ACS Biomaterials Science & Engineering

Decellularized extracellular matrices (ECMs) are widely used in regenerative medicine, yet current evaluation criteria prioritize cellular removal rather than preservation of the ECM characteristics that govern tissue behavior. Here, we demonstrate that efficient decellularization is achieved across a broad range of chemical conditions, whereas preservation of structurally and biologically relevant ECM components is confined to narrow, tissue-specific windows defined by coupled detergent interactions. Quantitative proteomics revealed that intrinsic ECM composition is strongly associated with tissue-specific susceptibility to decellularization-induced damage and provided molecular context for the distinct preservation responses between tissues. Optimized matrices retained major structural ECM components and supported tissue-specific cellular organization and cell-mediated mechanical reinforcement following cellular repopulation despite uniformly low residual DNA across protocols. Together, these findings support a shift in decellularization quality assessment from DNA-based evaluation toward preservation of biologically relevant ECM and establish a composition-driven strategy for the rational design of regenerative biomaterials with tissue-relevant biological and mechanical properties.

We report the generation and characterization of a human induced pluripotent stem cell (iPSC) line derived from dermal fibroblasts of a patient with Skogholt disease, a rare maternally inherited neurodegenerative syndrome associated with choroid plexus dysfunction and impaired cerebrospinal fluid (CSF) homeostasis. Patient fibroblasts were reprogrammed using the non-integrating Repro-OSKGM kit. The resulting iPSC line exhibited typical pluripotent morphology, expressed canonical pluripotency markers, maintained a normal karyotype, retained the disease-associated genetic variant, was mycoplasma-free, and demonstrated trilineage differentiation potential. We also made choroid plexus (ChP) like organoids from the generated iPSCs. This patient-specific iPSC line provides a valuable resource for generating choroid plexus organoids and neurons to investigate disease mechanisms and develop therapeutic strategies.

Neurodegenerative diseases are increasingly associated with vascular dysfunction beyond progressive neuronal degeneration, yet how vascular pathology contributes to disease progression remains poorly understood, largely due to the lack of a neurodegenerative disease model capable of capturing neuronal pathology alongside associated vascular dysfunction. Here, we developed a microengineered 3D vascularized brain tissue model that integrates neurospheroids with a self-assembled, perfusable vascular network to recapitulate key features of the neurovascular interface. Using this model, we investigated the vascular contribution to Parkinson's disease pathology by introducing -synuclein preformed fibrils into the engineered vasculature. Intravascular -syn fibril exposure induced endothelial barrier disruption, vascular leakage, inflammation, and vascular regression. Notably, this vascular insult was accompanied by intraneuronal -synuclein aggregation within neurospheroids, suggesting that vascular dysfunction may facilitate the exposure of neural tissue to pathogenic -synuclein. Our neurodegenerative disease modeling approach establishes a versatile and tractable platform for investigating vascular contributions to neurodegenerative disease progression.

Embedded extrusion printing can process collagen-rich bioinks, but their low viscosity and slow fibrillogenesis compromise print fidelity and post-deposition stability. Here, we developed a collagen fibril-inducing support bath (FIB) that combines mechanical support for embedded printing with biochemical induction of collagen assembly. Microfibrillated or nanofibrillated cellulose was incorporated into a fibril-inducing buffer, and formulations were screened at 37 degrees Celsius for rheological behaviour and optical transparency. The selected FIB was evaluated by printing 1% and 5% (w/v) articular cartilage-derived extracellular matrix (ECM) inks at 10-20 mm/s and compared with a cellulose-only control bath. FIB exhibited yield-stress, shear-thinning and rapid recovery behaviour that supported reproducible filament deposition. Unlike the control bath, FIB enabled intact construct retrieval following stabilisation and promoted the formation of fibrillar collagen within the printed strands. Scanning electron microscopy revealed D-banded collagen fibrils preferentially oriented along the deposition direction, with dominant orientation peaks within +/- 10-15 degrees. The platform supported the fabrication of 15 x 15 x 1.5 mm sheets and 6 x 6 x 6 mm scaffolds whose macroscopic dimensions were retained after processing. Constructs produced from 5% ECM inks exhibited approximately fourfold higher ramp and relaxation moduli than those produced from 1% ECM inks. Extracts from both formulations caused no detectable reduction in cell metabolic activity after 24 h or 72 h. Mesenchymal stem/stromal cells (MSCs) seeded onto printed sheets became markedly elongated and aligned by day 3, with approximately 80% of cells having an aspect ratio exceeding 1.5, significantly greater than cells seeded onto casted ECM controls, with a mean deviation of ~9 degrees from the filament print direction. These findings establish FIB as a bioactive support bath that couples embedded printability with collagen fibrillogenesis, enabling recoverable collagen-rich constructs with aligned fibrillar architecture that directs early cellular organisation.

