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Healthcare-seeking behavior is a key factor in how well a health system performs and how fair it is. In Saudi Arabia, public healthcare services are free, yet many people still choose private healthcare, especially in cities like Riyadh. It is important to understand why people seek care from private clinics to help shape health policies, distribute resources better, and improve services across the healthcare system. This study aimed to examine the frequency of private healthcare use, defined as the reported usual or concurrent use of private healthcare services, and to identify sociodemographic, behavioral, and health-related factors associated with this choice among adults in Riyadh, Saudi Arabia. A cross-sectional study was carried out in Riyadh from March to July 2023 using a multistage cluster sampling method. We randomly selected 48 government primary healthcare centers and invited adults aged 18 and older who visited these centers to participate. We collected data electronically with a validated questionnaire that covered sociodemographic details, patterns of healthcare use, reasons for choosing private healthcare, behavioral risk factors, and existing health conditions. We used multivariate logistic regression analysis to find independent predictors of private healthcare use, reporting adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Of 14,239 participants, 72.4% reported using private healthcare services either as a usual source of care or alongside public services. The multivariable analysis revealed several factors to be positively related to private healthcare utilization. Those who were married were more likely to use private healthcare services (AOR 1.23, 95% CI 1.11-1.36). Those with insurance coverage were threefold higher odds of private healthcare use (AOR 3.51, 95% CI 3.13-3.94). Smokers were more likely to seek private healthcare (AOR 1.60, 95% CI 1.45-1.77) than non-smokers, and those who exercised reported increased utilization (AOR 1.83, 95% CI 1.67-2.00). Obesity was also positively related to private healthcare utilization (AOR 1.38, 95% CI 1.12-1.71), and those with heart disease had substantially higher odds of using private healthcare services (AOR 2.09, 95% CI 1.59-2.76). Private healthcare use in Riyadh is common and associated with insurance coverage, marital status, behavioral factors, and certain chronic conditions. These findings provide descriptive insights into factors related to private healthcare utilization among public PHC attendees in Riyadh, without implying causal effects or policy recommendations beyond the scope of the data.

Health-related social needs (HRSN) data are used in referral and treatment decisions, in population health management strategies, and in health services research. However, evidence suggests HRSN data are at risk for bias. To identify and classify sources of bias in HRSN data and the implications for usage for patient care and population health. In this qualitative study, key informant interviews with patients and health care professionals in Indiana and Florida (recruited using multiple recruitment methods and snowball sampling) were conducted from January to May 2025. Key informants in Indiana were primarily associated with a public safety-net system including federally qualified health centers, or a multihospital system with services statewide. In Florida, key informants were associated with a large academic medical center, with some dual-affiliated with a US Department of Veterans Affairs hospital. Health care professionals had the titles such as physician, social worker, and community health worker. Data collection occurred via video or telephone call. Interviews followed a semistructured interview guide grounded in a framework describing sources of potential bias in health data. Participants were asked about HRSN data collection practices and experiences, documentation practices, responses to HRSN data collection, and how, in their own words, they defined food insecurity, housing instability, financial strain, and transportation barriers. Thematic analysis followed a consensus coding approach. A total of 20 patients and 20 health care professionals were recruited (40 informants total; 22 aged 40-64 years [42.5%]; 27 female [67.5%]). Participants described variation in HRSN data collection and differing availability of organizational resources that contributed to sampling bias. Patients and professionals reported detection bias was possible because HRSNs could be intentionally sought during visits or not collected at all. Concerns about stigma or embarrassment, power distance, and privacy could result in nonresponse bias. Health care professionals and patients could all offer slightly different, or nuanced, definitions of different HRSNs. These more expansive or restrictive definitions could lead to misclassification bias. In this qualitative study, both patients and health care professionals described opportunities for bias in HRSN data collection and documentation. These findings suggest that, while HRSN data are potentially valuable to patient care, their fitness for use in organizational decision-making, research, and health policy may need improvement.

Clinical Informatics is wide-ranging field that engages with nearly every aspect of clinical care that is documented in the electronic health record (EHR). While studies from the informatics literature had been gradually introducing more sophisticated machine learning and artificial intelligence (AI) techniques into clinical settings, the explosive growth of Large Language Models (LLMs) has enticed both entrepreneurs and clinicians to rapidly introduce LLMs into the Emergency Department. Clinical Informaticists possess a deep understanding of both the clinical significance and underlying architecture of clinical data. Misunderstanding how data is represented can pose significant hazards for clinical care, research, and AI systems. Despite the seemingly high performance of LLMs on some clinical measures, evidence for their ability to reason clinically is lacking, and they often provide confident, false answers. Emergency Physicians (EPs) who are board-certified in Clinical Informatics could be a natural constituency to help to integrate these technologies safely into the ED. However, there are very few EPs with this board-certification, due to high demand, few training programs, and a lack of visibility of the subspecialty. LLMs and other AI systems are likely to play a growing role within the ED as technology improves and hospitals partner with commercial vendors. Working EPs need to have a strong understanding of the potential benefits and limitations of these technologies, and EPs with training in Informatics will play an essential role. Increasing exposure to Clinical Informatics within Emergency Medicine residencies and supporting EPs to go into Informatics fellowships is paramount.

