Engineering of a Low-Cost Open-Source Hypoxia Chamber for Rodent Models of Obstructive Sleep Apnea

Engineering of a Low-Cost Open-Source Hypoxia Chamber for Rodent Models of Obstructive Sleep Apnea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Engineering of a Low-Cost Open-Source Hypoxia Chamber for Rodent Models of Obstructive Sleep Apnea SAAD AL-ANAZI, Mohamed Mekhitche, Prof Dr. Abeer Al-Masri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8633334/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: Reproducible intermittent hypoxia (IH) exposure is essential for investigating the pathophysiology of obstructive sleep apnea (OSA) and its cardiopulmonary consequences. However, commercially available IH systems are often prohibitively expensive, technically restrictive, and reliant on proprietary architectures. This study aimed to develop and validate a low-cost, open-source IH chamber capable of automated oxygen cycling, real-time environmental monitoring, and multi-animal capacity for translational sleep-apnea research. Methods An open-source IH system was constructed using a Raspberry Pi–based control unit, solenoid-valve gas switching, and custom Python software for automated hypoxia–normoxia cycling and data logging. Chamber-level oxygen concentration (FiO₂) was continuously monitored using a galvanic O₂ sensor, alongside temperature and humidity sensors; a carbon monoxide sensor was included solely as a qualitative safety indicator. Two validated IH regimens were tested: moderate IH (10–12% O₂; 20–30 cycles·h⁻¹) and severe IH (5–7% O₂; 30–60 cycles·h⁻¹). Precision, reproducibility, and environmental stability were assessed using coefficient of variation analysis and one-way ANOVA (p < 0.05). Results The chamber reliably achieved rapid and reproducible FiO₂ oscillations between normoxia (21%) and target hypoxic nadirs (10–12% or 5–7%) within 20–25 s, with cycle-to-cycle variability < 5%. Environmental conditions remained stable during prolonged exposures (22–24°C; 55–80% relative humidity), and effective chamber ventilation prevented abnormal gas accumulation. The total system cost was approximately SAR 2,990 (≈ US $ 800), representing < 5% of the cost of a commercial OxyCycler while delivering comparable desaturation dynamics. Conclusion This validated, low-cost, open-source IH chamber provides a scalable and reproducible platform for modeling OSA-related intermittent hypoxia in rodents. Its affordability, transparency, and multi-animal capacity support broader adoption and standardization of IH research across laboratories worldwide. intermittent hypoxia open-source chamber Raspberry Pi rodent model obstructive sleep apnea Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Obstructive sleep apnea (OSA) is one of the most prevalent sleep‑related breathing disorders worldwide and is characterized by recurrent episodes of partial or complete upper airway collapse during sleep, leading to intermittent hypoxemia, hypercapnia, intrathoracic pressure swings, and sleep fragmentation [1,2]. Epidemiological studies estimate that moderate‑to‑severe OSA affects nearly one billion adults globally, with prevalence increasing in parallel with obesity, aging, and cardiometabolic disease burden [3,4]. A defining physiological hallmark of OSA is intermittent hypoxia (IH), arising from repetitive cycles of oxygen desaturation and reoxygenation secondary to obstructive respiratory events [2,5]. Importantly, IH is a consequence of OSA pathophysiology, not a causal factor in the development of the disorder. These repetitive hypoxia–reoxygenation cycles trigger oxidative stress, sympathetic activation, systemic inflammation, endothelial dysfunction, and metabolic dysregulation, which together contribute to the development of OSA‑associated comorbidities, including hypertension, atrial fibrillation, stroke, insulin resistance, cognitive impairment, and increased all‑cause mortality [6–9]. Rodent Intermittent Hypoxia Models and Their Physiological Scope Over the past several decades, rodent IH models have played a central role in advancing mechanistic understanding of OSA-related cardiovascular, metabolic, and neurocognitive consequences by exposing animals to precisely controlled hypoxia–normoxia cycles, which isolate the hypoxic stimulus and enable investigation of downstream molecular and physiological responses under highly reproducible conditions [10–12]. However, it is critical to distinguish rodent IH exposure from human OSA physiology. While IH models faithfully reproduce the hypoxia–reoxygenation component of OSA, they do not recapitulate upper airway obstruction, increased respiratory effort, sleep fragmentation, arousal responses, or carbon dioxide retention that occur in patients with OSA [13–15]. Consequently, IH models should be viewed as reductionist experimental tools designed to study the biological effects of intermittent hypoxemia rather than comprehensive surrogates of the full OSA syndrome. Recognizing these limitations is essential for appropriate interpretation and translational relevance of IH-based findings. Evolution of Hypoxia Chamber Systems Early IH exposure systems consisted of small, fully enclosed animal chambers with manually adjusted gas inflow rates. While these designs provided initial proof-of-concept for hypoxia research, they were limited by slow gas exchange kinetics, imprecise oxygen control, and poor reproducibility, particularly for short hypoxic cycles relevant to rodent models [16,17]. The introduction of automated hypoxia systems—most notably computer-controlled platforms such as the OxyCycler (BioSpherix, USA)—represented a major technical advance by enabling programmable FiO₂ cycling through solenoid-regulated gas delivery [18]. These systems allow highly reproducible desaturation profiles and precise protocol control but are associated with substantial financial cost, often exceeding USD 40,000, and rely on proprietary hardware and closed-source software architectures [19]. As a result, their use remains largely confined to well-funded laboratories, limiting accessibility and scalability across institutions. In addition, many commercial platforms are optimized for single-animal exposure, which constrains experimental throughput and complicates studies requiring multiple exposure groups, pharmacological interventions, or longitudinal designs [20]. To address cost and accessibility barriers, several groups have developed custom hypoxia chambers using plexiglass enclosures with manually or semi-automated gas regulation. Although more affordable, such systems often lack closed-loop control and exhibit delayed gas exchange, limiting their ability to reproduce rapid hypoxia–reoxygenation cycles typical of rodent IH protocols (e.g., 20–30 s nadirs) [20,21]. These constraints may lead to attenuated or inconsistent oxygen profiles, reducing experimental reproducibility. Another limitation of many legacy and custom designs is the restricted scope of environmental monitoring . Oxygen concentration is frequently the sole parameter measured, whereas carbon dioxide accumulation, temperature, and humidity are often unmonitored despite their independent influence on respiratory drive, metabolic rate, and airway physiology [22–24]. Insufficient environmental monitoring complicates cross-laboratory comparisons and introduces uncontrolled variability. While recent open-source IH chamber designs using Arduino or Raspberry Pi controllers have been described [25,26], most remain limited by single-animal capacity, incomplete environmental sensing, or lack of detailed validation and reproducibility documentation. Together, these limitations underscore the need for an affordable, scalable, and transparent IH chamber system that combines precise oxygen control with comprehensive environmental monitoring and multi-animal capacity. Advances in open-source electronics, including low-cost single-board computers, modular sensors, and open programming frameworks, now enable the development of sophisticated laboratory systems at a fraction of the cost of commercial alternatives [27]. Open-source biomedical engineering solutions have already demonstrated feasibility and reliability across a range of applications, including ventilators, infusion systems, and environmental monitoring platforms [26,27]. Applying similar principles to hypoxia research offers the opportunity to enhance accessibility, reproducibility, and methodological transparency. Study Objectives In the present study, we designed and validated a low-cost, open-source intermittent hypoxia chamber for rodent models that addresses key limitations of existing systems. Specifically, the objectives were to: Increase experimental throughput by constructing a chamber capable of housing up to 10 rats simultaneously. Enhance usability and transparency through a real-time, user-friendly monitoring interface for chamber environmental conditions. Improve environmental control and reproducibility by continuously monitoring oxygen concentration, temperature, and humidity during programmable hypoxia–normoxia cycling. While the system is optimized for modeling the intermittent hypoxia component of OSA, its modular design also enables application to other experimental paradigms, including ischemia–reperfusion injury, chronic respiratory disease models, and high-altitude physiology research. 2. System Design and Methods 2.1 Study Design This study employed a controlled experimental design using a rat model to investigate the effects of intermittent hypoxia (IH) exposure. The experimental paradigm was designed to reproduce the hypoxia–reoxygenation component of obstructive sleep apnea (OSA), which is widely used in preclinical research to study downstream cardiovascular, pulmonary, and systemic consequences of OSA-associated hypoxemia [ 28 ]. It is important to emphasize that IH is a reductionist model that isolates cyclic hypoxemia and does not fully recapitulate key features of human OSA such as upper airway obstruction, sleep fragmentation, ventilatory effort swings, or CO2 retention. Nevertheless, IH models are extensively validated and remain among the most widely used approaches for investigating mechanisms of OSA-related hypoxic stress in cardiopulmonary systems [ 29 ]. All experimental procedures were conducted according to predefined protocols with tightly controlled environmental and exposure parameters to ensure reproducibility and minimize variability. 2.1.1 Study Area and Experimental Setting The study was conducted between April 1 and July 29, 2025, at the Animal Experimental Center of King Saud University (KSU), Riyadh, Saudi Arabia. All procedures were performed in accordance with internationally accepted standards for laboratory animal care and use, including the NIH Guide for the Care and Use of Laboratory Animals and the ARRIVE 2.0 guidelines [ 30 ]. Animals were housed under ambient room air conditions, with temperature maintained at 22–24°C and relative humidity at 40–60%, under a 12:12 h light–dark cycle (lights on at 07:00) [ 31 ]. Rats were provided ad libitum access to standard laboratory chow and water. Prior to experimental interventions, all animals underwent a 4-day acclimatization period. 2.1.2 Animal Selection and Rationale A total of fifty (50) adult male Sprague–Dawley rats, weighing 300–400 g, were included in the study. This strain was selected due to its well-characterized physiology, docile temperament, and widespread use in cardiopulmonary research, making it suitable for controlled IH exposure, physiological monitoring, blood sampling, and tissue collection [32] . Rats share substantial genetic and physiological similarities with humans, including high conservation of protein-coding genes and comparable cardiopulmonary structural and functional characteristics, supporting their translational relevance for hypoxia-related cardiopulmonary injury studies [33] . Only clinically healthy animals free from overt respiratory or cardiovascular abnormalities were included following routine veterinary assessment. 2.1 Control Unit The control system was built around a Raspberry Pi 4 Model B (Raspberry Pi Foundation, UK) featuring a quad-core 1.5 GHz ARM Cortex-A72 CPU and 4 GB RAM [ 34 ]. The system operates on Raspberry Pi OS (Linux-based) and executes custom Python 3.9 scripts to control hypoxia–normoxia cycling, valve actuation, and sensor data acquisition. System control and real-time data visualization were provided via a 5-inch capacitive touchscreen [35] . High-current devices (solenoid valves) were actuated via a dual-channel 5 V relay module, electrically isolated from Raspberry Pi GPIO outputs (Fig. 1 ). 2.2 Gas Delivery System FiO₂ was regulated using electrically actuated solenoid valves connected to compressed gas sources. Two 12 V DC solenoid valves (Model YS-12, Ningbo King Shengda Solenoid Tech., China) controlled nitrogen inflow and room air/oxygen inflow [36] . Gas sources were supplied via medical-grade cylinders equipped with precision pressure regulators and flowmeters (Aalborg Instruments, USA) [37] . A continuous background airflow (~ 1–2 L/min) was maintained via a needle valve to prevent accumulation of expired gases, consistent with established IH protocols [38] . The timing-based on–off solenoid switching strategy is consistent with widely used IH systems employing either timers or sensor-based control [ 39 ]. 2.3 Environmental Sensors Chamber FiO₂ was monitored using an electrochemical oxygen sensor module (Gravity I²C O₂ Sensor, DFRobot, China) incorporating a galvanic cell (Teledyne R-17A type) [ 40 ]. Calibration was performed using certified gas mixtures (5%, 10%, and 21% O₂; balance N₂) [ 41 ]. An MQ-7 semiconductor sensor (Hanwei Electronics, China) was included solely as a qualitative carbon monoxide (CO) safety indicator and was not used for CO₂ measurement [42] . Temperature and humidity were monitored using a BME280 sensor (Bosch Sensortec, Germany). Analog signals were digitized using an ADS1115 16-bit ADC where required. 