Life

Objective A diversity of approaches and cohorts have been published in research literature for suicide risk estimation, leading to some uncertainty around which factors and conditions are most strongly contributing to risk. Methods exist for weighting estimates by propensity score, removing simplifying the interpretation of model effects. Few studies have explored the use of these tools to assess when the effect of certain factors is uncertain or inconsistent. Method A mix of 1,001,737 suicide decedents and living patients were evaluated for suicide risk, relying on a subset of commonly identified factors: demographics, psychiatric conditions, substance use disorders, and social needs. Care utilization patterns were then assessed controlling for these factors in a hospital care setting. Four methods were considered for controlling multivariate confounding of odds predicting death by suicide: multivariate logistic regression, 1-to-1 sample matching, inverse propensity weighting (IPTW), and entropy balancing (EBAL). Results Test statistics showed suicide decedents were much more likely to have various psychiatric and substance use diagnoses, social needs and more likely to heightened care utilization across sampling intervals. Greater utilization was found close to date of death. Effect sizes after IPTW and EBAL weighting were also generally greater than would have been documented by regression alone. Conclusions Propensity weighting is a useful alternative to exploring nuanced effects, especially when the direction and strength of an association differ from traditional statistics. By weighting, the overall imbalance of cases remains consistent and effect terms might reflect more nuanced associations than regression or sample matching alone.

Multi-agent prompting of large language models has produced contradictory diagnostic results, and it remains unclear whether any benefit comes from specialist personas or from perspective independence. We compared a single direct call, five personas in one context, and the same five roles as isolated agents integrated by a moderator, with five repeat runs per case, on 87 CPC cases, 406 MedCaseReasoning cases, and 364 emergency department encounters, under an LLM judge validated against clinicians. On the external benchmark the team beat the single call on both pre-specified recall endpoints (top-3 +3.0 points, p = 0.0079; top-5 +3.9, p = 3.8 x 10^-5;); a factorial attributes the gain to independent generation plus moderated synthesis, not the specialist roles. On real emergency presentations the benefit reversed (top-1 40.1% versus 34.3%, p < 0.0001), carried by the specialist role lists and surviving added objective results. Deployment should key on the question and the input at hand.

Abstract Background Health-related quality of life (HRQoL) is an important outcome in patients with chronic kidney disease (CKD) receiving kidney replacement therapy (KRT), but the relationship between comprehensive metabolic control and the Symptoms/Problems domain of kidney diseasespecific HRQoL remains uncertain. Objective To evaluate the association between composite metabolic control and the Symptoms/Problems domain of the Kidney Disease Quality of Life-36 (KDQOL-36) in patients with stage 5 CKD receiving KRT, and to explore the relationship between individual metabolic parameters and the continuous Symptoms/Problems score. Materials and Methods We conducted an observational, cross-sectional, analytical study of 177 adults receiving hemodialysis or peritoneal dialysis at a nephrology unit in Chihuahua, Mexico. Metabolic control included blood pressure, glycated hemoglobin (HbA1c), total cholesterol, and triglycerides. Adequate control was defined as achievement of at least three of four studydefined operational thresholds. The KDQOL-36 Symptoms/Problems score was analyze as a continuous variable and categorized as low, moderate, or high according to the study protocol. Group comparisons, Spearman correlations, Pearson chi-square tests, and ordinal logistic regression were performed. Results Adequate metabolic control was present in 106 patients (59.9%). No, statistically significant association was observed between achievement of at least three of four study-defined operational thresholds and Symptoms/Problems score category (x2=3.983; p=0.137; Cramers V=0.150). None of the individual metabolic parameters differed significantly across Symptoms/Problems score categories or showed significant correlations with the continuous score. In ordinal logistic regression, adequate metabolic control yielded an odds ratio of 1.81 (95% CI: 0.98-3.35; p=0.057) after adjustment for age, sex, time receiving KRT, and KRT modality. Conclusions No, statistically significant association was identified between the operational definition of metabolic control used in this study and the KDQOL-36 Symptoms/Problems domain in patients with stage 5 CKD receiving KRT. The findings support evaluating HRQoL multidimensionally and warrant confirmation in larger longitudinal studies. Categories: Family/General Practice, Internal Medicine, Nephrology Keywords: chronic kidney disease (ckd), hemodialysis, kdqol-36, kidney replacement therapy, metabolic control, peritoneal dialysis (pd)

