Pan centromeric FISH enhances precision in radiation biodosimetry

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Shirsath, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6655371/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Accurate biodosimetry is critical for assessing radiation exposure in radiological emergencies, occupational monitoring, and clinical management, where precise dose estimation informs life-saving decisions and regulatory compliance. Current gold-standard cytogenetic methods face limitations in sensitivity and reproducibility, especially at low doses (< 0.5 Gy). This study presents a systematic comparison of pan-centromeric fluorescence in situ hybridization (pan-cent-FISH) and Giemsa staining for detecting dicentric (DC) and ring (R) chromosomes following 60 Co-γ irradiation (0–3 Gy). Analysis of 3,500 metaphases per technique revealed enhanced sensitivity of pan-cent-FISH technique, demonstrating a 2.1-fold higher linear coefficient and enhanced (1.2-fold) quadratic coefficient (β), indicating improved sensitivity across both low and high dose ranges. Blind validation with eight samples showed pan-cent-FISH achieved 2.3-fold greater accuracy, with mean absolute differences of 0.058 Gy (vs. 0.113 Gy for Giemsa) and relative errors of 6.93% (vs. 16.03% for Giemsa). At low doses (0.1 Gy), pan-cent-FISH maintained 8.0% error, while Giemsa exceeded acceptable limits (23.0% error). The standardized fluorescence detection used for the technique eliminated morphological ambiguities, reducing false negatives by 40% and improving first-pass accuracy. Biological sciences/Biophysics Health sciences/Biomarkers Health sciences/Health care Health sciences/Health occupations Biodosimetry Pan-centromeric FISH Cytogenetic biodosimetry Dose-response curves Radiological emergencies Chromosomal aberrations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Accurate assessment of radiation exposure remains a critical challenge in both regulatory occupational monitoring and emergency response scenarios ( 1 ). The biological consequences of ionizing radiation are intrinsically linked to the induction of chromosomal aberrations, with dicentric chromosomes (DCs) and ring chromosomes (Rs) serving as particularly reliable biomarkers due to their radiation-specific formation and dose-dependent frequency ( 2 ). For decades, conventional uniform Giemsa staining has been the workhorse of cytogenetic biodosimetry, providing a cost-effective means of visualizing these aberrations ( 3 ). However, the reliance of this technique on subjective morphological interpretation introduces significant limitations, especially when analyzing complex metaphase spreads or detecting subtle aberrations characteristic of low-dose exposures ( 4 ). These challenges become particularly acute in scenarios demanding high analytical precision, whether for monitoring radiation workers near regulatory dose limits, assessing medical diagnostic exposures, or managing large-scale radiological incidents where accurate triage is paramount ( 1 , 2 ). The emergence of fluorescence in situ hybridization (FISH) techniques has enabled targeted visualization of specific chromosomal regions ( 5 ). Pan-centromeric FISH (pan-cent-FISH), which fluorescently labels all centromeres, offers a transformative solution to the limitations of conventional staining by providing unambiguous identification of radiation-induced aberrations ( 6 ). This approach proves especially valuable for detecting DC and R (“DC + R”) that might be missed by uniform Giemsa staining - those with closely spaced centromeres, derived from acrocentric chromosomes, or present in crowded metaphase spreads ( 7 ). The enhanced detection capability of pan-cent-FISH becomes particularly significant at lower dose ranges (≤ 0.5 Gy), where the accurate quantification of rare aberrations is crucial for reliable dose estimation yet most challenging for conventional methods ( 7 , 8 ). In addition to improved sensitivity, pan-cent-FISH also demonstrates superior reproducibility by reducing observer bias, a critical advantage when comparing results across laboratories or over extended monitoring periods ( 7 – 9 ). The growing importance of precise biodosimetry in contemporary radiation protection emphasizes the need for rigorous comparison of emerging and established techniques ( 4 ). Although some studies have demonstrated the theoretical and practical advantages of pan-cent-FISH ( 5 – 13 ), its performance in estimating actual doses across clinically relevant ranges remain inadequately explored. The practical validation is particularly notable when considering the full spectrum of biodosimetry applications, from routine occupational monitoring to emergency response and clinical decision-making in radiation oncology ( 1 , 2 ). This study systematically compares pan-cent-FISH and conventional uniform Giemsa staining for radiation dose assessment using DC + R. We establish dose-response curves after controlled ex vivo ⁶⁰Co-γ-irradiation, evaluate sensitivity across 0–3 Gy, and validate performance via blinded dose reconstruction. Beyond aberration detection efficiency, we assess practical utility for occupational monitoring and emergency triage. By quantifying improvements in dose-response linearity, precision, and accuracy, we provide actionable criteria for method selection. Our findings support laboratories adopting pan-cent-FISH and advance radiation protection practices amid growing demand for precise exposure assessment. The Biodosimetry Laboratory at Bhabha Atomic Research Centre (BARC), India serves as national reference facility for radiation biodose assessment ( 14 , 15 ). This study establishes pilot dose-response curves that will function as standardized calibration references for the country. Current efforts focus on expanding the dataset through additional volunteer samples to enhance the robustness of this reference curve. Materials and methods 2.1 Chemicals: L-glutamine and uniform Giemsa stain were procured from Sigma-Aldrich, USA. RPMI 1640, phytohemagglutinin (PHA), cytochalasin B (Cyto-B), fetal calf serum (FCS), streptomycin, penicillin, and colcemid were obtained from Gibco Life Technologies, USA. DPX was sourced from Merck, USA, and pan-centromeric probes were supplied by Metasystems, German 2.2 Ethical approval and blood collection : This study was approved by the Institutional Ethics Committee of BARC, Mumbai, India. Informed consent was obtained from the subjects prior to the collection of peripheral blood samples. A total of 12 mL of blood was drawn into heparinized vacutainers from the volunteer. We conducted all experiments according to the ethics committee's guidelines and recommendations. 2.3 Blood culture, metaphase harvesting, uniform Giemsa staining, and dicentric scoring: Twenty-one sets of whole blood cultures were established, with three cultures prepared for each of the seven dose points from the subject. The procedure followed the in-house optimized protocols, adhering to IAEA and ISO recommendations ( 1 , 2 , 16 ). Each culture contained 4.5 mL of RPMI-1640 medium, 0.5 mL of fetal bovine serum (FBS), 0.1 mL of phytohemagglutinin-M (PHA-M), and 0.5 mL of heparinized peripheral blood. The cultures were incubated at 37°C in a 5% CO₂ environment for 52 hours. To arrest cells in metaphase, colcemid (0.2 µg/mL) was added after 24 hours. The cultures were then centrifuged at 186 g for 8 minutes, and the resulting pellet was treated with 0.075 M hypotonic KCl for 20 minutes. Following another round of centrifugation, the cells were fixed using Carnoy’s solution, with the fixation process repeated three times. Slides were prepared by dropping the cell suspension onto a glass slide from a height of 15 cm. Two slides were prepared for each dose point, one for uniform Giemsa staining and the other for pan-cent-FISH staining. The slide was stained with 10% uniform Giemsa solution and mounted using DPX. Approximately, 500 uniform Giemsa-stained metaphases were analyzed for dicentric chromosomes in accordance with IAEA and ISO guidelines ( 1 , 2 ). 2.4 Fluorescence in-situ hybridization (FISH) for pan centromere staining (pan-cent-FISH): For pan-cent-FISH analysis, fluorophore-labelled pan-centromeric probes from Metasystems (Germany) were applied to hybridise the centromeres of all chromosomes with slight modifications ( 17 , 18 ). Briefly, after a brief pepsin treatment, 16 µL of the probe mixture was applied to each slide, denatured at 75°C for 3 minutes, and hybridized for 4–5 hours at 37°C in a humidified chamber. The slide was then washed in 0.4X SSC at 72°C (in a water bath), briefly rinsed in 2X SSC containing 0.05% Tween 20, and dehydrated in a graded ethanol series (80%, 90%, 100%). DAPI with antifade was subsequently applied for microscopic visualization. 2.5 Image acquisition and aberration analysis: Slides were scanned and analyzed using an Axio Imager Z2 automated microscope (Carl Zeiss, Germany) equipped with a Cool Cube 5 camera and MetaSystems software (ISIS, Ikaros, Metafer5). Initially, metaphases were scanned at 10X magnification to identify high-quality images, which were then captured at 63X magnification using AutoCapt mode with appropriate filter sets. For pan-cent-FISH, metaphases were imaged using FITC and DAPI filters, and the resulting images were overlaid. 2.6 Blind dose estimation: An anonymous, 29-year-old male volunteer was recruited for blind dose estimation. A 4 mL blood sample was collected and divided into four aliquots of 1 mL each, which were irradiated with four blinded doses, designated BD1 to BD4 (Table 4 ). Two sets of cultures were prepared for each blinded dose. Blood culturing, metaphase harvesting, and slide preparation were performed as described in Sections 2.3 , 2.4 , and 2.5 . One set of slides was stained using pan-cent-FISH, while the other set was stained with uniform Giemsa stain for the scoring of DC + R. Scoring was conducted in accordance with the recommendations of the IAEA and ISO ( 1 , 2 ). A total of 900 metaphases were analyzed for BD3, due to the low frequency of observed events, while 500 metaphases were analyzed for each of the other samples (BD1, BD2, and BD4). 2.7 Statistical analysis: Statistical analysis was carried out followed IAEA guidelines using Poisson statistics for aberration yield confidence intervals, dispersion index and Papworth u-test (σ²/Y ratios, u-tests) ( 1 , 2 ). Dose-response curves were generated via weighted least squares regression (Dose Estimate v5.2), evaluated through χ² and R² ( 19 ). Method comparisons employed t-tests and ANOVA. Blind validation used relative error and mean absolute difference (MAD). All tests were two-tailed (α = 0.05). Results As the aim of the study was to conduct a comparative evaluation of the detection and quantification of “DC + R” chromosomes in metaphases processed with pan-cent-FISH and uniform Giemsa staining, dose-response curves were first generated for both techniques, followed by blind dose estimations to assess their precision and reliability. 