Background: Chronic wounds, ulcers, and lacerations require staged debridement and irrigation to promote healing. However, conventional techniques of debridement, such as surgical, chemical, or autolytic, struggle to fully remove residual necrotic tissue, slough, and unhealthy granulation from wound sites, especially when lodged within wound clefts and cavities, and in wounds with exposed structures. This promotes polymicrobial biofilms, delays wound closure, and causes significant discomfort with increased morbidity. Objective: To demonstrate the feasibility of using an indigenously developed tunable flat-jet hydro-debridement device (presently termed as CleanseJet), a frugal wound debridement system designed for deployment in resource-constrained clinical settings. Methods: An open-label, interventional, single-centre, parallel-group pilot randomized controlled trial was conducted to clinically evaluate an indigenously developed tunable flat-jet hydro-debridement device in patients with wounds of varied aetiology. The device provided adjustable spray impact force and coverage area tailored to wound characteristics. Outcomes were compared with a control group receiving standard wound care alone, with time to complete granulation serving as the primary healing endpoint. Outcomes were compared with a control cohort receiving standard of care alone. Results: The removal of loose devitalized tissue, slough, and biofilms from the wound bed improved the healing, which were monitored using the SINBAD scoring system. No adverse events were reported, supporting the feasibility and safety of CleanseJet. Conclusion: While commercial hydro-debridement systems are effective, they are often costly, rely on disposable components, and require specialized training. In contrast, CleanseJet provides a low-cost, easy-to-use alternative that can be operated with minimal training, making it suitable for broader clinical use without observed adverse effects.

Osteochondral repair requires restoration of zonally organised articular cartilage and subchondral bone, yet translatable implants rarely reproduce this spatial complexity. Here, we developed an off-the-shelf, cell-free multilayer scaffold comprising a superficial 2% (w/v) articular cartilage extracellular matrix (AC-ECM) phase, an intermediate 5% AC-ECM phase and a basal 6% bone ECM (BN-ECM) phase. The scaffold formed continuous interfaces, displayed regionally distinct pore sizes and resisted permanent deformation during cyclic compression. In vitro, constructs seeded with caprine mesenchymal stromal and articular cartilage progenitor cells supported cell expansion and the accumulation of sulfated glycosaminoglycan- and collagen-rich matrix, with regional differences in collagen I, II and X deposition. Following eight weeks of subcutaneous implantation, cell-seeded scaffolds contained more collagenous matrix than unseeded controls, while vascularisation preferentially localised to the BN-ECM phase. The scaffold was then evaluated against empty defects in a caprine osteochondral model for six months. Scaffold treatment significantly improved macroscopic and histological repair, increased chondral tissue fill (~60% versus ~40%), limited cartilage-like tissue extension into the subchondral region and generated a more native-like superficial collagen organisation. Repair tissue further exhibited greater collagen II immunoreactivity, increased ACAN and COL2A1 expression and reduced COL1A2 expression relative to empty defects, although deeper bone repair was not significantly improved. These findings demonstrate that tissue-specific ECM layering can spatially guide endogenous repair and substantially improve cartilage restoration without exogenous cells or growth factors in a clinically relevant large-animal model, while identifying subchondral bone regeneration as the remaining design challenge for complete osteochondral repair.