Background: Chronic kidney disease (CKD) may progress to stage 5 CKD (end-stage renal disease, ESRD), but stage-associated molecular alterations remain incompletely characterized. This study aimed to identify candidate biomarkers associated with CKD and ESRD. Methods: Two discovery datasets comparing CKD or ESRD with healthy controls were analyzed using differential expression analysis and weighted gene co-expression network analysis. Candidate genes were identified by intersecting differentially expressed genes with key co-expression modules, followed by protein-protein interaction network analysis using Degree, Closeness, and Maximal Clique Centrality algorithms. Candidate genes were evaluated in independent GEO validation datasets and further examined by quantitative real-time PCR and western blotting using clinical blood samples. Results: Ten hub genes were identified, among which EPB42, KLF1, and H3F3A were prioritized for further evaluation. EPB42 showed decreased expression in CKD but increased expression in ESRD, indicating an opposite-direction pattern between the two disease states. KLF1 showed no significant difference between healthy controls and CKD but was increased in ESRD, whereas H3F3A was increased in CKD and further increased in ESRD. Functional enrichment analysis highlighted processes related to oxygen transport, mitochondrial function, muscle-associated processes, and inflammation, suggesting possible involvement of hypoxia-associated oxidative injury and ferroptosis-related processes. Conclusions: Integrated transcriptomic and experimental analyses identified EPB42, KLF1, and H3F3A as candidate stage-associated biomarkers in CKD and ESRD. In particular, the opposite-direction expression pattern of EPB42 may provide additional molecular information for distinguishing CKD from ESRD.

Abstract Background: Respiratory failure (RF) and atrial fibrillation/flutter (AF/AFL) often occur together in older adults, but long-term national mortality trends for the two conditions combined have not been described. We examined trends and disparities in deaths with co-listed RF and AF/AFL among U.S. adults aged 65 years and older from 1999 to 2024, and projected rates through 2035. Methodology: We used the CDC WONDER Multiple Cause of Death database to identify deaths with ICD 10 codes J96 (RF) and I48 (AF/AFL) listed together. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population and stratified by sex, race/ethnicity, census region, urbanization, state, and place of death. Joinpoint regression was used to estimate annual percent changes (APCs) and average annual percent changes (AAPCs). Forecasts for 2025 to 2035 were generated with an exponential smoothing (ETS) model. Results: A total of 433,598 deaths were recorded. The overall AAMR rose from 12.8 in 1999 to 66.7 in 2024 (AAPC 8.11%; 95% CI, 7.35 to 8.87; P < 0.0001). Three segments were identified: an increase from 1999 to 2018 (APC 7.73%), a sharp rise from 2018 to 2021 (APC 18.10%), and a decline from 2021 to 2024 (APC -5.25%). Males had a higher AAMR and a steeper increase than females (AAPC 8.44% vs 7.81%). Non-Hispanic White adults had the highest mean AAMR, Hispanic adults had the largest relative increase (AAPC 8.72%), and Non-Hispanic Asian/Pacific Islander adults had the smallest (AAPC 6.23%). The Midwest had the fastest regional growth (AAPC 9.35%). From 1999 to 2020, non-metropolitan areas increased faster than metropolitan areas (AAPC 10.09% vs 8.21%). State AAMRs ranged from 24.7 in Ohio to 85.1 in Louisiana. Most deaths occurred in hospital inpatient settings (64.1%), followed by nursing homes (14.1%) and the home (11.9%). The model projects an AAMR of 122.96 per 100,000 by 2035 (95% prediction interval, 85.42 to 160.50). Conclusions: Mortality involving RF with AF/AFL among older U.S. adults increased more than fivefold over 25 years, with a marked surge during the COVID 19 period and only a partial decline afterward. Differences by sex, race/ethnicity, region, and urbanization point to groups that may benefit from earlier detection and coordinated cardiopulmonary care.

