Journal of Personalized Medicine

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

Carsickness impairs comfort and affects a large proportion of the population. However, interventions that provide a therapeutic solution to carsickness have yet to be established. Here we introduce a wearable mindfulness meditation brain-computer interface (MM-BCI) system as a closed-loop training therapy for carsickness. The system records electroencephalographic activity, decodes meditative state in real time and delivers audiovisual neurofeedback to scaffold meditation practice. In a 10-week randomized controlled trial, 60 individuals susceptible to carsickness were assigned to practice mindfulness meditation with either real-time MM-BCI neurofeedback or sham feedback, both during real-world car riding and at home. Critically, pre-intervention, post-intervention, and one-month follow-up assessments of carsickness severity were conducted during regular car riding without any task or feedback system. Relative to the sham group, the MM-BCI group showed significantly reduced carsickness severity at post-intervention and follow-up. At baseline, carsickness-susceptible participants exhibited a reduced aperiodic exponent in occipito-parietal cortex relative to non-susceptible controls, identifying a candidate neural signature of carsickness susceptibility. MM-BCI training increased this exponent toward non-susceptible levels, and the magnitude of this neural normalization was associated with the degree of symptom improvement. This study provides the first demonstration that BCI-enhanced mindfulness meditation can induce promising treatment effect on carsickness, offering a transformative non-pharmacological approach to enhance passenger well-being in everyday transit.

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assistant developed for Finnish wellbeing services counties. We tested the system using 36 clinically relevant queries, each rendered in three gendered variants (male, female, gender-neutral), and evaluated responses using both an LLM-as-a-judge approach and a human expert panel consisting of a physician and a sociologist specializing in ethics. We observed substantial and clinically significant differences across gendered variants, including differential treatment urgency, inappropriate symptom associations, and misidentification of clinical context. Female variants disproportionately framed responses around childcare and reproductive health regardless of clinical relevance, reflecting societal stereotypes rather than medical reasoning. Bias manifested both at the LLM generation stage and the RAG retrieval stage, in several cases causing the model to hallucinate responses entirely. Some bias patterns were persistent across repeated runs, while others appeared inconsistently, highlighting the challenge of distinguishing systematic bias from stochastic variation.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Automating Hierarchical Condition Category (HCC) assignment directly from unstructured electronic health record (EHR) notes remains an important but understudied problem in clinical informatics. We present HCC-Coder, an end to end NLP system that maps narrative documentation to 115 Centers for Medicare & Medicaid Services(CMS) HCC codes in a multi-label setting. On the test dataset, HCC-Coder achieves a macro-F1 of 0.779 and a micro-F1 of 0.756, with a macro-sensitivity of 0.819 and macro-specificity of 0.998. By contrast, Generative Pre-trained Transformer (GPT)-4o achieves highest score of a macro-F1 of 0.735 and a micro-F1 of 0.708 under five-shot prompting. The fine-tuned model demonstrates consistent absolute improvements of 4%-5% in F1-scores over GPT-4o. To address severe label imbalance, we incorporate inverse-frequency weighting and per-label threshold calibration. These findings suggest that domain-adapted transformers provide more balanced and reliable performance than prompt-based large language models for hierarchical clinical coding and risk adjustment.

Despite the global prevalence of postpartum depression (PPD), current referral uptake rates are far from satisfactory. While some qualitative studies have investigated factors affecting PPD referrals, a gap in quantitative analysis remains. Addressing this, our study utilized a discrete choice experiment (DCE) to understand the procedural elements influencing PPD referral uptake among diagnosed women. The DCE was conducted via home visits by healthcare providers and a comprehensive mobile app questionnaire. We constructed seven distinct referral attributes to explore participants' preferences, analyzed using mixed logit models and latent class analysis. This analysis identified key determinants and revealed the heterogeneities in referral preferences. A total of 698 individuals completed the DCE questionnaire. All assessed attributes, except for Accompaniment (going to clinic with a family member), were important determinants of preference. Participants generally preferred referrals to psychiatric clinics, face-to-face consultations, lower costs, and shorter waiting times. Significantly, participants' personal and socio-demographic characteristics also played a critical role in their referral preferences. Latent class analysis categorized participants into four distinct groups based on their preferences, with treatment cost and waiting times being the most decisive factors. In conclusion, the preference for PPD referrals is predominantly driven by convenience and access to specialist care. To enhance referral uptake, developing flexible and personalized referral programs that cater to these preferences is crucial.