Background: Pressure ulcers often develop complex three-dimensional morphologies that extend beyond the visible wound surface. Subsurface extensions such as tunneling and undermining create hidden cavities that complicate clinical assessment and wound management. Despite their clinical relevance, the prevalence and spatial characteristics of these subsurface wound morphologies have not been well characterized at scale. Methods: We performed a registry-based analysis using data from the LIFT-OFF Pressure Ulcer Registry, which captures longitudinal clinical documentation of pressure ulcers treated in routine care. The registry included approximately 18,000 patients with 32,000 documented pressure ulcers. Spatial characteristics of tunneling and undermining were analyzed using measurements recorded during routine wound assessments, including tract length, direction, and circumferential extent. Directional and circumferential distributions of subsurface defects were examined to characterize wound geometry. Results: Tunneling was present in 764 of 14,700 full-thickness pressure ulcers (5.2%), whereas undermining occurred in 2,293 wounds (15.6%). Tunneling tracts were typically short and exhibited directional clustering relative to the wound bed. In contrast, undermining demonstrated broader circumferential distributions and frequently involved larger subsurface separations beneath the wound margin. Both morphologies demonstrated distinct spatial patterns across anatomical locations and wound stages. Conclusion: Tunneling and undermining are common subsurface features of pressure ulcers and exhibit distinct spatial geometries. Whereas tunneling manifests as directional tract-like extensions, undermining more frequently produces circumferential tissue separation beneath wound margins. Improved characterization of subsurface wound architecture may enhance assessment of wound complexity and provide information not captured by surface measurements alone. Future studies should evaluate whether these features contribute to wound severity assessment, prognosis, and risk stratification.

Amyloid fibrils are highly ordered protein aggregates characterized by a conserved cross-{beta}-sheet architecture despite originating from structurally diverse precursor proteins. Growing evidence suggests that interactions between different amyloidogenic proteins can modulate aggregation pathways through heterologous cross-seeding; however, the influence of seed polymorphism on the structure and biological properties of cross-seeded fibrils remains poorly understood. Here, we investigated the cross-seeding of native human insulin by two structurally distinct polymorphs of hen egg-white lysozyme (HEWL): flexible fibrils (FFs) and rigid fibrils (RFs). Native insulin remained stable under physiological conditions and underwent spontaneous fibrillation only under acidic conditions. In contrast, both HEWL polymorphs efficiently induced insulin aggregation at physiological pH, bypassing the nucleation barrier. Thioflavin T fluorescence, circular dichroism spectroscopy, and transmission electron microscopy revealed that lysozyme FFs templated the formation of insulin flexible fibrils (IFFs), whereas lysozyme RFs produced insulin rigid fibrils (IRFs), demonstrating that the structural characteristics of the parental HEWL polymorphs were propagated during heterologous cross-seeding. The toxicity of the resulting insulin fibrils was evaluated in SH-SY5Y neuronal cells and CCF-STTG1 astrocytes. IFFs exhibited minimal cytotoxicity and only subtle morphological alterations, whereas IRFs caused modest reductions in cell viability accompanied by more pronounced cellular damage. These findings demonstrate that the structural polymorphism of HEWL fibrils governs both the architecture and biological activity of cross-seeded insulin fibrils, highlighting amyloid polymorphism as an important determinant of heterologous amyloid propagation and a potential design principle for engineering functional amyloid-based biomaterials and protein delivery platforms.

Objective and scalable approaches for detecting subtle motor impairment in isolated REM sleep behavior disorder (iRBD), a prodromal stage of Parkinson's disease, remain limited. We investigated whether markerless motion capture from single RGB-camera videos can identify gait abnormalities in people living with iRBD and provide interpretable digital biomarkers. We retrospectively analyzed 93 standardized walking videos from three clinical sites. Human pose estimation extracted 12 body markers and 14 kinematic time series. Thirty-five machine learning approaches classified healthy controls (HC) and people with iRBD. The Movement Disorder Society Unified Parkinson's Disease Rating Scale Part 3 (MDS-UPDRS III) served as the clinical baseline. The best-performing model (tsfresh+XGBoost) achieved an AUROC of 0.739, significantly outperforming the MDS-UPDRS III sum score when trained on data from all three sites. Harmonized multi-site training improved performance. SHAP identified hip-related temporal features as key contributors, which differed between groups and showed stronger associations with regional dopaminergic deficits than clinical scores. Single-camera gait analysis may provide scalable digital biomarkers for low-cost screening and monitoring of prodromal PD.

