A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

[Negative Poisson's Ratio Material Structure Based on 3D Printing and Its Application in Medical Field].

First of all, the overall framework of 3D printing is briefly introduced, including the basic principles of the additive manufacturing process, the classification and summary of the seven processes. Secondly, the common negative Poisson's ratio structure is introduced. Compared with the conventional structure, the negative Poisson's ratio structure has stronger energy absorption capacity, better fracture resistance and better indentation resistance, which are its advantages in printing manufacturing. Finally, 3D printing, the application of negative Poisson's ratio structure and the combination of the two are introduced from the different perspective of medical field, for example, the application of cardiovascular stent, biomedical material structure preparation, and lumbar disc implants. This paper suggests that the structural design of negative Poisson's ratio in 3D printing guides the development of new application directions in the medical field. Negative Poisson's ratio materials have a wide range of applications, not only in the medical field but also in mechanical equipment, automotive manufacturing, aerospace, and other high-tech industries.

A comparison of the MPN and pour plate methods for estimating shellfish contamination by Escherichia coli.

AIMS: Shellfish production areas are classified for suitability for human consumption using counts of Escherichia coli in shellfish samples. Two alternative laboratory methods are approved in the European Union and UK for measuring E. coli in shellfish samples; the most probable number (MPN) and pour plate methods. These methods have inherently different statistical uncertainty and may give different counts for the same sample. Using two approaches: simulated data and spiking experiments, we investigate the theoretical properties of the two methods to determine their reliability for shellfish waters classification. METHODS AND RESULTS: Assuming a Poisson distribution of E. coli in shellfish samples, we simulate concentrations in 10 000 samples using the MPN and pour plate methods. We show that for higher concentrations of E. coli the pour plate method becomes increasingly more reliable than the MPN method. The MPN method has higher probabilities than pour plate of generating results exceeding shellfish classification thresholds, while conversely having higher probabilities of failing to detect counts that exceed regulatory thresholds. The theoretical analysis also demonstrates that the MPN method can produce genuine extreme outliers, even when E. coli are randomly distributed within the sampled material. A laboratory spiking experiment showed results consistent with the theoretical analysis, suggesting the Poisson assumption used in the theoretical analysis is reasonable. CONCLUSION: The large differences in statistical properties between the pour plate and MPN methods should be taken into consideration in classifying shellfish beds, with the pour plate method being more reliable over the crucial range of E. coli concentrations used to determine class boundaries.

Association between underlying dentin shadows (ICDAS 4) and OHRQoL among adolescents from southern Brazil.

This study aimed to assess the association between underlying dentin shadows (UDS) and oral health-related quality of life (OHRQoL) among 15-19-year-old adolescents from southern Brazil. This population-based cross-sectional study included a representative sample of 1,197 15-19-year-old adolescents attending 31 public and private schools from Santa Maria, Brazil. The Oral Health Impact Profile-14 (OHIP-14) was used to evaluate the OHRQoL, and clinical examinations were performed by two calibrated examiners (intra/interexaminer kappa values for caries examination ≥ 0.80) to diagnose UDS (ICDAS code 4 caries lesions). Sociodemographic information and clinical characteristics (overall caries experience, traumatic dental injury, malocclusion, and gingivitis) were also collected as adjusting variables. Multilevel Poisson regression models were used to assess the association between UDS and OHRQoL. Rate ratios (RR) and 95% confidence intervals (CI) were estimated. The UDS prevalence was 8.8% (n = 106 adolescents). In the adjusted models, adolescents with UDS had poorer OHRQoL than those without UDS, and the strength of the association was dependent on the number of lesions per individual. Individuals with 1-2 UDS had a mean OHIP-14 score 8% higher (RR = 1.08; 95%CI: 1.01-1.17) than adolescents without UDS, while those with 3-4 UDS had a mean score 35% higher (RR = 1.35; 95%CI: 1.12-1.63). This negative association was related to physical disability, psychological disability, social disability, and handicap domains. This study showed that UDS was associated negatively with OHRQoL among 15-19-year-old adolescents from southern Brazil. The negative effect of UDS on OHRQoL emphasizes the importance of addressing issues regarding OHRQoL even in the posterior teeth of adolescents.

Socioeconomic status in life course is associated with dental appearance dissatisfaction.

