Life

COVID-19 has spread rapidly and caused a global pandemic making it one of the deadliest in history. Early identification of patients with coronavirus disease 2019 who may develop critical illness is of immense importance. Therefore, novel biomarkers were needed to identify patients who will suffer rapid disease progression to severe complications and death. Many treatments were adopted including the antiviral Remdesivir, the antiretroviral Lopinavir /Ritonavir and Tocilizumab. Our study aimed not only to specify high-risk factors and biomarkers of fatal outcome in hospitalized subjects with coronavirus but also to compare the efficacy of the three considered treatments to help clinicians better choose a therapeutic strategy and reduce mortality. We divided the population (n=711) into four main groups based according to the WHO ordinal severity scale. The percentage of mortality, in and out the hospital, the length of stay in the hospital, the pulmonary inflammatory lesion and its distribution, the SARS-CoV-2 IgM and IgG variations at admission, the inflammatory markers, the complete blood count, the coagulation factors and enzymes, proteins and electrolytes profile, glucose and lipid profile, and other relevant markers were measured. The significance of the observed variation was assessed by multivariate and ANOVA analyses. We succeeded to establish a novel predictive scoring model of disease progression based on a cohort of Lebanese hospitalized patients relying on the pulmonary inflammatory lesions, inflammation biomarkers such as LDH, D-Dimer, CRP, IL-6 and the lymphocyte count, the number of comorbidities and the age of the patient which all were significantly correlated with the illness severity showing best outcomes with immunomodulatory and anticoagulant treatments by the results. As top tier, Tocilizumab was more efficient than the two other treatments in non-severe cases but none of the used treatments was insanely effective alone to reduce mortality in severe cases.

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

Objectives: Clinical prediction models estimate the risk of a future outcome in patients. Such models are often externally validated using independent datasets; however, even when a model has been rigorously validated in a new setting and patient population, its performance across other clinical settings remains unclear. Therefore, we systematically evaluated model performance and clinical utility across diverse patient populations to quantify the limits of transportability. Methods: Using liver transplantation as an example, we used the UK donation-after-circulatory-death (DCD) risk score and descriptive statistics from Swiss DCD liver transplant populations to simulate realistic target populations with varying donor and recipient characteristics. The risk score's ability to predict one-year graft failure was evaluated using calibration intercept, calibration slope, area under the receiver operating characteristic (ROC) curve, and net benefit. Results: The UK DCD Risk Score's performance depended heavily on the simulated population characteristics. While the score performed adequately in settings similar to those where it was derived, it was not satisfactory in others. Discussion: The study showed, using a risk score in liver transplantation as an example, that the application of a prediction model can be limited in certain external populations when they differ, and that its transportability in new settings is not guaranteed. Conclusion: This study highlights the importance of external validation of clinical prediction models to determine transportability to various target populations. Their application requires careful consideration and potential model re-estimation.

BackgroundThere is currently no approved antiviral therapy against measles virus (MeV). Repurposing available compounds with broad antiviral activity may rapidly identify candidate drugs for clinical evaluation. Here we evaluated the antiviral activity of the clinically approved drugs azelastine hydrochloride and zafirlukast as well as the flavonoids quercetin and isoquercetin against MeV in preventative and therapeutic in vitro studies. MethodsCompounds were tested for antiviral activity against MeV in preventative (prophylactic and virucidal) and therapeutic (steady-state and persistent) assays in Vero/hSLAM cells. Viral loads and cell viability were measured 48h post-infection, and dose-response curves were used to calculate EC50 values. Flavonoids were also tested in the presence of 1 mM ascorbic acid. ResultsAzelastine hydrochloride did not show evidence of antiviral activity against MeV under these conditions, whereas zafirlukast, quercetin, and isoquercetin showed therapeutic activity against MeV. The addition of ascorbic acid enhanced the therapeutic potency of quercetin to 4.2-4.8 {micro}M and of isoquercetin to 10.7-10.9 {micro}M. Antiviral activity was dose-dependent when administered post-infection. ConclusionAmong the four compounds tested, quercetin showed the most potent therapeutic antiviral activity against MeV in vitro. Isoquercetin and zafirkulast also showed therapeutic activity. These findings support further evaluation of quercetin, isoquercetin, and zafirlukast as candidate antiviral drugs for MeV and highlight the utility of in vitro platforms for rapid antiviral drug screening.