We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient's DE. The first method uses a non linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra rare, patient' specific trials. They can inform methodological design for future ARCA precision therapies.

Improvements in data availability, sharing, and integration, together with the development of explainable artificial intelligence (XAI) techniques, are advancing precision medicine for pediatric cancer by facilitating diagnosis, biomarker discovery, and drug development. Data sharing commons and initiatives like the Childhood Cancer Data Initiative (CCDI) provide access to pediatric-specific genomic and clinical data cohorts and improve data availability for pediatric cancer research. Based on CCDI, a scalable AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), integrates various data modalities from bulk and single-cell omics data to clinical information. Such multi-modal data facilitates the training and development of advanced XAI models for pediatric cancers. We then developed an end-to-end multi-modality framework, PCGS, for pediatric cancer by incorporating omics-specific representation learning via GNN models with cross-attention fusion and multi-objective learning for downstream tasks such as classification, clustering, and survival analysis. This framework outperforms previous supervised multi-omics integration baseline approaches based on glioma and Wilms tumor cohorts and enables GNN model explainability via Shapley value-based feature attribution approaches to explain the contributions of gene-level features across various biomedical tasks, including classification and survival. Given specific background samples (e.g., age groups, sex, grades) as baselines, this explainable GNN model estimates and ranks the importance scores for input features from each omics modality. It identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers.

Perioperative safety checklists standardize critical actions, but reliable completion depends on the surrounding work system and team behavior. We conducted a prospective observational analytic study from April to May 2026 in the central surgical unit of a high-volume public teaching referral hospital in Indonesia to examine whether patient safety culture and teamwork were associated with directly observed perioperative safety compliance and whether teamwork mediated the culture-compliance relationship. Patient safety culture was measured with the Hospital Survey on Patient Safety Culture 2.0, teamwork with a 35-item TeamSTEPPS Teamwork Perceptions Questionnaire research adaptation, and compliance by direct role-based observation using a 45-item checklist derived from the AORN Comprehensive Surgical Checklist. Eighty of 92 recruited professionals contributed 240 person-operation observations across 50 operations. Overall compliance was 74.75%, with sign-out lowest at 70.68%. Patient safety culture was associated with teamwork ({beta} = 0.590; 95% CI 0.510-0.770) and directly with compliance ({beta} = 0.407; 95% CI 0.187-0.712). The teamwork-compliance coefficient was positive ({beta} = 0.285; p = 0.046), but the prespecified percentile 95% CI included zero (-0.045 to 0.517). The indirect effect through teamwork was not supported ({beta} = 0.168; p = 0.079). These findings support a system-level interpretation of perioperative safety and identify learning-oriented responses to error, situation monitoring, and sign-out fidelity as measurable targets for future improvement efforts.

Predicting disease risk from prior diagnoses is fundamental to clinical decision-making, particularly during health emergencies such as the COVID-19 pandemic, when individuals with long-term conditions may be disproportionately vulnerable to adverse outcomes. Despite intense interest in developing models to predict disease risk from prior diagnoses (1-3), most prediction models do not estimate effects of each prior diagnosis on disease risk conditional on other diagnoses, limiting interpretability and clinical utility. We developed the Comorbidity Risk Score (CRS), trained on 13 million individuals (age 40-69) from linked electronic health record (EHR) datasets of the entire population of England, to predict COVID-19 hospitalisation and 87 other disease outcomes. CRS was trained at close to saturated sample size and precisely estimated the effects of 212 prior diagnoses on the 88 disease outcomes, conditional on all other prior diagnoses. Correlations of CRS effect sizes across outcomes (e.g. 0.76 for myocardial infarction vs. hyperlipidaemia) matched the corresponding genetic correlations (e.g. 0.79 for myocardial infarction vs. hyperlipidaemia), confirming that comorbidity architectures capture disease aetiology. On average, CRS identified 5% of the population with 3.4-fold higher disease risk, including myocardial infarction (4.4-fold), lung cancer (6.5-fold), and COVID-19 hospitalisation (6.3-fold). Using prior diagnoses alone, CRS outperformed state-of-the-art clinical COVID-19 models (4). Furthermore, CRS (N=13 million) substantially outperformed state-of-the-art AI (1) (N=0.5 million) and linear (3) (N=0.5 million) models in predicting disease risk, suggesting that training sample size outweighs model complexity. CRS attained near-perfect transferability across self-reported ethnicities (e.g., Black vs. White: AUROC ratio = 97.3%). Finally, CRS distinguished independently predictive comorbidities from indirect associations, e.g., lipid metabolism disorder was a strong predictor of myocardial infarction risk but not ischaemic stroke, after conditioning on other prior diagnoses. In conclusion, CRS provides a comprehensive resource for understanding the impact of comorbidities on COVID-19 and other future diseases, revealing disease aetiology while enabling powerful prediction of disease risk.