The present study aimed to investigate the prevalence of dissatisfaction with dental appearance among 24-year-old Brazilian adults and the associated factors in life course. A subsample (n = 720) of the 1982 Pelotas Birth Cohort in southern Brazil was investigated at the ages of 15 and 24 years using clinical (caries and periodontal) examinations and interviews. The outcome was dissatisfaction with dental appearance at the age of 24 years. Covariate variables included socioeconomic factors, oral health, and dissatisfaction with general appearance collected during different periods of life. Poisson regression models with robust variance were applied. The prevalence of dissatisfaction with dental appearance was 43.5% (95%CI: 39.8-47.1). Individuals with downward income mobility (PR = 1.22, 95%CI: 1.07-1.79) and those always poor (PR = 1.21, 95%CI: 1.00-1.57) presented a higher prevalence of dissatisfaction with their dental appearance even after oral health variables and dissatisfaction with general appearance were controlled for. Moderate/severe malocclusion at 15 years (PR = 1.34, 95%CI: 1.13-1.59), highest experience of untreated dental caries at 24 years (PR = 1.82, 95%CI: 1.46-2.27), and dental pain experience at 24 years (PR = 1.29, 95%CI: 1.22-1.75) were associated with the outcome. Also, the prevalence of dissatisfaction with dental appearance was 20% higher (PR = 1.20, 95%CI: 1.01-1.43) among those dissatisfied with their general appearance. Our findings demonstrated a high prevalence of dissatisfaction with dental appearance among young adults. Lifetime economic disadvantage and dental problems (malocclusion at 15 years, untreated dental caries at 24 years, and dental pain at 24 years) were associated with dissatisfaction with dental appearance among young adults.

Exact Distribution of the Quantal Content in Synaptic Transmission.

During electrochemical signal transmission through synapses, triggered by an action potential (AP), a stochastic number of synaptic vesicles (SVs), called the "quantal content," release neurotransmitters in the synaptic cleft. It is widely accepted that the quantal content probability distribution is a binomial based on the number of ready-release SVs in the presynaptic terminal. But the latter number itself fluctuates due to its stochastic replenishment, hence the actual distribution of quantal content is unknown. We show that exact distribution of quantal content can be derived for general stochastic AP inputs in the steady state. For fixed interval AP train, we prove that the distribution is a binomial, and corroborate our predictions by comparison with electrophysiological recordings from MNTB-LSO synapses of juvenile mice. For a Poisson train, we show that the distribution is nonbinomial. Moreover, we find exact moments of the quantal content in the Poisson and other general cases, which may be used to obtain the model parameters from experiments.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

INTRODUCTION: Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. METHOD: The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. RESULT: The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. CONCLUSION: In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation.

Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences.

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Analysis of hospital dental care for patients with special needs in Brazil.

This analytical cross-sectional study aimed to analyze the access of patients with special needs (PSN) in Brazilian municipalities to hospital dental care of the Unified Health System (Sistema Único de Saúde - SUS), based on data from the Hospital Information System of the Unified Health System (Sistema de Informações Hospitalares do SUS- SIH/SUS - SIH), from 2010 to 2018. The Kolmogorov-Smirnov normality test was performed; the Poisson regression was used to verify factors associated with the variable total number of hospitalization authorizations with the main procedure of dental treatment for PSN ("Total de Autorizações de Internação Hospitalar" - AIH), the Spearman correlation test with a significance level of 5% was used to characterize the relationships between the Municipal Human Development Index per municipality - (Índice de Desenvolvimento Humano Municipal - HDI) and the Oral Health Coverage in the Family Health Strategy by municipality (Cobertura de saúde bucal na estratégia saúde da família por município - SBSF Coverage), and the relationship of the AIH with SBSF Coverage. A total of 127,691 procedures were performed, of which 71,517 (56%) were clinical procedures, such as restorations, endodontic treatments, supra and subgingival scaling, among others. Municipalities in the Midwest (PR=5.117) and Southeast (RP = 4.443) regions had more precedures than the others. A weak correlation was found between AIH and SBSF Coverage (r = -0.2, p < 0.001) and HDI and SBSF Coverage (r = -0.074, p < 0.001). Population size, region, health coverage, oral hygiene, and number of dentists in hospitals affected the availability of dental procedures in PSN.

A resampling-based approach to share reference panels.

