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.

BackgroundPatient-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. MethodsWe 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. ResultsThe 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%). ConclusionsOverall, 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 registrationApproval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

PurposeClinical translation of CAR T cell therapies has accelerated, yet preclinical evidence still often originates from single-center studies lacking sufficient robustness. Preclinical confirmatory multicenter studies have been proposed to improve the translational success, but their feasibility in cellular therapies remains unexplored. MethodsWe performed a confirmatory multicenter study validating C-C-motive-receptor-8 (CCR8) overexpression in CAR T cells--a strategy previously shown to enhance solid tumor infiltration. In vitro experiments covering activation, cytotoxicity, and migration using three CAR constructs were conducted across two centers with harmonized materials, preregistered protocols, randomization, and blinding. ResultsThe data from the two centers confirmed key findings of the exploratory study: CCR8 overexpression in anti-EpCAM and anti-mesothelin CAR T cells leads to enhanced selective migration towards a CCL1-gradient, while not compromising antigen-specific T cell activatory capacity and cytotoxicity in vitro. The study furthermore broadened the applicability of CCR8 overexpression to anti-CEA CAR T cells. ConclusionsThis first-of-its-kind preclinical confirmatory CAR T study demonstrates the feasibility of a multicenter confirmation in cellular therapy, with technical and logistical challenges resolved through transparent communication between all parties involved. Both exploratory and confirmatory studies aim to downselect CAR candidates with the highest clinical success potential, as they compete for limited resources in preclinical research. It is therefore mandatory to clarify the extent of replications required to validate the experimental methodology and identify CAR candidates with most likelihood of success. TRANSLATIONAL RELEVANCEPreclinical evidence for novel CAR T cell therapeutic strategies relies mostly on exploratory single-center studies lacking robustness, with recent findings substantiating their limited predictive value for cellular therapies tested outside hematology. Here, the function of CCR8-armored CARs in vitro was confirmed in a preclinical confirmatory multicenter study, demonstrating the feasibility of such studies in adding value to the transition of preclinical concepts to clinical development. Our first-of-its-kind study may contribute to define new routes for preclinical testing and further raises the general question of what level of preclinical evidence is reasonably achievable in an academic context. It indicates the need for strong collaborative efforts to realize dedicated preclinical infrastructure for clinical translation of reprogrammed immune cellular therapeutics.

Advances in NASAs astrobiology program have demonstrated the feasibility of cultivating plants in space and in analog extraterrestrial habitats. In addition to abiotic stressors, plants grown in terrestrial and space-like environments are challenged by both phytopathogens and opportunistic human pathogens, with implications for plant productivity and human health. The persistence of human-associated pathogens in spacecraft and space stations raises significant concerns regarding food safety. The molecular, biochemical, and signaling mechanisms governing stomatal development and function under microgravity remain poorly understood. We employed an experimental system incorporating human pathogen Salmonella enterica and lettuce microgreens exposed to simulated microgravity through two-dimensional clinorotation to investigate plant innate immunity and stomatal development and function. We further evaluated four lettuce cultivars to determine whether genetic variation impacts these factors under simulated microgravity conditions. Our findings indicate that simulated microgravity significantly influences stomatal development and function, as evidenced by an increase in stomatal density and variable changes to stomatal aperture. Notably, cultivar-dependent variation in stomatal traits and responses to Salmonella enterica was observed under microgravity conditions. Although increased stomatal density was hypothesized to enhance pathogen ingression, internalization was more strongly predicted by cultivar selection and simulated microgravity; simulated microgravity increased ingression, with red pigmented cultivars having less pathogen than green cultivars. These results suggest that targeted selection of cultivars with favorable physiological traits may improve food safety and the viability of crop production systems in space environments. They also suggest that development and function of stomata may change in spaceflight conditions.

Introduction Recent respiratory illness, especially influenza, may trigger acute cardiac events via elevated inflammatory mediators. During the 2018 influenza season in Bangladesh, this study examined whether recent acute clinical respiratory illness (CRI) or laboratory-confirmed influenza was associated with elevated hs-CRP and IL-6, linked to acute cardiac events. Methods A total of 139 participants aged [&ge;]40 were recruited from a Dhaka cardiac hospital: 70 with acute myocardial infarction (AMI), 30 with other acute cardiac events, and 39 healthy individuals. CRI was defined as fever with cough and/or respiratory symptoms within seven days. Respiratory swabs were tested for influenza, and blood was analyzed for hs-CRP and IL-6. Results Median hs-CRP and IL-6 were higher in participants with CRI or influenza but not significantly. Cardiac patients had elevated hs-CRP (9.98 mg/L in other cardiac; 4.86 mg/L in AMI vs. 1.73 mg/L in healthy) and IL-6 (0.1 pg/mL in other cardiac; 0.145 pg/mL in AMI vs. 0.08 pg/mL in healthy) (p<0.001). CRI was not significantly associated with elevated hs-CRP or IL-6, though influenza in healthy participants was linked to higher IL-6. Cardiac patients had a higher risk of hs-CRP [&ge;]3 mg/L and elevated IL-6. Conclusion Cardiac patients showed significantly increased inflammatory markers, but CRI was not clearly linked to inflammation. Further research should assess biomarker utility for early cardiac risk.