Purpose: Suicide and self-harm are major public health concerns characterized by substantial clinical and psychosocial heterogeneity. While latent class analysis has been used to identify subgroups of people with suicidal behavior, the extent to which such population-level phenotyping complements explainable artificial intelligence-based classification models remain unclear. Methods: We applied latent class analysis to a cross-sectional, publicly available dataset of 1000 individuals presenting with self-harm and suicide-related behaviors at Colombo South Teaching Hospital, Kalubowila, Sri Lanka. Sociodemographic, psychosocial, and clinical variables were used to identify latent subgroups. Class characteristics and suicide prevalence were examined and compared with variable importance patterns reported in a previously published explainable artificial intelligence (XAI)-based suicide classification study using the same dataset. Results: Four latent classes were identified. Two classes exhibited very high suicide prevalence (91.2% [95% CI: 87.7-93.8] and 99.0% [95% CI: 96.4-99.7]), whereas two classes showed low prevalence (<1%). The two high-prevalence classes differed markedly in lifetime psychiatric hospitalization history, with one class showing a 100% prevalence of prior hospitalization and the other substantially lower hospitalization rates. These patterns partially aligned with, and extended beyond, variable importance findings from the XAI-based model. Conclusion: Latent class analysis identified distinct subgroups with substantially different suicide prevalence and clinical profiles, underscoring the heterogeneity of individuals presenting with self-harm. Comparison with XAI-based suicide classification model findings suggest that unsupervised phenotyping and supervised classification provide complementary perspectives, offering population-level context that may enhance the interpretability of suicide assessment frameworks. Keywords: suicide; self-harm; latent class analysis; explainable artificial intelligence; machine learning

Salinity and sodicity stresses adversely affect rice growth and yield. To overcome yield losses, suitable tolerant rice cultivars can be developed through a marker-assisted breeding (MAB) program. In the present study, genomic regions associated with sodicity stress tolerance at the reproductive stage were identified using a high-density 50kSNP array in a recombinant inbred line (RIL) population derived from the contrasting rice genotypes CSR11 and MI48. A total of 50 QTLs were detected for various yield-related traits; further, 19 QTLs with [&ge;]15% of phenotypic variance were selected for integrated (omics) analysis. RNA sequencing of leaves and panicles at the reproductive stage under sodic stress conditions was employed to find differentially expressed genes. A total of 1368 and 1410 SNPs; 104 and 144 indels were found for MI48 and CSR11, respectively, within the QTL regions from resequencing. At chromosomes 1 and 6, colocalized QTLs (qPH1-1/qGP1-1 and qGP6-2/qSSI6-2) were discovered. Differentially expressed genes (DEGs) were mapped over the QTL regions selected, and SNP variations and indels were screened for colocalized QTLs. Potential candidate genes, namely Os-pGlcT1 (Os01g0133400), OsHKT2;1 (Os06g0701600) and OsHKT2;4 (Os06g0701700), OsANTH12 (Os06g0699800), and OsPTR2 (Os06g0706400), were identified as being responsible for glucose transport, ion homeostasis, pollen germination, and nitrogen use efficiency, respectively, under salt stress. Finally, our study provides important insights into the genes and potential mechanisms affecting grain yield under sodic stress in rice, which will contribute to the development of molecular markers for rice breeding programs.