Microglia represent a highly dynamic and heterogeneous cell type that is critically implicated in states of health and pathology. Microglial morphological subgroups have been identified that correspond to functional characteristics determining health-related outcomes. The identification of states based on morphological characteristics will therefore provide invaluable insights into the microglia-specific functional mechanisms driving treatment effects. The application of clustering analyses enables the detection of groupings within samples reflecting differences in morphological features. Here we propose the application of three custom-created modules to be used within the open-source software CellProfiler. These modules enable the automated detection of clusters present within the sample of microglia, as well as the assessment of the abundance of these clusters across conditions. The application of the analysis is conducted in a highly user-friendly manner, with a user interface integrated into the pipeline, enabling the performance of the analysis with only minimal user input. The workflow thereby includes the conduction of an outlier assessment, followed by hierarchical clustering and k-means clustering and the generation of interactive graphs to determine the number of microglia states present in the sample. Bar plots displaying the abundance of the microglia states across conditions included in the sample will be created. This approach will facilitate faster and more comparable detection of microglial morphological clusters across studies.

We show that the p53-Mdm2 gene interaction can be understood as a form of antithetic feedback, but with mixed feedback loops. In particular, we show that this viewpoint sheds light on the emergence and robustness properties of p53's oscillatory behaviour, as observed experimentally.

Early risk stratification of COVID-19 patients at admission informs triage and resource allocation, particularly in low- and middle-income settings where intensive-care capacity is constrained. Most published models are not interpretable, rarely externally validated, and developed almost exclusively in high-income cohorts. We developed an interpretable random-forest classifier for three-class COVID-19 severity (Mild: not ventilated, survived; Severe: ventilated, survived; Fatal: died) using only data available within 24 hours of admission in 321 hospitalised PCR-confirmed COVID-19 patients in Pakistan. From 2,278 raw variables we retained 20 predictors by clinical screening and permutation-importance selection, with leakage-controlled multiple imputation by chained equations and SHapley Additive exPlanations (SHAP) for interpretation. A 15-feature variant was evaluated in two independent public cohorts: the Chinese iCTCF cohort (n = 894) and an Italian complete-blood-count cohort (n = 1,218). The development model achieved a macro F1 of 0.55, identifying the Fatal class most reliably (F1 0.67, recall 0.73) and Mild cases least well. SHAP identified a parsimonious set of admission-time predictors (oxygen saturation, lactate dehydrogenase, urea, C-reactive protein, age and radiographic pneumonia), including a raised urea-to-creatinine ratio in fatal cases most consistent with dehydration (median urea 80 vs 50 mg/dL, fatal vs mild; p < 0.001). Externally, the model retained useful discrimination in the Chinese cohort (mortality AUC 0.82 in 719 patients with recorded outcomes; severe-or-worse AUC 0.75; macro F1 0.50) but only modest discrimination in the Italian cohort (AUC 0.62, 95% CI 0.56-0.68), which we treat as a feature-availability stress test rather than a second validation. Cross-cohort SHAP rankings correlated strongly in China (Spearman {rho} = 0.91) and moderately in Italy ({rho} = 0.76), where most laboratory predictors were imputed and carried no information beyond the complete blood count; discrimination tracked how many predictors were actually measured. The admission-time signature converges with established markers of hypoperfusion and inflammation.

Survival prediction frequently exhibit unstable and irreproducible performance, limiting their clinical applicability. Rather than proposing a new predictive architecture, this work presents a diagnostic investigation into the sources of this instability. Using a transformer-based deep fusion model combining RNA sequencing and histopathology data from the TCGA-BRCA cohort as a diagnostic probe, we demonstrate that global survival prediction is highly unstable across data splits. We then analyze the learned multimodal embeddings in an unsupervised manner, revealing stable latent patient subgroups with significantly divergent survival outcomes. These subgroups are not explained by canonical receptor status or immune infiltration, but instead align with tumor-intrinsic proliferative activity. Conditioning survival modeling on the largest discovered subgroup recovers strong and stable prognostic performance, substantially outperforming unimodal baselines. These findings indicate that survival predictability is conditional on latent heterogeneity and suggest that heterogeneity-aware modeling strategies are essential for robust multimodal prognostication. This work establishes a methodological framework for interpreting instability in multimodal survival learning.

