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.

Rooting systems of plants perceive environmental stimuli and flexibly regulate their growth. Therefore, understanding stimulus perception and response mechanisms is essential for optimizing cultivation. During the transition from aquatic to terrestrial environments, land plants have acquired mechanisms to adapt to gravitational force on land. Thus, elucidating gravity responses of rhizoids in bryophytes, early diverging land plants, provides important insights into how gravity-response mechanisms were established during land plant evolution. Analyzing rhizoid morphology under microgravity, where gravitational effects are largely eliminated, provides an effective approach to examine the gravity-response mechanisms that evolved after terrestrialization. In this study, to elucidate microgravity effects on rhizoid growth of Physcomitrium patens, we analyzed 3D datasets obtained by refraction-contrast micro-CT using synchrotron radiation after fixation and embedding of samples from the Space Moss experiment conducted on the International Space Station. Because each CT volume contains numerous rhizoids, we optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy. Comparison of 3D morphological indices between manually segmented rhizoids and predicted results supported the validity of the proposed method for morphological analysis. Morphological analyses revealed that, compared with both ground and artificial 1 x g conditions, rhizoid elongation and gravitropic responses were suppressed under microgravity, leading to reduced vertical growth. These findings indicate that gravity plays a fundamental role in rhizoid morphogenesis, and their absence affects growth orientation and elongation. This study provides foundational data for research on the rooting systems of bryophytes in space.

BackgroundGene expression profiling in radiation-exposed blood is a valuable tool for biodosimetry and clinical research. Evaluating the bloods transcriptomic radiation response provides insight into absorbed dose, hematotoxicities, and immune reactions. However, detailed analysis using long-read RNA sequencing is currently limited in its diffusion, despite the potential additional insights that could be extracted, including novel isoform discovery and on-the-field gene expression studies, owing to its portability. ResultsIn this study, we utilized Oxford Nanopore Technologies long-read RNA sequencing on human whole-blood samples from three healthy donors 6 hours after exposure to 4 Gy of X-rays. Compared to sham-irradiated (0 Gy) blood, gene-level differential expression analysis identified 117 upregulated and 66 downregulated genes, including canonical DNA damage repair and inflammatory responses. At the transcript level, 102 transcripts were significantly upregulated, and 17 were downregulated, revealing isoform-specific regulation that was not captured at the gene level. Notably, IL32, which showed no significant change at the gene level, exhibited strong upregulation of two transcript isoforms, while WDR74, ITM2B, AK2, and RPS19 displayed changes in transcript usage following irradiation. Leveraging the power of long-read RNA sequencing, we further identified 26 novel transcript isoforms, expanding the catalog of radiation-responsive transcripts. ConclusionsThis is the first comprehensive study of long-read RNA-seq for transcriptomic profiling of human whole blood following ionizing radiation. These findings highlight the ability of long-read RNA sequencing to provide a more detailed view of radiation-induced transcriptomic alterations, underscoring its potential for biodosimetry and clinical applications.