2.4 Sensor Calibration and Verification The chamber O₂ sensor (Gravity I²C O₂ sensor; galvanic cell) was calibrated prior to experimental runs using certified reference gases (21% O₂, 10% O₂, and 5% O₂; balance N₂). The chamber was flushed with each reference gas until the O₂ reading stabilized, and the corresponding raw sensor outputs were recorded to generate a calibration curve converting sensor output to %FiO₂. Calibration was repeated at regular intervals (e.g., weekly) and whenever the sensor was replaced. Temperature and humidity measurements (BME280) were verified before experiments by comparison to an external reference thermometer/hygrometer under stable ambient conditions (22–24°C; 40–60% RH). Agreement within the manufacturer-stated accuracy (± 1°C; ±3% RH) was required prior to data collection. All calibration and verification steps were performed to ensure accuracy and reproducibility of chamber environmental monitoring. 2.5 Hypoxia Chamber Construction The hypoxia chamber was a custom-built rectangular enclosure fabricated from translucent Plexiglas sheets (100 cm × 40 cm × 60 cm), corresponding to an internal volume of approximately 240 L , which supports multi-rat housing while maintaining efficient gas exchange kinetics [43] . The chamber was constructed in the Biomedical Engineering Workshop at King Saud University. To ensure airtight integrity, all panel joints and seams were sealed using silicone gasket material, and animal access was provided via a tight-fitting Plexiglas lid incorporating a rubber gasket to maintain a secure seal. Two bulkhead ports were installed to connect the gas delivery tube to a dedicated inlet and outlet. The inlet port was positioned to direct incoming gas toward the central chamber space to promote mixing, while the outlet port was placed near the upper region of the chamber to facilitate effective flushing and venting via an exhaust line [44] . To reduce visual stress during exposures, reflective one-way film was applied to selected interior surfaces, enabling external observation while limiting animals’ perception of surrounding movement. The chamber included a detachable stainless-steel raised mesh floor positioned a few centimeters above the base to support the animals, improve gas diffusion, and separate waste; the mesh floor and bedding were removable for cleaning and sterilization. Before each experimental session, leak integrity was verified by sealing the chamber, flushing with test gas, and monitoring FiO₂ decline and stability; failure to maintain the expected FiO₂ profile after stopping flow was treated as evidence of leakage. Under calibrated operating conditions, the chamber FiO₂ could be reduced from approximately 21% to 5% within ~ 60 s, confirming adequate sealing and gas-switching performance (Fig. 2 ). 2.6 Software and Control Interface A custom Python 3.9 application was developed to automate intermittent hypoxia cycling and environmental data acquisition, operating on a Linux-based Raspberry Pi OS. The system incorporates a Tkinter-based graphical user interface (GUI) that allows real-time visualization of chamber conditions and configuration of key protocol parameters, including cycle duration, target FiO₂ nadir, and session length. Remote monitoring and control were enabled via RealVNC , permitting oversight of prolonged exposure sessions without direct interaction with the chamber and minimizing disturbance to the animals [ 45 ]. Owing to its open-source architecture, the software framework supports future expansion, including integration of additional sensors and implementation of advanced control strategies such as closed-loop feedback algorithms based on FiO₂ measurements. Table 1.0 System components of the custom-built intermittent hypoxia chamber Component Category Item / Model Brand / Manufacturer Country of Origin Estimated Price (SAR) Microcontroller & Control Unit Raspberry Pi 4 Model B (Quad-core ARM, 4 GB RAM) Raspberry Pi Foundation UK 350 SAR 5-inch Raspberry Pi Touchscreen Display Raspberry Pi Foundation UK 200 SAR Breadboard and Jumper Wires Elegoo China 50 SAR Relay Module (5V, 2-Channel) Songle / SainSmart China 40 SAR Gas Control System 12V DC Solenoid Valves (Model YS-12) Ningbo King Shengda Solenoid Tech. China 120 SAR (×3 ≈ 360 SAR) Sensors O₂ Sensor (Gravity I²C) DFRobot China 300 SAR Carbon monoxide safety sensor (MQ-7) Hanwei Electronics China 50 SAR Temperature & Humidity Sensor (BME280) Bosch Sensortec Germany 40 SAR Hypoxia Chamber Custom Plexiglass Chamber (100 × 40 × 60 cm, airtight, mesh floor) Biomedical Engineering Workshop, KSU Saudi Arabia 1,500 SAR Software & Programming Python 3.9 Script (Linux-based, Raspberry Pi OS) Open source (Python Software Foundation) International Free RealVNC Viewer (License) RealVNC Ltd. UK 150 SAR Estimated Total System Cost ≈ 2,990 SAR 2.7 Hypoxia–Normoxia Cycling Protocol Two intermittent hypoxia (IH) paradigms were employed to model moderate and severe OSA-like hypoxic exposure. Moderate IH consisted of cycles lasting 2–3 min , yielding 20–30 hypoxic events per hour , during which chamber FiO₂ was reduced to 10–12% , followed by reoxygenation to normoxia. Severe IH consisted of 1-min cycles (up to 60 events per hour ), with FiO₂ rapidly reduced to 5–7% and subsequently restored to normoxic levels [46] . All exposures were conducted during the light (inactive) phase to align with the rodents’ natural rest period and mimic the sleep-associated hypoxic stress of human OSA. Control animals were housed under identical experimental conditions, including chamber environment, airflow, and noise exposure, but were continuously supplied with room air (21% O₂) throughout each session. 2.8 Statistical Analysis Descriptive data are presented as mean ± standard deviation (SD) . The coefficient of variation (CV%) was used to assess the reproducibility of chamber FiO₂ cycling across cycles and experimental days. Differences in FiO₂ nadir values across sessions were analyzed using one-way analysis of variance (ANOVA) . Statistical significance was defined as p < 0.05. All analyses were performed using GraphPad Prism (version 10.0) and Python (NumPy and SciPy libraries). 3. Results 3.1 Chamber Oxygen Profile The intermittent hypoxia (IH) chamber operated reliably in accordance with the programmed protocols, with continuous monitoring confirming accurate and reproducible control of chamber oxygen concentration (FiO₂) throughout all exposure sessions. During the severe intermittent hypoxia (IHS) protocol, FiO₂ decreased from normoxic levels (21%) to the target hypoxic range of 5–7% within approximately 20–25 seconds , depending on gas flow conditions. Reoxygenation was initiated immediately following the hypoxic phase, with FiO₂ returning to normoxia within 30–60 seconds (Fig. 3). Across repeated severe IH cycles, FiO₂ nadirs were consistently achieved with minimal variability. The mean FiO₂ nadir remained within the predefined hypoxic range, with a coefficient of variation (CV) < 5% , demonstrating stable solenoid valve actuation, effective chamber sealing, and precise gas delivery. Similarly, during moderate intermittent hypoxia (IHM) , chamber FiO₂ reproducibly decreased to 10–12% and returned to normoxia following each programmed cycle (Fig. 4). Analysis of 30 consecutive moderate IH cycles showed tight clustering of FiO₂ nadir values within each session, confirming high cycle-to-cycle reproducibility and precision of the gas delivery system. The characteristic desaturation–reoxygenation waveform observed in the FiO₂ traces verified accurate timing of solenoid valve switching and appropriate calibration of gas flow rates. No progressive drift in FiO₂ nadirs or recovery peaks was observed across sessions, indicating consistent chamber performance over time. 3.2 Environmental Conditions and Stability Continuous environmental monitoring facilitated maintenance of stable chamber conditions during prolonged exposure sessions. Ambient temperature remained tightly controlled, with an average value of 22.5 ± 0.3°C , while temperature within the chamber was maintained at approximately 22.4°C throughout the experiments. Relative humidity remained stable during the 8-h exposure periods, typically ranging between 55% and 80% . A transient increase in humidity was observed following animal placement, which normalized within approximately 1 hour , consistent with animal respiration and chamber ventilation dynamics. Carbon monoxide sensing was employed solely as a qualitative safety indicator to detect abnormal gas accumulation associated with ventilation failure. Under normal operating conditions with continuous outflow, no abnormal accumulation signal was detected, indicating effective chamber flushing. In contrast, preliminary validation tests conducted without active outflow resulted in rapid gas accumulation signals within approximately 30 minutes , underscoring the importance of continuous ventilation for maintaining chamber air quality. Noise levels generated by solenoid valve actuation during gas switching were approximately 50–60 dB . Animals habituated rapidly to these sounds, with no overt behavioral signs of sustained stress observed after the initial exposure period. Collectively, these findings demonstrate that the chamber maintained a stable and controlled microenvironment during prolonged intermittent hypoxia exposures, effectively isolating oxygen concentration (FiO₂) as the primary experimental variable (Fig. 5 ). 3.3 System Performance and Reliability The system performed consistently across multiple 8-hour daily runs . Raspberry Pi–based control software exhibited valve-switching accuracy with 10,000 actuation cycles without mechanical or electrical failure, indicating robust long-term durability. Continuous data logging yielded approximately 2,880 FiO₂ measurements per 8-hour session (1-second sampling interval), providing high-resolution oxygen concentration traces for system validation. Over seven consecutive days of exposure , FiO₂ nadirs exhibited negligible drift, with a standard deviation ≤ 0.5% O₂ . One-way ANOVA revealed no significant between-day differences in FiO₂ nadir values ( p > 0.5), confirming reproducibility of hypoxia–normoxia cycling across repeated sessions (Table 2 ). Minor FiO₂ overshoot observed in early prototype testing was resolved by optimizing reoxygenation flow rates and incorporating a passive bleed valve. These refinements eliminated transient hyperoxic excursions (> 21% O₂) and improved pressure stability during gas infusion. Overall, the system demonstrated durable, hands-off, and highly reproducible performance , supporting its suitability for long-term intermittent hypoxia experimental protocols. Table 2 System performance metrics are summarized in demonstrating stable FiO₂ control, hardware durability, and reproducible operation across multi-day exposure sessions. Performance Metric Measurement / Outcome Validation Context Exposure duration per session 8 hours Continuous unattended operation Control software timing drift 10,000 cycles No mechanical or electrical failure observed Relay module performance Stable No switching errors or overheating FiO₂ sampling rate 1 sample per second Continuous chamber-level monitoring FiO₂ data points per session ~ 2,880 readings Per 8-hour exposure Severe IH FiO₂ nadir range 5–7% O₂ Achieved within 20–25 s Moderate IH FiO₂ nadir range 10–12% O₂ Stable across cycles FiO₂ nadir variability (day-to-day) SD ≤ 0.5% O₂ Over 7 consecutive days FiO₂ reproducibility across days No significant difference (ANOVA, p > 0.5) Confirms session-to-session stability FiO₂ overshoot above normoxia None after optimization Bleed valve + flow tuning Pressure stability during infusion Stable No detectable pressure spikes Data logging reliability 100% No data loss observed Long-term operational reliability High Suitable for multi-day IH protocols 4. Discussion The present study demonstrates that a low-cost, open-source intermittent hypoxia (IH) chamber can achieve precise, stable, and reproducible control of chamber oxygen concentration (FiO₂) during prolonged and repeated exposure sessions. The system reliably generated moderate and severe IH profiles with rapid desaturation and reoxygenation kinetics, minimal variability across cycles, and negligible drift across multiple days. These findings confirm that robust engineering performance can be achieved using widely available components and open-source software, providing a practical alternative to high-cost commercial platforms. 4.1 FiO₂ Control and Reproducibility in Relation to Existing IH Systems A critical requirement for IH research is the ability to achieve rapid and repeatable oxygen transitions that approximate the hypoxia–reoxygenation dynamics used in rodent models of obstructive sleep apnea (OSA) [47]. In the present system, severe IH protocols consistently reduced FiO₂ from 21% to 5–7% within approximately 20–25 s, with recovery to normoxia within 30–60 s. Moderate IH cycles reproducibly achieved nadirs of 10–12% FiO₂ with cycle-to-cycle variability below 5%. These transition kinetics and reproducibility metrics are comparable to those reported in validated rodent IH studies using both commercial hypoxia systems and custom-built chambers [18]. Commercial platforms such as the OxyCycler provide high precision but rely on proprietary hardware and closed software ecosystems, limiting accessibility and customization [48]. In contrast, the present system achieved comparable FiO₂ control while remaining fully open-source and substantially less expensive, facilitating broader adoption and protocol transparency. 