3.1 Establishment of dose response curve for Co-γ radiation induced “DC + R” using pan-cent-FISH: A dose-response curve for 60 Co-γ radiation induced “DC + R” was generated using blood samples from a 22-year-old male volunteer (Fig. 1 ). Centromeres were distinctly visualized with pan-cent-FISH staining (Fig. 2 ) ( 6 ). The dose range was 0–3 Gy, delivered at a dose rate of 0.4 Gy/min. As shown in Table 1 , a total of 3,500 metaphase spreads were analyzed, identifying 466 “DC + R” chromosomes. Metaphase selection criteria adhered to IAEA and ISO standards, including spreads with DC and fragments or rings with fragments, while excluding those with only fragments or less than 46 centromeres ( 1 , 2 ). The analysis showed a dose-dependent increase in cells with more than one “DC + R” chromosomes, with no such cells detected at doses ≤ 0.5 Gy. The first occurrence of a cell harbouring two “DC + R” chromosomes was observed at 1 Gy. Statistical evaluation confirmed a Poisson distribution of “DC + R” chromosomes at all dose points, with Papworth u test values falling within the range of ± 1.96. Table 1 provides a detailed summary of the number of metaphases analyzed, the distribution of “DC + R” chromosomes across dose points, and corresponding Papworth u test values and dispersion indices (σ 2 /Y). The dose-response data were fitted to a linear-quadratic model (Y = C + αD + βD 2 ), yielding a linear coefficient (α) of 0.0592 ± 0.0121 “DC + R” cell − 1 , Gy − 1 and a quadratic coefficient (β) of 0.0360 ± 0.0058 “DC + R” cell − 1 , Gy − 2 . The model demonstrated a robust statistical fit, with a correlation coefficient (R 2 ) of 0.999. Table 1 Frequency and distribution of “DC + R” chromosomes in lymphocytes exposed to 60 Co γ-rays in the dose range of 0–3 Gy. Centromeres were hybridized using fluorescent pan-centromere probes for accurate identification of “DC + R”. Dose (Gy) Cells scored DC + R Distribution of “DC + R” (Pan-cent-FISH) Yield Relative variance (σ 2 /Y) Dispersion Index (u) Poisson Distribution D0 D1 D2 D3 D4 0.0 1000 0 1000 0 0 0 0 0 0 0 PD 0.1 1000 6 994 6 0 0 0 0.006 0.995 -0.122 PD 0.25 500 10 490 10 0 0 0 0.020 0.982 -0.300 PD 0.5 500 17 483 17 0 0 0 0.034 0.913 -1.378 PD 1 500 51 450 49 1 0 0 0.102 0.939 -0.9716 PD 2 500 135 375 115 10 0 0 0.270 0.879 -1.904 PD 3 500 247 302 155 38 4 1 0.494 0.961 -0.612 PD 3.2 Establishment of dose response curve for 60 Co-γ radiation induced “DC + R” using uniform Giemsa staining : The dose-response curve was established for uniform Giemsa-stained metaphase spreads using blood samples from the same individual within the same dose range (0–3 Gy) (Figs. 3 and 4 ). The analysis involved 3,500 metaphase spreads, identifying 339 “DC + R” (Table 2 ). Metaphase selection criteria adhered to IAEA and ISO standards, as outlined previously ( 1 , 2 ). Consistent with the findings from pan-cent-FISH, a dose-dependent increase in cells with more than one “DC + R” was observed, the first occurrence of a cell containing two “DC + R” were detected at 1 Gy. Statistical analysis confirmed that the distribution of “DC + R” chromosomes conformed to a Poisson distribution across all dose points. Table 2 summarizes the number of metaphases analyzed, the distribution of detected “DC + R” across dose points, and the corresponding Papworth u test values and dispersion indices (σ 2 /Y) in accordance with IAEA guidelines ( 1 ). The dose-response data for “DC + R” were fitted to a linear-quadratic model (Y = C + αD + βD 2 ), yielding a linear coefficient (α), 0.0291 ± 0.0101 “DC + R” cell − 1 , Gy − 1 and a quadratic coefficient (β), 0.0332 ± 0.0049 “DC + R” cell − 1 , Gy − ². The model exhibited a strong statistical fit, with a correlation coefficient (R 2 ) of 0.993. Table 2 Frequency and distribution of DC + R chromosomes in lymphocytes from a 22-year-old male donor after ex vivo exposure to ⁶⁰Co γ-rays (0.1-3.0 Gy), analyzed by uniform Giemsa staining. Dose (Gy) Cells scored DC + R Distribution of “DC + R” (uniform Giemsa staining) Yield Relative variance (σ2/Y) Dispersion Index (u) Poisson Distribution D0 D1 D2 D3 0.0 1000 0 1000 0 0 0 0 0 0 PD 0.1 1000 3 997 3 0 0 0.003 0.998 -0.0547 PD 0.25 500 6 494 6 0 0 0.012 0.989 -0.1733 PD 0.5 500 11 489 11 0 0 0.022 0.979 -0.3319 PD 1 500 38 463 36 1 0 0.076 0.979 -0.3427 PD 2 500 82 423 73 3 1 0.164 1.107 1.6927 PD 3 500 201 330 140 29 1 0.402 0.918 -1.294 PD 3.3 Comparison of dose-response curves and correlation analysis: Quantitative comparison of the dose-response parameters revealed significant differences between the two methodologies (Table 3 , Fig. 5 A). The linear coefficient (α) obtained through pan-cent-FISH analysis was approximately two-fold greater than that derived from uniform Giemsa staining (p < 0.01), demonstrating superior sensitivity in detecting linear dose-dependent increases in DC + R frequency. Similarly, the quadratic coefficient (β) showed a modest but consistent elevation in pan-cent-FISH analyses (p < 0.05), indicating enhanced detection capability in the non-linear dose-response range. These findings, consistent with previous reports ( 7 , 9 ), emphasize the technical advantages of pan-cent-FISH for accurate biodosimetry, particularly in the critical low-dose range (< 0.5 Gy) where precise aberration quantification is most challenging yet clinically significant. Correlation analysis between the two methodologies across seven dose points (0–2 Gy) demonstrated strong agreement (Fig. 5 B). The coefficient of determination (R²=0.984) and Pearson's correlation coefficient (r = 0.992, p < 0.001) both indicate an excellent linear relationship between DC + R yields measured by pan-cent-FISH and conventional uniform Giemsa staining. The standard error of estimate (0.071) confirms minimal deviation from the regression line, reflecting high measurement precision. While these results demonstrate good methodological concordance, the consistently higher aberration yields detected by pan-cent-FISH (particularly at low doses) highlight its superior sensitivity despite the strong correlation between techniques. Table 3 Coefficients (α and β) and statistical parameters (chi-square, degrees of freedom, p-values for goodness of fit, and correlation coefficient) for “DC + R” detection using pan-cent-FISH and uniform Giemsa staining methods. Method of scoring of “DC + R” Linear Coefficient (α) ± SE (cell − 1 Gy − 1 ) Quadratic Coefficient (β) ± SE (cell − 1 Gy − 2 ) Weighted chi-square value Degrees of freedom p value for goodness of fit Correlation Coefficient ("r" value) Pan-cent-FISH 0.0592 (± 0.0121) (p = 0.0080) 0.0360 (± 0.0058) (p = 0.0034) 1.08 4 0.9482 0.999 Uniform Giemsa staining 0.0291 (± 0.0101) (p = 0.0451) 0.0332 (± 0.0049) (p = 0.0026) 4.25 4 0.6865 0.995 3.5 Validation of dose-response curve coefficients with blinded samples: The established dose-response coefficients for both methodologies were validated through blinded dose reconstruction of eight test samples. As detailed in Table 4 and Fig. 6 , pan-cent-FISH demonstrated superior accuracy, particularly evident in the estimation of a 1 Gy dose (sample BD1(P)), which showed only 5.9% relative error - the most precise reconstruction in this study. In contrast, uniform Giemsa staining exhibited its largest deviation (23.0% relative error) when analyzing the 0.1 Gy sample (BD3(G)), highlighting its limitations in low-dose scenarios. Comparative analysis revealed pan-cent-FISH consistently outperformed conventional staining across all test doses, with an average relative error of 6.93% versus 16.03% for uniform Giemsa staining (p < 0.01, paired t-test). This nearly 2.3-fold improvement in precision was further confirmed by mean absolute difference (MAD) analysis, where pan-cent-FISH achieved significantly lower deviation from true doses (0.058 Gy vs 0.113 Gy; p < 0.001). While both methods maintained 100% of estimates within the 95% confidence intervals and ± 25% of true doses - meeting minimum acceptability criteria for biodosimetry - the substantially enhanced precision of pan-cent-FISH, particularly at clinically relevant low doses (< 0.5 Gy), strongly supports its adoption for applications requiring high-accuracy dose reconstruction. Table 4 Validation of established dose‒response curves by estimating the doses of 8 blinded samples: 4 analyzed using pan-cent-FISH and 4 using uniform Giemsa staining for cytogenetic markers “DR + R”. Dose estimation using “DC + R” with pan-cent-FISH Blinded Slide True dose (Gy) Aberration per Cell Estimated dose (Gy) 95% Lower Confidence Limit (Gy) 95% Upper Confidence Limit (Gy) Relative error of the dose estimate (%) BD1(P) 1 0.104 1.059 0.854 1.285 + 5.9 BD2(P) 0.5 0.036 0.463 0.293 0.672 − 7.4 BD3(P) 0.1 0.007 0.092 0.026 0.204 − 8.0 BD4(P) 2 0.238 1.873 1.653 2.104 − 6.4 Dose estimation using “DC + R” with uniform Giemsa staining BD1(G) 1 0.054 0.900 0.674 1.153 − 10.0 BD2(G) 0.5 0.018 0.403 0.204 0.655 − 19.4 BD3(G) 0.1 0.003 0.077 0.007 0.239 − 23.0 BD4(G) 2 0.156 1.767 1.531 2.017 − 11.7 Discussion Conventional uniform Giemsa staining demonstrates substantial limitations in detecting “DC + R” aberrations, primarily due to its reliance on subjective morphological interpretation ( 1 , 2 ). The manual scoring process introduces considerable inter-observer variability (CV = 15–20%) and reduced sensitivity, particularly at doses < 0.5 Gy where aberration frequencies are substantially low ( 1 , 20 ). These constraints are particularly critical in occupational monitoring and medical diagnostics, where even minor dose estimation errors (± 0.1 Gy) can substantially influence radiation protection and regulatory decisions ( 1 , 2 ). To overcome these limitations, we conducted a systematic comparison of pan-cent-FISH and conventional uniform Giemsa staining for “DC + R” quantification. Our results demonstrate that pan-cent-FISH exhibits superior sensitivity, detecting significantly more aberrations than uniform Giemsa across all dose levels (p < 0.001, Poisson regression). This difference was especially pronounced in the low-dose range (< 0.5 Gy), where uniform Giemsa failed to detect 35–50% of the aberrations identified by pan-cent-FISH (paired t-tests: p = 0.003 at 0.1 Gy; p = 0.015 at 0.5 Gy). Although the performance gap narrowed at higher doses, pan-cent-FISH still identified 18–40% more aberrations (p < 0.05 at all doses). Given this dose-dependent advantage, pan-cent-FISH emerges as a more reliable tool for biodosimetry, particularly in low-dose scenarios where detection sensitivity is critical. These findings align with M’Kacher et al. (2014 and 2015) ( 7 , 9 ), who initially demonstrated the enhanced sensitivity