The digital transformation of healthcare, driven by electronic health records, together with the IoT-enabled medical devices and AI-driven analytics, has provided the healthcare with more care and innovation but also created unprecedented levels of cybersecurity vulnerabilities in medical informatics ecosystem. Recent incidents, such as ransomware attacks on pharmaceutical companies, breaches at the European Medicines Agency, and the Indian Council of Medical Research, are just a couple of examples of how the incident that was restricted by one jurisdiction can spread to the research, manufacturing, and data networks around the globe. The study gives a comparative overview of cybersecurity regulation across the United States, the European Union, and India, and examines how the three approaches to risk-based, rights-based, and hybrid regulatory models affect the resilience of the system as a whole. The analysis also indicates structural imbalances in breach notification, medical-device regulation and cross-border data management that disrupts global interoperability. The study ends with recommendations on harmonized international standards, secure-by-design, empower breach-response networks, and improved cooperation by WHO, ITU, and GDHP to make the global health cybersecurity resilient. The paper concentrates on the fact that by redefining cybersecurity as a networked socio-technical challenge, there is a need to have collaboratively designed, globally consistent governance policies to protect digital health systems and deliver continuity, trust, and innovation across jurisdictions.

Social vulnerability (SV) influences rehabilitation and postoperative care for patients with hip fracture. However, most previous work relies on area-level measures that overlook interindividual variation. The recent adoption of ICD-10 Z-codes allows clinical identification of patient-level SV and may offer a better understanding of its impact. This study aimed to evaluate healthcare utilization, including readmissions, discharge disposition, and length of stay (LOS) in surgically treated hip fracture patients with and without clinically acknowledged SV. Adults surgically treated for hip fracture between 2016 and 2020 were included from the Nationwide Readmissions Database. SV was defined as having at least one documented relevant ICD-10 Z-code. Primary outcome measures included complications, LOS, discharge disposition, and 30- and 90-day readmissions, stratified by SV and evaluated using chi-square analyses. Multivariable logistic regression assessed long LOS (≥ 5 days) and discharge to home, adjusting for age, insurance/income status, and substance use. Patients with SV were younger (35.6% with SV vs. 50.1% without SV were 81+), had a lower median household income (38.8% with SV vs. 25.7% without SV were in the lowest quartile), and were more often insured by Medicaid (19.3% vs. 3.8%). Alcohol/drug use disorders were significantly more prevalent in patients with SV (18.5% vs. 4.5%). SV was associated with 47% higher odds for long LOS (1.47, 1.41-1.54) and 23% higher odds for discharge to home (1.23, 1.16-1.30) but comparable 90-day readmissions (21.2% vs. 19.8%). Among surgically treated hip fracture patients, SV was associated with higher odds of long LOS and discharge to home but no meaningful difference in readmissions. The small number of patients with clinically documented SV highlights the limited reporting by healthcare workers. This analysis of a nationwide all-payer database highlights the need to identify these higher risk patients and implement appropriate care pathways to reduce healthcare utilization.

In Ethiopia, a substantial proportion of women experience physical, psychological, or sexual violence perpetrated by their husbands or intimate partners. There is limited evidence on interventions aiming to improve awareness, alter attitudes, and control behavior related to IPV in Ethiopia. Therefore, this study aimed to evaluate the effectiveness of community-based health education (CBHE) targeting couples on knowledge, attitudes, and controlling behavior among women in Hadiya zone, central Ethiopia. A community-based, parallel-group, two-arm cluster randomized controlled trial design was employed to evaluate the effect of a CBHE intervention on knowledge, attitude, and controlling behavior related to IPV in Hadiya zone, central Ethiopia. A total of 432 women (216 in the intervention groups and 216 in the control groups) were involved in the study. The intervention was provided for couples over a period of six consecutive months. Generalized Estimating Equation (GEE) and difference-in-difference analysis were conducted to evaluate the effectiveness of the intervention on the outcomes. About 94.4% of the mothers in the intervention groups and 95% of the women in the control groups were available for intention-to-treat analysis at the end of the intervention. Women in the intervention groups were about 5 times more likely to have good knowledge of IPV than those in the control groups (AOR = 4.8; 95% CI 2.9-7.9). Mothers in the intervention were 70% less likely to have a supportive attitude towards wife-beating compared to mothers in the control group (AOR = 0.3; 95% CI 0.2, 0.5). Likewise, mothers in the intervention groups were 60% less likely to justify controlling behavior from their husbands compared to those in the control groups (AOR = 0.4; 95% CI 0.3, 0.7). This study highlights that CBHE intervention led to a significant improvement in participants' knowledge of IPV against women. It also resulted in a marked reduction in the acceptance of wife-beating and justification of controlling behaviors. These findings provide strong evidence to support the broader scale-up of this intervention. This trial was recorded in the ClinicalTrials.gov registry with the identifier NCT05856214 on May 4, 2023.