4.2 Comparison with Published Open-Source and Custom IH Chambers Several open-source or low-cost IH chamber designs have been described in recent years, each addressing specific limitations of commercial systems. Gaspar et al. developed a microcontroller-based hypoxia chamber emphasizing affordability and automation but lacking comprehensive environmental monitoring and scalability [50]. Hillman et al. introduced an automated system with PID-based oxygen control, achieving high precision but restricting experiments to single-animal exposures [49]. Roshan et al. reported a compact Arduino-based design optimized for simplicity, though without integrated environmental sensing or multi-animal capacity [50]. Compared with these systems, the present chamber advances the field in several respects. It supports multi-animal exposure (up to 10 rats) while maintaining stable FiO₂ kinetics, integrates continuous oxygen, temperature, and humidity monitoring , and provides real-time graphical control through a Raspberry Pi interface. These features enhance experimental throughput, reproducibility, and usability relative to previously published open-source IH chambers [24]. 4.3 Environmental Stability and Control of Experimental Confounders Beyond oxygen control, maintaining a stable chamber microenvironment is essential for isolating hypoxia as the primary experimental variable. Continuous monitoring demonstrated tight regulation of chamber temperature (~22.4–22.5 °C) and stable relative humidity (55–80%) during prolonged 8-h exposures. Transient humidity increases following animal placement resolved within approximately one hour, consistent with expected respiratory moisture contributions and effective ventilation. Continuous chamber flushing prevented abnormal gas accumulation, as indicated by qualitative safety monitoring, and solenoid-related noise (~50–60 dB) was rapidly tolerated by animals. These findings are consistent with previous reports showing that uncontrolled temperature, humidity, or ventilation can confound physiological outcomes in long-duration IH studies [51]. 4.4 System Durability and Long-Term Performance The system demonstrated strong durability during extended use, completing more than 10,000 solenoid valve cycles without mechanical or electrical failure. Control software-maintained valve-switching accuracy with less than one second of cumulative timing drift over several hours. High-resolution FiO₂ logging (1-s sampling) enabled detailed validation of oxygen profiles across sessions. Across seven consecutive days, FiO₂ nadirs remained stable with no significant between-day differences, confirming long-term reproducibility. Minor hyperoxic overshoot observed in early prototypes was eliminated through flow optimization and incorporation of a passive bleed valve, highlighting the importance of iterative engineering validation in IH system development [16]. 4.5 Scope and Limitations The present work constitutes an engineering validation of chamber performance rather than a physiological validation of animal responses. While precise chamber-level FiO₂ control is a prerequisite for IH research, biological outcomes depend on animal-specific factors such as ventilation, perfusion, activity, and metabolic rate [52]. Accordingly, downstream physiological and molecular measures remain essential when applying this platform to specific disease models. 4.6 Conclusions In summary, the developed low-cost, open-source IH chamber provides precise, stable, and reproducible control of chamber oxygen concentration with integrated environmental monitoring and robust long-term performance. Compared with both commercial and previously published open-source systems, it offers a favorable balance between affordability, scalability, transparency, and engineering rigor. This platform has the potential to expand access to standardized intermittent hypoxia paradigms and support reproducible preclinical research across diverse laboratory settings. Abbreviations Abbreviation Definition OSA Obstructive Sleep Apnea IH Intermittent Hypoxia FiO₂ Fraction of Inspired Oxygen SpO₂ Peripheral Oxygen Saturation CPU Central Processing Unit GUI Graphical User Interface GPIO General-Purpose Input/Output IHM Moderate IH Protocol IHS Severe IH Protocol CV Coefficient of Variation SD Standard Deviation RH Relative Humidity Declarations Author Contribution Author Contributions StatementS.A.A. conceived and designed the study, developed the intermittent hypoxia chamber, performed system validation, conducted data acquisition, and drafted the original manuscript. A.A.A.-M. provided scientific supervision, conceptual guidance, and critical revision of the manuscript. S.A.A. contributed to the physiological interpretation of the data and manuscript review. assisted with experimental planning and data interpretation. M.A.M. contributed to the engineering design, fabrication, and technical optimization of the chamber. contributed to methodological refinement and critical manuscript editing.All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Ethical Approval and Animal Welfare All experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of King Saud University, in accordance with the Guide for the Care and Use of Laboratory Animals (8th Edition, National Academies Press, 2011). To minimize stress, rats were acclimatized to the chamber before the intermittent hypoxia exposure. Light–dark cycle, temperature, and humidity were kept constant, and the well-being of the animals was observed throughout all the sessions. Acknowledgements The authors would like to acknowledge the Biomedical Engineering Workshop at King Saud University for technical assistance in the fabrication and testing of the intermittent hypoxia chamber. Special thanks are extended to Prof. Abeer. Al-Masri and Eng Mohammed Amine for her scientific guidance and constructive feedback throughout the development process. Part of this work was derived from a doctoral dissertation submitted to King Saud University (2025). Open-source design and software components were developed within an academic framework to promote reproducibility and accessibility in preclinical sleep and respiratory physiology research. Data Availability Statement The open-source hardware designs and control software developed in this study are available from the corresponding author upon reasonable request. Experimental datasets generated during the study are not publicly available due to institutional and ethical constraints but can be accessed from the corresponding author upon reasonable request. References Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol . 2013;177(9):1006–14. doi:10.1093/aje/kws342. https://doi.org/10.1093/aje/kws342 Jordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. Lancet . 2014;383(9918):736–47. doi:10.1016/S0140-6736(13)60734-5. https://doi.org/10.1016/S0140-6736(13)60734-5 Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med . 2019;7(8):687–98. doi:10.1016/S2213-2600(19)30198-5.https://doi.org/10.1016/S2213-2600(19)30198-5 Heinzer R, Vat S, Marques-Vidal P, et al. Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. Lancet Respir Med . 2015;3(4):310–8. doi:10.1016/S2213-2600(15)00043-0.https://doi.org/10.1016/S2213-2600(15)00043-0 Gozal D, Kheirandish-Gozal L. Neurocognitive and behavioral morbidity in children with sleep disorders. Curr Opin Pulm Med . 2007;13(6):505–9. doi:10.1097/MCP.0b013e3282f1d6f4. https://doi.org/10.1097/MCP.0b013e3282f1d6f4 Lavie L. Oxidative stress inflammation and endothelial dysfunction in obstructive sleep apnea. Front Biosci . 2012;17:273–88. doi:10.2741/3927. https://doi.org/10.2741/3927 Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med . 2005;353(19):2034–41. doi:10.1056/NEJMoa043104. https://doi.org/10.1056/NEJMoa043104 Shan X, et al. Intermittent hypoxia-induced hypertension. Hypertens Res . 2021;44(4):305–14. doi:10.1038/s41440-020-00569-z. https://doi.org/10.1038/s41440-020-00569-z Pan Y, et al. Intermittent hypoxia and its role in OSA-induced neurocognitive dysfunction. Sleep Breath . 2022;26(1):195–203. doi:10.1007/s11325-021-02382-0 https://doi.org/10.1007/s11325-021-02382-0 Farré R, Montserrat JM, Gozal D, Almendros I, Navajas D. Intermittent hypoxia severity in animal models of sleep apnea. Front Physiol . 2018;9:1556. doi:10.3389/fphys.2018.01556. https://doi.org/10.3389/fphys.2018.01556 Davis EM, O’Donnell CP. Rodent models of sleep apnea. Respir Physiol Neurobiol . 2013;188(3):355–61. doi:10.1016/j.resp.2013.05.022. https://doi.org/10.1016/j.resp.2013.05.022 Alvente S, Gozal D, et al. How to study sleep apneas in mouse models of human pathology. J Neurosci Methods . 2023;395:109923. doi:10.1016/j.jneumeth.2023.109923. https://doi.org/10.1016/j.jneumeth.2023.109923 Zong S, Du P, Li H, et al. Advances in animal models of obstructive sleep apnea. Front Med (Lausanne) . 2023;10:988752. doi:10.3389/fmed.2023.988752. https://doi.org/10.3389/fmed.2023.988752 Crossland RF, et al. Effects of intermittent hypoxia and hypercapnia on cardiovascular and metabolic function. J Appl Physiol . 2013;115(2):193–204. doi:10.1152/japplphysiol.00355.2013. https://doi.org/10.1152/japplphysiol.00355.2013 McNicholas WT. Obstructive sleep apnea: transition from pathophysiology to comorbidity. J Sleep Res . 2022;31(1):e13616. doi:10.1111/jsr.13616. https://doi.org/10.1111/jsr.13616 Hillman TC, Idnani R, Wilson CG. An inexpensive open-source chamber for controlled hypoxia/hyperoxia exposure. Front Physiol . 2022;13:891005. doi:10.3389/fphys.2022.891005. https://doi.org/10.3389/fphys.2022.891005 Plantier L, et al. Affordable hypoxic chamber with environmental control. bioRxiv . 2023. doi:10.1101/2023.06.21.546032. https://doi.org/10.1101/2023.06.21.546032 Polšek D, et al. Automated intermittent hypoxia system in mice. PLoS One . 2017;12(3):e0174896. doi:10.1371/journal.pone.0174896. https://doi.org/10.1371/journal.pone.0174896 Saxena K, Sharma SK, Gupta A. Cost and access barriers to OSA diagnosis. Sleep . 2018;41(4):zsy021. doi:10.1093/sleep/zsy021. https://doi.org/10.1093/sleep/zsy021 Almendros I, Wang Y, Becker L, et al. Intermittent hypoxia-induced changes in tumor macrophages. Am J Respir Crit Care Med . 2014;189(5):593–601. doi:10.1164/rccm.201310-1830OC. https://doi.org/10.1164/rccm.201310-1830OC Marchus CRN, Wang L, Joubert LM, et al. Open-source cell culture chamber. HardwareX . 2021;10:e00213. doi:10.1016/j.ohx.2021.e00213. https://doi.org/10.1016/j.ohx.2021.e00213 Otero J, et al. Open-source hypoxia exposure setup for rodents. Preprints . 2025. doi:10.20944/preprints202512.0632.v1.https://doi.org/10.20944/preprints202512.0632.v1 Powley J, et al. Portable hypoxic chamber with smart sensing. Cancers (Basel) . 2023;15(14):3645. doi:10.3390/cancers15143645. https://doi.org/10.3390/cancers15143645 Mathupala SP, Kiousis S, Szerlip NJ. Microcontroller oxygen sensor for hypoxia chambers. PLoS One . 2016;11(2):e0148923. doi:10.1371/journal.pone.0148923. https://doi.org/10.1371/journal.pone.0148923 Acho L, Vargas AN, Pujol-Vázquez G. Low-cost open ventilator for COVID-19. Actuators (Basel) . 2020;9(3):84. doi:10.3390/act9030084.https://doi.org/10.3390/act9030084 Farré R, et al. Open-source hardware for low-resource respiratory care. J Pers Med . 2022;12(9):1498. doi:10.3390/jpm12091498. https://doi.org/10.3390/jpm12091498 Pearce JM. Digital manufacturing of open medical hardware. Addit Manuf . 2020;36:101413. doi:10.1016/j.addma.2020.101413. https://doi.org/10.1016/j.addma.2020.101413 Crossland RF, Durgan DJ, Lloyd EE, Phillips SC, Bryan RM. A new rodent model for obstructive sleep apnea: chronic intermittent hypoxia mimics OSA characteristics. J Appl Physiol . 2013;115(2):193–204. doi:10.1152/japplphysiol.00355.2013. https://doi.org/10.1152/japplphysiol.00355.2013 Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLoS Biol . 2020;18(7):e3000411. doi: 10.1371/journal.pbio.3000411.https://doi.org/10.1371/journal.pbio.3000411 National Research Council. Guide for the Care and Use of Laboratory Animals . 8th ed. Washington (DC): The National Academies Press; 2011. doi:10.1258/la.2010.010031. https://doi.org/10.1258/la.2010.010031 Dillmann WH, Kranias EG. The rat as a model for cardiovascular disease research: advantages and limitations . Cardiovasc Res . 2008;79(1):9‑12. doi:10.1016/j.cardiores.2008.03.006. https://www.sciencedirect.com/science/article/abs/pii/S174067570900022X ScienceDirect Gill L, et al. Evaluating the use of rodents as in vivo, in vitro, and ex vivo models of human disease. Toxicol Pathol . 2025;xx(x):xx–xx. doi:10.1007/s00204-025-04159-0. https://link.springer.com/article/10.1007/s00204-025-04159-0 Springer Jolles JW, et al. Broad‑scale applications of the Raspberry Pi: a review and case studies in scientific research . Methods Ecol Evol . 2021;12(10):1563‑1575. doi:10.1111/2041-210X.13652. https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13652 besjournals.onlinelibrary.wiley.com Schatz A, Winter Y. LabNet hardware control software for the Raspberry Pi . eLife . 2022;11:e77973. doi:10.7554/eLife.77973. https://doi.org/10.7554/eLife.77973 eLife Shyu D, Bliss P, Adams A, Cho RJ. Development and performance evaluation of a solenoid‑valve assisted low‑cost ventilator on gas exchange and respiratory mechanics in a porcine model . PLoS One. 2024;19(5):e0303443. doi:10.1371/journal.pone.0303443. https://doi.org/10.1371/journal.pone.0303443 Polšek D, Bago M, Živaljić M, Rosenzweig I, Lacza Z, Gajović S. A novel adjustable automated system for inducing chronic intermittent hypoxia in mice . PLoS One. 2017;12(3):e0174896. doi:10.1371/journal.pone.0174896. https://doi.org/10.1371/journal.pone.0174896 PubMed Li S, Sen S, Johnston JA, et al. Time‑dependent inflammatory factor production and NF‑κB activation in intermittent hypoxia exposure with timed gas regulation . Swiss Med Wkly. 2011;141:w13892. doi:10.4414/smw.2011.13892. https://pubmed.ncbi.nlm.nih.gov/21771632/ Swiss Medical Weekly Akmal N, Ansari R. Electrochemical oxygen sensors: principles, designs, and applications . In: Oxygen Sensors: Principles and Applications. American Chemical Society; 1998: pp. 209–230. doi:10.1021/bk‑1998‑0690.ch013. https://pubs.acs.org/doi/full/10.1021/bk‑1998‑0690.ch013 ACS Publications Warburton PR, Pagano M, Hoover R, Logman K, Crytzer M. Failure prediction and performance characteristics of galvanic oxygen sensors . Sensors Actuators B Chem. 2001;73(1–2):109–115. doi:10.1016/S0925‑4005(00)00534‑7. https://doi.org/10.1016/S0925-4005(00)00534-7 ScienceDirect Karbach N, Höhler L, Hoor P, Bozem H, Bobrowski N, Hoffmann T. Preparation of low‑concentration calibration gas mixtures in ambient air for electrochemical sensor characterization. Atmos Meas Tech . 