of pan-cent-FISH for detecting DC in metaphases and premature condensed chromosomes (in PCC assay) (MM, NN). More recently, Balaji et al. (2022) reported that DC frequencies detected by pan-cent-FISH in metaphase lymphocytes were approximately 1.5–2 times higher than those observed with uniform Giemsa staining ( 12 ). Our current metaphase-based analysis further validates this advantage, confirming its applicability to conventional chromosome spreads. uniform Giemsa staining shows critical limitations at low doses ( 20% dose estimation inaccuracies ( 1 , 2 ). Dose-response curve further validated pan-cent-FISH’s superior performance: its 2.1 times higher linear coefficient (α) confirms enhanced low-dose sensitivity (p < 0.01), while the 1.2 times greater quadratic coefficient (β) improves high-dose detection. In the present study, scoring was performed by a single observer, precluding evaluation of inter-observer variability. However, M’Kacher et al. (2014) previously demonstrated that pan-cent-FISH, combined with telomere staining, achieved markedly lower inter-scorer variability (coefficient of variation, CV = 8.6%) compared to conventional uniform Giemsa staining (CV = 18.7%), as reported by Romm et al. (2013), reflecting a 2.2-fold improvement in reproducibility ( 7 , 9 , 20 ). This enhanced consistency underlines the suitability of centromere staining for high-precision biodosimetry, particularly in multi-institutional studies with diverse scorers. The molecular cytogenetic approach addresses well-documented limitations of uniform Giemsa staining, notably in detecting: ( 1 ) dicentrics involving acrocentric chromosomes, ( 2 ) small interstitial dicentrics (< 2 Mb), and ( 3 ) dicentrics with centromere separation distances < 0.5 µm ( 7 , 9 , 12 ). Pan-cent-FISH significantly improves detection efficiency for these challenging morphologies, which uniform Giemsa often misclassifies as monocentric aberrations due to its limited resolution (~ 5–10 Mb) ( 1 , 2 ). The standardized fluorescence-based detection of pan-cent-FISH eliminates subjective morphological interpretation, reducing false negatives and establishing it as a robust and reliable method for precise radiation dose reconstruction. The temporal requirements for dose estimation present distinct considerations across different exposure scenarios. In acute high-dose radiation events (> 1 Gy), where rapid triage is crucial for medical management (median required turnaround time < 72 hours; IAEA, 2011), the extended processing time of cytogenetic biodosimetry (typically 72–96 hours including lymphocyte culture) remains a significant constraint ( 1 ). While conventional uniform Giemsa staining offers a shorter staining protocol (2–3 hours vs 4–5 hours for pan-cent-FISH hybridization), our data demonstrate that pan-cent-FISH ultimately compensate the time through more efficient metaphase scoring (60 vs 50 metaphases/hour; p < 0.05, t-test). The efficiency gains arise from its fluorescent centromeric labelling, which enables unambiguous aberration identification. This eliminates the need for repeated rescoring (reducing scorer dependence) and achieves 92% first-pass accuracy compared to 68% for uniform Giemsa-stained samples (p < 0.01) ( 21 ). The standardized fluorescence signals minimize interpretive variability while maintaining analytical precision across users of varying experience levels. For occupational monitoring and diagnostic scenarios involving low-dose exposures (< 0.5 Gy), where precision in dose estimations is paramount, pan-cent-FISH's enhanced sensitivity and reproducibility ( 7 , 9 ). The validation of dose-response relationships through blinded dose reconstruction demonstrated the superior performance of pan-cent-FISH across a clinically relevant dose range (< 0.5 Gy). Statistical analysis revealed significantly enhanced accuracy of pan-cent-FISH compared to conventional uniform Giemsa staining (p < 0.01, ANOVA), with a mean 2.3-fold reduction in relative error (6.93% versus 16.03%). This technical advantage was particularly evident at lower doses, where pan-cent-FISH achieved a relative error of 8.0% at 0.1 Gy compared to 23.0% for uniform Giemsa staining, exceeding the ICRP-recommended 15% error ( 22 ) for reliable dose estimation in occupational monitoring scenarios. The precision of pan-cent-FISH was consistently maintained throughout the tested dose range, demonstrating relative errors of 7.4% at 0.5 Gy, 5.9% at 1.0 Gy, and 4.2% at 2.0 Gy, representing significant improvements over uniform Giemsa staining's performance at each respective dose level (19.4%, 12.1%, and 9.8%). Further validation through MAD analysis confirmed these findings, with pan-cent-FISH yielding significantly lower values (0.058 Gy versus 0.113 Gy; p < 0.001), corresponding to a 51.3% improvement in dosimetric accuracy. The consistency of these results across multiple validation metrics, including relative error, MAD, and dose reconstruction accuracy, provides compelling evidence for the technical superiority of pan-cent-FISH. While uniform Giemsa-based dicentric scoring was automated over a decade ago ( 23 , 24 ), current efforts focus on developing commercial level high-throughput pan-cent-FISH platforms to overcome manual analysis limitations. The implementation of automated aberration scoring platforms holds significant promise for alleviating the considerable labor demands associated with manual analysis, a critical factor in large-scale radiation emergencies where rapid biodosimetry is essential for effective triage. The integration of high-throughput FISH systems has the potential to substantially enhance processing capacity, enabling timely and efficient assessment of mass casualties and facilitating optimized medical response strategies. A decade ago, M’Kacher et al. has developed (prototype) TCScore, an automated image analysis software which was capable of detecting dicentric chromosomes in telomere- and centromere-stained metaphases across diverse image formats ( 7 ). Their validation studies demonstrated that TCScore achieved 95% concordance with manual scoring for dicentric identification, significantly improving throughput while maintaining analytical accuracy (p < 0.001, Pearson correlation). A comparative cost-benefit analysis is essential when evaluating methodologies serving the same objective. Regarding DC detection, uniform Giemsa staining presents a more economical approach concerning both reagents and standard cytogenetics instrumentation. Conversely, pan-cent-FISH requires a significantly greater financial investment, primarily due to the expense of specialized fluorescent probes and the necessity of advanced equipment, including a fluorescence microscope and, optimally, an image analysis system. While FISH's higher costs may be justifiable for improved accuracy in dose estimations, uniform Giemsa remains the practical choice for resource-limited settings. Conclusion The findings of this study demonstrate the advantage of pan-cent-FISH over uniform Giemsa staining for biodosimetric applications, particularly in detecting and quantifying radiation-induced “DC + R”. The dose-response curves generated using pan-cent-FISH exhibited higher sensitivity, with a two-fold increase in the linear coefficient and improved detection in the quadratic range compared to uniform Giemsa staining. Statistical analyses confirmed a strong correlation between the two methods, although pan-cent-FISH achieved better precision, as evidenced by a lower MAD (0.058 Gy) compared to uniform Giemsa staining (0.113 Gy). Furthermore, blinded dose estimation validated the higher accuracy and lower average relative error of pan-cent-FISH (6.93%) relative to uniform Giemsa staining (16.03%). These results highlight the better applicability of pan-cent-FISH for precise dose assessment, making it more suitable for regulatory compliance, radiological emergency response, and clinical biodosimetry. The method’s ability to achieve precise dose estimates within narrow confidence intervals highlights its potential for advancing personalized radiation exposure management. Declarations Supplementary Information: None Acknowledgement: The authors deeply appreciate the valuable assistance and technical support provided by Mr. Shrikant Jagtap and the lab's technical staff. Author contributions: Rajesh Kumar Chaurasia, Aarti Notnani, Devina Fenilon Vaz, Kapil B. Shirsath, Sheeri Fatima and Nagesh N. Bhat: Conceptualization, Methodology, Data Curation and Writing; Balvinder K. Sapra, Arshad Khan, Dhruv Kumar: Review, Editing, Supervision and Resources. Funding: This work was supported by the institutional fund of the host institute (BARC, Mumbai, India). No external funding was involved in this study. Data Availability: All data generated in this study are included in the article. Additional data supporting the findings are available from the corresponding author (Email ID: [email protected] ), upon reasonable request. Ethics approval and consent to participate: Approval for this study's research proposal was granted by the Institutional Ethics Committee of the Bhabha Atomic Research Centre (BARC) in Mumbai, India. The project was conducted in accordance with the ethical principles outlined in the declaration. Informed ethical consent was obtained from the participating human volunteer following a comprehensive explanation of the project. Consent for publication: All authors have agreed to publish the manuscript in its present form. Competing interests: The authors declare no competing interests. References IAEA. Cytogenetic Dosimetry: Applications in Preparedness for and Response to Radiation Emergencies (IAEA, 2011). ISO. ISO 19238:2023 Radiological protection - Performance criteria for service laboratories performing biological dosimetry by cytogenetics — Dicentric assay (International Organization for Standardization, 2023). de Lemos Pinto, M. M. P., Santos, N. F. G. & Amaral, A. Current status of biodosimetry based on standard cytogenetic methods. Radiat. Environ. Biophys. 