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validations of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features. Graphical abstractWorkchart for multiomics integrative studies in the liquid biopsy field. Note: CTCs, circulating tumor cells; ctDNA, circulating tumor-DNA; TEPs, tumor-educated platelets; miRNA, microRNA; cfRNAs, cell-free RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/724535v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1f250b2org.highwire.dtl.DTLVardef@18fe36corg.highwire.dtl.DTLVardef@19c02b9org.highwire.dtl.DTLVardef@176f6e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

The growth of cultures and formation of mucilage blooms in reaction to salt stress of cyanobacterial cultures are investigated with a focus on the influence of pH. In non-buffered medium, cultures show their pH increasing from 6.5 just after inoculation, up to 11 during the exponential phase. We record the time-evolution of concentration and pH, with different initial OD0. In a second set of experiments, we extract the doubling time of the unbuffered cultures in comparison with those inoculated in pH-buffered BG11 media at four different pH from 6.3 to 10.5 : in the most acid media, all cultures die or grow very slowly. At pH = 10.5, we obtain the fastest growth for all four strains, allowing to qualify these cyanobacteria as being alkaliphiles, though for all strains with comparable initial OD0, the doubling time is shorter for unbuffered cultures. Following a previous study [31]), we finally investigate the influence of pH on mucilage formation and biomass uplift induced by salt stress, involving EPS floculation by cations. Our results show that operating in buffered media significantly influences the mucilage formation, though the observed regimes cannot be simply correlated to the pH value.

As human spaceflight becomes increasingly relevant, understanding how microgravity affects the human brain is an important but largely unexplored question, particularly in the context of neuronal function and vulnerability to neurodegeneration. Direct investigation of these processes in humans is not feasible, necessitating the use of physiologically relevant in vitro model systems. Three-dimensional human brain organoids recapitulate key aspects of brain development and organization and provide an experimentally accessible platform to study neuronal responses under controlled conditions. Here, within the framework of the student competition "Uberflieger 2", we investigated the effects of long-term microgravity on human midbrain organoids cultured for 40 days aboard the International Space Station (ISS). Midbrain organoids reproduce essential features of dopaminergic neuron development and are widely used to model Parkinsons disease and related neurodegenerative processes. To enable spaceflight experiments, we developed and implemented an autonomous culture system adapted to the constraints of the ISS environment. During the mission, a hardware malfunction impaired scheduled medium exchange, introducing an additional metabolic stress condition. Despite these limitations, ISS-cultured organoids remained viable and showed robust neurite outgrowth. Molecular and imaging analyses revealed that exposure to microgravity in combination with nutrient limitation induced a coordinated response involving cytoskeletal remodeling, neuronal plasticity, and selective vulnerability of dopaminergic neurons. These findings demonstrate that human midbrain organoids can maintain key structural and functional properties under prolonged spaceflight-associated stress while activating adaptive response programs. This work highlights the potential of organoid-based systems to investigate neurobiological effects of microgravity and provides a foundation for future studies addressing mechanisms relevant to neurodegenerative disease.

In the next years, several space missions will search for evidence of life on exoplanets, focusing on robust biosignatures associated with oxygenic photosynthesis, including atmospheric oxygen accumulation and the Vegetation Red-Edge in surface reflectance spectra. Many potentially habitable rocky exoplanets orbit M-dwarf stars, whose spectral energy distribution may challenge oxygenic photosynthesis. Differently from the Sun, M-dwarf stars emit predominantly far-red (700- 750 nm) and infrared (750-1000 nm) light, and relatively little visible (400-700 nm) radiation, which constitutes photosynthetically active radiation. Some organisms have been found to photosynthesize under such spectrum but less efficiently than under solar light, as their photosynthetic apparatus evolved to harvest visible light emitted by the Sun. Around M-dwarfs, such different irradiation might have selected adaptations optimized for harvesting far-red / infra-red light. On Earth, similar selection can be found in Acaryochloris marina strains, constitutively presenting high chlorophyll d content in photosystem II & I, with in vivo absorption peaks beyond 700 nm. Here we tested the Moss Beach strain under a simulated M-dwarf spectrum and a simulated primeval atmosphere - anoxic and enriched in carbon dioxide. Results underline how this permanently red-shifted photosynthetic apparatus does not require acclimation to the stellar spectrum and enables for a strong growth and oxygen production, higher than under simulated solar light. Moreover, cells reflectance spectrum highlights a shift of the canonical red-edge toward longer wavelengths, resulting in a Chl d-near-infrared edge, suggesting a similar metabolism on exoplanets orbiting M-dwarfs could successfully produce both a gaseous biosignature and a characteristic surface biosignature. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/719884v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@7f91bdorg.highwire.dtl.DTLVardef@1391bdborg.highwire.dtl.DTLVardef@53f7b4org.highwire.dtl.DTLVardef@ab59fa_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Liistro, E. (2026) https://BioRender.com/j2de4ay

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

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.