2024;17(17):4081–4086. doi:10.5194/amt‑17‑4081‑2024.https://doi.org/10.5194/amt‑17‑4081‑2024 AMT Ughade Y, Bodkhe S, Pawar S, et al. Progress in CO₂ Gas Sensing Technologies: Insights into chemiresistive CO₂ sensors based on nanostructured materials. Micromachines . 2025;16(4):466. doi:10.3390/mi16040466. https://doi.org/10.3390/mi16040466 MDPI Li C, Lu J, Zhang B. Development of a novel chronic intermittent hypoxia chamber. Sleep Breath . 2012;16(1):177–179. doi:10.1007/s11325‑011‑0518‑1. https://doi.org/10.1007/s11325‑011‑0518‑1 PubMed Hillman TC, Idnani R, Wilson CG. An inexpensive open‑source chamber for controlled hypoxia/hyperoxia exposure. Front Physiol . 2022;13:891005. doi:10.3389/fphys.2022.891005. https://doi.org/10.3389/fphys.2022.891005 Frontiers Álvarez Ariza J, Nomesqui Galvis C. RaspyControl Lab: A fully open‑source and real‑time remote laboratory for education in automatic control systems using Raspberry Pi and Python. HardwareX. 2023;13:e00396. doi:10.1016/j.ohx.2023.e00396. https://doi.org/10.1016/j.ohx.2023.e00396 ResearchGate Wei Q, Li W, Xu X, Xin F, Sun X. Chronic intermittent hypoxia induces cardiac inflammation and dysfunction in rats. Exp Physiol . 2016;101(1):203–213. doi:10.1113/EP086986. https://physoc.onlinelibrary.wiley.com/doi/10.1113/EP086986 Farré R, Montserrat JM, Gozal D, Almendros I, Navajas D. Intermittent hypoxia severity in animal models of sleep apnea. Front Physiol . 2018;9:1556. doi:10.3389/fphys.2018.01556. https://doi.org/10.3389/fphys.2018.01556 [BioSpherix OxyCycler Technical Literature]. Animal Modeling Literature – OxyCycler A41OV. BioSpherix Ltd; 2017. https://biospherix.com/wp-content/uploads/2017/11/Animal-Modeling-Literature_OxyCycler-A41OV-.pdf https://doi.org/10.1371/journal.pone.0148923 PLOS Roshan S, Jayachandran SK, Kandasamy M, Anusuyadevi M. Design and evaluation of an affordable hypoxic chamber with comprehensive environmental control for research applications. bioRxiv . 2023. doi:10.1101/2023.06.21.546032. https://doi.org/10.1101/2023.06.21.546032 ResearchGate Otero J, Rodríguez‑Lázaro MA, Salama R, et al. Optimized open‑source setting for subjecting rodents to chronic normobaric hypoxia. Preprints . 2025. doi:10.20944/preprints202512.0632.v1. https://doi.org/10.20944/preprints202512.0632.v1 Preprints Showalter A, Murphy DJ, Liu SY, Merriman D. Long‑term reliability and drift assessment of environmental control sensors in automated lab systems. J Lab Autom . 2019;24(4):380–392. doi:10.1177/2211068218805313. https://doi.org/10.1177/2211068218805313 Reeves SR, Dada LA, Henderson KS, et al. Effect of long‑term intermittent and sustained hypoxia on hypoxic ventilatory and metabolic responses in the adult rat . J Appl Physiol . 2003;94(5):1951–1958. doi:10.1152/japplphysiol.00759.2002. https://doi.org/10.1152/japplphysiol.00759.2002 Physiology Journals Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 May, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 18 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8633334","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596340037,"identity":"12ffc43c-cc17-4cb9-8311-b913a59629ed","order_by":0,"name":"SAAD AL-ANAZI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJACiQ8VbMz8EhC2DFFaJGec4WOXnMHA2ADUwkOUFmneNjl+gxtgLQyEtci3H354c8YZM2nj283HH92oseBhYD98dAM+LQZn0owtPlSkGZvdOZbYnHMM6DCetLQbeLUwJJgB/XIs2exGjmFzDhtQiwSPGV4t8v3PvwH98r9+8wyQln9EaGG4kWMG1MLGbCAB1JLbRoQWgxtvii1nnGFjlriRljg7t0+Ch42QX+T70zfeAEfljOQDn3O+1cnxsx8+ht9hGICNNOWjYBSMglEwCrABAFKKSFe+td1UAAAAAElFTkSuQmCC","orcid":"","institution":"King Saud University","correspondingAuthor":true,"prefix":"","firstName":"SAAD","middleName":"","lastName":"AL-ANAZI","suffix":""},{"id":596340038,"identity":"9baedfba-4074-4277-bf44-81985353f8f6","order_by":1,"name":"Mohamed Mekhitche","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Mekhitche","suffix":""},{"id":596340039,"identity":"23d3b758-dd54-4c43-b402-b8725edae0a7","order_by":2,"name":"Prof Dr. Abeer Al-Masri","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Prof","middleName":"Dr. Abeer","lastName":"Al-Masri","suffix":""}],"badges":[],"createdAt":"2026-01-18 19:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8633334/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8633334/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397872,"identity":"2c613638-ef65-4c50-a1cd-f47dcacb3430","added_by":"auto","created_at":"2026-03-11 11:58:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2249833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectronic architecture and sensor integration of the open-source intermittent hypoxia(IH)chamber.\u003c/strong\u003eThe system is controlled by a Raspberry Pi 4 Model B interfaced with a custom signal-conditioning board (ADS1115 ADC) for sensor acquisition. Environmental monitoring includes a galvanic oxygen sensor for chamber FiO₂ measurement, a carbon monoxide safety sensor (MQ-7) used exclusively for qualitative safety monitoring, and a temperature and humidity sensor (BME280). All sensors measure chamber environmental conditions only. Sensor data are processed and logged via Python-based software, enabling real-time monitoring and automated hypoxia–normoxia cycling.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/7a8e1bd7d0a1e93515b08e6e.png"},{"id":103537822,"identity":"d3f01bed-3a83-4224-bb89-c8a763ff1f3a","added_by":"auto","created_at":"2026-02-26 18:55:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1518708,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the custom-built open-source intermittent hypoxia (IH) chamber.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/2da82d859c753cfc3549a0ea.png"},{"id":103537821,"identity":"b96c3d13-03f5-4807-a2b5-e03fbe171935","added_by":"auto","created_at":"2026-02-26 18:55:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":356774,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;Legend not included with this version.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/9df74018ee396d64e8e67b1f.png"},{"id":103537826,"identity":"0b4c7fac-7ee2-4ae7-8731-cba61f50d5e8","added_by":"auto","created_at":"2026-02-26 18:55:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439737,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;Legend not included with this version.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/f962b6979be93461524f5bc2.png"},{"id":103537824,"identity":"fd224cc5-6c91-468b-88b1-4944c5461371","added_by":"auto","created_at":"2026-02-26 18:55:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA Chamber Temperature vs Time: \u003c/strong\u003eStable temperature maintained around \u003cstrong\u003e22.5 ± 0.3 °C\u003c/strong\u003e, Minor physiological fluctuations only, No drift across the \u003cstrong\u003e8-hour IH session\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Chamber Relative Humidity vs Time: Initial\u003c/strong\u003e transient rise in humidity after animal placement\u003cstrong\u003e, \u003c/strong\u003eGradual stabilization within ~1 hour\u003cstrong\u003e, \u003c/strong\u003eMaintained within \u003cstrong\u003e~55–80% RH\u003c/strong\u003e for the remainder of exposure\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/0459664f7a4cd1410c775314.png"},{"id":104407378,"identity":"8da933dd-5821-4827-bfd5-e1713a343eb6","added_by":"auto","created_at":"2026-03-11 12:37:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5806448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8633334/v1/abc7b415-5163-439d-ba3b-16e7412e5086.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Engineering of a Low-Cost Open-Source Hypoxia Chamber for Rodent Models of Obstructive Sleep Apnea","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObstructive sleep apnea (OSA) is one of the most prevalent sleep‑related breathing disorders worldwide and is characterized by recurrent episodes of partial or complete upper airway collapse during sleep, leading to intermittent hypoxemia, hypercapnia, intrathoracic pressure swings, and sleep fragmentation [1,2]. Epidemiological studies estimate that moderate‑to‑severe OSA affects nearly one billion adults globally, with prevalence increasing in parallel with obesity, aging, and cardiometabolic disease burden [3,4]. A defining physiological hallmark of OSA is intermittent hypoxia (IH), arising from repetitive cycles of oxygen desaturation and reoxygenation secondary to obstructive respiratory events [2,5]. Importantly, IH is a consequence of OSA pathophysiology, not a causal factor in the development of the disorder. These repetitive hypoxia\u0026ndash;reoxygenation cycles trigger oxidative stress, sympathetic activation, systemic inflammation, endothelial dysfunction, and metabolic dysregulation, which together contribute to the development of OSA‑associated comorbidities, including hypertension, atrial fibrillation, stroke, insulin resistance, cognitive impairment, and increased all‑cause mortality [6\u0026ndash;9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRodent Intermittent Hypoxia Models and Their Physiological Scope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver the past several decades, rodent IH models have played a central role in advancing mechanistic understanding of OSA-related cardiovascular, metabolic, and neurocognitive consequences by exposing animals to precisely controlled hypoxia\u0026ndash;normoxia cycles, which isolate the hypoxic stimulus and enable investigation of downstream molecular and physiological responses under highly reproducible conditions [10\u0026ndash;12]. However, it is critical to distinguish rodent IH exposure from human OSA physiology. While IH models faithfully reproduce the hypoxia\u0026ndash;reoxygenation component of OSA, they do not recapitulate upper airway obstruction, increased respiratory effort, sleep fragmentation, arousal responses, or carbon dioxide retention that occur in patients with OSA [13\u0026ndash;15]. Consequently, IH models should be viewed as reductionist experimental tools designed to study the biological effects of intermittent hypoxemia rather than comprehensive surrogates of the full OSA syndrome. Recognizing these limitations is essential for appropriate interpretation and translational relevance of IH-based findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvolution of Hypoxia Chamber Systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly IH exposure systems consisted of small, fully enclosed animal chambers with manually adjusted gas inflow rates. While these designs provided initial proof-of-concept for hypoxia research, they were limited by slow gas exchange kinetics, imprecise oxygen control, and poor reproducibility, particularly for short hypoxic cycles relevant to rodent models [16,17].\u003c/p\u003e\n\u003cp\u003eThe introduction of automated hypoxia systems\u0026mdash;most notably computer-controlled platforms such as the OxyCycler (BioSpherix, USA)\u0026mdash;represented a major technical advance by enabling programmable FiO₂ cycling through solenoid-regulated gas delivery [18]. These systems allow highly reproducible desaturation profiles and precise protocol control but are associated with substantial financial cost, often exceeding USD 40,000, and rely on proprietary hardware and closed-source software architectures [19]. As a result, their use remains largely confined to well-funded laboratories, limiting accessibility and scalability across institutions.\u003c/p\u003e\n\u003cp\u003eIn addition, many commercial platforms are optimized for single-animal exposure, which constrains experimental throughput and complicates studies requiring multiple exposure groups, pharmacological interventions, or longitudinal designs [20].\u003c/p\u003e\n\u003cp\u003eTo address cost and accessibility barriers, several groups have developed custom hypoxia chambers using plexiglass enclosures with manually or semi-automated gas regulation. Although more affordable, such systems often lack closed-loop control and exhibit delayed gas exchange, limiting their ability to reproduce rapid hypoxia\u0026ndash;reoxygenation cycles typical of rodent IH protocols (e.g., 20\u0026ndash;30 s nadirs) [20,21]. These constraints may lead to attenuated or inconsistent oxygen profiles, reducing experimental reproducibility.\u003c/p\u003e\n\u003cp\u003eAnother limitation of many legacy and custom designs is the \u003cstrong\u003erestricted scope of environmental monitoring\u003c/strong\u003e. Oxygen concentration is frequently the sole parameter measured, whereas carbon dioxide accumulation, temperature, and humidity are often unmonitored despite their independent influence on respiratory drive, metabolic rate, and airway physiology [22\u0026ndash;24]. Insufficient environmental monitoring complicates cross-laboratory comparisons and introduces uncontrolled variability.\u003c/p\u003e\n\u003cp\u003eWhile recent open-source IH chamber designs using Arduino or Raspberry Pi controllers have been described [25,26], most remain limited by single-animal capacity, incomplete environmental sensing, or lack of detailed validation and reproducibility documentation.\u003c/p\u003e\n\u003cp\u003eTogether, these limitations underscore the need for an \u003cstrong\u003eaffordable, scalable, and transparent IH chamber system\u003c/strong\u003e that combines precise oxygen control with comprehensive environmental monitoring and multi-animal capacity. Advances in open-source electronics, including low-cost single-board computers, modular sensors, and open programming frameworks, now enable the development of sophisticated laboratory systems at a fraction of the cost of commercial alternatives [27].