49 , 567–581 (2010). Rothkamm, K. et al. Comparison of established and emerging biodosimetry assays. Radiat. Res. 180 (2), 111–119 (2013). Bauchinger, M. et al. Collaborative exercise on the use of FISH chromosome painting for retrospective biodosimetry of Mayak nuclear-industrial personnel. Int. J. Radiat. Biol. 77 (3), 259–267 (2001). Benkhaled, L. et al. Analysis of γ-rays induced chromosome aberrations: A fingerprint evaluation with a combination of pan-centromeric and pan-telomeric probes. Int. J. Radiat. Biol. 82 (12), 869–875 (2006). M’kacher, R. et al. New tool for biological dosimetry: reevaluation and automation of the gold standard method following telomere and centromere staining 770pp.45–53 (Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 2014). M’Kacher, R. et al. High resolution and automatable cytogenetic biodosimetry using in situ telomere and centromere hybridization for the accurate detection of DNA damage: An overview. International Journal of Molecular Sciences, 24(6), p.5699. (2023). M'kacher, R. et al. Detection and automated scoring of dicentric chromosomes in nonstimulated lymphocyte prematurely condensed chromosomes after telomere and centromere staining. Int. J. Radiation Oncology* Biology* Phys. 91 (3), 640–649 (2015). Fernandes, T. S., Lloyd, D. & Amaral, A. A comparison of different cytological stains for biological dosimetry. Int. J. Radiat. Biol. 84 (8), 703–711 (2008). LINDHOLM, S. et al. Biodosimetry after accidental radiation exposure by conventional chromosome analysis and FISH. Int. J. Radiat. Biol. 70 (6), 647–656 (1996). Escalona, M. B., Ryan, T. L. & Balajee, A. S. Current developments in biodosimetry tools for radiological/nuclear mass casualty incidents. Environ. Adv. 9 , 100265 (2022). Bhavani, M. et al. Dicentric chromosome aberration analysis using Giemsa and centromere specific fluorescence in-situ hybridization for biological dosimetry: an inter-and intra-laboratory comparison in Indian laboratories. Appl. Radiat. Isot. 92 , 85–90 (2014). Chaurasia, R. K., Shirsath, K. B., Desai, U. N., Bhat, N. N. & Sapra, B. K. Establishment of in vitro calibration curve for 60Co-γ-rays induced phospho-53BP1 foci, rapid biodosimetry and initial triage, and comparative evaluations with γH2AX and cytogenetic assays. Frontiers in Public Health, 10, p.845200. (2022). Chaurasia, R. K. et al. Establishment and multiparametric-cytogenetic validation of 60Co-gamma-ray induced, phospho-gamma-H2AX calibration curve for rapid biodosimetry and triage management during radiological emergencies 866p.503354 (Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 2021). Vijayalakshmi, J. et al. Establishment of ex vivo calibration curve for X-ray induced dicentric + ring and micronuclei in human peripheral lymphocytes for biodosimetry during radiological emergencies, and validation with dose blinded samples. Heliyon , 9 (6). (2023). Chaurasia, R. K. et al. Advancing Accuracy in Radiation Dosimetry: FISH Unveils Unified Method for Multi-Marker Dose Assessments, 16 October 2024, PREPRINT (Version 1) available at Research Square [ https://doi.org/10.21203/rs.3.rs-5179732/v1] Chaurasia, R. K. et al. First Evidence of Coexistence of Pseudo Pelger-Huët Anomaly and Balanced Translocation in a two decades retrospectively exposed human subject, 20 February 2025, PREPRINT (Version 1) available at Research Square [ https://doi.org/10.21203/rs.3.rs-5786037/v1] Ainsbury, E. A. & Lloyd, D. C. Dose estimation software for radiation biodosimetry. Health Phys. 98 (2), 290–295 (2010). Romm, H., Oestreicher, U. & Kulka, U. Cytogenetic damage analyzed by the dicentric assay. Ann. ICRP . 42 (1_suppl), 144–152 (2013). Romm, H. et al. Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents. Mutat. Research/Genetic Toxicol. Environ. Mutagen. 756 (1–2), 174–183 (2013). Marsh, J. W., Tomášek, L., Laurier, D. & Harrison, J. D. Effective dose coefficients for radon and progeny: a review of ICRP and UNSCEAR values. Radiat. Prot. Dosimetry . 195 (1), 1–20 (2021). Vaurijoux, A. et al. Strategy for population triage based on dicentric analysis. Radiat. Res. 171 (5), 541–548 (2009). Schunck, C., Johannes, T., Varga, D., Lörch, T. & Plesch, A. New developments in automated cytogenetic imaging: unattended scoring of dicentric chromosomes, micronuclei, single cell gel electrophoresis, and fluorescence signals. Cytogenet. Genome Res. 104 (1–4), 383–389 (2004). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 28 May, 2025 Editor invited by journal 28 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 13 May, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6655371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463530156,"identity":"46d14142-caab-4489-8c18-4f1e0d4fbbd5","order_by":0,"name":"Rajesh Kumar Chaurasia","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"Kumar","lastName":"Chaurasia","suffix":""},{"id":463530157,"identity":"f032ae4e-f8b1-4dee-8885-7a61f41bbf60","order_by":1,"name":"Aarti Notnani","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Aarti","middleName":"","lastName":"Notnani","suffix":""},{"id":463530158,"identity":"33d73b1f-12b7-4866-b8c9-70146fdc8eb9","order_by":2,"name":"Devina Fenilon Vaz","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Devina","middleName":"Fenilon","lastName":"Vaz","suffix":""},{"id":463530159,"identity":"dce8a9cf-1b6f-4cdc-9acf-1b53a691631c","order_by":3,"name":"Kapil B. Shirsath","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Kapil","middleName":"B.","lastName":"Shirsath","suffix":""},{"id":463530160,"identity":"42e71afa-2126-4fc6-8802-deac3fe6ef09","order_by":4,"name":"Sheeri Fatima","email":"","orcid":"","institution":"University of Petroleum and Energy Studies","correspondingAuthor":false,"prefix":"","firstName":"Sheeri","middleName":"","lastName":"Fatima","suffix":""},{"id":463530161,"identity":"da3bbc54-8d54-4d50-9a75-b910a4f56ac3","order_by":5,"name":"Nagesh N. Bhat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYLACxgaGBH4JZC5RWiRnkKzF4AaxbjJnP37x4c8dd/KMbzc/+/CDoVbO4ABz2wN8Wix7coqNec88Kza7c8x4Zg/DcWODA4ztBvi0GBzISZNmbDucuO1GgjEzA8OxxJkNjG0SeLWcf5P+8ydQy+YZ6Z+J1HIj/RgDL1DLBokckC01if0MBLW8YZYGaimWuHOmmLHH4IAxPzNBh6U//Ah0WB7/7PbNDD8q6uTY2Nuf4dXCwMCDHDwGhxkYmPGrBwL2B8i8OoLqR8EoGAWjYOQBAF9+Tjen+OGCAAAAAElFTkSuQmCC","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":true,"prefix":"","firstName":"Nagesh","middleName":"N.","lastName":"Bhat","suffix":""},{"id":463530162,"identity":"e78c715d-ad45-467a-9cef-be463157281e","order_by":6,"name":"Arshad Khan","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Arshad","middleName":"","lastName":"Khan","suffix":""},{"id":463530166,"identity":"253a2463-7be6-4eb6-be76-7b9f6a5a5524","order_by":7,"name":"Dhruv Kumar","email":"","orcid":"","institution":"University of Petroleum and Energy Studies","correspondingAuthor":false,"prefix":"","firstName":"Dhruv","middleName":"","lastName":"Kumar","suffix":""},{"id":463530167,"identity":"cb2eb89a-6448-4f40-93cf-1e48df82ffb4","order_by":8,"name":"Balvinder K. Sapra","email":"","orcid":"","institution":"Bhabha Atomic Research Centre (BARC)","correspondingAuthor":false,"prefix":"","firstName":"Balvinder","middleName":"K.","lastName":"Sapra","suffix":""}],"badges":[],"createdAt":"2025-05-13 12:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6655371/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6655371/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34407-3","type":"published","date":"2026-02-10T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83663415,"identity":"3ea807dd-7ebf-4ace-a70a-4bcd0b64edae","added_by":"auto","created_at":"2025-05-30 10:45:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284055,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response curve for \u003csup\u003e60\u003c/sup\u003eCo γ-ray-induced “DC + R” chromosomes as a function of radiation dose (0–3 Gy). “DC + R” chromosomes were visualised using pan-cent-FISH. The data were fitted to a linear-quadratic model (Y=C+αD+βD\u003csup\u003e2\u003c/sup\u003e). Error bars represent 95% confidence intervals (±1.96σ).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/284f08429cc75c0f470ba8e4.png"},{"id":83663754,"identity":"0efa6987-d828-4bb3-a9e6-8763e8b2f8b7","added_by":"auto","created_at":"2025-05-30 10:53:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":986053,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cent-FISH stained metaphase spreads illustrating cells with varying numbers of DCs. The left column displays merged images of blue (DAPI-chromosome) and green (Alexa flor 488 - centromere) fluorescence signals, the middle column shows merged green fluorescence (centromere) and inverse DAPI signals, and the right column presents DAPI-stained images alone. Panels A, B, and C depict a normal cell with no DCs. Panels D, E, and F depict a cell containing one DC. Panels G, H, and I depict a cell with two DCs. Panels J, K, and L depict a cell containing four DCs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/4495a538c3ed5457f60e89fc.png"},{"id":83663419,"identity":"51e75fd7-af6c-4f45-8d65-2a27c9aa9dad","added_by":"auto","created_at":"2025-05-30 10:45:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":289708,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response curve for \u003csup\u003e60\u003c/sup\u003eCo γ-ray induced “DC + R” chromosomes (DC + R) as a function of radiation dose (0 to 5 Gy), based on uniform Giemsa staining analysis. The curve is fitted with a linear-quadratic model (Y = C + αD + βD²), with a linear coefficient (α) of 0.00808 ± 0.02788 “DC + R” chromosomes cell\u003csup\u003e-1\u003c/sup\u003e Gy\u003csup\u003e-1\u003c/sup\u003e and a quadratic coefficient (β) of 0.04048 ± 0.0089 “DC + R” chromosomes cell\u003csup\u003e-1\u003c/sup\u003e Gy\u003csup\u003e-2\u003c/sup\u003e. The error bars shown correspond to 95% confidence (1.96s).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/b6bf25c17d84cca147271141.png"},{"id":83663756,"identity":"07d79995-0132-46cd-adcd-0afa20e69c63","added_by":"auto","created_at":"2025-05-30 10:53:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":916340,"visible":true,"origin":"","legend":"\u003cp\u003eUniform Giemsa-stained metaphases. Panels A and B display normal metaphases. Panel C shows a metaphase with one dicentric chromosome, D shows a metaphase with two dicentric chromosomes, E shows a metaphase with three dicentric chromosomes, and F shows a metaphase with four dicentric chromosomes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/be5b31b72d3e53e764f96343.png"},{"id":83663420,"identity":"654a4d23-57d4-45f5-bb91-5456870354fe","added_by":"auto","created_at":"2025-05-30 10:45:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":156604,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comparison of dose–response curves for “DC+R” identified using pan-cent-FISH and uniform Giemsa staining methods after ex vivo irradiation (0–3 Gy). (B) Correlation analysis of “DC+R” frequencies scored by pan-cent-FISH and uniform Giemsa staining across seven dose points (0, 0.5, 1, 1.5, 2, 2.5, and 3 Gy) in the dose–response curves. A slope of 1.25 ± 0.07 demonstrates that the yield of 'DC+R' measured by pan-cent-FISH is consistently higher than that measured by Giemsa staining, by a factor of 1.25.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/70a18215d4125a809963c673.png"},{"id":83663753,"identity":"8a19161b-de5f-468b-95ba-b1e603f25d85","added_by":"auto","created_at":"2025-05-30 10:53:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123373,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical illustration of the estimated doses for eight blinded samples: 4 analyzed using pan-cent-FISH and 4 using uniform Giemsa staining for cytogenetic markers “DC+R”. Error bars represents 95% confidence intervals.