The post-detachment drifting phase of macrophytes, during which they can be alive, dead, or senescent, plays a crucial ecological and biogeochemical role by influencing long-range dispersal, transporting rafting species, affecting carbon sequestration, promoting blooms, and leading to beaching events. In order to predict the dispersal of macrophytes and macroplastic particles and where they will affect the ecosystem, it is important to be able to model how their drift velocities are influenced by hydrodynamic and aerodynamic factors. In this study, we investigated the drift velocity of macrophytes with diverse morphologies and macroplastic particles in a racetrack flume under different current conditions, in combination with and without wind in the same direction as the water current. Our data show that the drift velocity of macrophytes is highly dependent on their buoyancy and affected by morphological characteristics. Wind increased the velocity of the surface water, which in turn increased the drift velocity of both macrophytes and macroplastic particles. However, wind-induced turbulences reduced the overall effect, especially for macrophytes, which protruded minimally above the water surface in comparison to macroplastic particles. For positively buoyant specimens, an existing particle model was experimentally confirmed to predict macrophyte and macroplastic particle drift velocities reliably, irrespective of shape. For negatively buoyant species, we propose a novel equation to predict drift velocity, incorporating the diverse shapes of macrophytes, as well as their interaction with the bottom. These results represent the first step toward the development of trait-based models that represent macrophytes more realistically in dispersal simulations. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/709487v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1ab9f6aorg.highwire.dtl.DTLVardef@6ef75dorg.highwire.dtl.DTLVardef@132334forg.highwire.dtl.DTLVardef@c6a3d8_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundSpaceflight-associated neuro-ocular syndrome (SANS) poses significant risks to astronaut visual health during long-duration missions, yet its underlying molecular mechanisms remain incompletely understood. Oxidative stress and apoptosis are candidate molecular drivers, but their transcriptomic signatures in spaceflight-exposed retinal tissue have not been systematically characterized. MethodsWe applied a machine learning ensemble of linear regression models to predict two ocular phenotypes: 4-hydroxynonenal (4-HNE) immunostaining as a marker of lipid peroxidation-mediated oxidative damage; and TUNEL positivity as a marker of apoptotic cell death. In this observational study, we use bulk retinal gene expression data obtained from a controlled experiment with ground control and spaceflown mice to predict these phenotypes. Gene Ontology pathway enrichment was performed on the most predictive gene sets for each phenotype. ResultsThe 4-HNE phenotype was predicted by genes that converge on membrane-associated pathways, photoreceptor protein modification, synaptic dysfunction, and extracellular matrix dysregulation, including B2m, Tf, Cnga1, mt-Nd1, Snap25, and Efemp1. The genes predicting the TUNEL phenotype revealed a distinct signature emphasizing stress-induced apoptosis, rod photoreceptor degeneration, and endoplasmic reticulum dysfunction, with Ddit4, Nrl, Rom1, Reep6, and Gabarapl1 emerging as central regulators. ConclusionsOxidative lipid peroxidation and apoptotic cell death represent complementary and molecularly distinct pathological mechanisms in spaceflight-exposed murine retinal tissue. The gene signatures provide a putative molecular framework for developing noninvasive biomarkers and therapeutic targets to monitor and protect astronaut visual health during long-duration and deep-space missions.

ABSTRACTMotion sickness (MS) is commonly hypothesized to arise from sensory conflicts between incongruent sources of sensory information. Different types of sensory conflicts can induce MS, yet it remains unclear whether distinct contexts produce different physiological responses. Moreover, there is a lack of reliable objective predictors of MS, particularly for space motion sickness (SMS), which appears unrelated to motion sickness susceptibility on Earth. This study examined multiple physiological measures as potential objective markers of MS, including heart rate, blood pressure, salivary cortisol, skin conductance, skin surface temperature, and facial skin colorimetry. Subjective motion sickness severity and symptomatology were assessed using standardized questionnaires (SSQ, MSAQ, MSSQ). All measures were collected before and immediately after exposure to two sensory conflict paradigms: virtual reality (visuo-vestibular conflict) and parabolic flight (otolitho-canal conflict). Post-exposure, both paradigms were associated with increased cortisol, skin conductance, and skin greeness. Notably, increased skin greenness was associated with greater MS severity in parabolic flight and strongly correlated with subjective nausea ratings in both paradigms. Skin temperature and systolic blood were affected differently by VR and parabolic flight. No robust new physiological predictors of MS were identified. Overall, our findings suggest that facial skin color -particularly skin greenness- may serve as a simple, non-invasive, and reliable objective indicator of MS severity.