In our past study on human cilia from primary ciliary dyskinesia (PCD) patients using cryo-electron tomography and mass spectrometry, defects in the DNAH5 gene were proven to cause the loss of the entire outer dynein arm along with other proteins. This phenomenon was not observed in the unicellular green alga Chlamydomonas. In this study, we examined the loss of ciliary components caused by defects in various genes, based on proteomic and structural analyses of motile cilia from multiple PCD patients. The absence of single outer arm dynein genes causes a loss or significant decrease in other inner and outer arm dyneins. Interestingly, some patients with outer dynein defects show decreased intraflagellar transport (IFT) proteins, suggesting an influence of cargo components or assembly on transport. These phenomena were not observed in Chlamydomonas, suggesting a more complex mechanism of ciliogenesis in humans despite the high similarity in the final 3D architecture of the 9+2 axoneme. Defects in certain central pair proteins unexpectedly reduced components on the doublets as well. Our results demonstrate protein-protein interactions during stages of human motile ciliogenesis that are more complex than those in unicellular organisms.

The regulation of presynaptic mechanisms by the fragile X messenger ribonucleoprotein (FMRP) remains incompletely understood despite their potential importance to synaptic dysfunction reported in Fragile X syndrome. We combined post-developmental, cell-specific deletion of FMRP in dentate granule cells of the hippocampus with optogenetics, whole-cell patch-clamp electrophysiology and super resolution imaging. The selective loss of FMRP in DG cells did not alter their intrinsic excitability. The amplitude of synaptic currents was reduced at the output mossy fiber synapses in the absence of presynaptic FMRP, whereas short-term facilitation remained intact. We demonstrate that basal synaptic activity is regulated by PKA at mature mossy fiber synapses, and our results indicate alterations in cAMP-PKA signaling and a potential reduction in the readily releasable pool size in mossy fiber boutons lacking FMRP. Presynaptic FMRP is therefore required for efficient basal neurotransmission at mature mossy fiber synapses and likely acts through cAMP-PKA-dependent signaling pathway. Significance statementThe fragile X messenger ribonucleoprotein (FMRP) has been extensively studied as a regulator of postsynaptic mechanisms. However, its physiological functions in presynaptic terminals remain incompletely understood. Using post-developmental, cell-specific deletion of FMRP, we show that presynaptic FMRP is required to maintain efficient basal neurotransmission at hippocampal mossy fiber synapses and identify cAMP-PKA signaling as a potential mechanism for this effect. Taken together, these results highlight a physiological role for presynaptic FMRP during basal synaptic activity and demonstrate how time-controlled gene-editing approaches via viral infection can distinguish primary molecular functions from developmental compensations, thereby shedding new light on both the presynaptic function of FMRP and the pathophysiology of Fragile X syndrome. HighlightsO_LIPresynaptic loss of FMRP reduces basal synaptic activity at hippocampal Mf synapses. C_LIO_LIBasal synaptic activity is regulated by PKA in adult Mf synapses. C_LIO_LIAC-cAMP-PKA pathway is impaired during basal synaptic activity in the absence of presynaptic FMRP. C_LIO_LIRRP size is very likely reduced in AZs at Mf terminals. C_LI

Homozygosity for the HBB codon 8/9 (+G) frameshift (c.27dup (p.Ser10ValfsTer14)) causes transfusion-dependent {beta}-thalassaemia and is common in South Asia. Prime editing can reverse this insertion without double-strand breaks or donor DNA, but its efficiency depends on pegRNA design. We derived Sendai-reprogrammed iPSCs from a homozygous patient, optimised PEmax editing (spacer, pegRNA extension, secondary nick, MLH1dn) and compared patient, PEmax-treated and control iPSC-derived erythroid cells by flow cytometry, colony assays, RT-qPCR, western blotting and cation-exchange HPLC. Patient iPSCs had a normal 46,XY karyotype, expressed pluripotency markers, formed all three germ layers and were Sendai-free by passage 15. A PAM-disrupting spacer with a +68 nicking sgRNA gave the highest intended-edit frequency 8.4%; MLH1dn added little. PEmax-treated cultures matured and formed colonies more like control than patient cultures, restored HBB transcript (about 4-fold control iPSC-derived cells) and detectable {beta}-globin protein, and contained an HPLC fraction consistent with HbA ( undetectable in patient cells). Fetal haemoglobin (about 80%) and embryonic globin remained predominant. Prime editing corrects HBB c.27dupG in patient iPSCs and restores {beta}-globin expression, with partial restoration of adult haemoglobin in a fetal/embryonic-type erythroid background. Validation in additional donors, haematopoietic stem cells and in vivo is required.