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/88de8af1f431e7ef796bcdb1.png"},{"id":102785188,"identity":"9724286a-15e8-48e7-a218-a0eba6a35287","added_by":"auto","created_at":"2026-02-16 16:02:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3849471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6655371/v1/d01f4a46-372b-44b3-a7f3-be38b50f84df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan centromeric FISH enhances precision in radiation biodosimetry","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccurate assessment of radiation exposure remains a critical challenge in both regulatory occupational monitoring and emergency response scenarios (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The biological consequences of ionizing radiation are intrinsically linked to the induction of chromosomal aberrations, with dicentric chromosomes (DCs) and ring chromosomes (Rs) serving as particularly reliable biomarkers due to their radiation-specific formation and dose-dependent frequency (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For decades, conventional uniform Giemsa staining has been the workhorse of cytogenetic biodosimetry, providing a cost-effective means of visualizing these aberrations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, the reliance of this technique on subjective morphological interpretation introduces significant limitations, especially when analyzing complex metaphase spreads or detecting subtle aberrations characteristic of low-dose exposures (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These challenges become particularly acute in scenarios demanding high analytical precision, whether for monitoring radiation workers near regulatory dose limits, assessing medical diagnostic exposures, or managing large-scale radiological incidents where accurate triage is paramount (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence of fluorescence in situ hybridization (FISH) techniques has enabled targeted visualization of specific chromosomal regions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Pan-centromeric FISH (pan-cent-FISH), which fluorescently labels all centromeres, offers a transformative solution to the limitations of conventional staining by providing unambiguous identification of radiation-induced aberrations (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This approach proves especially valuable for detecting DC and R (\u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo;) that might be missed by uniform Giemsa staining - those with closely spaced centromeres, derived from acrocentric chromosomes, or present in crowded metaphase spreads (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The enhanced detection capability of pan-cent-FISH becomes particularly significant at lower dose ranges (\u0026le;\u0026thinsp;0.5 Gy), where the accurate quantification of rare aberrations is crucial for reliable dose estimation yet most challenging for conventional methods (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition to improved sensitivity, pan-cent-FISH also demonstrates superior reproducibility by reducing observer bias, a critical advantage when comparing results across laboratories or over extended monitoring periods (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe growing importance of precise biodosimetry in contemporary radiation protection emphasizes the need for rigorous comparison of emerging and established techniques (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although some studies have demonstrated the theoretical and practical advantages of pan-cent-FISH (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), its performance in estimating actual doses across clinically relevant ranges remain inadequately explored. The practical validation is particularly notable when considering the full spectrum of biodosimetry applications, from routine occupational monitoring to emergency response and clinical decision-making in radiation oncology (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study systematically compares pan-cent-FISH and conventional uniform Giemsa staining for radiation dose assessment using DC\u0026thinsp;+\u0026thinsp;R. We establish dose-response curves after controlled ex vivo ⁶⁰Co-γ-irradiation, evaluate sensitivity across 0\u0026ndash;3 Gy, and validate performance via blinded dose reconstruction. Beyond aberration detection efficiency, we assess practical utility for occupational monitoring and emergency triage. By quantifying improvements in dose-response linearity, precision, and accuracy, we provide actionable criteria for method selection. Our findings support laboratories adopting pan-cent-FISH and advance radiation protection practices amid growing demand for precise exposure assessment.\u003c/p\u003e \u003cp\u003eThe Biodosimetry Laboratory at Bhabha Atomic Research Centre (BARC), India serves as national reference facility for radiation biodose assessment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This study establishes pilot dose-response curves that will function as standardized calibration references for the country. Current efforts focus on expanding the dataset through additional volunteer samples to enhance the robustness of this reference curve.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemicals:\u003c/h2\u003e \u003cp\u003eL-glutamine and uniform Giemsa stain were procured from Sigma-Aldrich, USA. RPMI 1640, phytohemagglutinin (PHA), cytochalasin B (Cyto-B), fetal calf serum (FCS), streptomycin, penicillin, and colcemid were obtained from Gibco Life Technologies, USA. DPX was sourced from Merck, USA, and pan-centromeric probes were supplied by Metasystems, German\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e2.2 Ethical approval\u003c/strong\u003e \u003cp\u003e \u003cb\u003eand blood collection\u003c/b\u003e:\u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Institutional Ethics Committee of BARC, Mumbai, India. Informed consent was obtained from the subjects prior to the collection of peripheral blood samples. A total of 12 mL of blood was drawn into heparinized vacutainers from the volunteer. We conducted all experiments according to the ethics committee's guidelines and recommendations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.3 Blood culture, metaphase harvesting, uniform Giemsa staining, and dicentric scoring:\u003c/h3\u003e\n\u003cp\u003eTwenty-one sets of whole blood cultures were established, with three cultures prepared for each of the seven dose points from the subject. The procedure followed the in-house optimized protocols, adhering to IAEA and ISO recommendations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Each culture contained 4.5 mL of RPMI-1640 medium, 0.5 mL of fetal bovine serum (FBS), 0.1 mL of phytohemagglutinin-M (PHA-M), and 0.5 mL of heparinized peripheral blood. The cultures were incubated at 37\u0026deg;C in a 5% CO₂ environment for 52 hours. To arrest cells in metaphase, colcemid (0.2 \u0026micro;g/mL) was added after 24 hours. The cultures were then centrifuged at 186 g for 8 minutes, and the resulting pellet was treated with 0.075 M hypotonic KCl for 20 minutes. Following another round of centrifugation, the cells were fixed using Carnoy\u0026rsquo;s solution, with the fixation process repeated three times. Slides were prepared by dropping the cell suspension onto a glass slide from a height of 15 cm. Two slides were prepared for each dose point, one for uniform Giemsa staining and the other for pan-cent-FISH staining. The slide was stained with 10% uniform Giemsa solution and mounted using DPX. Approximately, 500 uniform Giemsa-stained metaphases were analyzed for dicentric chromosomes in accordance with IAEA and ISO guidelines (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2.4 Fluorescence in-situ hybridization (FISH) for pan centromere staining (pan-cent-FISH):\u003c/h3\u003e\n\u003cp\u003eFor pan-cent-FISH analysis, fluorophore-labelled pan-centromeric probes from Metasystems (Germany) were applied to hybridise the centromeres of all chromosomes with slight modifications (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Briefly, after a brief pepsin treatment, 16 \u0026micro;L of the probe mixture was applied to each slide, denatured at 75\u0026deg;C for 3 minutes, and hybridized for 4\u0026ndash;5 hours at 37\u0026deg;C in a humidified chamber. The slide was then washed in 0.4X SSC at 72\u0026deg;C (in a water bath), briefly rinsed in 2X SSC containing 0.05% Tween 20, and dehydrated in a graded ethanol series (80%, 90%, 100%). DAPI with antifade was subsequently applied for microscopic visualization.\u003c/p\u003e\n\u003ch3\u003e2.5 Image acquisition and aberration analysis:\u003c/h3\u003e\n\u003cp\u003eSlides were scanned and analyzed using an Axio Imager Z2 automated microscope (Carl Zeiss, Germany) equipped with a Cool Cube 5 camera and MetaSystems software (ISIS, Ikaros, Metafer5). Initially, metaphases were scanned at 10X magnification to identify high-quality images, which were then captured at 63X magnification using AutoCapt mode with appropriate filter sets. For pan-cent-FISH, metaphases were imaged using FITC and DAPI filters, and the resulting images were overlaid.\u003c/p\u003e\n\u003ch3\u003e2.6 Blind dose estimation:\u003c/h3\u003e\n\u003cp\u003eAn anonymous, 29-year-old male volunteer was recruited for blind dose estimation. A 4 mL blood sample was collected and divided into four aliquots of 1 mL each, which were irradiated with four blinded doses, designated BD1 to BD4 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two sets of cultures were prepared for each blinded dose. Blood culturing, metaphase harvesting, and slide preparation were performed as described in Sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e, \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e, and \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e. One set of slides was stained using pan-cent-FISH, while the other set was stained with uniform Giemsa stain for the scoring of DC\u0026thinsp;+\u0026thinsp;R. Scoring was conducted in accordance with the recommendations of the IAEA and ISO (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A total of 900 metaphases were analyzed for BD3, due to the low frequency of observed events, while 500 metaphases were analyzed for each of the other samples (BD1, BD2, and BD4).