Background: Polymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT). Methods: In a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg/kg <=300 mg; intermediate: 15 mg/kg <=900 mg; rapid: 25 mg/kg <=1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance. Results: Among 228 participants, 47.4% (108/228) were intermediate, 43.4% (99/228) slow, and 9.2% (21/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0-24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg h/L). Among slow acetylators, C24 >0.15 ug/mL decreased from 27/42 (64%) with standard dosing to 1/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24 similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg h/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively. Conclusions: NAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.

Background: The condition of a patient with acute stroke often changes within hours of ICU admission. Prognostic work here targets fixed endpoints predicted from admission data, and trajectory phenotyping assigns one label per patient. We used longitudinal ICU data to identify interpretable dynamic clinical states, characterize transitions between them, and relate the current state to later events. Methods: Retrospective cohort study of 6368 adults with acute stroke in MIMIC IV v3.1. The first 72 h were divided into twelve 6-hour windows, and a hidden Markov model was fitted to 21 neurological, physiological and organ support variables. State number was chosen against criteria fixed before fitting: statistical fit, restart stability, state occupancy and clinical interpretability. Generalized estimating equations related the current state to new mechanical ventilation and vasopressor use within 12 h, and to ICU death within 72 h. Eleven sensitivity analyses assessed the robustness of the state solution. Results: Four states were selected: neurologically preserved-low support, neurological impairment low support, impairment renal dysfunction and impairment-respiratory support (63.3%, 7.8%, 11.8% and 17.1% of windows). Within 72 h, 40.3% of patients changed state at least once, and transitions ran in both directions rather than along a single severity gradient. States were identified without outcome data, yet ICU mortality by last state ranged from 2.9% to 43.9%. Adjusted for age, sex, subtype and Charlson index, the current state remained associated with organ-support escalation and death. State prevalence differed by at most 1.1 percentage points between training and test sets, and 10 of 11 sensitivity analyses gave a stable four-state solution (ARI 0.754 0.955). Conclusions: The early ICU course of acute stroke can be represented as movement among a small number of clinically interpretable states. The representation was reproducible in a held out set and across admission eras, but requires validation in an independent database before any clinical use.

Introduction: Risk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch. Methods: We used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles. Results: Bup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries. Conclusion: This development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

Introduction Continuous Glucose Monitoring (CGM) is considered standard care in high-income countries. There is, however, limited published evidence on CGM use in low- and middle-income countries. The purpose of this study was to assess the usability, acceptability, and feasibility of CGM use among people living with type 1 diabetes (T1D) and caregivers in a low-resource setting. Research Design and Methods This prospective study conducted at the Kenyatta National Hospital purposively enrolled persons aged 4-25 years who had been on management for T1D for at least six months, and caregivers of those under 18 years. Fourty youth living with T1D used CGM for three months in place of self monitoring of blood glucose (SMBG). The System Usability Scale (SUS), a Theoretical Framework of Acceptability-based questionnaire, the Diabetes Distress Scale (DDS), the Glucose Monitoring Satisfaction Survey (GMSS), and a feasibility survey were administered. Outcomes were summarized descriptively, including means, medians, and frequencies using R statistical software. Results The median SUS score was 98.8 (IQR 92.5-100.0). Acceptability was high, and the median total GMSS score improved from 3.73 to 4.73. Among adolescents and adults, the median overall DDS score reduced from 1.54 to 1.36, with reductions in scores in all domains, except for hypoglycemia distress which increased, and physician distress which remained low. Among caregivers, the median overall DDS score declined from 2.05 (moderate distress) to 1.90 (low distress), with modest reductions in teen management and parent-teen relationship distress and a slight increase in personal distress. Median CGM active wear time was 89%. Conclusion This study comprehensively evaluated CGM across usability, acceptability, and feasibility outcomes, with the findings supporting the integration of CGM into routine diabetes management in low-resource settings. The short follow-up period, however, may not capture changing perceptions or long-term adherence.