Space travel is becoming accessible, yet our understanding of how space environment and microgravity ({micro}G) affect biology, physiology, and health remains incomplete. We investigated {micro}G effects on neuromuscular development and aging in Caenorhabditis elegans. Nematodes in {micro}G showed downregulation of genes related to synaptic signaling, dopamine response, locomotion, and cuticle development, with impaired synaptic vesicle dynamics, reduced motility, and shorter body lengths. Aged worms in {micro}G showed decreased collagen gene expression, increased motor neuron defects, synaptic vesicle accumulation and decreased release, and mitochondrial morphology collapse in body wall muscles, indicating accelerated aging. MEC-4 mechanoreceptor was identified as a key mediator of {micro}G-induced body length reduction and changes in extracellular matrix gene expression. {micro}G conditions suppressed mechanoreceptor genes, suggesting multiple mechanosensory systems are affected. Physical stimulation through culture medium with small beads in space mitigated many {micro}G-induced expression changes, including mechanoreceptors, neuromuscular defects, and aging-related phenotypes. These results highlight mechanical stimulis role in maintaining neuromuscular integrity during spaceflight and suggest restoring tactile input could counter health risks from reduced stimulation in long-term space missions. SIGNIFICACEWe found that microgravity ({micro}G) conditions suppress the expression of multiple mechanoreceptor genes in Caenorhabditis elegans, indicating that several mechanosensory systems are affected during spaceflight. Importantly, reintroducing physical stimulation by adding small beads to the culture medium in space partially reversed many of these {micro}G-induced gene expression changes. This intervention also mitigated neuromuscular defects and aging-related phenotypes observed under {micro}G conditions. Collectively, these findings underscore the essential role of mechanical stimuli in preserving neuromuscular integrity during space missions and suggest that restoring tactile input may be a promising strategy to counteract the health risks associated with reduced tactile stimulation during prolonged spaceflights.

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.

Mesenchymal stem cells (MSCs) cultured in hypoxic conditions have been suggested to have more therapeutic efficacy than those cultured under normoxic conditions, and there is growing interest in using hypoxic MSCs for clinical treatment, particularly human umbilical cord (hUC)-MSCs. We investigated how hUC-MSCs and human bone marrow (hBM)-MSCs change from normoxia to hypoxia (1% O2) for 2 weeks of culture. In the growth speed and population doubling time, hUC-MSCs cultured under hypoxia exhibited a significantly higher proliferation rate beyond cancerous cells, such as human glioblastoma and breast cancer cells, while hBM-MSCs did not show a significant difference between normoxia and hypoxia, and were statistically slower than these cancerous cells. Notably, hypoxic hUC-MSCs showed upregulation of genes related to metabolic reprogramming (cholesterol biosynthesis and fatty acid metabolism pathways) and cancer stem cell-like phenotype (factors related to Wnt and Hedgehog signaling pathways, cell proliferation drivers, and apoptosis-resistance), and lesser migration and homing to the traumatic brain injury than normoxic hUC-MSCs after intravenous injection. Thus, whether hUC-MSCs cultured under hypoxia offer clinical benefits and use are safe, given their extremely accelerated proliferation rate and partial cancer stem cell-like traits, requires comprehensive and careful investigation.

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.

Protein-DNA complexes are involved in vital cellular functions like gene regulation, replication, transcription, packaging, rearrangement, and damage repair. In this work, streamlined geometric formalism for computing the absolute binding free energy was used to obtain chemical accurate in silico estimation of binding free energy of three Protein-DNA complexes. Additionally, molecular interactions between Protein and DNA involved hydrogen bonds, electrostatic, van der Waals, and hydrophobic interactions. Using this formalism, researcher can obtain the absolute binding free energy for a Protein-DNA complex with remarkable accuracy and modest computational cost.