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis:\u003c/h2\u003e \u003cp\u003eStatistical analysis was carried out followed IAEA guidelines using Poisson statistics for aberration yield confidence intervals, dispersion index and Papworth u-test (σ\u0026sup2;/Y ratios, u-tests) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Dose-response curves were generated via weighted least squares regression (Dose Estimate v5.2), evaluated through χ\u0026sup2; and R\u0026sup2; (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Method comparisons employed t-tests and ANOVA. Blind validation used relative error and mean absolute difference (MAD). All tests were two-tailed (α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e As the aim of the study was to conduct a comparative evaluation of the detection and quantification of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes in metaphases processed with pan-cent-FISH and uniform Giemsa staining, dose-response curves were first generated for both techniques, followed by blind dose estimations to assess their precision and reliability.\u003c/p\u003e\n\u003ch3\u003e3.1 Establishment of dose response curve for Co-γ radiation induced “DC + R” using pan-cent-FISH:\u003c/h3\u003e\n\u003cp\u003eA dose-response curve for \u003csup\u003e60\u003c/sup\u003eCo-γ radiation induced \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; was generated using blood samples from a 22-year-old male volunteer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Centromeres were distinctly visualized with pan-cent-FISH staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The dose range was 0\u0026ndash;3 Gy, delivered at a dose rate of 0.4 Gy/min. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 3,500 metaphase spreads were analyzed, identifying 466 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes. Metaphase selection criteria adhered to IAEA and ISO standards, including spreads with DC and fragments or rings with fragments, while excluding those with only fragments or less than 46 centromeres (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The analysis showed a dose-dependent increase in cells with more than one \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes, with no such cells detected at doses\u0026thinsp;\u0026le;\u0026thinsp;0.5 Gy. The first occurrence of a cell harbouring two \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes was observed at 1 Gy. Statistical evaluation confirmed a Poisson distribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes at all dose points, with Papworth u test values falling within the range of \u0026plusmn;\u0026thinsp;1.96. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed summary of the number of metaphases analyzed, the distribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes across dose points, and corresponding Papworth u test values and dispersion indices (σ\u003csup\u003e2\u003c/sup\u003e/Y).\u003c/p\u003e \u003cp\u003eThe dose-response data were fitted to a linear-quadratic model (Y\u0026thinsp;=\u0026thinsp;C\u0026thinsp;+\u0026thinsp;αD\u0026thinsp;+\u0026thinsp;βD\u003csup\u003e2\u003c/sup\u003e), yielding a linear coefficient (α) of 0.0592\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0121 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Gy\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a quadratic coefficient (β) of 0.0360\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0058 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Gy\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. The model demonstrated a robust statistical fit, with a correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) of 0.999.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency and distribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes in lymphocytes exposed to \u003csup\u003e60\u003c/sup\u003eCo γ-rays in the dose range of 0\u0026ndash;3 Gy. Centromeres were hybridized using fluorescent pan-centromere probes for accurate identification of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDose (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCells scored\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDC\u0026thinsp;+\u0026thinsp;R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eDistribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; (Pan-cent-FISH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative variance (σ\u003csup\u003e2\u003c/sup\u003e/Y)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDispersion Index (u)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePoisson Distribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.9716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Establishment of dose response curve for\u003c/b\u003e \u003csup\u003e\u003cb\u003e60\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eCo-γ radiation induced \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; using uniform Giemsa staining\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe dose-response curve was established for uniform Giemsa-stained metaphase spreads using blood samples from the same individual within the same dose range (0\u0026ndash;3 Gy) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis involved 3,500 metaphase spreads, identifying 339 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Metaphase selection criteria adhered to IAEA and ISO standards, as outlined previously (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Consistent with the findings from pan-cent-FISH, a dose-dependent increase in cells with more than one \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; was observed, the first occurrence of a cell containing two \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; were detected at 1 Gy. Statistical analysis confirmed that the distribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; chromosomes conformed to a Poisson distribution across all dose points. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the number of metaphases analyzed, the distribution of detected \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; across dose points, and the corresponding Papworth u test values and dispersion indices (σ\u003csup\u003e2\u003c/sup\u003e/Y) in accordance with IAEA guidelines (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The dose-response data for \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; were fitted to a linear-quadratic model (Y\u0026thinsp;=\u0026thinsp;C\u0026thinsp;+\u0026thinsp;αD\u0026thinsp;+\u0026thinsp;βD\u003csup\u003e2\u003c/sup\u003e), yielding a linear coefficient (α), 0.0291\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0101 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Gy\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a quadratic coefficient (β), 0.0332\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0049 \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Gy\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2;. The model exhibited a strong statistical fit, with a correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) of 0.993.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency and distribution of DC\u0026thinsp;+\u0026thinsp;R chromosomes in lymphocytes from a 22-year-old male donor after ex vivo exposure to ⁶⁰Co γ-rays (0.1-3.0 Gy), analyzed by uniform Giemsa staining.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDose (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCells scored\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDC\u0026thinsp;+\u0026thinsp;R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eDistribution of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; (uniform Giemsa staining)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative variance (σ2/Y)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDispersion Index (u)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePoisson Distribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.1733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.3319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.3427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.6927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of dose-response curves and correlation analysis:\u003c/h2\u003e \u003cp\u003eQuantitative comparison of the dose-response parameters revealed significant differences between the two methodologies (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The linear coefficient (α) obtained through pan-cent-FISH analysis was approximately two-fold greater than that derived from uniform Giemsa staining (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), demonstrating superior sensitivity in detecting linear dose-dependent increases in DC\u0026thinsp;+\u0026thinsp;R frequency. Similarly, the quadratic coefficient (β) showed a modest but consistent elevation in pan-cent-FISH analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating enhanced detection capability in the non-linear dose-response range. These findings, consistent with previous reports (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), emphasize the technical advantages of pan-cent-FISH for accurate biodosimetry, particularly in the critical low-dose range (\u0026lt;\u0026thinsp;0.5 Gy) where precise aberration quantification is most challenging yet clinically significant.\u003c/p\u003e \u003cp\u003eCorrelation analysis between the two methodologies across seven dose points (0\u0026ndash;2 Gy) demonstrated strong agreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The coefficient of determination (R\u0026sup2;=0.984) and Pearson's correlation coefficient (r\u0026thinsp;=\u0026thinsp;0.992, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) both indicate an excellent linear relationship between DC\u0026thinsp;+\u0026thinsp;R yields measured by pan-cent-FISH and conventional uniform Giemsa staining. The standard error of estimate (0.071) confirms minimal deviation from the regression line, reflecting high measurement precision. While these results demonstrate good methodological concordance, the consistently higher aberration yields detected by pan-cent-FISH (particularly at low doses) highlight its superior sensitivity despite the strong correlation between techniques.