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with complex pathophysiology and limited therapeutic options. Identifying key molecular drivers and potential drug candidates is critical for improving clinical outcomes. MethodsWe integrated multi-cohort transcriptomics (GSE155489, GSE138518, GSE226146) with weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and drug repurposing. Differential expression analysis identified 1,039 DEGs, and WGCNA identified 10 PCOS-associated modules. Intersection of DEGs with module genes yielded 498 core candidate genes, which were subjected to functional enrichment, PPI network analysis, and connectivity map-based drug repurposing (CLUE/LINCS). Candidate drugs were further evaluated by molecular docking and ADMET prediction using a triple intersection strategy (hub genes, high differential expression, drug-target evidence). ResultsFunctional enrichment revealed significant enrichment in cell adhesion and TGF-beta signaling. PPI network analysis identified CD44 as the top hub gene (degree=42). Drug repurposing identified 106 candidate drugs, including troglitazone and enzalutamide. Using the triple intersection strategy, five genes (ID2, NR4A1, GJA5, ID1, MYH11) were prioritized for molecular docking. GJA5 showed strong predicted binding affinity with flufenamic acid (-7.88 kcal/mol), and cytosporone B exhibited favorable druglikeness (0 Lipinski violations). ConclusionThis study systematically characterizes PCOS-associated gene networks and provides a prioritized set of candidate targets and drugs through a purely computational framework. CD44 emerges as a key network node with potential relevance in PCOS pathophysiology. These findings offer testable hypotheses for future mechanistic studies and drug discovery efforts in PCOS.

Metabolic syndrome (MetS), characterized by abdominal obesity, insulin resistance, dyslipidemia, and hypertension, affects a substantial proportion of the global population and increases the risk for cardiovascular disease, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). Despite its prevalence, there are currently no effective pharmacological therapies targeting MetS, highlighting the need to identify novel etiological mechanisms, particularly within the gastrointestinal (GI) tract. Using a mouse model of MetS and healthy lean controls, we assessed the colonic microenvironment through metabolomic, transcriptomic, and microbiome analyses. Colonic organoids were cultured to further explore epithelial alterations. Additionally, human MetS fecal metabolomics data were cross-compared with the mouse model to validate translational relevance. MetS mice exhibited upregulation of colonic anabolic pathways, including glycolysis, the pentose phosphate pathway, and the tryptophan/kynurenine pathway, without evidence of intestinal inflammation. Microbiome analysis revealed an increased abundance of the genus Lactobacillus in MS NASH mice. Colonic organoids from MetS mice showed altered goblet cell differentiation. Comparative analysis with human MetS fecal metabolomics demonstrated similar dysregulated pathways, underscoring the translational relevance of these findings. Our study reveals significant metabolic and microbial alterations in the colon of MS NASH mice, implicating a dysfunctional GI tract as a potential etiological factor in MetS. These findings highlight specific metabolic pathways and microbial signatures that could serve as future therapeutic targets for MetS. NEW & NOTEWORTHYThis study identifies the colon as a metabolically active tissue affected in metabolic syndrome. Despite the absence of intestinal inflammation, MS NASH mice displayed altered colonic metabolism and microbiota composition, with conserved metabolite changes matching those seen in humans with metabolic syndrome. These findings highlight colonic metabolic dysfunction as a potential driver of gut dysbiosis and disease progression in metabolic syndrome and MASLD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/716131v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@1b7c685org.highwire.dtl.DTLVardef@4a832aorg.highwire.dtl.DTLVardef@1e95c66org.highwire.dtl.DTLVardef@1b14209_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Atrial fibrillation (AFib) is the most common sustained arrhythmia in the world, imposing a heavy clinical and economic burden on global healthcare systems. Early detection of AFib can reduce mortality and morbidity, while helping to alleviate the growing economic burden of cardiovascular diseases. With the increasing availability of digital health technologies, computational solutions have great potential to support the timely diagnosis of cardiac abnormalities. Objectives: With the increasing availability of electrocardiogram (ECG) data from clinical and wearable devices, manual interpretation has become impractical due to its time-consuming and subjective nature. Existing automated approaches often rely on single classifiers or fixed ensembles that primarily optimize predictive accuracy while neglecting model diversity, which leads to limited robustness and generalization across heterogeneous datasets. Therefore, this study aims to develop a robust and diversity-aware framework for automatic AFib detection that simultaneously improves classification performance and model generalizability. To this end, we propose MOE-ECG, a multi-objective ensemble selection and fusion framework that explicitly optimizes both predictive performance and inter-model diversity for reliable AFib detection from ECG recordings. Methods: The proposed multi-objective ensemble (MOE) framework uses ensemble selection as a bi-objective optimization problem and employs multi-objective particle swarm optimization to identify complementary classifiers from a heterogeneous model pool. Unlike conventional ensembles, it explicitly optimizes both predictive performance and diversity and integrates Dempster-Shafer theory for uncertainty-aware decision fusion. After filtering the ECG signals to remove baseline wander and noise, they were segmented into windows of 20, 60, and 120 heartbeats with 50% overlap. The proposed approach was evaluated over five independent runs to assess its stability and generalization. Fifteen statistical and nonlinear features were obtained from the RR-intervals of the pre-processed ECG signals, of which eight features were selected with correlation analysis to capture subtle information from the ECG data. We trained and evaluated the performance of the proposed model in three open source databases, namely, the MIT-BIH Atrial Fibrillation Database, Saitama Heart Database Atrial Fibrillation, and Long-Term AF Database. Results: The proposed approach achieved the best overall performance on 60-beat segments, with an average accuracy of 89.85%, precision of 91.14%, recall of 94.19%, an F1-score of 92.64%, and area under the curve (AUC) of around 0.95. Statistical analysis using Holm-adjusted Wilcoxon tests confirmed significant improvements (p<0.05) compared to both the best individual classifier and the unoptimized average ensemble of all classifiers. These findings show that the proposed selection and evaluation methodology, rather than group aggregation alone, is the key driver of performance improvements. Conclusion: The results obtained demonstrate that the MOE-ECG model offers a robust, accurate, and reliable solution for the detection of AFib from short ECG segments. The empirical findings, in general, confirm that multi-objective ensemble fusion enhances diagnostic performance and offers robust predictions that will open up possibilities for real-time AFib detection in clinical and tele-health settings.