Background: CD276 has been proposed as a candidate gene associated with the biological characteristics of meningioma, but its predictive position and interpretive significance within a transcriptomic classifier have not yet been clearly established. Accordingly, this study aimed to evaluate CD276 stepwise across internal model development, external validation, calibration, decision-analytic assessment, feature stability, and robustness analyses using public transcriptomic cohorts. Methods: The analyses in this study were organized into two interconnected notebooks. In Notebook A, we reconstructed the internal training cohort (GSE183653), evaluated the CD276 single-gene signal, and then developed a transcriptome-wide multigene classifier. We also performed permutation importance, bootstrap confidence interval, label permutation test, repeated cross-validation, CD276 ablation, and internal calibration analyses. In Notebook B, we reproduced the external validation cohort (GSE136661) in a fixed common-gene space, applied train-only recalibration and train-only threshold transfer, and extended the interpretation through decision curve analysis, stability analysis, enrichment analysis, and one-factor-at-a-time robustness analysis. Results: The internal training cohort consisted of 185 samples and 58,830 genes, of which 25 were WHO grade III cases. CD276 expression showed a significant association with WHO grade, but the internal discrimination of the CD276-only baseline was limited (ROC-AUC 0.628, average precision 0.323, balanced accuracy 0.540). In contrast, the initial transcriptome-wide model showed ROC-AUC 0.834 and PR-AUC 0.509, and under 5-fold cross-validation, the canonical fulltranscriptome model and the CD276-forced 5,001-feature branch showed mean ROC-AUC/PR-AUC of 0.854/0.564 and 0.855/0.606, respectively, outperforming the CD276-only baseline at 0.644/0.391. CD276 was not included in the initial 5,000-feature filtered set and ranked 900th among 5,001 features even in the forcibly included 5,001-feature branch. In paired ablation analysis, the performance difference attributable to inclusion of CD276 was effectively close to zero (delta ROCAUC 0.000062, delta PR-AUC 0.000056). Internal calibration analysis showed an overconfident probability pattern (Brier score 0.10501, intercept -1.421392, slope 0.413241). In external validation, the fixed multigene pipeline achieved ROC-AUC 0.928 and PR-AUC 0.335. Train-only recalibration improved calibration metrics while preserving discrimination, and decision curve analysis showed threshold-dependent but limited external utility. Stability analysis showed overlap between core-stable genes and high-impact genes, but CD276 was not supported as a dominant stable core feature and remained in the target-of-interest tier. In robustness analysis, some perturbations preserved the primary interpretation, whereas others revealed transform sensitivity or an alternative high-performing feature-space solution. Conclusions: CD276 is a gene of interest associated with meningioma grade, but it was difficult to interpret it as a strong standalone predictor or a dominant stable classifier feature. In this study, the main basis of predictive performance lay not in CD276 alone but in a broader multigene transcriptomic structure, and probability output needed to be interpreted conservatively with calibration taken into account. These findings position CD276 not as a direct single-gene classifier but as a biologymotivated target-of-interest that should be interpreted within a broader transcriptomic program.

Background. Recent meta-analyses of randomized controlled trials (RCTs) claimed efficacy of higher-dose fluvoxamine (2 x 100 mg/day, as opposed to 2 x 50 mg/day) in prevention of disease deterioration in adults with mild - moderate COVID-19 disease. Objectives. Investigate whether such claims are supported by the data. Methods. Systematic review and meta-analysis of RCTs evaluating higher-dose fluvoxamine in this indication. Results. Seven studies declared as RCTs were identified, one of which was severely biased (open-label, non-standardized and unreported standard of care as a control), and eventually ended as non-randomized (huge attrition). Composite endpoints of deterioration in the 6 included placebo-controlled trials contained elements susceptible to error and bias. Three trials were small (<100 patients/arm), three were larger (270 - 750 patients/arm). Deaths and need for mechanical ventilation were sporadic and observed in only one trial. Hospitalizations were also sporadic in 5/6 trials. Frequentist methods generally appropriate for random-effects analysis of low number of trials with rare outcomes (generalized linear mixed models, beta-binomial or binomial-normal) greatly underestimated heterogeneity, but still did not document benefits regarding the composite endpoints or hospitalizations. Bayesian hierarchical models revealed huge heterogeneity and indicated no benefit regarding: (i) composites of deterioration, large trials OR = 0.78 (95% CrI 0.55 - 1.21); multiplicity corrected OR = 0.87 (0.64 - 1.21); (ii) hospitalizations, small trials OR = 0.88 (0.45 - 1.72); large trials OR = 0.94 (0.52 - 1.75); all trials OR = 0.81 (0.47 - 1.43). Heterogeneity was unlikely due to clinical particulars (vaccination status, treatment duration, time horizon), and more likely due to unidentified bias. Conclusions. RCTs do not support efficacy of higher-dose fluvoxamine in prevention of disease deterioration in adults with mild - moderate COVID-19 disease.