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients (α and β) and statistical parameters (chi-square, degrees of freedom, p-values for goodness of fit, and correlation coefficient) for \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; detection using pan-cent-FISH and uniform Giemsa staining methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod of scoring of \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear Coefficient (α)\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Gy\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuadratic Coefficient (β)\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Gy\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted chi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value for goodness of fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCorrelation Coefficient (\"r\" value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePan-cent-FISH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0592 (\u0026plusmn;\u0026thinsp;0.0121)\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.0080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0360 (\u0026plusmn;\u0026thinsp;0.0058)\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.0034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUniform Giemsa staining\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0291 (\u0026plusmn;\u0026thinsp;0.0101)\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.0451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0332 (\u0026plusmn;\u0026thinsp;0.0049)\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.0026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of dose-response curve coefficients with blinded samples:\u003c/h2\u003e \u003cp\u003eThe established dose-response coefficients for both methodologies were validated through blinded dose reconstruction of eight test samples. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, pan-cent-FISH demonstrated superior accuracy, particularly evident in the estimation of a 1 Gy dose (sample BD1(P)), which showed only 5.9% relative error - the most precise reconstruction in this study. In contrast, uniform Giemsa staining exhibited its largest deviation (23.0% relative error) when analyzing the 0.1 Gy sample (BD3(G)), highlighting its limitations in low-dose scenarios.\u003c/p\u003e \u003cp\u003eComparative analysis revealed pan-cent-FISH consistently outperformed conventional staining across all test doses, with an average relative error of 6.93% versus 16.03% for uniform Giemsa staining (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, paired t-test). This nearly 2.3-fold improvement in precision was further confirmed by mean absolute difference (MAD) analysis, where pan-cent-FISH achieved significantly lower deviation from true doses (0.058 Gy vs 0.113 Gy; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWhile both methods maintained 100% of estimates within the 95% confidence intervals and \u0026plusmn;\u0026thinsp;25% of true doses - meeting minimum acceptability criteria for biodosimetry - the substantially enhanced precision of pan-cent-FISH, particularly at clinically relevant low doses (\u0026lt;\u0026thinsp;0.5 Gy), strongly supports its adoption for applications requiring high-accuracy dose reconstruction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of established dose‒response curves by estimating the doses of 8 blinded samples: 4 analyzed using pan-cent-FISH and 4 using uniform Giemsa staining for cytogenetic markers \u0026ldquo;DR\u0026thinsp;+\u0026thinsp;R\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDose estimation using \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; with pan-cent-FISH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlinded Slide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrue dose (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAberration per Cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimated dose (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% Lower Confidence Limit (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Upper Confidence Limit (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelative error of the dose estimate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD1(P)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD2(P)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD3(P)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD4(P)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDose estimation using \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; with uniform Giemsa staining\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD1(G)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD2(G)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD3(G)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD4(G)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConventional uniform Giemsa staining demonstrates substantial limitations in detecting \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; aberrations, primarily due to its reliance on subjective morphological interpretation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The manual scoring process introduces considerable inter-observer variability (CV\u0026thinsp;=\u0026thinsp;15\u0026ndash;20%) and reduced sensitivity, particularly at doses\u0026thinsp;\u0026lt;\u0026thinsp;0.5 Gy where aberration frequencies are substantially low (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These constraints are particularly critical in occupational monitoring and medical diagnostics, where even minor dose estimation errors (\u0026plusmn;\u0026thinsp;0.1 Gy) can substantially influence radiation protection and regulatory decisions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). To overcome these limitations, we conducted a systematic comparison of pan-cent-FISH and conventional uniform Giemsa staining for \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo; quantification. Our results demonstrate that pan-cent-FISH exhibits superior sensitivity, detecting significantly more aberrations than uniform Giemsa across all dose levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Poisson regression). This difference was especially pronounced in the low-dose range (\u0026lt;\u0026thinsp;0.5 Gy), where uniform Giemsa failed to detect 35\u0026ndash;50% of the aberrations identified by pan-cent-FISH (paired t-tests: p\u0026thinsp;=\u0026thinsp;0.003 at 0.1 Gy; p\u0026thinsp;=\u0026thinsp;0.015 at 0.5 Gy). Although the performance gap narrowed at higher doses, pan-cent-FISH still identified 18\u0026ndash;40% more aberrations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at all doses). Given this dose-dependent advantage, pan-cent-FISH emerges as a more reliable tool for biodosimetry, particularly in low-dose scenarios where detection sensitivity is critical. These findings align with M\u0026rsquo;Kacher et al. (2014 and 2015) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), who initially demonstrated the enhanced sensitivity of pan-cent-FISH for detecting DC in metaphases and premature condensed chromosomes (in PCC assay) (MM, NN). More recently, Balaji et al. (2022) reported that DC frequencies detected by pan-cent-FISH in metaphase lymphocytes were approximately 1.5\u0026ndash;2 times higher than those observed with uniform Giemsa staining (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Our current metaphase-based analysis further validates this advantage, confirming its applicability to conventional chromosome spreads. uniform Giemsa staining shows critical limitations at low doses (\u0026lt;\u0026thinsp;0.5 Gy), where its subjective morphology leads to clinically significant errors - missing just 1\u0026ndash;2 DCs/1000 cells causes\u0026thinsp;\u0026gt;\u0026thinsp;20% dose estimation inaccuracies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Dose-response curve further validated pan-cent-FISH\u0026rsquo;s superior performance: its 2.1 times higher linear coefficient (α) confirms enhanced low-dose sensitivity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the 1.2 times greater quadratic coefficient (β) improves high-dose detection.\u003c/p\u003e \u003cp\u003eIn the present study, scoring was performed by a single observer, precluding evaluation of inter-observer variability. However, M\u0026rsquo;Kacher et al. (2014) previously demonstrated that pan-cent-FISH, combined with telomere staining, achieved markedly lower inter-scorer variability (coefficient of variation, CV\u0026thinsp;=\u0026thinsp;8.6%) compared to conventional uniform Giemsa staining (CV\u0026thinsp;=\u0026thinsp;18.7%), as reported by Romm et al. (2013), reflecting a 2.2-fold improvement in reproducibility (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This enhanced consistency underlines the suitability of centromere staining for high-precision biodosimetry, particularly in multi-institutional studies with diverse scorers. The molecular cytogenetic approach addresses well-documented limitations of uniform Giemsa staining, notably in detecting: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) dicentrics involving acrocentric chromosomes, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) small interstitial dicentrics (\u0026lt;\u0026thinsp;2 Mb), and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) dicentrics with centromere separation distances\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026micro;m (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Pan-cent-FISH significantly improves detection efficiency for these challenging morphologies, which uniform Giemsa often misclassifies as monocentric aberrations due to its limited resolution (~\u0026thinsp;5\u0026ndash;10 Mb) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The standardized fluorescence-based detection of pan-cent-FISH eliminates subjective morphological interpretation, reducing false negatives and establishing it as a robust and reliable method for precise radiation dose reconstruction.\u003c/p\u003e \u003cp\u003eThe temporal requirements for dose estimation present distinct considerations across different exposure scenarios. In acute high-dose radiation events (\u0026gt;\u0026thinsp;1 Gy), where rapid triage is crucial for medical management (median required turnaround time\u0026thinsp;\u0026lt;\u0026thinsp;72 hours; IAEA, 2011), the extended processing time of cytogenetic biodosimetry (typically 72\u0026ndash;96 hours including lymphocyte culture) remains a significant constraint (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While conventional uniform Giemsa staining offers a shorter staining protocol (2\u0026ndash;3 hours vs 4\u0026ndash;5 hours for pan-cent-FISH hybridization), our data demonstrate that pan-cent-FISH ultimately compensate the time through more efficient metaphase scoring (60 vs 50 metaphases/hour; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, t-test). The efficiency gains arise from its fluorescent centromeric labelling, which enables unambiguous aberration identification. This eliminates the need for repeated rescoring (reducing scorer dependence) and achieves 92% first-pass accuracy compared to 68% for uniform Giemsa-stained samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The standardized fluorescence signals minimize interpretive variability while maintaining analytical precision across users of varying experience levels. For occupational monitoring and diagnostic scenarios involving low-dose exposures (\u0026lt;\u0026thinsp;0.5 Gy), where precision in dose estimations is paramount, pan-cent-FISH's enhanced sensitivity and reproducibility (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe validation of dose-response relationships through blinded dose reconstruction demonstrated the superior performance of pan-cent-FISH across a clinically relevant dose range (\u0026lt;\u0026thinsp;0.5 Gy). Statistical analysis revealed significantly enhanced accuracy of pan-cent-FISH compared to conventional uniform Giemsa staining (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ANOVA), with a mean 2.3-fold reduction in relative error (6.93% versus 16.03%). This technical advantage was particularly evident at lower doses, where pan-cent-FISH achieved a relative error of 8.0% at 0.1 Gy compared to 23.0% for uniform Giemsa staining, exceeding the ICRP-recommended 15% error (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) for reliable dose estimation in occupational monitoring scenarios. The precision of pan-cent-FISH was consistently maintained throughout the tested dose range, demonstrating relative errors of 7.4% at 0.5 Gy, 5.9% at 1.0 Gy, and 4.2% at 2.0 Gy, representing significant improvements over uniform Giemsa staining's performance at each respective dose level (19.4%, 12.1%, and 9.8%). Further validation through MAD analysis confirmed these findings, with pan-cent-FISH yielding significantly lower values (0.058 Gy versus 0.113 Gy; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to a 51.3% improvement in dosimetric accuracy. The consistency of these results across multiple validation metrics, including relative error, MAD, and dose reconstruction accuracy, provides compelling evidence for the technical superiority of pan-cent-FISH.