\u003c/p\u003e\n\u003cp\u003eOpen-source biomedical engineering solutions have already demonstrated feasibility and reliability across a range of applications, including ventilators, infusion systems, and environmental monitoring platforms [26,27]. Applying similar principles to hypoxia research offers the opportunity to enhance accessibility, reproducibility, and methodological transparency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, we designed and validated a \u003cstrong\u003elow-cost, open-source intermittent hypoxia chamber for rodent models\u003c/strong\u003e that addresses key limitations of existing systems. Specifically, the objectives were to:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eIncrease experimental throughput\u003c/strong\u003e by constructing a chamber capable of housing up to 10 rats simultaneously.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnhance usability and transparency\u003c/strong\u003e through a real-time, user-friendly monitoring interface for chamber environmental conditions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eImprove environmental control and reproducibility\u003c/strong\u003e by continuously monitoring oxygen concentration, temperature, and humidity during programmable hypoxia\u0026ndash;normoxia cycling.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhile the system is optimized for modeling the intermittent hypoxia component of OSA, its modular design also enables application to other experimental paradigms, including ischemia\u0026ndash;reperfusion injury, chronic respiratory disease models, and high-altitude physiology research.\u003c/p\u003e"},{"header":"2. System Design and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a controlled experimental design using a rat model to investigate the effects of intermittent hypoxia (IH) exposure. The experimental paradigm was designed to reproduce the \u003cb\u003ehypoxia\u0026ndash;reoxygenation component\u003c/b\u003e of obstructive sleep apnea (OSA), which is widely used in preclinical research to study downstream cardiovascular, pulmonary, and systemic consequences of OSA-associated hypoxemia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to emphasize that IH is a \u003cb\u003ereductionist model\u003c/b\u003e that isolates cyclic hypoxemia and does not fully recapitulate key features of human OSA such as upper airway obstruction, sleep fragmentation, ventilatory effort swings, or CO2 retention. Nevertheless, IH models are extensively validated and remain among the most widely used approaches for investigating mechanisms of OSA-related hypoxic stress in cardiopulmonary systems [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All experimental procedures were conducted according to predefined protocols with tightly controlled environmental and exposure parameters to ensure reproducibility and minimize variability.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Study Area and Experimental Setting\u003c/h2\u003e \u003cp\u003eThe study was conducted between April 1 and July 29, 2025, at the Animal Experimental Center of King Saud University (KSU), Riyadh, Saudi Arabia. All procedures were performed in accordance with internationally accepted standards for laboratory animal care and use, including the \u003cb\u003eNIH Guide for the Care and Use of Laboratory Animals\u003c/b\u003e and the \u003cb\u003eARRIVE 2.0 guidelines\u003c/b\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Animals were housed under ambient room air conditions, with temperature maintained at 22\u0026ndash;24\u0026deg;C and relative humidity at 40\u0026ndash;60%, under a 12:12 h light\u0026ndash;dark cycle (lights on at 07:00) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Rats were provided ad libitum access to standard laboratory chow and water. Prior to experimental interventions, all animals underwent a 4-day acclimatization period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Animal Selection and Rationale\u003c/h2\u003e \u003cp\u003eA total of fifty (50) adult male Sprague\u0026ndash;Dawley rats, weighing 300\u0026ndash;400 g, were included in the study. This strain was selected due to its well-characterized physiology, docile temperament, and widespread use in cardiopulmonary research, making it suitable for controlled IH exposure, physiological monitoring, blood sampling, and tissue collection \u003cb\u003e[32]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eRats share substantial genetic and physiological similarities with humans, including high conservation of protein-coding genes and comparable cardiopulmonary structural and functional characteristics, supporting their translational relevance for hypoxia-related cardiopulmonary injury studies \u003cb\u003e[33]\u003c/b\u003e. Only clinically healthy animals free from overt respiratory or cardiovascular abnormalities were included following routine veterinary assessment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Control Unit\u003c/h2\u003e \u003cp\u003eThe control system was built around a Raspberry Pi 4 Model B (Raspberry Pi Foundation, UK) featuring a quad-core 1.5 GHz ARM Cortex-A72 CPU and 4 GB RAM [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The system operates on Raspberry Pi OS (Linux-based) and executes custom Python 3.9 scripts to control hypoxia\u0026ndash;normoxia cycling, valve actuation, and sensor data acquisition. System control and real-time data visualization were provided via a 5-inch capacitive touchscreen \u003cb\u003e[35]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eHigh-current devices (solenoid valves) were actuated via a dual-channel 5 V relay module, electrically isolated from Raspberry Pi GPIO outputs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Gas Delivery System\u003c/h2\u003e \u003cp\u003eFiO₂ was regulated using electrically actuated solenoid valves connected to compressed gas sources. Two 12 V DC solenoid valves (Model YS-12, Ningbo King Shengda Solenoid Tech., China) controlled nitrogen inflow and room air/oxygen inflow \u003cb\u003e[36]\u003c/b\u003e. Gas sources were supplied via medical-grade cylinders equipped with precision pressure regulators and flowmeters (Aalborg Instruments, USA) \u003cb\u003e[37]\u003c/b\u003e. A continuous background airflow (~\u0026thinsp;1\u0026ndash;2 L/min) was maintained via a needle valve to prevent accumulation of expired gases, consistent with established IH protocols \u003cb\u003e[38]\u003c/b\u003e. The timing-based on\u0026ndash;off solenoid switching strategy is consistent with widely used IH systems employing either timers or sensor-based control [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Environmental Sensors\u003c/h2\u003e \u003cp\u003eChamber FiO₂ was monitored using an electrochemical oxygen sensor module (Gravity I\u0026sup2;C O₂ Sensor, DFRobot, China) incorporating a galvanic cell (Teledyne R-17A type) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Calibration was performed using certified gas mixtures (5%, 10%, and 21% O₂; balance N₂) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn MQ-7 semiconductor sensor (Hanwei Electronics, China) was included \u003cb\u003esolely\u003c/b\u003e as a qualitative carbon monoxide (CO) safety indicator and was not used for CO₂ measurement \u003cb\u003e[42]\u003c/b\u003e. Temperature and humidity were monitored using a BME280 sensor (Bosch Sensortec, Germany). Analog signals were digitized using an ADS1115 16-bit ADC where required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sensor Calibration and Verification\u003c/h2\u003e \u003cp\u003eThe chamber O₂ sensor (Gravity I\u0026sup2;C O₂ sensor; galvanic cell) was calibrated prior to experimental runs using certified reference gases (21% O₂, 10% O₂, and 5% O₂; balance N₂). The chamber was flushed with each reference gas until the O₂ reading stabilized, and the corresponding raw sensor outputs were recorded to generate a calibration curve converting sensor output to %FiO₂. Calibration was repeated at regular intervals (e.g., weekly) and whenever the sensor was replaced.\u003c/p\u003e \u003cp\u003eTemperature and humidity measurements (BME280) were verified before experiments by comparison to an external reference thermometer/hygrometer under stable ambient conditions (22\u0026ndash;24\u0026deg;C; 40\u0026ndash;60% RH). Agreement within the manufacturer-stated accuracy (\u0026plusmn;\u0026thinsp;1\u0026deg;C; \u0026plusmn;3% RH) was required prior to data collection. All calibration and verification steps were performed to ensure accuracy and reproducibility of chamber environmental monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hypoxia Chamber Construction\u003c/h2\u003e \u003cp\u003eThe hypoxia chamber was a custom-built rectangular enclosure fabricated from translucent Plexiglas sheets (100 cm \u0026times; 40 cm \u0026times; 60 cm), corresponding to an internal volume of approximately \u003cb\u003e240 L\u003c/b\u003e, which supports multi-rat housing while maintaining efficient gas exchange kinetics \u003cb\u003e[43]\u003c/b\u003e. The chamber was constructed in the Biomedical Engineering Workshop at King Saud University. To ensure airtight integrity, all panel joints and seams were sealed using silicone gasket material, and animal access was provided via a tight-fitting Plexiglas lid incorporating a rubber gasket to maintain a secure seal.\u003c/p\u003e \u003cp\u003eTwo bulkhead ports were installed to connect the gas delivery tube to a dedicated inlet and outlet. The inlet port was positioned to direct incoming gas toward the central chamber space to promote mixing, while the outlet port was placed near the upper region of the chamber to facilitate effective flushing and venting via an exhaust line \u003cb\u003e[44]\u003c/b\u003e. To reduce visual stress during exposures, reflective one-way film was applied to selected interior surfaces, enabling external observation while limiting animals\u0026rsquo; perception of surrounding movement. The chamber included a detachable stainless-steel raised mesh floor positioned a few centimeters above the base to support the animals, improve gas diffusion, and separate waste; the mesh floor and bedding were removable for cleaning and sterilization.\u003c/p\u003e \u003cp\u003eBefore each experimental session, leak integrity was verified by sealing the chamber, flushing with test gas, and monitoring FiO₂ decline and stability; failure to maintain the expected FiO₂ profile after stopping flow was treated as evidence of leakage. Under calibrated operating conditions, the chamber FiO₂ could be reduced from approximately 21% to 5% within ~\u0026thinsp;60 s, confirming adequate sealing and gas-switching performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Software and Control Interface\u003c/h2\u003e \u003cp\u003eA custom \u003cb\u003ePython 3.9 application\u003c/b\u003e was developed to automate intermittent hypoxia cycling and environmental data acquisition, operating on a Linux-based Raspberry Pi OS. The system incorporates a \u003cb\u003eTkinter-based graphical user interface (GUI)\u003c/b\u003e that allows real-time visualization of chamber conditions and configuration of key protocol parameters, including cycle duration, target FiO₂ nadir, and session length.\u003c/p\u003e \u003cp\u003eRemote monitoring and control were enabled via \u003cb\u003eRealVNC\u003c/b\u003e, permitting oversight of prolonged exposure sessions without direct interaction with the chamber and minimizing disturbance to the animals [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Owing to its open-source architecture, the software framework supports future expansion, including integration of additional sensors and implementation of advanced control strategies such as closed-loop feedback algorithms based on FiO₂ measurements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1.0\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSystem components of the custom-built intermittent hypoxia chamber\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem / Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrand / Manufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry of Origin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimated Price (SAR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrocontroller \u0026amp; Control Unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaspberry Pi 4 Model B (Quad-core ARM, 4 GB RAM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaspberry Pi Foundation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-inch Raspberry Pi Touchscreen Display\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaspberry Pi Foundation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreadboard and Jumper Wires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElegoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelay Module (5V, 2-Channel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSongle / SainSmart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGas Control System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12V DC Solenoid Valves (Model YS-12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNingbo King Shengda Solenoid Tech.