The transcriptomic interaction between altered gravity and innate immunity remains largely unexplored. In this study, we established a novel dual-stress model combining 3g hypergravity with lipopolysaccharide (LPS) immune challenge in 5 days post-fertilization (dpf) zebrafish (Danio rerio) larvae. RNA sequencing revealed that exposure to hypergravity alone induced no differentially expressed genes (DEGs), indicating a remarkable transcriptional resilience. In contrast, the addition of an immune challenge under hypergravity conditions triggered a substantial transcriptomic response involving 2,706 DEGs. Functional enrichment analyses identified significant alterations in pathways related to immune function, development, and circadian regulation. Targeted qPCR analyses further validated the expression patterns of selected genes. These findings suggest that while zebrafish larvae can effectively tolerate hypergravity alone, the combination of gravitational and immune stressors elicits profound molecular responses. This study provides new insights into the interplay between altered gravity and innate immunity using an animal model, contributing to future strategies aimed at safeguarding astronaut health and supporting space-based aquaculture systems.

Roseoloviruses, notably human herpesviruses 6A and 6B (HHV-6A and HHV-6B), are neurotropic viruses implicated as agents in neurological disorders, including: epilepsy, multiple sclerosis, and chronic fatigue syndrome. However, the effects of roseolovirus infection on neuronal signaling and network activity are not characterized. This is, in part, due to the complexities of monitoring electrical activity in individual neurons and across neuronal connections during viral infection. This protocol describes a human induced pluripotent stem cell (iPSC)-derived neuronal culture system that employs multi-electrode array (MEA) recordings to study neurophysiological changes during viral infection. Two culture platforms are described: (a) NGN2-induced forebrain neuronal cultures; and, (b) progenitor cell-derived neuron-astrocyte mixed cultures. Cell composition in cultures is validated by immunofluorescence staining using neuronal, glial, and viral markers. Functional activity is assessed using extracellular recordings detected via the MEA2100 system. Mean firing rate, inter-spike interval, single-electrode bursting, and network burst activity are characterized between roseolovirus-infected versus uninfected (control) states. Pharmacological treatment with bicuculline, gabazine, and nicotine in conjunction with immunofluorescence is used to confirm functional responsiveness of defined neuronal neurotransmitter chemotypes. Here, we show that HHV-6A infection changes neuronal firing compared to uninfected controls. The methods/workflows described provide a strategy to study how virus infection alters neuronal excitability and may be adapted to compare the effects of different viruses on nerve cell function, infection time courses, different multiplicities of infection (MOI), and therapeutic interventions.

Accurate prediction of adverse events in the intensive care unit (ICU) following cardiothoracic surgery is crucial for timely interventions, improving patient outcomes and optimizing value in healthcare. By leveraging advanced machine learning techniques, this study demonstrates the transformative potential of predictive analytics to enhance postoperative care using a dataset from the Society of Thoracic Surgeons' (STS) database and time-series intraoperative data. We developed a multi-modal late fusion approach integrating static patient variables with intraoperative time-series data, utilizing an ensemble of models including residual neural networks for static features, CNN and Bidirectional GRU for time-series processing, XGBoost, logistic regression, SVM and Gradient Boosting. Through five-fold cross-validation, our ensemble model with balanced threshold selection achieved an AUC of 0.87, sensitivity of 0.76, specificity of 0.83, PPV of 0.40, and NPV of 0.96. Preoperative heart failure, IABP insertion, cardiopulmonary bypass (CPB) duration and white blood cell (WBC) count emerged as key predictors. Training the same pipeline on each modality separately showed that the static registry variables alone reached the same AUC of 0.87, and that the intraoperative signals alone reached 0.83, so on this cohort the two modalities act as substitutes rather than complements and fusion changed the sensitivity and precision trade-off rather than the amount of separable signal. A per-signal ablation found that no individual monitoring channel was load bearing, with mean arterial pressure and central venous pressure the least substitutable. This balanced approach optimizes the trade-off between sensitivity and specificity, providing a screening tool with high negative predictive value for monitoring high-risk patients, and the modality comparison indicates that comparable performance is attainable at sites holding registry data alone.