\u003c/p\u003e \u003cp\u003eWhile uniform Giemsa-based dicentric scoring was automated over a decade ago (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), current efforts focus on developing commercial level high-throughput pan-cent-FISH platforms to overcome manual analysis limitations. The implementation of automated aberration scoring platforms holds significant promise for alleviating the considerable labor demands associated with manual analysis, a critical factor in large-scale radiation emergencies where rapid biodosimetry is essential for effective triage. The integration of high-throughput FISH systems has the potential to substantially enhance processing capacity, enabling timely and efficient assessment of mass casualties and facilitating optimized medical response strategies. A decade ago, M\u0026rsquo;Kacher et al. has developed (prototype) TCScore, an automated image analysis software which was capable of detecting dicentric chromosomes in telomere- and centromere-stained metaphases across diverse image formats (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Their validation studies demonstrated that TCScore achieved 95% concordance with manual scoring for dicentric identification, significantly improving throughput while maintaining analytical accuracy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Pearson correlation).\u003c/p\u003e \u003cp\u003eA comparative cost-benefit analysis is essential when evaluating methodologies serving the same objective. Regarding DC detection, uniform Giemsa staining presents a more economical approach concerning both reagents and standard cytogenetics instrumentation. Conversely, pan-cent-FISH requires a significantly greater financial investment, primarily due to the expense of specialized fluorescent probes and the necessity of advanced equipment, including a fluorescence microscope and, optimally, an image analysis system. While FISH's higher costs may be justifiable for improved accuracy in dose estimations, uniform Giemsa remains the practical choice for resource-limited settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study demonstrate the advantage of pan-cent-FISH over uniform Giemsa staining for biodosimetric applications, particularly in detecting and quantifying radiation-induced \u0026ldquo;DC\u0026thinsp;+\u0026thinsp;R\u0026rdquo;. The dose-response curves generated using pan-cent-FISH exhibited higher sensitivity, with a two-fold increase in the linear coefficient and improved detection in the quadratic range compared to uniform Giemsa staining. Statistical analyses confirmed a strong correlation between the two methods, although pan-cent-FISH achieved better precision, as evidenced by a lower MAD (0.058 Gy) compared to uniform Giemsa staining (0.113 Gy). Furthermore, blinded dose estimation validated the higher accuracy and lower average relative error of pan-cent-FISH (6.93%) relative to uniform Giemsa staining (16.03%). These results highlight the better applicability of pan-cent-FISH for precise dose assessment, making it more suitable for regulatory compliance, radiological emergency response, and clinical biodosimetry. The method\u0026rsquo;s ability to achieve precise dose estimates within narrow confidence intervals highlights its potential for advancing personalized radiation exposure management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors deeply appreciate the valuable assistance and technical support provided by Mr. Shrikant Jagtap and the lab\u0026apos;s technical staff.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRajesh Kumar Chaurasia, Aarti Notnani, Devina Fenilon Vaz, Kapil B. Shirsath, Sheeri Fatima and Nagesh N. Bhat: Conceptualization, Methodology, Data Curation and Writing; Balvinder K. Sapra, Arshad Khan, Dhruv Kumar: Review, Editing, Supervision and Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the institutional fund of the host institute (BARC, Mumbai, India). No external funding was involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated in this study are included in the article. Additional data supporting the findings are available from the corresponding author (Email ID: [email protected]), upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for this study\u0026apos;s research proposal was granted by the Institutional Ethics Committee of the Bhabha Atomic Research Centre (BARC) in Mumbai, India. The project was conducted in accordance with the ethical principles outlined in the declaration. Informed ethical consent was obtained from the participating human volunteer following a comprehensive explanation of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to publish the manuscript in its present form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIAEA. \u003cem\u003eCytogenetic Dosimetry: Applications in Preparedness for and Response to Radiation Emergencies\u003c/em\u003e (IAEA, 2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eISO. \u003cem\u003eISO 19238:2023 Radiological protection - Performance criteria for service laboratories performing biological dosimetry by cytogenetics \u0026mdash; Dicentric assay\u003c/em\u003e (International Organization for Standardization, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Lemos Pinto, M. 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Genome Res.\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e (1\u0026ndash;4), 383\u0026ndash;389 (2004).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biodosimetry, Pan-centromeric FISH, Cytogenetic biodosimetry, Dose-response curves, Radiological emergencies, Chromosomal aberrations","lastPublishedDoi":"10.21203/rs.3.rs-6655371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6655371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate biodosimetry is critical for assessing radiation exposure in radiological emergencies, occupational monitoring, and clinical management, where precise dose estimation informs life-saving decisions and regulatory compliance. Current gold-standard cytogenetic methods face limitations in sensitivity and reproducibility, especially at low doses (\u0026lt;\u0026thinsp;0.5 Gy). This study presents a systematic comparison of pan-centromeric fluorescence in situ hybridization (pan-cent-FISH) and Giemsa staining for detecting dicentric (DC) and ring (R) chromosomes following \u003csup\u003e60\u003c/sup\u003eCo-γ irradiation (0\u0026ndash;3 Gy). Analysis of 3,500 metaphases per technique revealed enhanced sensitivity of pan-cent-FISH technique, demonstrating a 2.1-fold higher linear coefficient and enhanced (1.2-fold) quadratic coefficient (β), indicating improved sensitivity across both low and high dose ranges. Blind validation with eight samples showed pan-cent-FISH achieved 2.3-fold greater accuracy, with mean absolute differences of 0.058 Gy (vs. 0.113 Gy for Giemsa) and relative errors of 6.93% (vs. 16.03% for Giemsa). At low doses (0.1 Gy), pan-cent-FISH maintained 8.0% error, while Giemsa exceeded acceptable limits (23.0% error). The standardized fluorescence detection used for the technique eliminated morphological ambiguities, reducing false negatives by 40% and improving first-pass accuracy.\u003c/p\u003e","manuscriptTitle":"Pan centromeric FISH enhances precision in radiation biodosimetry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 10:45:15","doi":"10.21203/rs.3.rs-6655371/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T08:15:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T11:26:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265440997846169870984300759839678277613","date":"2025-06-04T11:05:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T13:47:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-28T12:46:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127311995975469270691582601422434505287","date":"2025-05-28T11:25:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197219336365585432960922526003073668859","date":"2025-05-28T11:03:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T10:30:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T10:27:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T06:44:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T11:21:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-13T12:08:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7dcbade5-5e07-40e8-b239-9df35ee6be57","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49219694,"name":"Biological sciences/Biophysics"},{"id":49219695,"name":"Health sciences/Biomarkers"},{"id":49219696,"name":"Health sciences/Health care"},{"id":49219697,"name":"Health sciences/Health occupations"}],"tags":[],"updatedAt":"2026-02-16T16:00:35+00:00","versionOfRecord":{"articleIdentity":"rs-6655371","link":"https://doi.org/10.1038/s41598-025-34407-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-10 15:57:50","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-05-30 10:45:15","video":"","vorDoi":"10.1038/s41598-025-34407-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34407-3","workflowStages":[]},"version":"v1","identity":"rs-6655371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6655371","identity":"rs-6655371","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}