Integrating Histology and Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy to Estimate the Postmortem Interval in Rat Liver

Integrating Histology and Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy to Estimate the Postmortem Interval in Rat Liver | 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 Integrating Histology and Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy to Estimate the Postmortem Interval in Rat Liver Samuel T. Adeleke, David O. Adedokun, Sina Iyiola, Christopher Igbeneghu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8388033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The precise determination of the postmortem interval (PMI) remains a significant challenge within the field of forensic science. Traditional methods, including algor mortis, rigor mortis, and livor mortis, are often constrained by variations in environmental conditions. Other methods have considerably involved pH, ions and metabolic byproduct measurements with inherent limitations and inaccuracies. Advanced techniques like flow cytometry, single-cell gel electrophoresis, polymerase chain reaction, some of which have been found expensive and cumbersome, have also largely been reported to investigate PMI in different tissues with almost similar consensus. This research aimed to explore the time-dependent postmortem alterations in rat liver tissue through histological analysis and Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy, focusing on effect sizes and their practical relevance in forensic applications. Results Histological examination revealed the presence of intact hepatocytes at the 1-hour mark, followed by autolytic changes at 12 hours, and necrotic processes accompanied by liquefaction by 48 hours. The ATR-FTIR spectral analysis paralleled these identified phases, demonstrating a consistent decrease in protein absorbance (Cohen’s d = 2.4–2.7), while glycogen levels exhibited the most pronounced decline (d = 14.4). Additionally, there was an observed increase in nucleic acids and the cytoplasmic to nuclear ratio (d = − 7.7; R = 0.913, η² = 0.834; d = − 6.9; R = 0.822, η² = 0.676), indicating substantial and reproducible time-dependent changes. Conclusions The liver shows discernible histological and biochemical transformations postmortem within a 48-hour timeframe. ATR-FTIR spectroscopy effectively detected these molecular modifications, yielding objective and quantifiable metrics such as the cytoplasm to nucleus (C:N) ratio. The integration of histological findings with spectral biomarkers enhances the accuracy, reproducibility, and forensic reliability of PMI estimations. Postmortem interval liver Autolysis ATR-FTIR spectroscopy Forensic biomarkers Glycogen Protein Nucleic acid Figures Figure 1 Figure 2 Background The postmortem interval (PMI), defined as the duration that has passed since an individual’s death, constitutes a fundamental aspect of forensic science. It plays a crucial role in reconstructing the timeline associated with criminal activities, legal inquiries, and victim identification. Nevertheless, accurately estimating the PMI presents significant difficulties, primarily due to multiple factors including environmental conditions, the specific characteristics of the body, the cause of death, and intrinsic biochemical processes (Shedge et al. 2020 ; Khalil et al. 2024 ). Traditional techniques such as algor mortis, rigor mortis, and livor mortis have been employed in preliminary postmortem assessments for many years. These observable indicators are typically beneficial within the first 24 hours postmortem; however, their reliability diminishes outside this timeframe due to various external influences, including ambient temperature, body dimensions, and clothing. Furthermore, during advanced decomposition, these signs may be absent or confounded, thereby restricting their forensic applicability (Khalil et al. 2024 ; Goff 2020 ). To address these limitations, there is an increasing focus within forensic research on biochemical and molecular markers that exhibit time-dependent variations using specific analytical interventions. Specific markers such as excited iris, vitreous potassium, hypoxanthine, lactate, denaturation of proteins, depletion or metabolism of glycogen, and stepwise degradation of nucleic acids characterize postmortem processes (Hackett et al. 2015; Tozzo et al. 2020 ; Franceschetti et al. 2023 ). These alterations can be quantified, presenting opportunities for more objective PMI estimations (Secco et al. 2025 ). Multi-marker or omics methodologies such as image analysis, single cell gel electrophoresis, terminal deoxynucleotidyl transferase and DNA amplification methods enhance accuracy by integrating multiple biochemical indicators, however these are expensive and laborious in low-income and poor-resource settings where critical forensic analysis are always demanded to unravel causes, time and onset of deaths; (Tozzo et al. 2020 ; Zhang et al. 2020 ). Advanced analytical techniques, such as Fourier Transform Infrared (FTIR) spectroscopy—and particularly Attenuated Total Reflectance (ATR)-FTIR—provides cost-effective alternative which enable fast profiling of the molecular makeup of tissues with minimal preparation. Previous research indicates that spectral variations in proteins, lipids, and nucleic acids correlate with different stages of decomposition (Ke et al., 2012 ; Scheurer et al., 2005 ). ATR-FTIR has demonstrated efficacy for PMI estimation with tissues including muscle, blood, vitreous humor, and adipose tissue (Yu et al., 2021 ; Notarstefano et al., 2025 ; Zhang et al., 2020 ). Beyond estimating PMI, its applications extend to areas such as toxicology and investigations into causes of death (Alkhuder, 2022 ; Mitu et al., 2023 ). The liver serves as a model organ for PMI assessment, having been established to be reliable (Wang et al. 2019 ; Tozzo et al. 2020 ; Zhang et al. 2020 ; Wojtowicz et al. 2025 ). The current study, first to incorporate spectroscopy with histological method in Nigeria and sub-saharan Africa, aims to explore the organ’s characteristics. We examined histological and biochemical changes in rat liver tissue at various time points (1 hour, 12 hours, and 48 hours postmortem) under controlled conditions, particularly focusing on proteins, glycogen, nucleic acids, and the cytoplasm-to-nucleus (C:N) ratio derived from spectral ratios measured on hepatic tissues. The ATR-FTIR spectroscopy was utilized alongside histological analysis to document molecular alterations. By correlating these markers with established time intervals, we aspire to identify reliable PMI indicators and advance forensic methodologies. Methods Study design and Sample Collection This research utilized an experimental animal framework to replicate the initial postmortem alterations under regulated conditions. All methodologies adhered to international standards regarding the welfare and utilization of laboratory animals (National Research Council 1985 ). The protocol received endorsement from the Research and Ethics Committee at Bowen University Teaching Hospital, Ogbomoso, Nigeria, with the approval number BUTH/REC-1197. Need for consent of participation and clinical trial number does not apply to this work. Eighteen adult Wistar rats were employed in this study. In accordance with ethical stipulations, all animals were euthanized in a humane manner, and confirmation of death was ensured prior to the collection of samples. The cadavers were preserved at ambient temperature (20–22°C) to facilitate natural autolytic and putrefactive processes. Liver tissue specimens were obtained at three distinct postmortem time intervals: 1 hour (immediate), 12 hours (early), and 48 hours (advanced early stage). These intervals were chosen to correspond to previous ATR-FTIR PMI studies focused on the early postmortem phase (Ke et al. 2012 ; Zhang et al. 2020 ; Tozzo et al. 2020 ). Each sampling time point comprised six liver samples (n = 6), with one liver extracted from each rat. Histological Analysis and Examination Tissue specimens were promptly preserved in 10% neutral-buffered formalin and subsequently processed according to established histological methods (Carleton 2019). Staining with hematoxylin and eosin (H&E) was carried out, and the prepared slides were analyzed using a light microscope. Qualitative evaluation of morphological alterations was conducted by two independent evaluators, who reported representative observations. Spectral Features Acquisition Molecular characterization was performed utilizing attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. The analysis involved unstained formalin-fixed paraffin-embedded liver tissue sections with a thickness of 15 µm, assessed using a portable ATR-FTIR spectrometer (Agilent Cary 630 FTIR, Agilent Technologies, Santa Clara, CA, USA) that featured a diamond ATR crystal. Infrared spectra were captured across the range of 4000 cm⁻¹ to 600 cm⁻¹, within the mid-infrared spectrum, at a resolution of 16 cm⁻¹. Moderate pressure was applied to ensure adequate contact between the tissue and the ATR crystal. For each spectrum, thirty-two scans were averaged. The samples underwent analysis in triplicate, with spectra recorded from three distinct regions to guarantee comprehensive representation. The raw spectral data were subjected to preprocessing via SpectraGryph software, involving baseline normalization, noise reduction, and correction processes. The absorbance intensities were quantified for specific wavenumbers that were selected based on recognized FTIR designations for biological tissues were determined using ratioing of certain peaks to give biomarkers. Protein was derived from Amide I (~ 1650 cm⁻¹) to Amide II (~ 1540 cm⁻¹) ratio, glycogen from ratio of ~ 1040 cm⁻¹ (C–O stretching in polysaccharides) to ~ 1540 cm⁻¹, and ratio nucleic acids with asymmetric (~ 1237 cm⁻¹) and symmetric (~ 1080 cm⁻¹) PO₂⁻ stretching. A cytoplasmic-to-nuclear (C:N) spectral ratio was computed as Amide II (~ 1540 cm⁻¹) divided by PO₂⁻ (~ 1080 cm⁻¹) to serve as an indicator of nuclear degradation in relation to cytoplasmic stability (Adeleke et al. 2025 ). Statistical Analysis Statistical evaluations were conducted utilizing IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Descriptive statistics, specifically mean values accompanied by standard deviations (SD), were computed for all biomarkers at time points of 1 hour, 12 hours, and 48 hours post-mortem interval (PMI). To compare various groups, independent-samples t-tests were employed for pairwise comparisons between the time points (1 h vs. 12 h; 1 h