The goal of this work was to leverage a large corpus of text based psychotherapy data to create novel machine learning algorithms that can identify suicide risk in asynchronous text therapy. Advances in the field of natural language processing and machine learning have allowed us to include novel data sources as well as use encoding models that can represent context. Our models utilize advanced natural language processing techniques, including fine-tuned transformer models like RoBERTa, to classify risk. Subsequent model versions incorporated non-text data such as demographic features and census-derived social determinants of health to improve equitable and culturally responsive risk assessment, as well as multiclass models that can identify tiered levels of risk. All new models demonstrated significant improvements over our previous model. Our final version, a multiclass model, provides a tiered system that classifies risk as "no risk," "moderate," or "severe" (weighted F1 of 0.85). This tiered approach enhances clinical utility by allowing providers to quickly prioritize the most urgent cases, ensuring a more accurate and timely intervention for clients in need.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

AO_SCPLOWBSTRACTC_SCPLOWNitrogen fertilizers are essential for crop productivity but cause environmental harm, necessitating the development of cultivars that thrive under limited nitrogen. This study investigates the transcriptomic response to nitrate in Arabidopsis thaliana (a model dicot), Brachypodium distachyon (a model Pooideae), and Hordeum vulgare (barley, a domesticated Pooideae) to identify conserved and species-specific molecular mechanisms. Using RNA-seq after 1.5 and 3 hours of nitrate treatment, we found that core nitrate-responsive biological processes - such as nitrate transport, assimilation, carbon metabolism, and hormone signaling - are largely conserved across species. However, comparative analysis at gene level based on orthology revealed specificities between the species. For instance, rRNA processing was uniquely stimulated in Arabidopsis, while cysteine biosynthesis from serine and gibberellin biosynthesis were specifically regulated in Brachypodium and barley. Orthologs of key nitrate-responsive genes (e.g., NRT, NLP, TCP20) exhibited variable regulation, reflecting potential adaptations linked to domestication or nutrient acquisition strategies. These findings highlight the importance of integrating model and crop species to uncover targets for improving nitrogen use efficiency in cereals. The study provides a pipeline integrating gene ontology and orthology analyses to compare transcriptomic responses between species.

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

Background & aimsConstitutive activation of the {beta}-catenin pathway is a determining feature in the pathogenesis of two primary liver cancers, namely HCC and hepatoblastoma (HB). Activating alterations in CTNNB1 gene and, to a lesser extent, inhibiting alterations in APC gene are observed in 30 to 40% of HCC cases and 80 to 90% of HB cases. For both tumours, therapeutic management is far from optimal. Therefore, relevant experimental models are needed to increase our knowledge and test new therapeutic approaches. MethodsOrganoids and tumouroids were established from APC{Delta}hep and {beta}cat{Delta}ex3 mouse models, which are clinically relevant models for {beta}-catenin-activated HCC and mesenchymal HB. We developed a new methodological approach based on a dynamic suspension culture in a rotating bioreactor. Morphological and molecular characteristics and sensitivity to WNTinib, a treatment already successfully tested on human HCC and HB tumouroids, were evaluated by histology, immunohistochemistry, immunofluorescence, and RT-qPCR. ResultsThis easy-to-implement methodology allows for the rapid generation of a large number of organoids and tumouroids that are uniform in size and show no signs of cell death in their core. The robustness of the methodology is illustrated by the maintenance of the histological architecture, cell diversity and gene expression in organoids and tumouroids in comparison with the native liver tissues. In addition, the value of the HCC-derived tumouroids for evaluating cancer treatment was assessed based on their responsiveness to the {beta}-catenin antagonist WNTinib. ConclusionsThe organoids and tumouroids that we present here are new reliable in vitro cancer models, recapitulating the main features of {beta}-catenin-driven HCC and mesenchymal HB. They can be integrated into an appropriate platform for drug screening and could enable the development of "a la carte" therapies that are urgently needed for these indications. Impact and implicationsThis study addresses the critical need for representative in vitro models to investigate {beta}-catenin-driven liver cancers. The organoids and tumouroids developed here are particularly valuable for researchers seeking robust, reproducible models that accurately reflect the cellular diversity and gene expression profiles of native liver tumours. These findings have practical applications in exploring cancer mechanisms, screening new drugs, optimizing personalized treatment strategies, and reducing reliance on animal models, which ultimately benefits patients. HighlightsO_LIEasy and rapid generation of mouse liver organoids and tumouroids from {beta}-catenin activated tumours using culture in a bioreactor C_LIO_LITumouroids preserve histology, cell diversity, and gene expression of native tissue C_LIO_LIHCC-derived tumouroids respond to {beta}-catenin inhibitor WNTinib C_LIO_LIThese reliable 3D models reduce reliance on animal experiments for drug testing C_LI