BackgroundType 2 diabetes mellitus (T2DM) is a leading global public health challenge. Machine learning (ML) combined with Explainable AI (XAI) is increasingly applied to T2DM risk prediction, but the field lacks a quantitative overview of methodological trends and integration gaps. MethodsWe present a structured synthesis and critical analysis of the XAI literature on T2DM risk prediction, combining (i) quantitative bibliometric analysis of a two-database corpus (N = 2,048 documents from Scopus and PubMed/MEDLINE, deduplicated via a transparent three-tier pipeline) and (ii) an in-depth selective review of 15 highly cited papers. Reporting follows PRISMA 2020, adapted for metadata-based synthesis; analyses include keyword frequency, rule-based thematic clustering, and publication trend analysis. ResultsThe field grew rapidly, from 36 documents (2020) to 866 (2025). SHAP and LIME dominate XAI methods; XGBoost and Random Forest dominate ML models. Critically, KG/GNN terms appeared in only 17 documents ([~]0.83%) compared with 906 for XAI methods, a 53.3:1 disparity. This gap is consistent across both databases, which share 33.2% of their records, ruling out a single-database artifact. The selective review confirmed that none of the 15 highly cited papers combined all three components, ML, XAI, and KG, in T2DM risk prediction. ConclusionsThe XAI for T2DM risk prediction field exhibits a clinical interpretability gap: statistical explanations are rarely linked to structured clinical pathways. We propose a three-layer conceptual framework (Predictive [-&gt;] Explainability [-&gt;] Knowledge) that integrates KG as a supplementary semantic layer, with potential applications in clinical decision support and population-level screening. The framework does not perform true causal inference but structures explanations around established pathophysiological knowledge. This study contributes a transferable methodology and a quantified research gap to guide future work integrating ML, XAI, and structured medical knowledge.