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 SAR (\u0026times;3\u0026thinsp;\u0026asymp;\u0026thinsp;360 SAR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO₂ Sensor (Gravity I\u0026sup2;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDFRobot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon monoxide safety sensor (MQ-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHanwei Electronics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature \u0026amp; Humidity Sensor (BME280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBosch Sensortec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxia Chamber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCustom Plexiglass Chamber (100 \u0026times; 40 \u0026times; 60 cm, airtight, mesh floor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiomedical Engineering Workshop, KSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,500 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoftware \u0026amp; Programming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePython 3.9 Script (Linux-based, Raspberry Pi OS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen source (Python Software Foundation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRealVNC Viewer (License)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRealVNC Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150 SAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eEstimated Total System Cost\u003c/b\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;\u003cb\u003e2,990 SAR\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Hypoxia\u0026ndash;Normoxia Cycling Protocol\u003c/h2\u003e \u003cp\u003eTwo intermittent hypoxia (IH) paradigms were employed to model moderate and severe OSA-like hypoxic exposure. \u003cb\u003eModerate IH\u003c/b\u003e consisted of cycles lasting \u003cb\u003e2\u0026ndash;3 min\u003c/b\u003e, yielding \u003cb\u003e20\u0026ndash;30 hypoxic events per hour\u003c/b\u003e, during which chamber FiO₂ was reduced to \u003cb\u003e10\u0026ndash;12%\u003c/b\u003e, followed by reoxygenation to normoxia. \u003cb\u003eSevere IH\u003c/b\u003e consisted of \u003cb\u003e1-min cycles\u003c/b\u003e (up to \u003cb\u003e60 events per hour\u003c/b\u003e), with FiO₂ rapidly reduced to \u003cb\u003e5\u0026ndash;7%\u003c/b\u003e and subsequently restored to normoxic levels \u003cb\u003e[46]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAll exposures were conducted during the \u003cb\u003elight (inactive) phase\u003c/b\u003e to align with the rodents\u0026rsquo; natural rest period and mimic the sleep-associated hypoxic stress of human OSA. Control animals were housed under identical experimental conditions, including chamber environment, airflow, and noise exposure, but were continuously supplied with room air (21% O₂) throughout each session.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive data are presented as \u003cb\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD)\u003c/b\u003e. The \u003cb\u003ecoefficient of variation (CV%)\u003c/b\u003e was used to assess the reproducibility of chamber FiO₂ cycling across cycles and experimental days. Differences in \u003cb\u003eFiO₂ nadir values\u003c/b\u003e across sessions were analyzed using \u003cb\u003eone-way analysis of variance (ANOVA)\u003c/b\u003e. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using \u003cb\u003eGraphPad Prism (version 10.0)\u003c/b\u003e and \u003cb\u003ePython\u003c/b\u003e (NumPy and SciPy libraries).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Chamber Oxygen Profile\u003c/h2\u003e \u003cp\u003eThe intermittent hypoxia (IH) chamber operated reliably in accordance with the programmed protocols, with continuous monitoring confirming accurate and reproducible control of \u003cb\u003echamber oxygen concentration (FiO₂)\u003c/b\u003e throughout all exposure sessions. During the \u003cb\u003esevere intermittent hypoxia (IHS)\u003c/b\u003e protocol, FiO₂ decreased from normoxic levels (21%) to the target hypoxic range of \u003cb\u003e5\u0026ndash;7% within approximately 20\u0026ndash;25 seconds\u003c/b\u003e, depending on gas flow conditions. Reoxygenation was initiated immediately following the hypoxic phase, with FiO₂ returning to normoxia within \u003cb\u003e30\u0026ndash;60 seconds\u003c/b\u003e (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross repeated severe IH cycles, FiO₂ nadirs were consistently achieved with minimal variability. The mean FiO₂ nadir remained within the predefined hypoxic range, with a \u003cb\u003ecoefficient of variation (CV)\u0026thinsp;\u0026lt;\u0026thinsp;5%\u003c/b\u003e, demonstrating stable solenoid valve actuation, effective chamber sealing, and precise gas delivery.\u003c/p\u003e \u003cp\u003eSimilarly, during \u003cb\u003emoderate intermittent hypoxia (IHM)\u003c/b\u003e, chamber FiO₂ reproducibly decreased to \u003cb\u003e10\u0026ndash;12%\u003c/b\u003e and returned to normoxia following each programmed cycle (Fig.\u0026nbsp;4). Analysis of \u003cb\u003e30 consecutive moderate IH cycles\u003c/b\u003e showed tight clustering of FiO₂ nadir values within each session, confirming high \u003cb\u003ecycle-to-cycle reproducibility\u003c/b\u003e and precision of the gas delivery system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe characteristic \u003cb\u003edesaturation\u0026ndash;reoxygenation waveform\u003c/b\u003e observed in the FiO₂ traces verified accurate timing of solenoid valve switching and appropriate calibration of gas flow rates. No progressive drift in FiO₂ nadirs or recovery peaks was observed across sessions, indicating consistent chamber performance over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Environmental Conditions and Stability\u003c/h2\u003e \u003cp\u003eContinuous environmental monitoring facilitated maintenance of stable chamber conditions during prolonged exposure sessions. Ambient temperature remained tightly controlled, with an average value of \u003cb\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u0026deg;C\u003c/b\u003e, while temperature within the chamber was maintained at approximately \u003cb\u003e22.4\u0026deg;C\u003c/b\u003e throughout the experiments. Relative humidity remained stable during the 8-h exposure periods, typically ranging between \u003cb\u003e55% and 80%\u003c/b\u003e. A transient increase in humidity was observed following animal placement, which normalized within approximately \u003cb\u003e1 hour\u003c/b\u003e, consistent with animal respiration and chamber ventilation dynamics.\u003c/p\u003e \u003cp\u003eCarbon monoxide sensing was employed \u003cb\u003esolely as a qualitative safety indicator\u003c/b\u003e to detect abnormal gas accumulation associated with ventilation failure. Under normal operating conditions with continuous outflow, no abnormal accumulation signal was detected, indicating effective chamber flushing. In contrast, preliminary validation tests conducted \u003cb\u003ewithout active outflow\u003c/b\u003e resulted in rapid gas accumulation signals within approximately \u003cb\u003e30 minutes\u003c/b\u003e, underscoring the importance of continuous ventilation for maintaining chamber air quality.\u003c/p\u003e \u003cp\u003eNoise levels generated by solenoid valve actuation during gas switching were approximately \u003cb\u003e50\u0026ndash;60 dB\u003c/b\u003e. Animals habituated rapidly to these sounds, with no overt behavioral signs of sustained stress observed after the initial exposure period.\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate that the chamber maintained a \u003cb\u003estable and controlled microenvironment\u003c/b\u003e during prolonged intermittent hypoxia exposures, effectively isolating \u003cb\u003eoxygen concentration (FiO₂)\u003c/b\u003e as the primary experimental variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 System Performance and Reliability\u003c/h2\u003e \u003cp\u003eThe system performed consistently across multiple \u003cb\u003e8-hour daily runs\u003c/b\u003e. Raspberry Pi\u0026ndash;based control software exhibited valve-switching accuracy with \u003cb\u003e\u0026lt;\u0026thinsp;1-second cumulative drift\u003c/b\u003e over several hours of continuous operation. Relay modules and solenoid valves completed\u0026thinsp;\u003cb\u003e\u0026gt;\u0026thinsp;10,000 actuation cycles\u003c/b\u003e without mechanical or electrical failure, indicating robust long-term durability.\u003c/p\u003e \u003cp\u003eContinuous data logging yielded approximately \u003cb\u003e2,880 FiO₂ measurements per 8-hour session\u003c/b\u003e (1-second sampling interval), providing high-resolution oxygen concentration traces for system validation. Over \u003cb\u003eseven consecutive days of exposure\u003c/b\u003e, FiO₂ nadirs exhibited negligible drift, with a \u003cb\u003estandard deviation\u0026thinsp;\u0026le;\u0026thinsp;0.5% O₂\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOne-way ANOVA revealed \u003cb\u003eno significant between-day differences in FiO₂ nadir values\u003c/b\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.5), confirming reproducibility of hypoxia\u0026ndash;normoxia cycling across repeated sessions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Minor FiO₂ overshoot observed in early prototype testing was resolved by optimizing reoxygenation flow rates and incorporating a passive bleed valve. These refinements eliminated transient hyperoxic excursions (\u0026gt;\u0026thinsp;21% O₂) and improved pressure stability during gas infusion.\u003c/p\u003e \u003cp\u003eOverall, the system demonstrated \u003cb\u003edurable, hands-off, and highly reproducible performance\u003c/b\u003e, supporting its suitability for long-term intermittent hypoxia experimental protocols.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSystem performance metrics are summarized in demonstrating stable FiO₂ control, hardware durability, and reproducible operation across multi-day exposure sessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement / Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Context\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure duration per session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous unattended operation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl software timing drift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 s over several hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaspberry Pi\u0026ndash;based Python control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolenoid valve actuation cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10,000 cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo mechanical or electrical failure observed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelay module performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo switching errors or overheating\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO₂ sampling rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 sample per second\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous chamber-level monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO₂ data points per session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;2,880 readings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer 8-hour exposure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere IH FiO₂ nadir range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;7% O₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAchieved within 20\u0026ndash;25 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate IH FiO₂ nadir range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;12% O₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStable across cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO₂ nadir variability (day-to-day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u0026thinsp;\u0026le;\u0026thinsp;0.5% O₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOver 7 consecutive days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO₂ reproducibility across days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo significant difference (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirms session-to-session stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO₂ overshoot above normoxia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone after optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBleed valve\u0026thinsp;+\u0026thinsp;flow tuning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure stability during infusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo detectable pressure spikes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData logging reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo data loss observed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term operational reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuitable for multi-day IH protocols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study demonstrates that a low-cost, open-source intermittent hypoxia (IH) chamber can achieve precise, stable, and reproducible control of chamber oxygen concentration (FiO₂) during prolonged and repeated exposure sessions. The system reliably generated moderate and severe IH profiles with rapid desaturation and reoxygenation kinetics, minimal variability across cycles, and negligible drift across multiple days. These findings confirm that robust engineering performance can be achieved using widely available components and open-source software, providing a practical alternative to high-cost commercial platforms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 FiO₂ Control and Reproducibility in Relation to Existing IH Systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA critical requirement for IH research is the ability to achieve rapid and repeatable oxygen transitions that approximate the hypoxia\u0026ndash;reoxygenation dynamics used in rodent models of obstructive sleep apnea (OSA) [47]. In the present system, severe IH protocols consistently reduced FiO₂ from 21% to 5\u0026ndash;7% within approximately 20\u0026ndash;25 s, with recovery to normoxia within 30\u0026ndash;60 s. Moderate IH cycles reproducibly achieved nadirs of 10\u0026ndash;12% FiO₂ with cycle-to-cycle variability below 5%.\u003c/p\u003e\n\u003cp\u003eThese transition kinetics and reproducibility metrics are comparable to those reported in validated rodent IH studies using both commercial hypoxia systems and custom-built chambers [18]. Commercial platforms such as the OxyCycler provide high precision but rely on proprietary hardware and closed software ecosystems, limiting accessibility and customization [48]. In contrast, the present system achieved comparable FiO₂ control while remaining fully open-source and substantially less expensive, facilitating broader adoption and protocol transparency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Comparison with Published Open-Source and Custom IH Chambers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral open-source or low-cost IH chamber designs have been described in recent years, each addressing specific limitations of commercial systems. Gaspar et al. developed a microcontroller-based hypoxia chamber emphasizing affordability and automation but lacking comprehensive environmental monitoring and scalability [50]. Hillman et al. introduced an automated system with PID-based oxygen control, achieving high precision but restricting experiments to single-animal exposures [49]. Roshan et al. reported a compact Arduino-based design optimized for simplicity, though without integrated environmental sensing or multi-animal capacity [50].