vs. 48 h; 12 h vs. 48 h). A p-value threshold of less than 0.05 was established to denote statistical significance. Despite the relatively small sample sizes, the analysis offers preliminary insights into noteworthy differences. To explore linear relationships between PMI (considered as a continuous variable in hours) and biomarker levels, Pearson's correlation analysis was performed. The results included correlation coefficients (R) alongside p-values. Additionally, where feasible, effect sizes were determined (Cohen’s d for the t-tests and η² for the correlations) to evaluate the strength of the observed associations for practical uses. Results Table 1 Macroscopic Observation of Postmortem Changes on Liver at 1 hour, 12 hours, 48 hours. Parameters 1hr 12hr 48hr Blood Present Absent Absent Size normal Shrunk Shrunk Odour Absent Present Present Insects Absent Absent Present Appearance normal Pale pale weight 4.2g 3.1g 1.9g colour Pinkish red Pale red Black brown Upon initial evaluation, livers harvested one hour postmortem demonstrated typical characteristics, exhibiting a reddish-brown hue, a firm texture, and unblemished surfaces. In contrast, at the 12-hour mark, while significant external alterations were absent, certain samples exhibited slight softening. By the 48-hour point, all livers exhibited unmistakable signs of decomposition, revealing a darker appearance with discoloration ranging from brown to black, a softened texture, and surface alterations indicative of tissue degradation. The weights of liver post-1 hour showed a gradual and progressive decline, further attesting to post mortem evolution. These macroscopic observations furnish a preliminary indication of the advancing decomposition correlating with the duration of the postmortem interval ( Table 1 ). Figure 1(a-c) ×400 Photomicrograph of post-mortem changes on liver tissue section at 1 hour, 12 hours, 48 hours Microscopic examination elucidated distinct phases of autolytic and putrefactive alterations in hepatic tissue. At the one-hour postmortem mark, the liver tissue exhibited relatively preserved integrity, with hepatocytes maintaining their polygonal or cuboidal morphology and exhibiting well-defined cellular boundaries. The nuclei presented as round to oval in shape, displaying discernible nuclear membranes and chromatin structures. The sinusoids remained open and unobstructed, characterized by intact endothelial linings. Central veins and portal triads were readily identifiable, with no evidence of necrotic changes. Only a few hepatocytes presented small cytoplasmic vacuoles, indicative of initial organelle degradation (see figure 1a ). By twelve hours postmortem, structural deterioration became apparent. Hepatocytes often appeared shrunken, while their cell boundaries became less distinct. Pyknosis and karyolysis were observed in numerous cells, and cytoplasmic vacuolation became more pronounced. Hepatic cords were observed to be disorganized, with partial collapse of the sinusoidal spaces and some shedding of endothelial cells. Additionally, patchy necrosis was documented (see figure 1b ). After forty-eight hours postmortem, advanced decomposition was prevalent. Most hepatocytes were either lysed or exhibited a ghost-like appearance characterized by eosinophilic cytoplasm. The majority of nuclei were either absent or fragmented, indicating a loss of lobular architecture. Liquefactive necrosis was significantly evident, featuring areas of dissolved tissue. These findings illustrate a progression from intact cellular morphology after one hour, to autolytic changes by twelve hours, culminating in substantial structural collapse by forty-eight hours (see figure 1c ). Figure 2 Spectral comparison of kinetics of postmortem changes on liver left unfixed for different times. Table 2 Spectral Biomarkers of Postmortem Changes on Liver at 1hour, 12 hours, 48 hours respectively Biomarkers Time(hours) Mean ± S.D F-value p-value Protein 1 1.31±0.01 4.12 0.088ns 12 1.20±0.02 48 1.14±0.03 Nucleic acid 1 1.82±0.02 108.95 0.000s 12 1.30±0.12 48 1.11±0.02 Cytoplasm-Nucleus ratio 1 0.96±0.02 31.22 0.001s 12 1.29±0.24 48 1.49±0.04 Glycogen 1 1.27±0.02 76.76 0.000s 12 0.99±0.21 48 0.79±0.02 Table 3 Spectral Biomarkers of Postmortem Changes on Liver between 1hour, and 12 hours Biomarkers Time(hours) Mean ± S.D t-test p-value Cohen’s d Protein 1 1.31±0.01 4.67 0.02s 2.7ve 12 1.20±0.02 Nucleic acid 1 1.82±0.02 -1.56 0.218ns -0.9e 12 1.30±0.12 Cytoplasm-Nucleus ratio 1 0.96±0.02 -1.24 0.303ns -0.72e 12 1.29±0.24 Glycogen 1 1.27±0.02 1.17 0.328ns 0.68e 12 0.99±0.21 Table 4 Comparison of Spectral Biomarkers of Postmortem Changes on Liver between 1hour, and 48 hours Biomarkers Time (hours ) Mean ± S.D t-test p-value Cohen’s d Protein 1 1.31±0.01 4.22 0.05ns 2.44ve 48 1.14±0.03 Nucleic acid 1 1.82±0.02 -13.35 0.006s -7.72ve 48 1.11±0.02 Cytoplasm-Nucleus ratio 1 0.96±0.02 -11.93 0.00s -6.89ve 48 1.49±0.04 Glycogen 1 1.27±0.02 25.00 0.00s 14.43ve 48 0.79±0.02 Table 5 Comparison of Spectral Biomarkers of Post-mortem Changes on Liver between 12 hours, and 48 hours Biomarkers Time(hours) Mean ± S.D t-test p-value Cohen’s d Protein 12 1.20±0.02 3.02 0.03s 1.75ve 48 1.14±0.03 Nucleic acid 12 1.30±0.12 -1.82 0.13ns -1.06ve 48 1.11±0.02 Cytoplasm-Nucleus ratio 12 1.29±0.24 -1.40 0.22ns -0.82e 48 1.49±0.04 Glycogen 12 0.99±0.21 1.58 0.18ns 0.91e 48 0.79±0.02 Table 6 Correlation Between Post-mortem Timing and ATR-FTIR biomarkers. Biomarkers Time(hr) R p-value η² Protein 1 12 -0.822h 0.012s -0.822e 48 Nucleic acid 1 12 0.913h 0.002s 0.834e 48 Cytoplasm-Nucleus ratio 1 12 0.822h 0.012s 0.676e 48 Glycogen 1 12 -0.848h 0.008s 0.719e 48 ATR-FTIR spectroscopy offered valuable biochemical insights into temporal changes. At the 1-hour mark, protein bands corresponding to amide I and II manifested significantly, accompanied by pronounced glycogen peaks, while nucleic acid peaks remained minimal. After 12 hours, a slight decrease in protein intensity was noted, glycogen levels also showed a modest reduction, and nucleic acid depreciated with PMI which is indicative of an ongoing, progressive postmortem events. By 48 hours, there was a notable decline in protein absorbance, glycogen became nearly undetectable, and nucleic acid peaks also crashing down. The C:N spectral ratio exhibited a sharp rise, correlating with histological findings of nuclear disappearance ( Table 2 ). Thus, ATR-FTIR spectroscopy revealed consistent, time-sensitive biochemical modifications in the rat liver across postmortem intervals of 1, 12, and 48 hours. The degradation of proteins exhibited a gradual decline, with mean absorbance decreasing from 1.31 ± 0.01 at 1 hour to 1.20 ± 0.02 at 12 hours, and further to 1.14 ± 0.03 at 48 hours. Although the overall ANOVA results were not statistically significant (F = 4.12, p = 0.088) ( Table 2 ), pairwise comparisons validated statistically significant reductions between 1 and 12 hours (t = 4.67, p = 0.02, Cohen’s d = 2.70) ( Table 3 ) as well as between 1 and 48 hours (t = 4.22, p = 0.05, d = 2.44) (Table 4), demonstrating considerable effect sizes. Nucleic acid concentrations decreased progressively with postmortem interval (PMI), declining from 1.82 ± 0.02 at 1 hour to 1.30 ± 0.12 at 12 hours and reaching 1.11 ± 0.02 at 48 hours. The ANOVA highlighted a significant effect (F = 108.95, p < 0.001) (Table 2). The decreased from 1 hour to 12 hours was not significant (Table 3), yet became highly significant by 48 hours (t = –13.35, p = 0.006, d = –7.72) ( Table 4 ). Pearson correlation analysis indicated a strong positive relationship with large effects sizes compared with PMI (R = 0.913, p = 0.002, η² = 0.834) ( Table 6 ). The cytoplasm-to-nucleus ratio (C:N) increased from 0.96 ± 0.02 at 1 hour to 1.29 ± 0.24 at 12 hours, and 1.49 ± 0.04 at 48 hours. ANOVA results indicated significant differences (F = 31.22, p = 0.001) ( Table 2 ), with the most notable effect observed between 1 hour and 48 hours (t = –11.93, p < 0.001, d = –6.89) (Table 4). Correlation assessments confirmed its efficacy as a PMI biomarker (R = 0.822, p = 0.012, η² = 0.676) ( Table 6 ). Glycogen exhibited the most prominent decline, decreasing from 1.27 ± 0.02 at 1 hour to 0.99 ± 0.21 at 12 hours, and further to 0.79 ± 0.02 at 48 hours. ANOVA indicated a significant effect (F = 76.76, p < 0.001) (Table 2). The difference between 1 hour and 48 hours (t = 25.0, p < 0.001, d = 14.43) reflected an extraordinarily large effect size. Correlation analysis reaffirmed a robust negative association with PMI (R = –0.848, p = 0.008, η² = 0.719) ( Table 6 ). Analysis of ATR-FTIR biomarkers indicated a significant decline in protein levels between 12 hours (1.20 ± 0.02) and 48 hours (1.14 ± 0.03) (t = 3.02, p = 0.03), with a substantial effect size (Cohen’s d = 1.75). Nucleic acid content saw a modest depression from 1.30 ± 0.12 at 12 hours to 1.11 ± 0.02 at 48 hours, yet this difference did not achieve statistical significance (t = –1.82, p = 0.13, d = –1.06). Similarly, the cytoplasm-to-nucleus ratio increased from 1.29 ± 0.24 to 1.49 ± 0.04, but did not reach significance (t = –1.40, p = 0.22, d = –0.82). Glycogen levels also fell from 0.99 ± 0.21 at 12 hours to 0.79 ± 0.02 at 48 hours, indicating a large effect size (d = 0.91) but lacking statistical significance (t = 1.58, p = 0.18) ( Table 5 ). Discussion This investigation conducted a comprehensive analysis of the early postmortem alterations in rat liver tissues, employing histological examination, and Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy as methodologies. Three significant findings were identified. Initially, histological observations revealed a progressive deterioration from well-preserved tissues at 1 hour to pronounced autolytic changes by 12 hours, culminating in advanced necrosis and liquefactive processes by 48 hours characterized by visible signs of decomposition (Table 1 ). Secondly, in the fingerprint region (1800 − 600 cm − 1 Fig. 2 ), the spectral characterization of post mortem onset and changes on liver for different time interval showed three distinguishing peaks (Amide I peaks, Amide II peaks, Glycogen peaks assigned to 1650 cm − 1 , 1535 cm − 1 and 1040 cm − 1 respectively (Movasaghi et al. 2008 ; Talari et al., 2015a ; Huan et al., 2016 ). The amide I is assigned to alpha-helix secondary protein structures and Amide II, to beta-sheet protein structure (Rehman et al. 2013; Talari et al. 2015a ;). The peak heights of liver at 12 hour and 48 hours showing similar spectral patterns compared with liver spectra at 1 hour reflects more intense absorbances for liver samples that had undergone a much rather