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

Esophageal cancer (EC) remains one of the most lethal malignancies worldwide, with poor survival outcomes largely attributable to late-stage diagnosis and limited treatment effectiveness. Early detection and accurate risk stratification are therefore essential for improving clinical management. In this study, we investigate the predictive value of socio-demographic, dietary, behavioral, environmental, and clinical variables collected from 312 individuals (104 EC cases and 208 controls) in the Arsi Zone, Ethiopia. An ensemble features ranking approach based on Random Forest machine learning was first applied to identify the most relevant predictive features. Subsequently, multiple ensemble machine learning models were evaluated, including Histogram-based Gradient Boosting (Model I), Extreme Gradient Boosting (Model II), AdaBoost (Model III), Random Forest (Model IV), and k-Nearest Neighbors (Model V). These models were tested under multiple experimental settings using both full and reduced feature subsets. To enhance robustness and minimize variability, a multi-seed ensemble framework was employed. Different seed values generate distinct train-test splits and slight variations in model initialization and optimization, leading to minor differences in training outcomes; aggregating results across multiple seeds mitigates this variability and provides more stable and reliable performance estimates. The experimental results demonstrate that boosting-based ensemble models consistently outperform other classifiers across all evaluation metrics. Model I achieved the highest overall performance, reaching an accuracy of 0.983, with precision of 0.982, recall of 0.980, and F1-score of 0.981 using the reduced feature set, while maintaining nearly identical performance with the full feature set. Model II also showed stable and strong predictive capability, achieving accuracies of 0.963 and 0.961 for the full and reduced feature sets, respectively, with balanced precision, recall, and F1-score values. These findings indicate that feature importance-based dimensionality reduction preserves essential predictive information without compromising classification performance. Overall, the results highlight the significant predictive contribution of dietary and environmental risk factors and demonstrate that ensemble learning provides a reliable, efficient, and clinically meaningful approach for early EC detection. The proposed framework offers a promising direction for supporting diagnostic decision-making and risk stratification in resource-limited healthcare settings. HighlightsO_LIMachine Learning Framework for Esophageal Cancer Classification A robust ensemble machine learning framework was developed to classify esophageal cancer using socio-demographic, dietary, behavioral, environmental, and clinical risk factors, enabling accurate and reliable disease prediction. C_LIO_LIMulti-Seed Ensemble Strategy for Improved Model Stability A novel multi-seed ensemble classification approach was implemented to reduce model variance and improve robustness by aggregating predictions across multiple randomized training and testing splits. C_LIO_LIEnsemble Feature Ranking for Optimal Feature Selection An ensemble Random Forest-based feature ranking framework was designed to identify the most predictive features, ensuring stable biomarker selection and improved model interpretability. C_LIO_LIHigh Classification Performance with Reduced Feature Set The proposed ensemble HGBC model achieved outstanding performance with 98.3% accuracy, 98.2% precision, 98.0% recall, and 98.1% F1-score using a reduced feature subset, demonstrating efficient dimensionality reduction without performance loss. C_LIO_LIExceptional Discriminative Ability with Near-Perfect AUC The ensemble HGBC model achieved an AUC of 0.994, indicating excellent discrimination between cancer and non-cancer cases and confirming its suitability for high-precision clinical decision support. C_LIO_LIZero False-Negative Predictions and Maximum Diagnostic Sensitivity The proposed model achieved zero false negatives in evaluation, resulting in 100% statistical power and perfect sensitivity, ensuring reliable detection of esophageal cancer cases. C_LIO_LIIdentification of Key Dietary and Environmental Risk Factors Feature importance analysis revealed that dietary habits, hot food consumption, environmental exposures, and behavioral factors are among the most significant predictors of esophageal cancer risk. C_LIO_LIEnsemble Learning Outperforms Traditional Machine Learning Models Boosting-based ensemble models, particularly HGBC and XGBoost, consistently outperformed other classifiers, demonstrating superior predictive accuracy, stability, and robustness. C_LIO_LIEfficient and Interpretable AI Framework for Clinical Decision Support The proposed framework balances high predictive accuracy with interpretability, making it suitable for assisting clinicians in early diagnosis and risk stratification of esophageal cancer. C_LIO_LIAI-Driven Solution for Resource-Constrained Healthcare Settings The proposed ensemble machine learning approach provides an effective and scalable diagnostic support tool, particularly valuable for healthcare systems with limited resources and access to specialized medical expertise. C_LI