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Lipodystrophies are a group of disorders featuring reduced adipose tissue mass or function, which often leads to significant metabolic disease, reduced lifespan and impaired quality of life. Individuals with congenital generalised lipodystrophy (CGL) have severely reduced adipose tissue mass. The loss of healthy systemic lipid storage typically causes hepatic steatosis and lipoatrophic diabetes. In addition, adipocyte-secreted hormones including leptin and adiponectin are dramatically reduced. Leptin has critical roles regulating appetite and broader effects on lipid and glucose metabolism. Daily injection with recombinant leptin is currently the only specific, approved treatment for CGL. The consequences of adiponectin loss in these patients are not fully understood. Likewise, the potential therapeutic benefit of adiponectin delivery is unclear. Here we examine the effect of delivering leptin or adiponectin by adeno-associated virus (AAV) as potential gene therapy treatment for metabolic disease in CGL using a well-characterised murine model of the condition. AAV-mediated leptin delivery significantly improved hepatic steatosis and hyperinsulinemia. However, adiponectin delivery did not lead to any observed beneficial effects. This demonstrates the potential of gene therapy approaches for long-term delivery of leptin in individuals with lipodystrophy, without the need for continuous supply of perishable therapeutics and painful daily injections.

Inadequate discharge communication is a well-documented contributor to medication non-adherence, missed follow-ups, and preventable readmissions across healthcare systems worldwide. In resource-limited oncology settings, where patients are often low-literate, speak non-dominant languages, and manage complex multi-drug regimens, this problem is acute and largely unaddressed. We present Aakhyan, a vernacular patient communication platform that addresses the full post-discharge arc: from converting English-language discharge summaries into structured, voice-based vernacular explanations, through medication adherence support, to proactive follow-up management - all delivered via WhatsApp. The architecture is novel in its strict separation of concerns: a vision-language model performs structured JSON extraction from discharge images; all patient-facing content is generated deterministically from clinician-approved templates with community-sensitive vocabulary registers. This design eliminates the hallucination risk inherent in generative AI patient communication (documented at 18-82% in prior studies) while preserving the extraction capability of large language models. The platform supports four language registers, Bengali, Hindi, simplified English for tribal populations, and Assamese, with text-to-speech synthesis across all registers, including a custom grapheme-to-phoneme engine developed for Assamese phonology. Beyond discharge communication, the platform includes scheduled medication adherence nudges, interactive follow-up reminders, and a Daily Availability and Patient Notification System (DAPNS) that notifies patients the evening before their follow-up whether their doctor and required investigations are available, preventing wasted trips by rural patients who travel 2-6 hours to reach the centre. A 100-patient stratified randomised controlled study is planned at Silchar Cancer Centre, with structured teach-back assessment at 48-72 hours post-discharge as the primary comprehension outcome and preliminary clinical efficacy as a secondary objective. This paper describes the clinical rationale, technical architecture, safety framework, and positioning of Aakhyan within the existing literature on mHealth patient communication interventions.

BackgroundElevated postprandial hypertriglyceridemia (PP-HTG) is a significant risk factor for development of cardiovascular diseases, however, the mechanisms underlying its exaggerated rise remains poorly understood. MicroRNAs (miRs) are known to be implicated in the regulation of lipid metabolism, thus identifying them as potential key players. We presently investigated whether miRs may control postprandial triglyceride (PP-TG) response. MethodsPostprandial changes in circulating miR expression as a function of the degree of postprandial TG response were evaluated in non-dyslipidemic healthy subjects (n=32). The impact of miR-100-5p on hepatic gene expression was evaluated in differentiated Caco2 and HepG2 cells by analysis of hepatic transcriptome (RNAseq), western blot and ELISA. In vivo studies were conducted in C57BL/6J mice overexpressing mimic miR-100-5p. ResultsPostprandial variation in circ-miR-100-5p levels inversely correlate with PP-TG response. Cir-miR-100-5p was preferentially associated with TGRL particles of intestinal origin in subjects exhibited a low PP TG response. Differential analysis of transcriptome from HepG2 cells transfected by either mimic miR-100-5p or scrambled mimic miR as control allowed us to identify PCSK9 as a down-regulated gene. Overexpression of miR-100-5p in HepG2 cells significantly decreased PCSK9 mRNA levels by 52% (p<0.0001), cellular protein content by 28 % (p<0.0001) as well as PCSK9 secretion by 39% (p<0.0001). In vivo systemic delivery of mimic miR-100-5p induced a two-fold reduction (p<0.0001) on PP-TG in mice, such effect being abolished by blocking the circulating form of PCSK9 with alirocumab. Finally, we revealed a significant inverse relationship between circulating miR-100-5p expression levels and both PCSK9 levels and the magnitude of postprandial hypertriglyceridemia. ConclusionTaken together, our observations reveal that miR-100-5p regulates postprandial hypertriglyceridemia by targeting PCSK9, thus enhancing hepatic triglyceride-rich lipoproteins (TGRL) uptake. Our findings allow us to propose circ-miR-100-5p as a potential biomarker for early identification of subjects at high cardiovascular risk, prior to appearance of classical clinical features of metabolic disorders. Postprandial clinical study, HDL-PP (NCT03109067) Lay summaryThis study examined whether miRs may control postprandial triglyceride response Key findingsOur data reveal that miR-100-5p regulates postprandial hypertriglyceridemia by targeting PCSK9 Our observations allow us to propose miR-100-5p as a potential biomarker for early identification of subjects at high cardiovascular risk