longer degenerative alteration. The absorbance at the two peak positions aforementioned also suggest a more plausible idea that liver tissue left for longer time (12 hours and 48 hours) to undergo postmortem changes are very much likely to appear much denser. The reason for this protein spectral behavior at this stage, as expected, might be autolysis-driven rather than putrefaction-driven in spurring the process of degradative changes due to much inherent intracellular enzymes (Survana et al. 2013 ; Carson and Capellano, 2019). It is also interesting to note that glycogen band seen were more intense for liver left to undergo post mortem modifications for 48 hours, followed by the 12 hours and finally, the 1hour liver. This findings in this present study, therefore, agrees with similar studies reported on autolytic process in liver organs (Karadzic et al. 2010; Ebuehi et al. 2015 ; Akib et al. 2020 ). Secondly, biochemical markers quantified these morphological transitions: there was a modest decrease in protein levels, a sharp reduction in glycogen levels by 48 hours, an expected decrease in nucleic acids, and an elevation in the cytoplasm-to-nucleus (C:N) ratio concomitant with nuclear degradation. The practical purpose for choice of these biochemical markers is to correlate them with often demonstrable histologic metrics like glycogen, protein markers, nucleic acid and nucleocytoplasmic index. Interestingly, ATR-FTIR spectroscopy offered a rapid molecular representation of these alterations, with spectral variations that aligned with both histological and biochemical data. These temporal variations in biomarkers are visually corroborated in Fig. 2 . Both protein and glycogen and nucleic acids exhibit declining trends, while the cytoplasm-to-nucleus ratio demonstrate consistent increases. The distinct separation of these trends emphasizes the potential utility of ATR-FTIR spectral biomarkers as reliable indicators for estimating post-mortem intervals. Statistical evaluations validated that these trends were not only statistically significant but also exhibited substantial effect sizes. For instance, the depletion of glycogen at the 48-hour mark registered a Cohen's d value exceeding 14, while the decrease in nucleic acids demonstrated a strong correlation with postmortem interval (PMI) (R = 0.913, η² = 0.834), and alterations in the C:N ratio exhibited approximately d = -6.9. Such pronounced effect sizes underscore the robust, reproducible nature of the observed changes, affirming their relevance in forensic contexts. Histological analysis is pivotal in forensic pathology as it delivers direct, visual affirmations regarding tissue integrity and disintegration. The findings in rat liver exhibited a decay pattern analogous to human decomposition. At 1 hour, hepatocytes maintained their polygonal outlines, intact nuclei, and unaltered sinusoids, which is indicative of the fresh stage of decomposition (Scheurer et al. 2005 ). By 12 hours, autolytic changes were observable, characterized by pyknosis, karyolysis, cytoplasmic vacuolation, and sinusoidal deterioration, indicative of lysosomal rupture. These observations corroborate existing literature indicating that parenchymal tissues undergo microscopic autolytic transformations within 8 to 24 hours postmortem (Shedge et al. 2020 ). By 48 hours, liquefactive necrosis became predominant, with bacterial infiltration hastening tissue degradation (Ceciliason et al. 2018 ; Obun et al. 2025 ). This phase corresponds with the early decomposition or bloat stage in forensic decomposition timelines. While histology is invaluable for staging, it is inherently qualitative and vulnerable to variability among observers. The incorporation of quantitative markers significantly enhances its forensic applicability. The substantial effect size obtained for changes in the C:N ratio (d ≈ -6.9; η² = 0.676) demonstrates how subjective evaluations can be transformed into coherent, reproducible metrics, thereby diminishing uncertainties associated with PMI determination. Furthermore, the analysis of protein degradation provides insights into the biochemical processes governing decomposition. A consistent decline in total protein levels was noted within the 48-hour period, accompanied by significant effect sizes (Cohen's d = 2.4–2.7). Even when the tissue morphology appeared unaffected, proteolytic activity was already in progress correlating with PMI (Zhang et al. 2019 ; Huang et al. 2020 ). This correlates with previous findings indicating that labile proteins are rapidly degraded while structural proteins exhibit greater resilience (Sacco et al. 2022 ). Forensic proteomics capitalizes on this differential stability by monitoring specific proteins, such as tropomyosin or titin, which degrade in a predictable sequence (Del Cabo et al. 2023 ). Although broad protein measurements offer less precision, their noteworthy effect sizes reaffirm their significance in a forensic context. Glycogen depletion experienced the steepest decline, ceasing after 48 hours with an extraordinary effect size (Cohen's d = 14.4), positioning glycogen as a potent mid-range PMI marker, especially useful for differentiating between deaths occurring within 24 hours versus those that are more prolonged. This dynamic reflects continuing anaerobic metabolism and metabolic breakdown until disrupted by acidosis (Gumus et al. 2016). While these findings underscore glycogen's forensic potential, they also reveal limitations stemming from variations due to premortem nutritional states, stress levels, environmental temperatures, and preservation conditions (Devos and Hers, 1979 ). The profiles of nucleic acids exhibited a marked divergence, with paradoxical reduction by the 48-hour mark, backed by substantial effect sizes (Cohen’s d = -7.7) and strong correlations with PMI (R = 0.913, η² = 0.834). Similar observations have been reported by several others (Gomaa et al. 2013 ; Ebuehi et al. 2015 ; )The underlying mechanism pertains to the rupture of nuclear membranes, which releases DNA and RNA, alongside contributions from microbial DNA during putrefaction (Moitas et al. 2024 ). Notably, the integrity of the nucleic acids, rather than their quantity, emerges as the critical forensic metric. DNA remains amplifiable for several days, but it becomes fragmented, whereas RNA is highly unstable, with only ribosomal RNA surviving beyond a day (Zarczynska et al. 2023; Bhovar et al. 2024). These observations reinforce the preference for integrity-focused measurements, such as the 28S:18S rRNA ratios or the detection of unstable mRNA, over bulk quantification (Ebuehi et al. 2015 ; Sacco et al. 2025). The C:N ratio and ATR-FTIR analyses elucidated that the C:N ratio surged sharply by the 48-hour mark, demonstrating impressive effects (Cohen’s d = -6.9; R = 0.822, η² = 0.676). This increase reflects the rapid disappearance of nuclei relative to more stable cytoplasmic proteins. ATR-FTIR enabled an objective and reproducible method for evaluating this ratio through spectral indicators (protein amide II around ~ 1540 cm⁻¹ contrasted with nucleic acid phosphate near ~ 1080 cm⁻¹). Unlike histological methods, which are dependent on interpretive variability, spectral ratios mitigate such inconsistencies. This underscores the potential of the C:N ratio as a standardized, non-invasive PMI marker. Comparable patterns have been observed in human liver (Ceciliason et al. 2018 ) and other rat tissues (Gomaa et al. 2013 ). However, its applicability diminishes after approximately 72 hours, as cytoplasmic structures begin to liquefy, thus limiting this marker’s efficiency primarily to the early-to-intermediate PMI. Each biomarker offered unique insights; for example, while proteins exhibited a steady degradation pattern, glycogen demonstrated a dramatic collapse after 24 hours, nucleic acids declined and fragmented, and the C:N ratio quantitatively represented nuclear loss in congruent with histopathological presentations. The complementary behaviors of these indices suggest that no single measurement can suffice; rather, integrated models yield enhanced accuracy. This aligns with the work of Secco et al. ( 2025 ), advocating for multimodal omics approaches in PMI estimation. Moreover, combining ATR-FTIR with chemometric techniques such as principal component analysis, partial least squares has the potential to create predictive models for PMI (Wang et al. 2019 ; Zhang et al. 2019 ; Huang et al. 2020 ; Yu et al. 2021 ). Furthermore, Notarstefano et al. extended this methodology to human vitreous humor utilizing machine learning with promising outcomes. Generally, the highlighted markers correlate well with specific criteria used in similar studies (Huang et al. 2009; Hackett et al. 2015; Zhang et al. 2019 ; Huang et al. 2020 ). The large effect sizes demonstrated for these biomarkers revealed their intrinsic practical applications in real-time, real-world forensic explorations. However, the application of rat liver as a model poses challenges for direct applicability to human settings. Furthermore, the experiments were conducted at ambient temperature, whereas actual scenarios entail a variety of environmental factors. Additionally, the limited sample size (n = 18) diminishes the statistical robustness due to the use of animal models in this investigation. Lastly, the implementation of ATR-FTIR necessitates calibration against extensive reference databases for consistent forensic application. Future studies should aim to authenticate these outcomes using human autopsy specimens, broaden the examination to include additional biomarkers such as lipids, metabolites, and proteins, as well as align ATR-FTIR results with other molecular methodologies. Moreover, research should encompass other tissues including muscle, kidney, brain, and vitreous humor. The incorporation of ATR-FTIR spectral data into machine learning frameworks could facilitate automated predictions of post-mortem intervals. Conclusion Liver tissue exhibits consistent histological and biochemical alterations within 48 hours after death. Initially, autolysis prevails during the first 24 hours, succeeded by bacterial decomposition. Concurrently, these phenomena are reflected in the breakdown of proteins, the reduction of glycogen, the release of nucleic acids, and an increase in the carbon-to-nitrogen ratio. The use of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy has effectively illustrated these patterns. By integrating histological analysis with ATR-FTIR spectroscopy, forensic practitioners can enhance the precision of post-mortem interval (PMI) estimations. The