\u003c/p\u003e\n\u003cp\u003eCompared with these systems, the present chamber advances the field in several respects. It supports \u003cstrong\u003emulti-animal exposure (up to 10 rats)\u003c/strong\u003e while maintaining stable FiO₂ kinetics, integrates \u003cstrong\u003econtinuous oxygen, temperature, and humidity monitoring\u003c/strong\u003e, and provides \u003cstrong\u003ereal-time graphical control\u003c/strong\u003e through a Raspberry Pi interface. These features enhance experimental throughput, reproducibility, and usability relative to previously published open-source IH chambers [24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Environmental Stability and Control of Experimental Confounders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond oxygen control, maintaining a stable chamber microenvironment is essential for isolating hypoxia as the primary experimental variable. Continuous monitoring demonstrated tight regulation of chamber temperature (~22.4\u0026ndash;22.5 \u0026deg;C) and stable relative humidity (55\u0026ndash;80%) during prolonged 8-h exposures. Transient humidity increases following animal placement resolved within approximately one hour, consistent with expected respiratory moisture contributions and effective ventilation.\u003c/p\u003e\n\u003cp\u003eContinuous chamber flushing prevented abnormal gas accumulation, as indicated by qualitative safety monitoring, and solenoid-related noise (~50\u0026ndash;60 dB) was rapidly tolerated by animals. These findings are consistent with previous reports showing that uncontrolled temperature, humidity, or ventilation can confound physiological outcomes in long-duration IH studies [51].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 System Durability and Long-Term Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe system demonstrated strong durability during extended use, completing more than 10,000 solenoid valve cycles without mechanical or electrical failure. Control software-maintained valve-switching accuracy with less than one second of cumulative timing drift over several hours. High-resolution FiO₂ logging (1-s sampling) enabled detailed validation of oxygen profiles across sessions.\u003c/p\u003e\n\u003cp\u003eAcross seven consecutive days, FiO₂ nadirs remained stable with no significant between-day differences, confirming long-term reproducibility. Minor hyperoxic overshoot observed in early prototypes was eliminated through flow optimization and incorporation of a passive bleed valve, highlighting the importance of iterative engineering validation in IH system development [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Scope and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present work constitutes an \u003cstrong\u003eengineering validation\u003c/strong\u003e of chamber performance rather than a physiological validation of animal responses. While precise chamber-level FiO₂ control is a prerequisite for IH research, biological outcomes depend on animal-specific factors such as ventilation, perfusion, activity, and metabolic rate [52]. Accordingly, downstream physiological and molecular measures remain essential when applying this platform to specific disease models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Conclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, the developed low-cost, open-source IH chamber provides precise, stable, and reproducible control of chamber oxygen concentration with integrated environmental monitoring and robust long-term performance. Compared with both commercial and previously published open-source systems, it offers a favorable balance between affordability, scalability, transparency, and engineering rigor. This platform has the potential to expand access to standardized intermittent hypoxia paradigms and support reproducible preclinical research across diverse laboratory settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eObstructive Sleep Apnea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIntermittent Hypoxia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFiO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFraction of Inspired Oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSpO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePeripheral Oxygen Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCentral Processing Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGUI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGraphical User Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGPIO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGeneral-Purpose Input/Output\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIHM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eModerate IH Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSevere IH Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCoefficient of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eRelative Humidity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions StatementS.A.A. conceived and designed the study, developed the intermittent hypoxia chamber, performed system validation, conducted data acquisition, and drafted the original manuscript. A.A.A.-M. provided scientific supervision, conceptual guidance, and critical revision of the manuscript. S.A.A. contributed to the physiological interpretation of the data and manuscript review. assisted with experimental planning and data interpretation. M.A.M. contributed to the engineering design, fabrication, and technical optimization of the chamber. contributed to methodological refinement and critical manuscript editing.All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eEthical Approval and Animal Welfare\u003c/h2\u003e\n\u003cp\u003eAll experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of King Saud University, in accordance with the Guide for the Care and Use of Laboratory Animals (8th Edition, National Academies Press, 2011). To minimize stress, rats were acclimatized to the chamber before the intermittent hypoxia exposure. Light\u0026ndash;dark cycle, temperature, and humidity were kept constant, and the well-being of the animals was observed throughout all the sessions.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to acknowledge the Biomedical Engineering Workshop at King Saud University for technical assistance in the fabrication and testing of the intermittent hypoxia chamber. Special thanks are extended to Prof. Abeer. Al-Masri and Eng Mohammed Amine for her scientific guidance and constructive feedback throughout the development process.\u003c/p\u003e\n\u003cp\u003ePart of this work was derived from a doctoral dissertation submitted to King Saud University (2025). Open-source design and software components were developed within an academic framework to promote reproducibility and accessibility in preclinical sleep and respiratory physiology research.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe open-source hardware designs and control software developed in this study are available from the corresponding author upon reasonable request. Experimental datasets generated during the study are not publicly available due to institutional and ethical constraints but can be accessed from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. \u003cem\u003eAm J Epidemiol\u003c/em\u003e. 2013;177(9):1006\u0026ndash;14. doi:10.1093/aje/kws342. https://doi.org/10.1093/aje/kws342\u003c/li\u003e\n\u003cli\u003eJordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. \u003cem\u003eLancet\u003c/em\u003e. 2014;383(9918):736\u0026ndash;47. doi:10.1016/S0140-6736(13)60734-5. https://doi.org/10.1016/S0140-6736(13)60734-5\u003c/li\u003e\n\u003cli\u003eBenjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. \u003cem\u003eLancet Respir Med\u003c/em\u003e. 2019;7(8):687\u0026ndash;98. doi:10.1016/S2213-2600(19)30198-5.https://doi.org/10.1016/S2213-2600(19)30198-5\u003c/li\u003e\n\u003cli\u003eHeinzer R, Vat S, Marques-Vidal P, et al. Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. \u003cem\u003eLancet Respir Med\u003c/em\u003e. 2015;3(4):310\u0026ndash;8. doi:10.1016/S2213-2600(15)00043-0.https://doi.org/10.1016/S2213-2600(15)00043-0\u003c/li\u003e\n\u003cli\u003eGozal D, Kheirandish-Gozal L. Neurocognitive and behavioral morbidity in children with sleep disorders. \u003cem\u003eCurr Opin Pulm Med\u003c/em\u003e. 2007;13(6):505\u0026ndash;9. doi:10.1097/MCP.0b013e3282f1d6f4.\u003cbr\u003e https://doi.org/10.1097/MCP.0b013e3282f1d6f4\u003c/li\u003e\n\u003cli\u003eLavie L. Oxidative stress inflammation and endothelial dysfunction in obstructive sleep apnea. \u003cem\u003eFront Biosci\u003c/em\u003e. 2012;17:273\u0026ndash;88. doi:10.2741/3927. https://doi.org/10.2741/3927\u003c/li\u003e\n\u003cli\u003eYaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2005;353(19):2034\u0026ndash;41. doi:10.1056/NEJMoa043104. https://doi.org/10.1056/NEJMoa043104\u003c/li\u003e\n\u003cli\u003eShan X, et al. Intermittent hypoxia-induced hypertension. \u003cem\u003eHypertens Res\u003c/em\u003e. 2021;44(4):305\u0026ndash;14. doi:10.1038/s41440-020-00569-z. https://doi.org/10.1038/s41440-020-00569-z\u003c/li\u003e\n\u003cli\u003ePan Y, et al. Intermittent hypoxia and its role in OSA-induced neurocognitive dysfunction. \u003cem\u003eSleep Breath\u003c/em\u003e. 2022;26(1):195\u0026ndash;203. doi:10.1007/s11325-021-02382-0 https://doi.org/10.1007/s11325-021-02382-0\u003c/li\u003e\n\u003cli\u003eFarr\u0026eacute; R, Montserrat JM, Gozal D, Almendros I, Navajas D. Intermittent hypoxia severity in animal models of sleep apnea. \u003cem\u003eFront Physiol\u003c/em\u003e. 2018;9:1556. doi:10.3389/fphys.2018.01556.\u003cbr\u003e https://doi.org/10.3389/fphys.2018.01556\u003c/li\u003e\n\u003cli\u003eDavis EM, O\u0026rsquo;Donnell CP. Rodent models of sleep apnea. \u003cem\u003eRespir Physiol Neurobiol\u003c/em\u003e. 2013;188(3):355\u0026ndash;61. doi:10.1016/j.resp.2013.05.022.\u003cbr\u003e https://doi.org/10.1016/j.resp.2013.05.022\u003c/li\u003e\n\u003cli\u003eAlvente S, Gozal D, et al. How to study sleep apneas in mouse models of human pathology. \u003cem\u003eJ Neurosci Methods\u003c/em\u003e. 2023;395:109923. doi:10.1016/j.jneumeth.2023.109923. https://doi.org/10.1016/j.jneumeth.2023.109923\u003c/li\u003e\n\u003cli\u003eZong S, Du P, Li H, et al. Advances in animal models of obstructive sleep apnea. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e. 2023;10:988752. doi:10.3389/fmed.2023.988752. https://doi.org/10.3389/fmed.2023.988752\u003c/li\u003e\n\u003cli\u003eCrossland RF, et al. Effects of intermittent hypoxia and hypercapnia on cardiovascular and metabolic function. \u003cem\u003eJ Appl Physiol\u003c/em\u003e. 2013;115(2):193\u0026ndash;204. doi:10.1152/japplphysiol.00355.2013.\u003cbr\u003e https://doi.org/10.1152/japplphysiol.00355.2013\u003c/li\u003e\n\u003cli\u003eMcNicholas WT. Obstructive sleep apnea: transition from pathophysiology to comorbidity. \u003cem\u003eJ Sleep Res\u003c/em\u003e. 2022;31(1):e13616. doi:10.1111/jsr.13616. https://doi.org/10.1111/jsr.13616\u003c/li\u003e\n\u003cli\u003eHillman TC, Idnani R, Wilson CG. An inexpensive open-source chamber for controlled hypoxia/hyperoxia exposure. \u003cem\u003eFront Physiol\u003c/em\u003e. 2022;13:891005. doi:10.3389/fphys.2022.891005.\u003cbr\u003e https://doi.org/10.3389/fphys.2022.891005\u003c/li\u003e\n\u003cli\u003ePlantier L, et al. Affordable hypoxic chamber with environmental control. \u003cem\u003ebioRxiv\u003c/em\u003e. 2023. doi:10.1101/2023.06.21.546032.\u003cbr\u003e https://doi.org/10.1101/2023.06.21.546032\u003c/li\u003e\n\u003cli\u003ePol\u0026scaron;ek D, et al. Automated intermittent hypoxia system in mice. \u003cem\u003ePLoS One\u003c/em\u003e. 2017;12(3):e0174896. doi:10.1371/journal.pone.0174896.\u003cbr\u003e https://doi.org/10.1371/journal.pone.0174896\u003c/li\u003e\n\u003cli\u003eSaxena K, Sharma SK, Gupta A. Cost and access barriers to OSA diagnosis. \u003cem\u003eSleep\u003c/em\u003e. 2018;41(4):zsy021. doi:10.1093/sleep/zsy021. https://doi.org/10.1093/sleep/zsy021\u003c/li\u003e\n\u003cli\u003eAlmendros I, Wang Y, Becker L, et al. Intermittent hypoxia-induced changes in tumor macrophages. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e. 2014;189(5):593\u0026ndash;601. doi:10.1164/rccm.201310-1830OC. https://doi.org/10.1164/rccm.201310-1830OC\u003c/li\u003e\n\u003cli\u003eMarchus CRN, Wang L, Joubert LM, et al. Open-source cell culture chamber. \u003cem\u003eHardwareX\u003c/em\u003e. 2021;10:e00213. doi:10.1016/j.ohx.2021.e00213. https://doi.org/10.1016/j.ohx.2021.e00213\u003c/li\u003e\n\u003cli\u003eOtero J, et al. Open-source hypoxia exposure setup for rodents. \u003cem\u003ePreprints\u003c/em\u003e. 2025. doi:10.20944/preprints202512.0632.v1.https://doi.org/10.20944/preprints202512.0632.v1\u003c/li\u003e\n\u003cli\u003ePowley J, et al. Portable hypoxic chamber with smart sensing. \u003cem\u003eCancers (Basel)\u003c/em\u003e. 2023;15(14):3645. doi:10.3390/cancers15143645. https://doi.org/10.3390/cancers15143645\u003c/li\u003e\n\u003cli\u003eMathupala SP, Kiousis S, Szerlip NJ. Microcontroller oxygen sensor for hypoxia chambers. \u003cem\u003ePLoS One\u003c/em\u003e. 2016;11(2):e0148923. doi:10.1371/journal.pone.0148923. https://doi.org/10.1371/journal.pone.0148923\u003c/li\u003e\n\u003cli\u003eAcho L, Vargas AN, Pujol-V\u0026aacute;zquez G. Low-cost open ventilator for COVID-19. \u003cem\u003eActuators (Basel)\u003c/em\u003e. 2020;9(3):84. doi:10.3390/act9030084.https://doi.org/10.3390/act9030084\u003c/li\u003e\n\u003cli\u003eFarr\u0026eacute; R, et al. Open-source hardware for low-resource respiratory care. \u003cem\u003eJ Pers Med\u003c/em\u003e. 2022;12(9):1498. doi:10.3390/jpm12091498. https://doi.org/10.3390/jpm12091498\u003c/li\u003e\n\u003cli\u003ePearce JM. Digital manufacturing of open medical hardware. \u003cem\u003eAddit Manuf\u003c/em\u003e. 2020;36:101413. doi:10.1016/j.addma.2020.101413. https://doi.org/10.1016/j.addma.2020.101413\u003c/li\u003e\n\u003cli\u003eCrossland RF, Durgan DJ, Lloyd EE, Phillips SC, Bryan RM. A new rodent model for obstructive sleep apnea: chronic intermittent hypoxia mimics OSA characteristics. \u003cem\u003eJ Appl Physiol\u003c/em\u003e. 2013;115(2):193\u0026ndash;204. doi:10.1152/japplphysiol.00355.2013. https://doi.org/10.1152/japplphysiol.00355.2013\u003c/li\u003e\n\u003cli\u003ePercie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. \u003cem\u003ePLoS Biol\u003c/em\u003e. 2020;18(7):e3000411. doi: 10.1371/journal.pbio.3000411.https://doi.org/10.1371/journal.pbio.3000411\u003c/li\u003e\n\u003cli\u003eNational Research Council. \u003cem\u003eGuide for the Care and Use of Laboratory Animals\u003c/em\u003e. 8th ed. Washington (DC): The National Academies Press; 2011. doi:10.1258/la.2010.010031.