Objective: This study examined the prevalence of psychotic experiences (PE) and how early onset and persistence of PE contribute to risk and severity of mental disorders in adolescents at familial high-risk of schizophrenia (FHR-SZ) or bipolar disorder (FHR-BP) and adolescents from a population-based control group (PBC). Methods: This is the second follow-up of a nationwide cohort study including 522 children at FHR-SZ (N=202), FHR-BP (N=120), and PBC (N=200). Participants were assessed at ages 7, 11, and 15 using a semi-structured interview to evaluate PE and mental disorders. Results: At age 15, adolescents at FHR-SZ reported more PE than PBC over the past six months (current) and the past four years, while adolescents at FHR-BP only reported more current PE. PE reported at two or three timepoints (persistent PE) predicted any Axis I disorder in mid-adolescence, corresponding to three- (OR 2.9, 95% CI [1.5-5.7]) and 21-fold (OR 21.4, 95% CI [2.8-162.3]) increased risks, respectively. Persistent PE also predicted multimorbidity, with three- (OR 2.8, 95% CI [1.0-7.6]) and four-fold (OR 4.1, 95% CI [1.2-14.1]) increased risks, respectively. This was after adjustment for sex, early mental disorders, and familial risk. Conclusions: This study demonstrates a strong link between persistent PE and mid-adolescence mental disorders. Our findings emphasize PE as important risk markers for mental disorders during mid-adolescence and highlight the importance of monitoring children with PE before age 7 who develop persistent symptoms.

The distribution of fat-soluble compounds (including antioxidants) in follicular fluid (FF) remains sparsely documented in relation to in vitro fertilization (IVF) outcomes and existing studies have reported diverging associations. This study aimed to describe plasma and FF concentrations of fat-soluble micronutrients in women undergoing IVF and to analyze their adjusted associations with ovarian function, embryo development and pregnancy outcomes. In 2021-2022, plasma and FF samples were collected from 82 women (first IVF cycle) at oocyte puncture, along with lifestyle data covering the three preceding months. Eleven compounds (two tocopherols, three xanthophylls, five carotenes and retinol) were quantified. All compounds were detected in both compartments (lowest in FF) except phytoene, undetectable in FF. Plasma and FF -tocopherol concentrations were positively associated with plasma estradiol levels before oocyte puncture (both p<0.01) while FF -carotene and lycopene were inversely associated with plasma progesterone concentrations (p=0.01 and 0.02, respectively). Plasma phytofluene and phytoene were positively associated with mature oocyte rate (p=0.03 and p=0.01, respectively), while FF retinol was negatively associated (p=0.03). Carotenes, tocopherols and retinol were inversely associated with later IVF outcomes: fertilization rate (p<0.001 for plasma g-tocopherol, 0.02 for FF retinol), top-quality embryo (p=0.02 for plasma phytofluene), biochemical pregnancy at day 7 post-embryo transfer (p=0.05 for plasma -tocopherol, 0.02 for plasma -carotene), clinical pregnancy (p=0.03 for plasma -tocopherol, 0.01 for plasma phytoene) and live birth (p=0.04 for plasma -tocopherol, 0.02 for plasma phytoene). Plasma and FF g-tocopherol were positively associated with embryo fragmentation (both p<0.05). Finally, among xanthophylls, only plasma {beta}-cryptoxanthin was positively associated with plasma progesterone concentrations (p=0.02). Our findings of heterogeneous associations between tocopherols, carotenes, retinol and IVF outcomes across the stages of IVF suggest a beneficial effect limited to early outcomes and support a complex and context-dependent role of these compounds in female reproduction. This manuscript has been submitted to PlosOne on August 19, 2026.