significance of the observed effect sizes highlights the reliability of these indicators, advocating for their incorporation into practical forensic applications. Abbreviations ATR Attenuated total reflectance BUTH Bowen University Teaching Hospital DNA Deoxyribonucleic acid FTIR Fourier transform infrared IBM International business machines PMI Postmortem interval RNA Ribonucleic acid SPSS Statistical package for the social sciences Declarations Ethics approval and consent to participate The protocol was reviewed and approved by the Research and Ethics Committee of the Bowen University Teaching Hospital, Ogbomoso, Nigeria. Approval number BUTH/REC-1197. Consent for publication Not applicable Funding This research did not receive grants from any funding agency in the public, commercial, or non-profit sectors. Author Contribution STA conceived the idea, search literatures, gathered materials, interpreted results wrote and revised the first and last draft. DOA analyzed the samples histologically and contributed to writing the first draft. IS analysed, interpreted data and contribute to writing and revising the final manuscript draft. CI read and revised the final manuscript draft. Data Availability Data is provided within the manuscript or supplementary information files References Shedge R, Krishan K, Warrier V, Kanchan T (2020) Postmortem changes. StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL) Khalil AM, Hegazy NI, Abouhashem AA, Shaheen MA, Hassan NM (2024) From death to decay: An overview of postmortem changes. Zagazig Univ Med J 30(6):2345–2355 Goff ML (2020) Early post-mortem changes and stages of decomposition in exposed cadavers. Exp Appl Acarol 82(3):347–359 Hackett MJ, Sylvain NJ, Hou H, Caine S, Alaverdashvili M, Pushie MJ et al (2016) Concurrent Glycogen and Lactate Imaging with FTIR Spectroscopy To Spatially Localize Metabolic Parameters of the Glial Response Following Brain Ischemia. Anal Chem 88(22):10949–10956. https://doi.org/10.1021/acs.analchem.6b02588 Tozzo P, Scrivano S, Sanavio M, Caenazzo L (2020) The role of DNA degradation in the estimation of post-mortem interval: a systematic review of the current literature. Int J Mol Sci 21(10):3540 Franceschetti L, Amadasi A, Bugelli V, Bolsi G, Tsokos M (2023) Estimation of late postmortem interval: where do we stand ? A literature review; Biology. 2023, 12(6); 783 Secco L, Palumbi S, Padalino P, Grosso E, Perilli M, Casonato M et al (2025) Omics and postmortem interval estimation: A systematic review. Int J Mol Sci 26(3):1034 Zhang K, Wang Q, Liu R, Wei X, Li Z, Fan S et al (2020) Evaluating the effects of causes of death on postmortem interval estimation by ATR-FTIR spectroscopy. Int J Legal Med 134(2):565–574 Ke Y, Li Y, Wang ZY (2012) The changes of Fourier transform infrared spectrum in rat brain. 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Spectrochim Acta Mol Biomol Spectrosc 280:121519 Alkhuder K (2022) Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses. Int J Legal Med 136(6):1717–1736 Mitu B, Cerda M, Hrib R, Trojan V, Halámková L (2023) Attenuated total reflection Fourier transform infrared spectroscopy for forensic screening of long-term alcohol consumption from human nails. ACS Omega 8(24):22203–22210 Wojtowicz A, Mitura A, Bracha U, Arent Z, Malek K, Wietecha-Pasluszny R (2025) Postmortem changes in tissue samples captured by infrared spectroscopy- studies in a rabbit model. Microchem J 213:113803 Carleton HM, Drury RAB, Wallington EA (2019) Carleton’s histological technique, 7th edn. Oxford University Press, Oxford, pp 66–75 Adeleke ST, Igbeneghu C, Iyiola S (2025) Diagnostic performance of ATR-FTIR spectroscopy in discriminating normal breast tissue and breast tumors. Cancer Screen Prev 4(2):79–88 Movasaghi Z, Rehman S, Rehman IU (2008) Fourier transform infrared (FTIR) spectroscopy of biological tissues. Appl Spectroscopic Reviews 43(2):134–179 Talari AC, Movasaghi Z, Rehman S, Rehman IU (2015a) Spectroscopy of bio-logical tissues. Appl Spectroscopic Reviews 50:46–111 Huan T, Troyer DA, Li L (2016) Metabolite analysis and Histology on the exact same tissue: comprehensive metabolomic profiling and metabolic classification of prostate cancer. Sci Rep 6:32272 Rehman IU (2013) Vibrational spectroscopy for tissue analysis. CRC Survana SK, Layton C, Bancroft JD (2013) Bancroft’s theory and practice of histological techniques. 7th edition. Churchill Livingstone. ; Pp 386–388 Carson FL, Cappellano CH, Histotechnology (2019) A Self Instructional Text. 4th Edition American Society College of Pathology press, USA. 264–282 Karadžić R, Ilić G, Antović A, Banović LK (2010) Autolytic ultrastructural changes in rat and human hepatocytes. Rom J Leg Med 18(4):247–252 Ebuehi OAT, Amode M, Balogun A, Fowora A (2015) Postmortem Time Affects Brain, Liver, Kidney and Heart DNA in Male Rat. Am J Biochem 5(1):1–5 Akib N, Atanda JO, Atanda AT, Bello ZM (2020) Rate of autolytic changes in different unfixed tissues at different time interval. Bayero J Med Lab Sci 5(1):11–17 National Research Council (1985) Guide for the care and use of laboratory animals. National Academies, Washington, DC Ceciliason AS, Andersson MG, Lindström A, Sandler H (2018) Quantifying human decomposition in an indoor setting and implications for postmortem interval estimation. Forensic Sci Int 283:180–189 Obun CO, Ibegbu OA, Ogugua AO, Esomchi CN, Ikpa JO, Adie M et al (2025) The role of histo-taphonomy in postmortem interval estimation: a preliminary study of porcine liver and kidney. J Exp Clin Anat 22(1):61–66 Zhang H, Wang Q, Zhang K, Liu R, Fan S, Wang Z (2019) Estimation of postmortem interval using attenuated total reflectance Fourier Transform Infrared Spectroscopy in adipose tissues. J Forensic Sci Med 5(1):7–12 Huang J, Zhou YY, Deng K, Luo Y, Sun Q, Li Z, Huang P et al (2020) Relationship between Postmortem interval and FTIR Spectroscopy changes of the rat skin. J Forensic Med 36(2):187–191 Sacco MA, Cordasco F, Scalise C, Ricci P, Aquila I (2022) Systematic review on post-mortem protein alterations: analysis of experimental models and evaluation of potential biomarkers of time of death. Diagnostics 12(6):1490 Del Cabo L, Bugelli V et al (2023) Forensic proteomics for the discovery of new postmortem interval biomarkers. Int J Mol Sci 24(19):14627 Gümüş A, Gümüş B, Özer E, Yücetaş E, Yücetaş U, Düz E et al (2016) Evaluation of postmortem glucose and glycogen levels in hepatic, renal, muscle and brain tissues: can these parameters estimate postmortem interval? J Forensic Sci 61(Suppl 1):S144–S149 Devos P, Hers HG (1979) A molecular order in the synthesis and degradation of glycogen in the liver. Eur J Biochem 99(1):161–167 Gomaa MS, El-Khalek A, A. M., and, Sameer MM (2013) The relationship between the postmortem interval and the DNA degradation in brain and liver of adult albino rats. J Am Sci 9(5):535–540 Moitas B, Caldas IM, Sampaio-Maia B (2024) Microbiology and postmortem interval: a systematic review. Forensic Sci Med Pathol 20(4):696–715 Żarczyńska M, Żarczyński P, Tomsia M (2023) Nucleic acids persistence—benefits and limitations in forensic genetics. Genes 14(8):1643 Bhoyar L, Mehar P, Chavali K (2024) An overview of DNA degradation and its implications in forensic caseworks. Egypt J Forensic Sci 14(1):15 Gomaa MS, Abd El-Khalek AM, Sameer MM (2013) The relationship between the postmortem interval and DNA degradation in brain and liver of adult albino rats. J Am Sci 9(5):535–540 Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx PPTforLiverForensics.pptx Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":100121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a-c)\u003c/strong\u003e ×400 Photomicrograph of post-mortem changes on liver tissue section at 1 hour, 12 hours, 48 hours\u003c/p\u003e\n\u003cp\u003e1a-c\u003c/p\u003e\n\u003cp\u003eD= round to oval nucleus\u003c/p\u003e\n\u003cp\u003eE= Hepatocytes are polygonal with distinct border\u003c/p\u003e\n\u003cp\u003eG= Hepatocytes are shrunken and margins are less conspicuous\u003c/p\u003e\n\u003cp\u003eH= Progressive nuclear loss due to karyolysis and pyknosis\u003c/p\u003e\n\u003cp\u003eI= Extensive vacuolation and patchy necrosis\u003c/p\u003e\n\u003cp\u003eK= Nucleus visibly absent\u003c/p\u003e\n\u003cp\u003eL= Eosinophilic background characteristics of extensive autolysis and putrefaction\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8388033/v1/85c5a9cd220095ef5877bf06.jpg"},{"id":100656826,"identity":"09f3c8e3-ffc9-446d-a406-0d0b800fb4aa","added_by":"auto","created_at":"2026-01-20 07:54:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpectral comparison of kinetics of postmortem changes on liver left unfixed for different times.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8388033/v1/d89266786ee2d6b101c76681.jpg"},{"id":100658092,"identity":"e49d8b27-b597-4059-8688-9cc1d96fd869","added_by":"auto","created_at":"2026-01-20 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07:55:03","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":473212,"visible":true,"origin":"","legend":"","description":"","filename":"PPTforLiverForensics.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8388033/v1/98572048c25e38d6b7e561d6.