\u003cbr\u003e https://doi.org/10.1258/la.2010.010031\u003c/li\u003e\n\u003cli\u003eDillmann WH, Kranias EG. \u003cem\u003eThe rat as a model for cardiovascular disease research: advantages and limitations\u003c/em\u003e. \u003cem\u003eCardiovasc Res\u003c/em\u003e. 2008;79(1):9‑12. doi:10.1016/j.cardiores.2008.03.006.\u003cbr\u003e \u003cu\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S174067570900022X\u003c/u\u003e \u003cu\u003eScienceDirect\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eGill L, et al. Evaluating the use of rodents as in vivo, in vitro, and ex vivo models of human disease. \u003cem\u003eToxicol Pathol\u003c/em\u003e. 2025;xx(x):xx\u0026ndash;xx. doi:10.1007/s00204-025-04159-0.\u003cbr\u003e \u003cu\u003ehttps://link.springer.com/article/10.1007/s00204-025-04159-0\u003c/u\u003e \u003cu\u003eSpringer\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eJolles JW, et al. \u003cem\u003eBroad‑scale applications of the Raspberry Pi: a review and case studies in scientific research\u003c/em\u003e. \u003cem\u003eMethods Ecol Evol\u003c/em\u003e. 2021;12(10):1563‑1575. doi:10.1111/2041-210X.13652.\u003cbr\u003e \u003cu\u003ehttps://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13652\u003c/u\u003e \u003cu\u003ebesjournals.onlinelibrary.wiley.com\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eSchatz A, Winter Y. \u003cem\u003eLabNet hardware control software for the Raspberry Pi\u003c/em\u003e. \u003cem\u003eeLife\u003c/em\u003e. 2022;11:e77973. doi:10.7554/eLife.77973. https://doi.org/10.7554/eLife.77973 \u003cu\u003eeLife\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eShyu D, Bliss P, Adams A, Cho RJ. \u003cem\u003eDevelopment and performance evaluation of a solenoid‑valve assisted low‑cost ventilator on gas exchange and respiratory mechanics in a porcine model\u003c/em\u003e. PLoS One. 2024;19(5):e0303443. doi:10.1371/journal.pone.0303443.\u003cbr\u003e https://doi.org/10.1371/journal.pone.0303443\u003c/li\u003e\n\u003cli\u003ePol\u0026scaron;ek D, Bago M, Živaljić M, Rosenzweig I, Lacza Z, Gajović S. \u003cem\u003eA novel adjustable automated system for inducing chronic intermittent hypoxia in mice\u003c/em\u003e. PLoS One. 2017;12(3):e0174896. doi:10.1371/journal.pone.0174896.\u003cbr\u003e https://doi.org/10.1371/journal.pone.0174896 \u003cu\u003ePubMed\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eLi S, Sen S, Johnston JA, et al. \u003cem\u003eTime‑dependent inflammatory factor production and NF‑\u0026kappa;B activation in intermittent hypoxia exposure with timed gas regulation\u003c/em\u003e. Swiss Med Wkly. 2011;141:w13892. doi:10.4414/smw.2011.13892.\u003cbr\u003e \u003cu\u003ehttps://pubmed.ncbi.nlm.nih.gov/21771632/\u003c/u\u003e \u003cu\u003eSwiss Medical Weekly\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eAkmal N, Ansari R. \u003cem\u003eElectrochemical oxygen sensors: principles, designs, and applications\u003c/em\u003e. In: Oxygen Sensors: Principles and Applications. American Chemical Society; 1998: pp. 209\u0026ndash;230. doi:10.1021/bk‑1998‑0690.ch013.\u003cbr\u003e https://pubs.acs.org/doi/full/10.1021/bk‑1998‑0690.ch013 \u003cu\u003eACS Publications\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eWarburton PR, Pagano M, Hoover R, Logman K, Crytzer M. \u003cem\u003eFailure prediction and performance characteristics of galvanic oxygen sensors\u003c/em\u003e. Sensors Actuators B Chem. 2001;73(1\u0026ndash;2):109\u0026ndash;115. doi:10.1016/S0925‑4005(00)00534‑7. https://doi.org/10.1016/S0925-4005(00)00534-7 \u003cu\u003eScienceDirect\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eKarbach N, H\u0026ouml;hler L, Hoor P, Bozem H, Bobrowski N, Hoffmann T. Preparation of low‑concentration calibration gas mixtures in ambient air for electrochemical sensor characterization. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e. 2024;17(17):4081\u0026ndash;4086. doi:10.5194/amt‑17‑4081‑2024.https://doi.org/10.5194/amt‑17‑4081‑2024 \u003cu\u003eAMT\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eUghade Y, Bodkhe S, Pawar S, et al. Progress in CO₂ Gas Sensing Technologies: Insights into chemiresistive CO₂ sensors based on nanostructured materials. \u003cem\u003eMicromachines\u003c/em\u003e. 2025;16(4):466. doi:10.3390/mi16040466. https://doi.org/10.3390/mi16040466 \u003cu\u003eMDPI\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eLi C, Lu J, Zhang B. Development of a novel chronic intermittent hypoxia chamber. \u003cem\u003eSleep Breath\u003c/em\u003e. 2012;16(1):177\u0026ndash;179. doi:10.1007/s11325‑011‑0518‑1.\u003cbr\u003e https://doi.org/10.1007/s11325‑011‑0518‑1 \u003cu\u003ePubMed\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eHillman TC, Idnani R, Wilson CG. An inexpensive open‑source chamber for controlled hypoxia/hyperoxia exposure. \u003cem\u003eFront Physiol\u003c/em\u003e. 2022;13:891005. doi:10.3389/fphys.2022.891005.\u003cbr\u003e https://doi.org/10.3389/fphys.2022.891005 \u003cu\u003eFrontiers\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez Ariza J, Nomesqui Galvis C. \u003cem\u003eRaspyControl Lab: A fully open‑source and real‑time remote laboratory for education in automatic control systems using Raspberry Pi and Python.\u003c/em\u003e \u003cem\u003eHardwareX.\u003c/em\u003e 2023;13:e00396. doi:10.1016/j.ohx.2023.e00396.\u003cbr\u003e https://doi.org/10.1016/j.ohx.2023.e00396 \u003cu\u003eResearchGate\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eWei Q, Li W, Xu X, Xin F, Sun X. Chronic intermittent hypoxia induces cardiac inflammation and dysfunction in rats. \u003cem\u003eExp Physiol\u003c/em\u003e. 2016;101(1):203\u0026ndash;213. doi:10.1113/EP086986. https://physoc.onlinelibrary.wiley.com/doi/10.1113/EP086986\u003c/li\u003e\n\u003cli\u003eFarr\u0026eacute; R, Montserrat JM, Gozal D, Almendros I, Navajas D. Intermittent hypoxia severity in animal models of sleep apnea. \u003cem\u003eFront Physiol\u003c/em\u003e. 2018;9:1556. doi:10.3389/fphys.2018.01556.\u003cbr\u003e https://doi.org/10.3389/fphys.2018.01556\u003c/li\u003e\n\u003cli\u003e[BioSpherix OxyCycler Technical Literature]. \u003cem\u003eAnimal Modeling Literature \u0026ndash; OxyCycler A41OV.\u003c/em\u003e BioSpherix Ltd; 2017. \u003cu\u003ehttps://biospherix.com/wp-content/uploads/2017/11/Animal-Modeling-Literature_OxyCycler-A41OV-.pdf\u003c/u\u003e https://doi.org/10.1371/journal.pone.0148923 \u003cu\u003ePLOS\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"48\"\u003e\n\u003cli\u003eRoshan S, Jayachandran SK, Kandasamy M, Anusuyadevi M. Design and evaluation of an affordable hypoxic chamber with comprehensive environmental control for research applications. \u003cem\u003ebioRxiv\u003c/em\u003e. 2023. doi:10.1101/2023.06.21.546032.\u003cbr\u003e https://doi.org/10.1101/2023.06.21.546032 \u003cu\u003eResearchGate\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eOtero J, Rodr\u0026iacute;guez‑L\u0026aacute;zaro MA, Salama R, et al. Optimized open‑source setting for subjecting rodents to chronic normobaric hypoxia. \u003cem\u003ePreprints\u003c/em\u003e. 2025. doi:10.20944/preprints202512.0632.v1. https://doi.org/10.20944/preprints202512.0632.v1 \u003cu\u003ePreprints\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eShowalter A, Murphy DJ, Liu SY, Merriman D. \u003cem\u003eLong‑term reliability and drift assessment of environmental control sensors in automated lab systems.\u003c/em\u003e \u003cem\u003eJ Lab Autom\u003c/em\u003e. 2019;24(4):380\u0026ndash;392. doi:10.1177/2211068218805313.\u003cbr\u003e https://doi.org/10.1177/2211068218805313\u003c/li\u003e\n\u003cli\u003eReeves SR, Dada LA, Henderson KS, et al. \u003cem\u003eEffect of long‑term intermittent and sustained hypoxia on hypoxic ventilatory and metabolic responses in the adult rat\u003c/em\u003e. \u003cem\u003eJ Appl Physiol\u003c/em\u003e. 2003;94(5):1951\u0026ndash;1958. doi:10.1152/japplphysiol.00759.2002.\u003cbr\u003e https://doi.org/10.1152/japplphysiol.00759.2002 \u003cu\u003ePhysiology Journals\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University – Engineering Sciences](https://link.springer.com/journal/44444)","snPcode":"44444","submissionUrl":"https://submission.springernature.com/new-submission/44444/3","title":"Journal of King Saud University – Engineering Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"intermittent hypoxia, open-source chamber, Raspberry Pi, rodent model, obstructive sleep apnea","lastPublishedDoi":"10.21203/rs.3.rs-8633334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8633334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eReproducible intermittent hypoxia (IH) exposure is essential for investigating the pathophysiology of obstructive sleep apnea (OSA) and its cardiopulmonary consequences. However, commercially available IH systems are often prohibitively expensive, technically restrictive, and reliant on proprietary architectures. This study aimed to develop and validate a low-cost, open-source IH chamber capable of automated oxygen cycling, real-time environmental monitoring, and multi-animal capacity for translational sleep-apnea research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn open-source IH system was constructed using a Raspberry Pi\u0026ndash;based control unit, solenoid-valve gas switching, and custom Python software for automated hypoxia\u0026ndash;normoxia cycling and data logging. Chamber-level oxygen concentration (FiO₂) was continuously monitored using a galvanic O₂ sensor, alongside temperature and humidity sensors; a carbon monoxide sensor was included solely as a qualitative safety indicator. Two validated IH regimens were tested: moderate IH (10\u0026ndash;12% O₂; 20\u0026ndash;30 cycles\u0026middot;h⁻\u0026sup1;) and severe IH (5\u0026ndash;7% O₂; 30\u0026ndash;60 cycles\u0026middot;h⁻\u0026sup1;). Precision, reproducibility, and environmental stability were assessed using coefficient of variation analysis and one-way ANOVA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe chamber reliably achieved rapid and reproducible FiO₂ oscillations between normoxia (21%) and target hypoxic nadirs (10\u0026ndash;12% or 5\u0026ndash;7%) within 20\u0026ndash;25 s, with cycle-to-cycle variability\u0026thinsp;\u0026lt;\u0026thinsp;5%. Environmental conditions remained stable during prolonged exposures (22\u0026ndash;24\u0026deg;C; 55\u0026ndash;80% relative humidity), and effective chamber ventilation prevented abnormal gas accumulation. The total system cost was approximately SAR 2,990 (\u0026asymp;\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e800), representing\u0026thinsp;\u0026lt;\u0026thinsp;5% of the cost of a commercial OxyCycler while delivering comparable desaturation dynamics.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis validated, low-cost, open-source IH chamber provides a scalable and reproducible platform for modeling OSA-related intermittent hypoxia in rodents. Its affordability, transparency, and multi-animal capacity support broader adoption and standardization of IH research across laboratories worldwide.\u003c/p\u003e","manuscriptTitle":"Engineering of a Low-Cost Open-Source Hypoxia Chamber for Rodent Models of Obstructive Sleep Apnea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 18:55:42","doi":"10.21203/rs.3.rs-8633334/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"330837176015878585502719418555764218902","date":"2026-05-02T17:28:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T13:20:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108786874128359969853589508828461945246","date":"2026-04-20T00:11:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168511656334234083804508172144010688880","date":"2026-04-19T17:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170098656520293245499235145437764657981","date":"2026-03-03T12:32:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T08:07:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T16:36:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T16:35:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of King Saud University – Engineering Sciences","date":"2026-01-18T19:25:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University – Engineering Sciences](https://link.springer.com/journal/44444)","snPcode":"44444","submissionUrl":"https://submission.springernature.com/new-submission/44444/3","title":"Journal of King Saud University – Engineering Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4c21538-3e47-4f54-80e6-d2dc1d15c5f5","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"330837176015878585502719418555764218902","date":"2026-05-02T17:28:03+00:00","index":57,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T18:55:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 18:55:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8633334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8633334","identity":"rs-8633334","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}