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are essential regulators of plant growth, development, and stress adaptation. In this study, we performed a comprehensive genome-wide identification of SNARE genes in cucumber (Cucumis sativus L.), uncovering 51 putative members designated as CsSNAREs. Phylogenetic analysis confirmed that these genes cluster into five major clades: Qa-CsSNARE (14), Qb-CsSNARE (9), Qc-CsSNARE (10), Qb+c-CsSNARE (3), and R-CsSNARE (15). Bioinformatic analysis of their promoter regions, coupled with expression profiling under diverse abiotic stress conditions, highlighted a heightened responsiveness within the Qa-CsSNARE subfamily. To validate this, we selected representative Qa-CsSNARE genes for quantitative real-time PCR analysis under drought and salt stress. Among these, CsSYP121 was notably induced by salt treatment. We subsequently generated transgenic cucumber lines overexpressing CsSYP121 and challenged them with salinity. Phenotypic assessment, combined with measurements of reactive oxygen species (ROS) accumulation and K+/Na+ ratios, demonstrated that CsSYP121 overexpression (OE) confers enhanced salt tolerance and boosts antioxidant capacity. We propose a model wherein CsSYP121 mitigates ROS-induced cellular damage under salt stress, potentially through promoting K+/Na+ homeostasis, thereby improving plant performance under saline conditions. Our findings identify CsSYP121 as a promising candidate gene for breeding salt-tolerant crops.

Background: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of global mortality, with persistent lung inflammation contributing to disease progression. This inflammation is partly associated with reduced levels of histone deacetylase 2 (HDAC2). Previous studies suggest that Vitamin D may modulate HDAC2 levels. This study aimed to evaluate the effect of Vitamin D supplementation on HDAC2 expression in stable COPD patients. This experimental study aimed to evaluate the effect of vitamin D supplementation on HDAC2 expression in stable COPD patients at Jemursari Islamic Hospital. Methods: Five COPD patients received a daily dose of 5000 IU of Vitamin D for three months. Serum levels of 25(OH)D3 and HDAC2 were measured before and after the intervention. Results: Vitamin D supplementation resulted in a significant increase in both 25(OH)D and HDAC2 levels. Pulmonary function parameters showed an increasing trend, however, no statistically significant differences were observed. Conclusion: Vitamin D supplementation was associated with increased HDAC2 levels, suggesting a potential anti-inflammatory effect. However, no significant improvement in pulmonary function was observed. Further studies are needed to determine its clinical impact.

BackgroundMixed-format lifestyle questionnaires contain both structured variables and free-text responses, but it remains unclear whether language-derived variables provide incremental predictive value beyond structured data, and under which representational condition. It was investigated whether variables derived from patient-reported free text improve ALS-versus-control classification beyond structured questionnaire data, and whether their value depends on how temporal information is represented. MethodsA leakage-free machine-learning pipeline was developed to classify ALS versus controls from questionnaire-derived data, including a schema-guided LLM-based text-to-table extraction and a compact longitudinal encoding strategy. Three feature configurations were compared: Pool1, containing structured baseline variables only; Pool2, adding compact summaries derived from first-time-point (T1) free-text responses; and Pool3, further incorporating compact descriptors of change between T1 and T2. Logistic Regression, linear Support Vector Classification, and Random Forest were evaluated using repeated stratified holdout (10 seeds) and repeated stratified 5-fold cross-validation. Final ablation analyses were performed to isolate the contribution of the compact text block and the compact temporal block. ResultsAfter leakage correction, performance estimates became more conservative, indicating that previous results had been optimistic. In the final configuration, Pool3 achieved the best performance, with Random Forest reaching a holdout accuracy of 0.673, F1-weighted score of 0.666, and Matthews correlation coefficient of 0.323; cross-validated F1-weighted score and Matthews correlation coefficient were 0.654 and 0.312, respectively. Pool2 did not show a robust improvement over Pool1. Ablation analysis showed that removing the compact temporal block markedly reduced Pool3 performance, whereas removing the compact text block had little overall effect. These findings indicate that the primary value of language-based processing in small clinical cohorts lies not in static feature enrichment, but in enabling compact representations of longitudinal change. ConclusionsIn this setting, the main predictive gain did not arise from static text-derived variables alone, but from representing questionnaire information as compact longitudinal change descriptors. These findings suggest that, in small clinical cohorts, the value of language-based processing may lie more in summarizing trajectories than in expanding static feature spaces.