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegrating Histology and Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy to Estimate the Postmortem Interval in Rat Liver\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe postmortem interval (PMI), defined as the duration that has passed since an individual\u0026rsquo;s death, constitutes a fundamental aspect of forensic science. It plays a crucial role in reconstructing the timeline associated with criminal activities, legal inquiries, and victim identification. Nevertheless, accurately estimating the PMI presents significant difficulties, primarily due to multiple factors including environmental conditions, the specific characteristics of the body, the cause of death, and intrinsic biochemical processes (Shedge et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Khalil et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional techniques such as algor mortis, rigor mortis, and livor mortis have been employed in preliminary postmortem assessments for many years. These observable indicators are typically beneficial within the first 24 hours postmortem; however, their reliability diminishes outside this timeframe due to various external influences, including ambient temperature, body dimensions, and clothing. Furthermore, during advanced decomposition, these signs may be absent or confounded, thereby restricting their forensic applicability (Khalil et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Goff \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these limitations, there is an increasing focus within forensic research on biochemical and molecular markers that exhibit time-dependent variations using specific analytical interventions. Specific markers such as excited iris, vitreous potassium, hypoxanthine, lactate, denaturation of proteins, depletion or metabolism of glycogen, and stepwise degradation of nucleic acids characterize postmortem processes (Hackett et al. 2015; Tozzo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Franceschetti et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These alterations can be quantified, presenting opportunities for more objective PMI estimations (Secco et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Multi-marker or omics methodologies such as image analysis, single cell gel electrophoresis, terminal deoxynucleotidyl transferase and DNA amplification methods enhance accuracy by integrating multiple biochemical indicators, however these are expensive and laborious in low-income and poor-resource settings where critical forensic analysis are always demanded to unravel causes, time and onset of deaths; (Tozzo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvanced analytical techniques, such as Fourier Transform Infrared (FTIR) spectroscopy\u0026mdash;and particularly Attenuated Total Reflectance (ATR)-FTIR\u0026mdash;provides cost-effective alternative which enable fast profiling of the molecular makeup of tissues with minimal preparation. Previous research indicates that spectral variations in proteins, lipids, and nucleic acids correlate with different stages of decomposition (Ke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Scheurer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). ATR-FTIR has demonstrated efficacy for PMI estimation with tissues including muscle, blood, vitreous humor, and adipose tissue (Yu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Notarstefano et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beyond estimating PMI, its applications extend to areas such as toxicology and investigations into causes of death (Alkhuder, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mitu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe liver serves as a model organ for PMI assessment, having been established to be reliable (Wang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tozzo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wojtowicz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The current study, first to incorporate spectroscopy with histological method in Nigeria and sub-saharan Africa, aims to explore the organ\u0026rsquo;s characteristics. We examined histological and biochemical changes in rat liver tissue at various time points (1 hour, 12 hours, and 48 hours postmortem) under controlled conditions, particularly focusing on proteins, glycogen, nucleic acids, and the cytoplasm-to-nucleus (C:N) ratio derived from spectral ratios measured on hepatic tissues. The ATR-FTIR spectroscopy was utilized alongside histological analysis to document molecular alterations. By correlating these markers with established time intervals, we aspire to identify reliable PMI indicators and advance forensic methodologies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and Sample Collection\u003c/h2\u003e \u003cp\u003eThis research utilized an experimental animal framework to replicate the initial postmortem alterations under regulated conditions. All methodologies adhered to international standards regarding the welfare and utilization of laboratory animals (National Research Council \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The protocol received endorsement from the Research and Ethics Committee at Bowen University Teaching Hospital, Ogbomoso, Nigeria, with the approval number BUTH/REC-1197. Need for consent of participation and clinical trial number does not apply to this work.\u003c/p\u003e \u003cp\u003eEighteen adult Wistar rats were employed in this study. In accordance with ethical stipulations, all animals were euthanized in a humane manner, and confirmation of death was ensured prior to the collection of samples. The cadavers were preserved at ambient temperature (20\u0026ndash;22\u0026deg;C) to facilitate natural autolytic and putrefactive processes. Liver tissue specimens were obtained at three distinct postmortem time intervals: 1 hour (immediate), 12 hours (early), and 48 hours (advanced early stage). These intervals were chosen to correspond to previous ATR-FTIR PMI studies focused on the early postmortem phase (Ke et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tozzo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Each sampling time point comprised six liver samples (n\u0026thinsp;=\u0026thinsp;6), with one liver extracted from each rat.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistological Analysis and Examination\u003c/h3\u003e\n\u003cp\u003eTissue specimens were promptly preserved in 10% neutral-buffered formalin and subsequently processed according to established histological methods (Carleton 2019). Staining with hematoxylin and eosin (H\u0026amp;E) was carried out, and the prepared slides were analyzed using a light microscope. Qualitative evaluation of morphological alterations was conducted by two independent evaluators, who reported representative observations.\u003c/p\u003e\n\u003ch3\u003eSpectral Features Acquisition\u003c/h3\u003e\n\u003cp\u003eMolecular characterization was performed utilizing attenuated total reflectance\u0026ndash;Fourier transform infrared (ATR-FTIR) spectroscopy. The analysis involved unstained formalin-fixed paraffin-embedded liver tissue sections with a thickness of 15 \u0026micro;m, assessed using a portable ATR-FTIR spectrometer (Agilent Cary 630 FTIR, Agilent Technologies, Santa Clara, CA, USA) that featured a diamond ATR crystal. Infrared spectra were captured across the range of 4000 cm⁻\u0026sup1; to 600 cm⁻\u0026sup1;, within the mid-infrared spectrum, at a resolution of 16 cm⁻\u0026sup1;. Moderate pressure was applied to ensure adequate contact between the tissue and the ATR crystal. For each spectrum, thirty-two scans were averaged. The samples underwent analysis in triplicate, with spectra recorded from three distinct regions to guarantee comprehensive representation. The raw spectral data were subjected to preprocessing via SpectraGryph software, involving baseline normalization, noise reduction, and correction processes. The absorbance intensities were quantified for specific wavenumbers that were selected based on recognized FTIR designations for biological tissues were determined using ratioing of certain peaks to give biomarkers. Protein was derived from Amide I (~\u0026thinsp;1650 cm⁻\u0026sup1;) to Amide II (~\u0026thinsp;1540 cm⁻\u0026sup1;) ratio, glycogen from ratio of ~\u0026thinsp;1040 cm⁻\u0026sup1; (C\u0026ndash;O stretching in polysaccharides) to ~\u0026thinsp;1540 cm⁻\u0026sup1;, and ratio nucleic acids with asymmetric (~\u0026thinsp;1237 cm⁻\u0026sup1;) and symmetric (~\u0026thinsp;1080 cm⁻\u0026sup1;) PO₂⁻ stretching. A cytoplasmic-to-nuclear (C:N) spectral ratio was computed as Amide II (~\u0026thinsp;1540 cm⁻\u0026sup1;) divided by PO₂⁻ (~\u0026thinsp;1080 cm⁻\u0026sup1;) to serve as an indicator of nuclear degradation in relation to cytoplasmic stability (Adeleke et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical evaluations were conducted utilizing IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Descriptive statistics, specifically mean values accompanied by standard deviations (SD), were computed for all biomarkers at time points of 1 hour, 12 hours, and 48 hours post-mortem interval (PMI). To compare various groups, independent-samples t-tests were employed for pairwise comparisons between the time points (1 h vs. 12 h; 1 h vs. 48 h; 12 h vs. 48 h). A p-value threshold of less than 0.05 was established to denote statistical significance. Despite the relatively small sample sizes, the analysis offers preliminary insights into noteworthy differences.\u003c/p\u003e \u003cp\u003eTo explore linear relationships between PMI (considered as a continuous variable in hours) and biomarker levels, Pearson's correlation analysis was performed. The results included correlation coefficients (R) alongside p-values. Additionally, where feasible, effect sizes were determined (Cohen\u0026rsquo;s d for the t-tests and η\u0026sup2; for the correlations) to evaluate the strength of the observed associations for practical uses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Macroscopic Observation of Postmortem Changes on Liver at 1 hour, 12 hours, 48 hours.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1hr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e12hr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e48hr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eShrunk\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eShrunk\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eOdour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eInsects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAppearance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003epale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e4.2g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3.1g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.9g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ecolour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePinkish red\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePale red\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBlack brown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUpon initial evaluation, livers harvested one hour postmortem demonstrated typical characteristics, exhibiting a reddish-brown hue, a firm texture, and unblemished surfaces. In contrast, at the 12-hour mark, while significant external alterations were absent, certain samples exhibited slight softening. By the 48-hour point, all livers exhibited unmistakable signs of decomposition, revealing a darker appearance with discoloration ranging from brown to black, a softened texture, and surface alterations indicative of tissue degradation. The weights of liver post-1 hour showed a gradual and progressive decline, further attesting to post mortem evolution. These macroscopic observations furnish a preliminary indication of the advancing decomposition correlating with the duration of the postmortem interval (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1(a-c)\u003c/strong\u003e \u0026times;400 Photomicrograph of post-mortem changes on liver tissue section at 1 hour, 12 hours, 48 hours\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Microscopic examination elucidated distinct phases of autolytic and putrefactive alterations in hepatic tissue. At the one-hour postmortem mark, the liver tissue exhibited relatively preserved integrity, with hepatocytes maintaining their polygonal or cuboidal morphology and exhibiting well-defined cellular boundaries. The nuclei presented as round to oval in shape, displaying discernible nuclear membranes and chromatin structures. The sinusoids remained open and unobstructed, characterized by intact endothelial linings. Central veins and portal triads were readily identifiable, with no evidence of necrotic changes. Only a few hepatocytes presented small cytoplasmic vacuoles, indicative of initial organelle degradation (see \u003cstrong\u003efigure 1a\u003c/strong\u003e). By twelve hours postmortem, structural deterioration became apparent. Hepatocytes often appeared shrunken, while their cell boundaries became less distinct. Pyknosis and karyolysis were observed in numerous cells, and cytoplasmic vacuolation became more pronounced. Hepatic cords were observed to be disorganized, with partial collapse of the sinusoidal spaces and some shedding of endothelial cells. Additionally, patchy necrosis was documented (see \u003cstrong\u003efigure 1b\u003c/strong\u003e). After forty-eight hours postmortem, advanced decomposition was prevalent. Most hepatocytes were either lysed or exhibited a ghost-like appearance characterized by eosinophilic cytoplasm. The majority of nuclei were either absent or fragmented, indicating a loss of lobular architecture. Liquefactive necrosis was significantly evident, featuring areas of dissolved tissue. These findings illustrate a progression from intact cellular morphology after one hour, to autolytic changes by twelve hours, culminating in substantial structural collapse by forty-eight hours (see \u003cstrong\u003efigure 1c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 Spectral comparison of kinetics of postmortem changes on liver left unfixed for different times.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Spectral Biomarkers of Postmortem Changes on Liver at 1hour, 12 hours, 48 hours respectively\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"499\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime(hours)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cbr\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.088ns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cbr\u003eNucleic acid \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.82\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.000s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.30\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCytoplasm-Nucleus ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.29\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cbr\u003eGlycogen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.27\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.000s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Spectral Biomarkers of Postmortem Changes on Liver between 1hour, and 12 hours\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eBiomarkers \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime(hours)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cbr\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.7ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cbr\u003eNucleic acid \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.82\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.218ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.9e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.30\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCytoplasm-Nucleus ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.303ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.72e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.29\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cbr\u003eGlycogen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.27\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.328ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.68e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.99\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of Spectral Biomarkers of Postmortem Changes on Liver between 1hour, and 48 hours\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime (hours )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cbr\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.05ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2.44ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cbr\u003eNucleic acid \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.82\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-13.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.006s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e-7.72ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCytoplasm-Nucleus ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-11.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e-6.89ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cbr\u003eGlycogen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.27\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e14.43ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Comparison of Spectral Biomarkers of Post-mortem Changes on \u0026nbsp;Liver between 12 hours, and 48 hours\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime(hours)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.75ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003eNucleic acid \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.30\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.13ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-1.06ve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCytoplasm-Nucleus ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.29\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.22ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.82e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003eGlycogen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.18ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.91e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u0026nbsp;Correlation Between Post-mortem Timing and ATR-FTIR biomarkers.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime(hr)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.822h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.012s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.822e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003eNucleic acid \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.913h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.002s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.834e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCytoplasm-Nucleus ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.822h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.012s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.676e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;Glycogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.848h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.719e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eATR-FTIR spectroscopy offered valuable biochemical insights into temporal changes. At the 1-hour mark, protein bands corresponding to amide I and II manifested significantly, accompanied by pronounced glycogen peaks, while nucleic acid peaks remained minimal. After 12 hours, a slight decrease in protein intensity was noted, glycogen levels also showed a modest reduction, and nucleic acid depreciated with PMI which is indicative of an ongoing, progressive postmortem events. By 48 hours, there was a notable decline in protein absorbance, glycogen became nearly undetectable, and nucleic acid peaks also crashing down. The C:N spectral ratio exhibited a sharp rise, correlating with histological findings of nuclear disappearance (\u003cstrong\u003eTable 2\u003c/strong\u003e). Thus, ATR-FTIR spectroscopy revealed consistent, time-sensitive biochemical modifications in the rat liver across postmortem intervals of 1, 12, and 48 hours.\u003c/p\u003e\n\u003cp\u003eThe degradation of proteins exhibited a gradual decline, with mean absorbance decreasing from 1.31 \u0026plusmn; 0.01 at 1 hour to 1.20 \u0026plusmn; 0.02 at 12 hours, and further to 1.14 \u0026plusmn; 0.03 at 48 hours. Although the overall ANOVA results were not statistically significant (F = 4.12, p = 0.088) (\u003cstrong\u003eTable 2\u003c/strong\u003e), pairwise comparisons validated statistically significant reductions between 1 and 12 hours (t = 4.67, p = 0.02, Cohen\u0026rsquo;s d = 2.70) (\u003cstrong\u003eTable 3\u003c/strong\u003e) as well as between 1 and 48 hours (t = 4.22, p = 0.05, d = 2.44) (Table 4), demonstrating considerable effect sizes.\u003c/p\u003e\n\u003cp\u003eNucleic acid concentrations decreased progressively with postmortem interval (PMI), declining \u0026nbsp;from 1.82 \u0026plusmn; 0.02 at 1 hour to 1.30 \u0026plusmn; 0.12 at 12 hours and reaching 1.11 \u0026plusmn; 0.02 at 48 hours. The ANOVA highlighted a significant effect (F = 108.95, p \u0026lt; 0.001) (Table 2). The decreased from 1 hour to 12 hours was not significant (Table 3), yet became highly significant by 48 hours (t = \u0026ndash;13.35, p = 0.006, d = \u0026ndash;7.72) (\u003cstrong\u003eTable 4\u003c/strong\u003e). Pearson correlation analysis indicated a strong positive relationship with large effects sizes compared with PMI (R = 0.913, p = 0.002, \u0026eta;\u0026sup2; = 0.834) (\u003cstrong\u003eTable 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe cytoplasm-to-nucleus ratio (C:N) increased from 0.96 \u0026plusmn; 0.02 at 1 hour to 1.29 \u0026plusmn; 0.24 at 12 hours, and 1.49 \u0026plusmn; 0.04 at 48 hours. ANOVA results indicated significant differences (F = 31.22, p = 0.001) (\u003cstrong\u003eTable 2\u003c/strong\u003e), with the most notable effect observed between 1 hour and 48 hours (t = \u0026ndash;11.93, p \u0026lt; 0.001, d = \u0026ndash;6.89) (Table 4). Correlation assessments confirmed its efficacy as a PMI biomarker (R = 0.822, p = 0.012, \u0026eta;\u0026sup2; = 0.676) (\u003cstrong\u003eTable 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGlycogen exhibited the most prominent decline, decreasing from 1.27 \u0026plusmn; 0.02 at 1 hour to 0.99 \u0026plusmn; 0.21 at 12 hours, and further to 0.79 \u0026plusmn; 0.02 