Flow cytometry is central to clinical hematology, generating extensive archives comprising billions of single-cell profiles linked to expert-derived diagnostic summaries. While these records represent a largely untapped repository of collective expert knowledge, the underlying single-cell annotations required for supervised machine learning are typically not recorded in a standardized, accessible format. We present IMPGAT (implicit gating), a weakly supervised multiple-instance learning framework that reconstructs single-cell gating strategies directly from existing sample-level cell-type frequency distributions. The model utilizes an induced set attention mechanism to map single-cell marker profiles to specific cell populations, requiring neither explicit cell-level labels nor unsupervised clustering during training. We evaluate the framework on acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) cohorts, totaling 711 measurements from 268 individuals and 240 million single cells. In ALL, the model's quantification of cell-type abundances shows no statistically significant difference from human inter-expert variability. In AML, the framework achieves blast quantification on par with a recently established supervised state-of-the-art model that relies on explicit single-cell annotations for training. Error analysis shows that mismatches between model predictions and expert gating primarily involve expert-labeled non-cellular events, reflecting the non-incorporation of any explicit physical prior knowledge (such as the geometric relationships traditionally used to identify doublets and debris in scatter cytograms) in model training. Conversely, model certainty correlates with phenotypic borderline cases where manual bisection is inherently difficult. Our approach enables the systematic utilization of retrospective clinical records for the development of automated, standardized gating logic and is applicable to broader high-throughput contexts, such as large-scale immune monitoring or phenotypic drug screening.

This paper shows that it is possible to predict which newly published biomedical research papers will be connected to subsequent clinical trials. We use CAR-T cell therapy as a proof-of-concept setting because its development can be followed from published research through clinical testing, regulatory approval, and commercialization. We reconstructed connections between research papers and clinical trials and trained machine-learning models to predict which papers were associated with research that progressed to clinical testing. Using 65 paper-inherent indicators, including scientific topic, language, biomedical orientation, and team structure, the models achieved a cross-validated AUC of 0.908. The resulting rankings were also practically informative. In a retrospective analysis using no information generated after 2016, 80% of the top 20 papers came from laboratories associated with one of 13 predefined major CAR-T clinical or commercial outcomes. As an additional finding, first-year citations alone achieved an AUC of 0.797, while provenance indicators based on author, journal, and institutional reputation performed less strongly and added little to the paper-inherent model. Our results show that whether biomedical research papers will be connected to subsequent clinical trials can be predicted from newly published papers with high discrimination in CAR-T. Our findings could help research organizations and pharmaceutical companies identify papers, laboratories, and therapeutic programs that warrant closer evaluation before clinical outcomes are known.