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Number of tetanus toxoid injections before birth and associated factors among pregnant women in low and middle income countries: Negative binomial poisson regression.

BACKGROUND: In low- and middle-income countries where vaccination rates are low, tetanus is still an important threat to public health. Although maternal and neonatal tetanus remains a major global health concern, its magnitude and determinates are not well studied. Therefore, this study aimed to assess the number of tetanus toxoid injections and associated factors among pregnant women in low- and middle-income countries. METHODS: Data from the most recent Demographic and Health Surveys, which covered 60 low- and middle-income countries from 2010 to 2022, was used for secondary data analysis. The study included a total of 118,704 pregnant women. A statistical software package, STATA 14, was used to analyze the data. A negative binomial regression of a cross-sectional study was carried out. Factors associated with the number of tetanus vaccinations were declared significant at a p-value of < 0.05. The incidence rate ratio and confidence interval were used to interpret the results. A model with the smallest Akaike Information Criterion and Bayesian Information Criterion values and the highest log likelihood was considered the best-fit model for this study. RESULTS: In low- and middle-income countries, 26.0% of pregnant women took at least two doses of the tetanus toxoid vaccine. Factors such as maternal education, primary (IRR = 1.22, 95% CI: 1.17, 1.26), secondary (IRR = 1.19, 95% CI: 1.15, 1.23), higher (IRR = 1.16, 95% CI: 1.12, 1.20), employment (IRR = 1.11, 95% CI: 1.09, 1.13), 1-3 ANC visits (IRR = 2.49, 95% CI: 2.41, 2.57), ≥4 visits (IRR = 2.94, 95% CI: 2.84, 3.03), wealth index (IRR = 1.06; 95% CI: 11.04, 1.08), ≥birth order (IRR = 1.04, 95% CI: 1.02, 1.27), distance to health facility (IRR = 1.02, 95% CI: 1.00, 1.03), and health insurance coverage (IRR = 1.08; 95% CI: 1.06, 1.10) had a significant association with the number of tetanus vaccinations among pregnant women. CONCLUSIONS AND RECOMMENDATIONS: This study concludes that the number of tetanus toxoid vaccinations among pregnant women in low- and middle-income countries is low. In the negative binomial model, the frequency of tetanus vaccinations has a significant association with maternal employment, educational status, wealth index, antenatal care visits, birth order, distance from a health facility, and health insurance. Therefore, the ministries of health in low and middle-income countries should give attention to those women who had no antenatal care visits and women from poor wealth quantiles while designing policies and strategies.

Population-average mediation analysis for zero-inflated count outcomes.

Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a new mediation methodology for zero-inflated count outcomes using the marginalized zero-inflated Poisson (MZIP) model and the counterfactual approach to mediation. This novel work gives population-average mediation effects whose variance can be estimated rapidly via delta method. This methodology is extended to cases with exposure-mediator interactions. We apply this novel methodology to explore if diabetes diagnosis can explain BMI differences in healthcare utilization and test model performance via simulations comparing the proposed MZIP method to existing zero-inflated and Poisson methods. We find that our proposed method minimizes bias and computation time compared to alternative approaches while allowing for straight-forward interpretations.

A Poisson-Independent Approach to Precision Nucleic Acid Quantification in Microdroplets.

Digital PCR (dPCR) has become indispensable in nucleic acid (NA) detection across various fields, including viral diagnostics and mutant detection. However, misclassification of partitions in dPCR can significantly impact accuracy. Despite existing methods to minimize misclassification bias, accurate classification remains elusive, especially for nonamplified target partitions. To address these challenges, this study introduces an innovative microdroplet-based competitive PCR platform for nucleic acid quantification in microfluidic devices independent of Poisson statistics. In this approach, the target concentration (T) is determined from the concentration of competitor DNA (C) at the equivalence point (E.P.), where C/T is 1. Competitive PCR ensures that the ratio of target to competitor DNA remains constant during amplification, reflected in the resultant fluorescence intensity, allowing the quantification of target DNA concentration at the equivalence point. The unique amplification technique eliminates Poisson distribution, addressing misclassification challenges. Additionally, our approach reduces the need for post-PCR procedures and shortens analytical time. We envision this platform as versatile, reproducible, and easily adaptable for driving significant progress in molecular biology and diagnostics.

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation.

This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.