Cardiovascular disease is one of the leading causes of death worldwide, with myocardial infarction (MI) being a major cause of both morbidity and mortality among cardiovascular patients. MI Patients face a higher risk of cardiovascular disease recurrence afterwards. Therefore, accurately predicting the risk of recurrence and identifying key risk factors are crucial for clinical decision-making. In this paper, we consider the interrelationships among cardiovascular factors from a systemic perspective. We first construct a differential network for each patient to capture individual-specific deviations in factor relationships and propose a novel method, termed Causal Factor-aware Graph Neural Network (CFGNN), which integrates factor interactions to predict the recurrence risk of MI patients while uncovering key risk factors from a causal perspective. Experimental results demonstrate that CFGNN performs well on hospital-derived datasets in real world, effectively identifying several key risk factors. This method not only deepens our understanding of cardiovascular disease, but also paves the way for more targeted and effective interventions.

Aspergillus fumigatus is a world-wide saprophyte filamentous fungus which released conidia, its infectious morphotype, in the atmosphere. These conidia are inhaled daily by humans and can colonize the respiratory tract, where they may develop into hyphae, the invasive morphotype. We previously showed that bronchial epithelial cells (BECs) restrict A. fumigatus virulence by inhibiting conidial germination and filament formation through a process requiring PI3K signaling and the conidial fucose-specific lectin FleA. In the present study, we are looking to identify host factors and cellular partners involved in the BEC antifungal response and to define the molecular interactions underpinning FleA recognition. For this, we analyzed transcriptome of BECs infected with A. fumigatus in the presence or absence of the PI3K inhibitor LY294002. Functional involvement of candidate genes was assessed by siRNA knockdown and readouts of fungal filamentation (microscopic scoring and galactomannan release). FleA-interacting host proteins were identified by biotin-FleA affinity co-precipitation coupled to Tandem mass spectrometry, and validated by surface plasmon resonance and biolayer interferometry. The spatiotemporal dynamics of FleA and candidate partners were analyzed by confocal microscopy and proximity ligation assay We demonstrated that BEC antifungal activity involves at least two complementary pathways: a PI3K/laminin-332 axis promoting conidial adhesion, and a FleA-dependent pathway engaging ITGB1 and MRC2 consistent with lectin uptake and trafficking toward LAMP1-positive compartments. These findings nominate FleA-host receptor interactions as attractive targets for anti-adhesive strategies against A. fumigatus. Author summaryFungal pathogens are an increasing threat to public health, as they are becoming more common and harder to treat due to rising drug resistance. Among them, Aspergillus fumigatus has been classified as a critical pathogen by the World Health Organization (WHO). This filamentous fungus delivers spores in the air daily, which are constantly inhaled by humans. In people with weakened immunity, these spores can cause a range of lung diseases known as aspergillosis, with severity ranging from mild to life-threatening. Lung epithelial cells are the first cells of the respiratory tract to encounter inhaled spores. In a previous study, we showed that bronchial cells can prevent spore from developing into filaments, the invasive form of A. fumigatus that is responsible for tissue damage. This protective effect depends of on the recognition of a fungal protein called FleA. In the present study, we identified host cell proteins that bind to FleA and transport it into intracellular compartments. Our findings suggest that these proteins help bronchial epithelial cells to internalize fungal spores, thereby blocking their transformation into the invasive filamentous form.