MOTIVATIONAdult cardiomyocytes are difficult to profile by whole-cell single-cell RNA sequencing because of their large size and fragility, which make them poorly compatible with standard workflows. Current approaches for adult cardiomyocyte transcriptomics often require a trade-off between data quality and throughput, thus, studies instead rely heavily on sequencing of nuclei alone. Therefore, we set out to develop a high-quality and scalable workflow for adult heart cells using in-cell ligation and split-pool barcoding strategies to address this methodological gap. This workflow may be further generalisable to other large cell types or samples containing cell populations with highly unequal RNA content. SUMMARYAdult cardiomyocytes are difficult to profile by whole-cell single-cell RNA sequencing (scRNA-seq). Here, we developed a high-quality and scalable workflow for adult heart cells using in-cell ligation and split-pool barcoding. We identified per-cell RNA content as a significant variable that must be accounted for. Separation of cardiomyocytes (large cells) and non-cardiomyocytes (small cells) before library construction, and allocation of deeper sequencing to cardiomyocytes, produced high-quality whole-cell datasets for both compartments. Compared with single-nucleus RNA sequencing, whole-cell cardiomyocyte profiling better recovered metabolic, mitochondrial, cytoplasmic translational, and contractile gene programs. This workflow provides a practical method for scalable, high-quality cardiomyocyte whole-cell scRNA-seq and offers general strategies for other large cell types or samples containing cell populations with highly unequal RNA content.

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.

BackgroundFerroptosis has emerged as a promising therapeutic target in IRI. However, it remains largely unclear how and when this iron-dependent regulated cell death manifests during IRI. Therefore, we explored malondialdehyde (MDA), a byproduct of lipid peroxidation, and glutathione peroxidase 4 (GPX4), as a marker of redox capacity, in multiple IRI models. With this explorative study, we aimed to uncover MDA dynamics in renal and hepatic IRI, which could provide valuable insights for future internal studies. MethodsHistorical plasma and tissue samples from rat and porcine models of renal and hepatic IRI were selected based on varying conditions of ischemic injury, reperfusion and perfusion. MDA was measured using a colorimetric assay with N-methyl-2-phenylindole, methanol, acetonitrile and hydrochloric acid and quantified at 595 nm. GPX4 protein concentrations were investigated using standard western blotting. ResultsIn rat clamping models, plasma MDA concentrations revealed no difference between control and IRI settings. However, an increasing trend could be observed in tissue samples after IRI. Similarly, a decrease in tissue GPX4 concentrations was observed after IRI. In porcine studies, MDA concentrations were increased during reperfusion of kidneys exposed to prolonged warm ischemia and livers exposed to short periods of cold ischemia. Dynamic preservation could attenuate MDA concentrations. ConclusionWe found that MDA and GPX4 are affected within the first hours after reperfusion, stressing the need for early sampling in studies focusing on characterizing ferroptosis. Moreover, MDA dynamics during organ perfusion revealed an increased vulnerability of ischemic organs to lipid peroxidation and a potential protective effect of dynamic preservation. These preliminary results should be confirmed in studies focusing on ferroptosis characterization, as notable observations regarding sample age and storage conditions and experimental design limit the validity of this study.

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.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients ([&ge;] 18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD {+/-} 0.09), sensitivity of 0.71 (SD {+/-} 0.16), and specificity of 0.78 (SD {+/-} 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification. Author SummaryDelirium is a sudden change in attention and awareness that commonly affects hospitalized patients. It is linked with longer hospital stays, cognitive decline, and death. Patients with delirium often show changes in movements and behaviors such as slowed movement, restlessness, or excessive scanning of the environment. Since current screening tools rely on intermittent human interactions, they can be subjective and miss the fluctuating nature of delirium, leading to underdiagnosis. We sought to explore whether short video recordings could be used to detect delirium automatically. In our study, we enrolled 50 hospitalized patients and conducted daily delirium assessments and video recordings. We used a machine learning model to analyze patients eye movements, facial expressions, and body postures. We found that video-derived features could be used to identify delirium in a small clinical cohort. While needing further validation in outside cohorts, this study shows an important proof-of-concept for objective delirium monitoring in heterogeneous clinical contexts without adding burden to clinical staff.

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