at 48 hours. ANOVA indicated a significant effect (F = 76.76, p \u0026lt; 0.001) (Table 2). The difference between 1 hour and 48 hours (t = 25.0, p \u0026lt; 0.001, d = 14.43) reflected an extraordinarily large effect size. Correlation analysis reaffirmed a robust negative association with PMI (R = \u0026ndash;0.848, p = 0.008, \u0026eta;\u0026sup2; = 0.719) (\u003cstrong\u003eTable 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAnalysis of ATR-FTIR biomarkers indicated a significant decline in protein levels between 12 hours (1.20 \u0026plusmn; 0.02) and 48 hours (1.14 \u0026plusmn; 0.03) (t = 3.02, p = 0.03), with a substantial effect size (Cohen\u0026rsquo;s d = 1.75). Nucleic acid content saw a modest depression from 1.30 \u0026plusmn; 0.12 at 12 hours to 1.11 \u0026plusmn; 0.02 at 48 hours, yet this difference did not achieve statistical significance (t = \u0026ndash;1.82, p = 0.13, d = \u0026ndash;1.06). Similarly, the cytoplasm-to-nucleus ratio increased from 1.29 \u0026plusmn; 0.24 to 1.49 \u0026plusmn; 0.04, but did not reach significance (t = \u0026ndash;1.40, p = 0.22, d = \u0026ndash;0.82). Glycogen levels also fell from 0.99 \u0026plusmn; 0.21 at 12 hours to 0.79 \u0026plusmn; 0.02 at 48 hours, indicating a large effect size (d = 0.91) but lacking statistical significance (t = 1.58, p = 0.18) (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation conducted a comprehensive analysis of the early postmortem alterations in rat liver tissues, employing histological examination, and Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy as methodologies. Three significant findings were identified. Initially, histological observations revealed a progressive deterioration from well-preserved tissues at 1 hour to pronounced autolytic changes by 12 hours, culminating in advanced necrosis and liquefactive processes by 48 hours characterized by visible signs of decomposition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, in the fingerprint region (1800\u0026thinsp;\u0026minus;\u0026thinsp;600 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the spectral characterization of post mortem onset and changes on liver for different time interval showed three distinguishing peaks (Amide I peaks, Amide II peaks, Glycogen peaks assigned to 1650 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1535 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1040 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively (Movasaghi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Talari et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e; Huan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The amide I is assigned to alpha-helix secondary protein structures and Amide II, to beta-sheet protein structure (Rehman et al. 2013; Talari et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e;). The peak heights of liver at 12 hour and 48 hours showing similar spectral patterns compared with liver spectra at 1 hour reflects more intense absorbances for liver samples that had undergone a much rather longer degenerative alteration. The absorbance at the two peak positions aforementioned also suggest a more plausible idea that liver tissue left for longer time (12 hours and 48 hours) to undergo postmortem changes are very much likely to appear much denser. The reason for this protein spectral behavior at this stage, as expected, might be autolysis-driven rather than putrefaction-driven in spurring the process of degradative changes due to much inherent intracellular enzymes (Survana et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Carson and Capellano, 2019). It is also interesting to note that glycogen band seen were more intense for liver left to undergo post mortem modifications for 48 hours, followed by the 12 hours and finally, the 1hour liver. This findings in this present study, therefore, agrees with similar studies reported on autolytic process in liver organs (Karadzic et al. 2010; Ebuehi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Akib et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, biochemical markers quantified these morphological transitions: there was a modest decrease in protein levels, a sharp reduction in glycogen levels by 48 hours, an expected decrease in nucleic acids, and an elevation in the cytoplasm-to-nucleus (C:N) ratio concomitant with nuclear degradation. The practical purpose for choice of these biochemical markers is to correlate them with often demonstrable histologic metrics like glycogen, protein markers, nucleic acid and nucleocytoplasmic index.\u003c/p\u003e \u003cp\u003eInterestingly, ATR-FTIR spectroscopy offered a rapid molecular representation of these alterations, with spectral variations that aligned with both histological and biochemical data. These temporal variations in biomarkers are visually corroborated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Both protein and glycogen and nucleic acids exhibit declining trends, while the cytoplasm-to-nucleus ratio demonstrate consistent increases. The distinct separation of these trends emphasizes the potential utility of ATR-FTIR spectral biomarkers as reliable indicators for estimating post-mortem intervals.\u003c/p\u003e \u003cp\u003eStatistical evaluations validated that these trends were not only statistically significant but also exhibited substantial effect sizes. For instance, the depletion of glycogen at the 48-hour mark registered a Cohen's d value exceeding 14, while the decrease in nucleic acids demonstrated a strong correlation with postmortem interval (PMI) (R\u0026thinsp;=\u0026thinsp;0.913, η\u0026sup2; = 0.834), and alterations in the C:N ratio exhibited approximately d = -6.9. Such pronounced effect sizes underscore the robust, reproducible nature of the observed changes, affirming their relevance in forensic contexts.\u003c/p\u003e \u003cp\u003eHistological analysis is pivotal in forensic pathology as it delivers direct, visual affirmations regarding tissue integrity and disintegration. The findings in rat liver exhibited a decay pattern analogous to human decomposition. At 1 hour, hepatocytes maintained their polygonal outlines, intact nuclei, and unaltered sinusoids, which is indicative of the fresh stage of decomposition (Scheurer et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). By 12 hours, autolytic changes were observable, characterized by pyknosis, karyolysis, cytoplasmic vacuolation, and sinusoidal deterioration, indicative of lysosomal rupture. These observations corroborate existing literature indicating that parenchymal tissues undergo microscopic autolytic transformations within 8 to 24 hours postmortem (Shedge et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By 48 hours, liquefactive necrosis became predominant, with bacterial infiltration hastening tissue degradation (Ceciliason et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Obun et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This phase corresponds with the early decomposition or bloat stage in forensic decomposition timelines.\u003c/p\u003e \u003cp\u003eWhile histology is invaluable for staging, it is inherently qualitative and vulnerable to variability among observers. The incorporation of quantitative markers significantly enhances its forensic applicability. The substantial effect size obtained for changes in the C:N ratio (d \u0026asymp; -6.9; η\u0026sup2; = 0.676) demonstrates how subjective evaluations can be transformed into coherent, reproducible metrics, thereby diminishing uncertainties associated with PMI determination. Furthermore, the analysis of protein degradation provides insights into the biochemical processes governing decomposition. A consistent decline in total protein levels was noted within the 48-hour period, accompanied by significant effect sizes (Cohen's d\u0026thinsp;=\u0026thinsp;2.4\u0026ndash;2.7). Even when the tissue morphology appeared unaffected, proteolytic activity was already in progress correlating with PMI (Zhang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This correlates with previous findings indicating that labile proteins are rapidly degraded while structural proteins exhibit greater resilience (Sacco et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Forensic proteomics capitalizes on this differential stability by monitoring specific proteins, such as tropomyosin or titin, which degrade in a predictable sequence (Del Cabo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although broad protein measurements offer less precision, their noteworthy effect sizes reaffirm their significance in a forensic context.\u003c/p\u003e \u003cp\u003eGlycogen depletion experienced the steepest decline, ceasing after 48 hours with an extraordinary effect size (Cohen's d\u0026thinsp;=\u0026thinsp;14.4), positioning glycogen as a potent mid-range PMI marker, especially useful for differentiating between deaths occurring within 24 hours versus those that are more prolonged. This dynamic reflects continuing anaerobic metabolism and metabolic breakdown until disrupted by acidosis (Gumus et al. 2016). While these findings underscore glycogen's forensic potential, they also reveal limitations stemming from variations due to premortem nutritional states, stress levels, environmental temperatures, and preservation conditions (Devos and Hers, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe profiles of nucleic acids exhibited a marked divergence, with paradoxical reduction by the 48-hour mark, backed by substantial effect sizes (Cohen\u0026rsquo;s d = -7.7) and strong correlations with PMI (R\u0026thinsp;=\u0026thinsp;0.913, η\u0026sup2; = 0.834). Similar observations have been reported by several others (Gomaa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ebuehi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; )The underlying mechanism pertains to the rupture of nuclear membranes, which releases DNA and RNA, alongside contributions from microbial DNA during putrefaction (Moitas et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, the integrity of the nucleic acids, rather than their quantity, emerges as the critical forensic metric. DNA remains amplifiable for several days, but it becomes fragmented, whereas RNA is highly unstable, with only ribosomal RNA surviving beyond a day (Zarczynska et al. 2023; Bhovar et al. 2024). These observations reinforce the preference for integrity-focused measurements, such as the 28S:18S rRNA ratios or the detection of unstable mRNA, over bulk quantification (Ebuehi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sacco et al. 2025).