Objective: Skin sympathetic nerve activity (SKNA) has emerged as a promising non-invasive surrogate measure of sympathetic drive, but its relevant physiological characteristics remain ill-defined. This observational study aims to investigate its regulatory patterns during rest and Valsalva maneuver (VM) in healthy participants. Method: Using a two-layer strategy integrating signal analysis and physiological modelling, we analyzed data recorded from 41 subjects performing repeated VMs. The observational layer includes time-domain feature comparisons using linear mixed-effect models, and time-varying spectral coherence analysis. The mechanistic layer proposes a mathematical model to investigate whether baroreflex and respiratory modulation are sufficient to reproduce the observed HR and average SKNA (aSKNA) dynamics. Main Results: Mean integrated SKNA (iSKNA) showed more significant change than HRV for VM induced effects. We also found mean iSKNA increase during VM varies with BMI and sex. The coherence analysis indicated that iSKNA strongly synchronized with EDR under resting conditions. The proposed model successfully reproduced main characteristics of aSKNA dynamics, yielding a high median Pearson correlation coefficient of 0.80 ([Q1, Q3] = [0.60, 0.91]). In contrast, HR dynamics were only partially captured, with a median PCC of 0.37 ([Q1, Q3] = [0.16, 0.55]). These results likely suggest SKNA provides a more direct representation of sympathetic burst dynamics during VM in healthy subjects. Significance: This study provides convergent evidence that SKNA reflects known autonomic regulatory influences in healthy subjects. These findings strengthen the physiological interpretability of SKNA while clarifying its appropriate use as a practical biomarker of sympathetic function.

Severe forms of inflammation-induced acute and chronic myocarditis have a poor prognosis. Promising therapeutic efforts focused on monoclonal antibodies (mAbs) inhibiting inflammation-inducing molecules. However, most mAbs target only one or a limited number of such molecules. Since inflammation involves multiple redundant pathways, we postulated that an mAb inhibiting multiple inflammatory pathways would be a potent therapeutic agent. We initially tested the commercially available anti-natural killer (NK) cell mAb (anti-NK1.1), which binds a receptor expressed on NK cells and depletes them. Since NK cells are key cellular orchestrators of inflammation, by reducing their number, we aimed to inhibit multiple inflammatory pathways. Our initial studies demonstrated that administration of this antibody significantly improved myocardial outcomes in mouse models of acute myocardial infarction and of heart failure. Since NK1.1 is not expressed in human cells, we built on these promising preclinical results by developing a novel mAb targeting CD160 on human NK cells for evaluation as an immunosuppressive therapy. We found that the anti-CD160 mAb depletes both murine and human NK cells. We also found that, while CD160+ cells were largely present in the NK population, they also occurred among CD8+ and {gamma}/{delta} T cell subsets in human cells. Anti-CD160 therapy entirely prevented the deterioration of the myocardial function of mice with autoimmune-induced acute myocarditis. This outcome suggests our novel approach for inhibiting multiple inflammatory pathways may provide a potent strategy for improving outcomes of inflammation-driven myocarditis, as well as of other inflammation-driven diseases. Key PointsO_ST_ABSQuestionC_ST_ABSCan the depletion of CD160+ cells prevent autoimmune-induced myocarditis? FindingsIn this study we found that CD160 is expressed by mouse and human natural killer cells and other subtypes of cytotoxic T cells, and that a monoclonal antibody targeting CD160 depletes NK cells. In a preclinical model of experimental autoimmune myocarditis, administration of the anti-CD160 monoclonal antibody prevented myocardial dysfunction and systemic inflammation. MeaningOur results are compatible with the hypothesis that early autoimmune-induced myocardial dysfunction is promoted by CD160+ cells, which elevate inflammation-induced circulating factors (or factors released by tissue-resident cytotoxic immune cells) that cause myocardial dysfunction in the absence of myocardial necrosis or fibrosis, and further, that targeting CD160+cells with a mAb that depletes NK cells (and probably CD160 expressing cytotoxic T cells) entirely prevents the deterioration of myocardial function in such mice. This outcome suggests our novel approach for inhibiting multiple inflammatory pathways may provide a potent strategy for improving outcomes of inflammation-driven myocarditis, as well as of other inflammation-driven diseases.