As human space exploration expands to the Moon, Mars, and beyond, there is a growing need to study the effects of altered gravity on the microbial systems that we will bring with us for life support. Because spaceflight experiment opportunities are rare and resource-intensive, most space biology experiments are conducted using ground-based simulators. The most common microgravity simulator for microbial experiments, the rotating wall vessel, can approximate the low-shear and low-turbulence conditions that characterize microgravity. However, current designs do not allow for real-time measurement of growth or metabolic activity during rotation: experiments require destructive sampling or disruption of the microgravity simulation conditions. Here, we describe the development of an in situ spectroscopy system compatible with the Cell Spinpod rotating wall vessel, which enables measurement of both optical absorbance and fluorescence with high temporal resolution, producing growth curves similar to those from an off-the-shelf plate reader. These results are validated using two common microbial hosts: Escherichia coli and Saccharomyces cerevisiae. The Spinpod Optical System has the potential to diversify the types of microbiology experiments possible in simulated microgravity, allowing the measurement of not only growth curve parameters but also metabolic activity, gene expression, or community dynamics. It thus has the potential to improve the quality of experiments seeking to characterize microbial responses to spaceflight conditions.

Accurate classification of SARS-CoV-2 genomic variants is essential for effective genomic surveillance, yet it is challenged by extreme class imbalance, limited representation of rare variants, and distribution shifts in real-world sequencing data. In this study, we employed hybrid RF-SVM framework designed for robust detection of rare SARS-CoV-2 variants. It integrates a random forest and a polynomial-kernel based support vector machine to enhance sensitivity to minority classes while maintaining overall predictive stability. We systematically compared classical machine learning models, deep learning approaches, and hybrid strategies under both standard and distribution-shifted evaluation settings. Our results show that classical models using TF-IDF-based k-mer features outperform deep learning methods on macro-averaged performance metrics. The Random Forest classifier using TF-IDF Feature achieved the best overall performance, with a macro-averaged F1-score of 0.8894 and an accuracy of 96.3%. The model also demonstrated strong generalization ability, as evidenced by stable cross-validation performance (CV accuracy = 0.9637). Hybrid RF-SVM model further improves rare variant detection under severe class imbalance. Calibration analysis indicates reliable probability estimates for common variants, although challenges persist for minority classes. Overall, this study highlights the limitations of deep learning in highly imbalanced genomic settings and demonstrates that carefully designed hybrid machine learning approaches provide an effective and interpretable solution for rare SARS-CoV-2 variant detection.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

The vast diversity of B and T cell receptors generated through the recombination of Variable (V), Diversity (D), and Joining (J) gene segments plays a critical role in adaptive immunity. Profiling immune repertoires at the DNA level provides a robust and stable approach to capture the clonal composition of these receptors. immunoPETE is an assay designed to target recombined human T-cell Receptor Beta (TRB), T-cell Receptor Delta (TRD), and Immunoglobulin Heavy (IGH) chain genes directly from genomic DNA. Simultaneous profiling of B and T cell receptor chains in a single reaction provides internally normalized clone counts and facilitates the study of B-T cell interactions. Full-length amplicon consensus sequences representative of original template DNA molecules are accurately reconstructed using Unique Molecular Identifiers (UMIs). An in-house pipeline compiles VDJ rearrangements from the Complementarity-Determining Region 3 (CDR3) of TRB, TRD and IGH chains into comprehensive readouts at cell-level resolution. In this study, we describe the immunoPETE end-to-end workflow, followed by a comprehensive benchmarking of its performance in adaptive immune profiling. Where applicable, we used both natural and contrived samples and characterized the assays accuracy, linearity, and reproducibility across several metrics: retrieving CDR3 sequences, determining B and T cell ratios, total cell count, yield, fraction of functional rearrangements, clonal diversity, composition of dominant clones, pairwise similarity, and V/J gene usage frequencies. Furthermore, we assessed its quantitative limits concerning the total number of lymphocytes and the detection of rare clones. As an example of its applications, we show that adding immune biomarkers extracted from immunoPETE data to clinical factors improves prediction of progression-free survival in a cohort of non-muscle invasive bladder cancer (NMIBC) patients. Finally, we discuss the broad applications of immunoPETE in the study of aging, cancers, infections, and autoimmune disorders with reference to select published studies.