\u003c/p\u003e \u003cp\u003eThe C:N ratio and ATR-FTIR analyses elucidated that the C:N ratio surged sharply by the 48-hour mark, demonstrating impressive effects (Cohen\u0026rsquo;s d = -6.9; R\u0026thinsp;=\u0026thinsp;0.822, η\u0026sup2; = 0.676). This increase reflects the rapid disappearance of nuclei relative to more stable cytoplasmic proteins. ATR-FTIR enabled an objective and reproducible method for evaluating this ratio through spectral indicators (protein amide II around ~\u0026thinsp;1540 cm⁻\u0026sup1; contrasted with nucleic acid phosphate near ~\u0026thinsp;1080 cm⁻\u0026sup1;). Unlike histological methods, which are dependent on interpretive variability, spectral ratios mitigate such inconsistencies. This underscores the potential of the C:N ratio as a standardized, non-invasive PMI marker. Comparable patterns have been observed in human liver (Ceciliason et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and other rat tissues (Gomaa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, its applicability diminishes after approximately 72 hours, as cytoplasmic structures begin to liquefy, thus limiting this marker\u0026rsquo;s efficiency primarily to the early-to-intermediate PMI.\u003c/p\u003e \u003cp\u003eEach biomarker offered unique insights; for example, while proteins exhibited a steady degradation pattern, glycogen demonstrated a dramatic collapse after 24 hours, nucleic acids declined and fragmented, and the C:N ratio quantitatively represented nuclear loss in congruent with histopathological presentations. The complementary behaviors of these indices suggest that no single measurement can suffice; rather, integrated models yield enhanced accuracy. This aligns with the work of Secco et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), advocating for multimodal omics approaches in PMI estimation. Moreover, combining ATR-FTIR with chemometric techniques such as principal component analysis, partial least squares has the potential to create predictive models for PMI (Wang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, Notarstefano et al. extended this methodology to human vitreous humor utilizing machine learning with promising outcomes.\u003c/p\u003e \u003cp\u003eGenerally, the highlighted markers correlate well with specific criteria used in similar studies (Huang et al. 2009; Hackett et al. 2015; Zhang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The large effect sizes demonstrated for these biomarkers revealed their intrinsic practical applications in real-time, real-world forensic explorations. However, the application of rat liver as a model poses challenges for direct applicability to human settings. Furthermore, the experiments were conducted at ambient temperature, whereas actual scenarios entail a variety of environmental factors. Additionally, the limited sample size (n\u0026thinsp;=\u0026thinsp;18) diminishes the statistical robustness due to the use of animal models in this investigation. Lastly, the implementation of ATR-FTIR necessitates calibration against extensive reference databases for consistent forensic application.\u003c/p\u003e \u003cp\u003eFuture studies should aim to authenticate these outcomes using human autopsy specimens, broaden the examination to include additional biomarkers such as lipids, metabolites, and proteins, as well as align ATR-FTIR results with other molecular methodologies. Moreover, research should encompass other tissues including muscle, kidney, brain, and vitreous humor. The incorporation of ATR-FTIR spectral data into machine learning frameworks could facilitate automated predictions of post-mortem intervals.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLiver tissue exhibits consistent histological and biochemical alterations within 48 hours after death. Initially, autolysis prevails during the first 24 hours, succeeded by bacterial decomposition. Concurrently, these phenomena are reflected in the breakdown of proteins, the reduction of glycogen, the release of nucleic acids, and an increase in the carbon-to-nitrogen ratio. The use of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy has effectively illustrated these patterns. By integrating histological analysis with ATR-FTIR spectroscopy, forensic practitioners can enhance the precision of post-mortem interval (PMI) estimations. The significance of the observed effect sizes highlights the reliability of these indicators, advocating for their incorporation into practical forensic applications.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eATR Attenuated total reflectance\u003c/p\u003e \u003cp\u003eBUTH Bowen University Teaching Hospital\u003c/p\u003e \u003cp\u003eDNA Deoxyribonucleic acid\u003c/p\u003e \u003cp\u003eFTIR Fourier transform infrared\u003c/p\u003e \u003cp\u003eIBM International business machines\u003c/p\u003e \u003cp\u003ePMI Postmortem interval\u003c/p\u003e \u003cp\u003eRNA Ribonucleic acid\u003c/p\u003e \u003cp\u003eSPSS Statistical package for the social sciences\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The protocol was reviewed and approved by the Research and Ethics Committee of the Bowen University Teaching Hospital, Ogbomoso, Nigeria. Approval number BUTH/REC-1197.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e Not applicable\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive grants from any funding agency in the public, commercial, or non-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSTA conceived the idea, search literatures, gathered materials, interpreted results wrote and revised the first and last draft. DOA analyzed the samples histologically and contributed to writing the first draft. IS analysed, interpreted data and contribute to writing and revising the final manuscript draft. CI read and revised the final manuscript draft.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShedge R, Krishan K, Warrier V, Kanchan T (2020) Postmortem changes. StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalil AM, Hegazy NI, Abouhashem AA, Shaheen MA, Hassan NM (2024) From death to decay: An overview of postmortem changes. Zagazig Univ Med J 30(6):2345\u0026ndash;2355\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoff ML (2020) Early post-mortem changes and stages of decomposition in exposed cadavers. 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M., and, Sameer MM (2013) The relationship between the postmortem interval and the DNA degradation in brain and liver of adult albino rats. J Am Sci 9(5):535\u0026ndash;540\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoitas B, Caldas IM, Sampaio-Maia B (2024) Microbiology and postmortem interval: a systematic review. Forensic Sci Med Pathol 20(4):696\u0026ndash;715\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŻarczyńska M, Żarczyński P, Tomsia M (2023) Nucleic acids persistence\u0026mdash;benefits and limitations in forensic genetics. Genes 14(8):1643\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhoyar L, Mehar P, Chavali K (2024) An overview of DNA degradation and its implications in forensic caseworks. Egypt J Forensic Sci 14(1):15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomaa MS, Abd El-Khalek AM, Sameer MM (2013) The relationship between the postmortem interval and DNA degradation in brain and liver of adult albino rats. J Am Sci 9(5):535\u0026ndash;540\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Postmortem interval, liver, Autolysis, ATR-FTIR spectroscopy, Forensic biomarkers, Glycogen, Protein, Nucleic acid","lastPublishedDoi":"10.21203/rs.3.rs-8388033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8388033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe precise determination of the postmortem interval (PMI) remains a significant challenge within the field of forensic science. Traditional methods, including algor mortis, rigor mortis, and livor mortis, are often constrained by variations in environmental conditions. Other methods have considerably involved pH, ions and metabolic byproduct measurements with inherent limitations and inaccuracies. Advanced techniques like flow cytometry, single-cell gel electrophoresis, polymerase chain reaction, some of which have been found expensive and cumbersome, have also largely been reported to investigate PMI in different tissues with almost similar consensus. This research aimed to explore the time-dependent postmortem alterations in rat liver tissue through histological analysis and Attenuated Total Reflectance\u0026ndash;Fourier Transform Infrared (ATR-FTIR) spectroscopy, focusing on effect sizes and their practical relevance in forensic applications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHistological examination revealed the presence of intact hepatocytes at the 1-hour mark, followed by autolytic changes at 12 hours, and necrotic processes accompanied by liquefaction by 48 hours. The ATR-FTIR spectral analysis paralleled these identified phases, demonstrating a consistent decrease in protein absorbance (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;2.4\u0026ndash;2.7), while glycogen levels exhibited the most pronounced decline (d\u0026thinsp;=\u0026thinsp;14.4). Additionally, there was an observed increase in nucleic acids and the cytoplasmic to nuclear ratio (d = \u0026minus;\u0026thinsp;7.7; R\u0026thinsp;=\u0026thinsp;0.913, η\u0026sup2; = 0.834; d = \u0026minus;\u0026thinsp;6.9; R\u0026thinsp;=\u0026thinsp;0.822, η\u0026sup2; = 0.676), indicating substantial and reproducible time-dependent changes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe liver shows discernible histological and biochemical transformations postmortem within a 48-hour timeframe. ATR-FTIR spectroscopy effectively detected these molecular modifications, yielding objective and quantifiable metrics such as the cytoplasm to nucleus (C:N) ratio. The integration of histological findings with spectral biomarkers enhances the accuracy, reproducibility, and forensic reliability of PMI estimations.\u003c/p\u003e","manuscriptTitle":"Integrating Histology and Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy to Estimate the Postmortem Interval in Rat Liver","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 07:31:23","doi":"10.21203/rs.3.rs-8388033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94346d41-302b-4702-a639-1e83b663872a","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T07:31:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 07:31:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8388033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8388033","identity":"rs-8388033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}