Background: Long COVID presents with one or multiple symptoms or diagnosable conditions after SARS-CoV-2 infection. To study whether use of the antiviral remdesivir in persons hospitalized with acute COVID-19 is associated with reduced Long COVID, we created a computational phenotype for Long COVID. Methods: In electronic health records (EHR) from a multistate healthcare system (US), hospital admissions from 5/1/20 - 9/30/22 were reviewed. The study group was hospitalized with acute COVID-19 and the control group was hospitalized for other reasons without prior SARS-CoV-2 infection. The populations were balanced with overlap weights based on a high-dimensional propensity score of pre-specified variables and the top 100 comorbidities differing between the groups. Hazard ratios (HR) were calculated for the combined primary outcome: U09.9 (Post-Covid Conditions) or any incident secondary outcome from 90 to 365 days after admission. Secondary outcomes included 27 individual incident diagnoses, corrected for multiplicity with Holm-Bonferroni. Results: Admissions included 45,540 with, and 409,186 without COVID-19 during the study period, evaluable for the primary outcome. After weighting, standardized difference was < 0.01 for all measured confounders including demographic and clinical features. In the COVID+ and non-COVID groups 38.0% and 29.3% met the combined primary outcome, respectively. Weighted HR (95%CI) for the primary outcome was 1.37 (1.35, 1.40), p < 0.0001. All secondary outcomes were associated with the COVID+ group, when adjusted for multiplicity. Incident diagnoses with strong associations (HR > 2) included thromboembolism, hair loss, diabetes mellitus, obesity, and hypoxia. Anosmia/dysgeusia was associated with COVID, but wide confidence intervals reflected few charted diagnoses. Conclusions: Manifestations of Long COVID at population scale are detectable as part of routine symptoms and clinical diagnoses in the EHR after admissions for COVID-19, compared with all other hospital admissions. This a prior computational phenotype for Long COVID will be used to assess whether remdesivir use is associated with decreased Long COVID.

Background: Population-based cancer registers (PBCR) are important for monitoring trends in cancer epidemiology, facilitating the implementation of effective cancer services. Adolescents and Young Adult (AYA) with cancer are a patient group with a unique set of needs. The utility of PBCR in AYA is limited by the lack of AYA-specific data items. STRONG AYA, an international multidisciplinary consortium is addressing this through federated learning (FL) methodology and novel data visualisation concepts. A Core Outcome Set (COS) has been developed to measure outcomes of importance through clinical data and Patient Reported Outcomes (PROs). We describe how data from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), a PBCR in the UK is being used within STRONG AYA and how the subsequent analyses can guide patient consultations. Methods: Data from the YSRCCYP were imported into a Vantage 6 node, from which FL analyses are performed along with data provided by other consortium members. The results are extracted into the PROMPT software and integrated into patient electronic healthcare records. Results: Healthcare professionals can view the results of individual PROs at various time points and in comparison, to summary analyses carried out within the STRONG AYA infrastructure. Results can be filtered by age, disease, country and stage. Conclusion: We have demonstrated how a regional PBCR can contribute to a pan-European infrastructure and analyses viewed to enhance patient consultations. Such analyses have the potential to be used for research and policy-making, improving outcomes for AYA.