BackgroundSymptom diaries are widely used in acute respiratory infection trials to capture patient-reported symptom severity and recovery. Longer questionnaires may provide a more complete clinical picture but can increase participant burden and reduce adherence. Evidence directly comparing long and short formats within the same trial is limited. ObjectiveTo compare adherence, symptom trajectories, agreement between recovery measures, and predictive performance for recovery-related outcomes between a short and long symptom diary in an outpatient SARS-CoV-2 trial MethodsThis secondary analysis of the CanTreatCOVID trial compared a 9-item Abbreviated Diary and a 34-item FLU-PRO Plus Diary over 14 days in non-hospitalized participants with confirmed SARS-CoV-2 infection. Outcomes included diary initiation, completion, completion rate, compliance, symptom trajectories, agreement between recovery outcomes, and predictive performance. Analyses used logistic regression, generalized estimating equations, survival models, and predictive modelling. ResultsOf 712 participants, 638 used the Abbreviated Diary and 74 the FLU-PRO Plus Diary. Baseline characteristics were similar between groups. Diary type was not significantly associated with diary initiation or full 14-day compliance, whereas treatment assignment was associated with higher adherence (p < 0.0001). Completion rates were slightly higher in the Abbreviated group (68.1% vs. 64.4%), but differences were not statistically significant. Agreement between "feeling recovered" and "return to usual health" was strong to excellent ({kappa} = 0.8371-0.8859), while agreement with "return to usual activities" was moderate ({kappa} = 0.5273-0.6583). Predictive models performed well for both diaries (AUCs 0.87-0.94), with only marginal gains from including the extended FLU-PRO Plus items. ConclusionIn this outpatient SARS-CoV-2 trial, abbreviated and extended symptom diaries produced comparable adherence, symptom trajectories, and predictive performance. Future research should extend follow-up beyond 14 days to capture longer-term patterns and test diary performance in more diverse and digitally underserved populations.

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.

Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older individuals, with non-small cell lung cancer (NSCLC) comprising 85% of cases. Despite advancements in diagnosis and treatment, prognosis for elderly patients remains poor. This study investigates the role of microRNAs (miRNAs) involved in lung cancer, focusing on individuals aged 60 and above. RNA sequencing data from The Cancer Genome Atlas (TCGA) was used to conduct differential expression analysis of miRNA profiles from elderly and senile patient groups. Results showed that out of 1,881 miRNA profiles, 801 were found to be differentially expressed. Filtering for significance identified that 25 miRNAs, with hsa-mir-1911 upregulated and 24, including hsa-mir-196a and hsa-mir-323b found to be downregulated. Studies showed that these miRNAs play roles in apoptosis, senescence, and inflammation. Another Experimental approach in this study, used Machine learning analysis which highlighted key miRNAs, including hsa-mir-181b, hsa-mir-542, hsa-mir-450b, hsa-mir-584, and hsa-mir-21 as crucial in lung cancer biology. Moreover, Functional enrichment analysis revealed their involvement in gene silencing, translational repression, and RNA-induced silencing complex (RISC) regulation. This research identifies the association of miRNAs and aging in lung cancer and finds potential biomarkers that can be helpful in early diagnosis and targets for personalized therapies.

Accurate breast cancer risk prediction remains a central challenge in precision oncology due to the complexity and heterogeneity of underlying biological processes. While single-modality models based on clinical, gene expression, or copy number variation (CNV) data provide valuable prognostic insights, they often fail to capture complementary information across data sources. Conventional stacking ensembles improve predictive performance through multimodal integration but remain susceptible to variance and overfitting. In this study, we propose a heterogeneous hybrid ensemble framework that combines stacking and bagging to enhance robustness and accuracy in multi-omics breast cancer classification. The framework integrates clinical features, gene expression profiles, and CNV data through stacked multimodal representations, followed by parallel stacking and bagging meta-learning and weighted fusion. Experiments conducted on the METABRIC cohort demonstrate that the proposed hybrid model achieves a ROC AUC of 0.9355, outperforming unimodal models (AUC range: 0.80-0.88) and a conventional stacking ensemble (AUC = 0.919). At the Youdens J optimal operating point, the hybrid approach yields balanced sensitivity (0.8571) and specificity (0.8792), with an overall accuracy of 87.4% and an F1-score of 0.7706. These results highlight the effectiveness of hybrid ensemble learning for robust multimodal integration and demonstrate its potential as a scalable and reliable approach for breast cancer risk prediction. The proposed framework offers a practical pathway toward improved predictive stability and supports the broader application of ensemble-based strategies in precision medicine.