Purpose. Neuroblastoma is the most common malignancy of infancy, and cancer cells strongly depend on iron metabolism for proliferation. Whether sub-cytotoxic iron load reprograms mitochondrial and redox metabolism, and how this relates to the malignant behavior of neuroblastoma cells, is poorly defined. We hypothesized that exposure of human neuroblastoma cells to a non-toxic iron overload perturbs mitochondrial and redox metabolism, thereby affecting cancer cell properties. Methods. SH-SY5Y neuroblastoma cells were exposed to 100 M iron sulfate for 24 h. The resulting changes were monitored using complementary methods, including cell viability, cytotoxicity, proliferation and migration assays, intracellular iron quantification by ICP-MS, confocal imaging of mitochondrial morphology and membrane potential ({Delta}{Psi}m), high-resolution respirometry with flux control ratios, probe-based oxidant and H2O2 measurements, and label-free quantitative proteomics with Ingenuity Pathway Analysis. Results. Non-toxic iron loading raised intracellular iron about four-fold, promoted cell proliferation and reduced migration. Iron induced mitochondrial fragmentation and {Delta}{Psi}m depolarization while preserving mitochondrial content and OXPHOS coupling efficiency. Among respiratory states, only proton-leak respiration increased, paralleled by a higher leak/ETS coupling ratio. Total cellular oxidant levels and H2O2 output were strongly reduced. The proteome showed downregulation of ribosomal proteins and translation/RNA-processing components and upregulation of histones and ferritin. Conclusion. Mild iron exposure reprograms mitochondrial and redox metabolism in neuroblastoma cells. The observed selective increase in proton leak lowers {Delta}{Psi}m to a range that biophysically limits oxidant production while OXPHOS coupling is preserved. Interfering with this adaptive, proton leak-dependent and preventive antioxidant program may offer a way to alter neuroblastoma progression.

Mesenchymal stromal cells (MSCs) are promising cell therapeutics, but their development as off-the-shelf products requires cryopreservation, which impairs cell function after thaw. Inflammatory licensing enhances MSC therapeutic attributes, yet protocols vary considerably and little is known about how these variables affect MSC metabolism or post-thaw recovery. We characterized mitochondrial and glycolytic flux in umbilical cord (UC) and bone marrow (BM) MSCs licensed with interferon-{gamma} (IFN-{gamma}), tumor necrosis factor- (TNF-), or both. Brief exposure to either cytokine shifted MSC metabolism toward glycolysis and reduced mitochondrial respiration, whereas prolonged exposure led to heterogeneous responses. Conversely, combined IFN-{gamma} and TNF- increased glycolytic flux and respiratory capacity in most donors, with responses detectable after 2 hours and at concentrations below those conventionally used. Cryopreservation impaired aerobic metabolism, followed by donor-dependent recovery. Preconditioning with combined IFN-{gamma} and TNF- partially improved post-thaw metabolism, particularly in BM MSCs, and enhanced early responsiveness to inflammatory challenge, particularly in UC MSCs, as detected by IDO and candidate biomarkers CTSS and C1S. However, donors with improved post-thaw metabolism did not consistently show increased biomarker responses. These findings demonstrate that metabolic fitness and inducible biomarkers capture complementary aspects of post-thaw MSC quality and may inform preconditioning strategies for cryopreserved MSC products.

Background: Commercial spaceflight now carries civilians on short missions, yet their stress-axis physiology is poorly described. Earlier shuttle data suggest that sympathetic responses predominate after short flights and glucocorticoid responses after long ones. The neutrophil-to-lymphocyte ratio (NLR) is a low-cost readout of both. Methods: Preregistered secondary analysis of open clinical laboratory data (NASA Open Science Data Repository OSD-569 and OSD-575) from the four Inspiration4 crew members, each sampled seven times: three draws before launch, one the day after return (R+1) and three during recovery (R+45 to R+194). Log-transformed NLR was modelled with a linear mixed model with a random intercept per crew member and Kenward-Roger inference. Lymphocyte and eosinophil counts and C-reactive protein were secondary outcomes with false discovery rate correction. Results: All 28 draws were analysed. NLR at R+1 did not differ from pre-flight (fold change 1.04; 95% CI 0.81 to 1.34; p = 0.73); two crew members increased (+25% and +46%) and two decreased (-27% and -11%). During recovery NLR was 23% lower than pre-flight (fold change 0.77; 95% CI 0.64 to 0.92; p = 0.005). No secondary outcome changed after correction; lymphocytes were 18% lower at R+1 (95% CI -35% to +4%; q = 0.31). Between-person differences explained 58% of the variance. An ordinary regression ignoring repeated measures missed the recovery effect (p = 0.08). Conclusions: A 3-day orbital flight produced no consistent leukocyte stress signature one day after return. The lower NLR during recovery suggests that pre-flight values, not recovery values, deviate from the resting state, which has implications for how baselines are defined in short-duration spaceflight studies.