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Despite recent advances in medical informatics, extracting tumor information from pathology reports remains a challenge in modern cancer registry and surveillance workflows. These documents often have an unstructured format, complex medical content, and a considerably lengthy context, creating significant challenges for automated phenotypic information extraction. Although some recent language models such as BERT, GatorTron, and GPT-4 have demonstrated efficacy in medical applications, they are either constrained by sequence length limitations or cloud-based computing that violates the handling of protected health information. We introduce two oncology pathology-optimized transformer models OncoPT, based on Longformer and BigBird architectures and trained on real-world pathology reports. OncoPT efficiently processes reports up to 4,096 tokens, making it suitable for hospitals' onsite deployment with limited resources. We apply OncoPT to a common malignancy (exemplified by breast cancer) and a rare malignancy (exemplified by gastric cancer), across five key tumor phenotypes: Subsite, Histology, Grade, Stage, and Laterality. The results demonstrate that OncoPT achieves state-of-the-art weighted F-1 on a private pathology dataset and surpasses commercial chatbots (ChatGPT 4o and o1) on the public CORAL dataset (up to 30% improvement). These findings highlight the robustness of OncoPT models with the added benefit of preserving the privacy of patient health information.

Testicular germ cell tumors are the most common solid malignancy in young men and are associated with high cure rates. Given the young age at diagnosis and prolonged survivorship, testicular cancer survivors (TCS) may be vulnerable to alcohol use disorder (AUD). We evaluated the incidence and predictors of AUD and examined whether chemotherapy increased risk. We conducted a retrospective cohort study using the VA national healthcare system through the Veterans Informatics and Computing Infrastructure, identifying patients with testicular germ cell tumors from 1990-2021. A cancer-free comparison group was created using 1:5 exact matching on birth year and race, with assigned index dates. Patients with preexisting AUD were excluded. AUD was defined using ICD-9/10 diagnosis codes and alcohol-related CPT codes. Cox proportional hazards models assessed associations, adjusting for demographic and clinical variables. Cumulative incidence was estimated using Kaplan-Meier methods. We identified 1,774 TCS and 3,224 matched controls. At 10 years, cumulative AUD incidence was 21.6% in TCS vs 1.5% in controls (HR 13.8, 95% CI 9.2-20.8, P < .001). Chemotherapy was not independently associated with AUD (HR 1.16, P = .26). Higher risk was observed with unemployment (HR 1.43, P = .001) and Black race (HR 1.60, P = .01), while never smoking was protective (HR 0.46, P < .001). AUD risk is markedly elevated in TCS and is driven by sociodemographic factors rather than treatment exposure, highlighting the need for targeted screening and survivorship interventions.

In an increasingly technology-driven healthcare environment, digital literacy and clinical decision-making (CDM) are essential competencies for undergraduate nursing students. This study investigates the relationship between digital literacy and clinical decision-making skills among student nurses. A cross-sectional correlational design was employed, involving a convenience sample of 201 undergraduate nursing students at Taif University, Saudi Arabia. Data were collected on campus between August and September 2025 via a secure Google Forms link distributed through official university channels. Analysis included descriptive statistics, independent t-tests to examine sex differences, and Pearson's correlation and linear regression to evaluate the relationship between variables. The nursing students possessed a high level of digital literacy (M&#x2009;=&#x2009;51.00, SD&#x2009;=&#x2009;8.44) and a high level of clinical decision-making ability (M&#x2009;=&#x2009;171.30, SD&#x2009;=&#x2009;12.60). Female students (M&#x2009;=&#x2009;51.78) scored significantly higher in digital literacy than male students (M&#x2009;=&#x2009;44.97), with t(199)&#x2009;=&#x2009;3.65, p < 0.001. A statistically significant positive correlation was found between the two variables (r&#x2009;=&#x2009;0.389, p < 0.001), indicating that higher digital competency is associated with stronger clinical decision-making skills. Digital literacy was a significant predictor, accounting for approximately 15.1% (R2&#x2009;=&#x2009;0.151) of the variance in CDM scores. Sex differences were highly significant across both domains. Female students reported significantly higher mean digital literacy scores (51.78, SD&#x2009;=&#x2009;7.73) compared to their male counterparts (44.97, SD&#x2009;=&#x2009;7.89; t = -3.87, p < 0.001). Furthermore, a significant disparity was observed in clinical decision-making, where female students scored 172.50 (SD&#x2009;=&#x2009;12.40) compared to 162.03 (SD&#x2009;=&#x2009;14.15) for males (t = -3.82, p < 0.001). The findings underscore the critical role of digital literacy in clinical performance. The results suggest a need for targeted educational strategies to bridge sex-based competency gaps within nursing education. This ensures all students are prepared for a digitalized healthcare landscape.

Alzheimer's disease (AD) is a growing public health concern, with neuroinflammation implicated in its pathogenesis. Allergic rhinitis (AR), a prevalent chronic inflammatory disorder, may contribute to systemic inflammation and potentially influence AD risk. This study sought to critically assess the association between a history of AR and subsequent AD development in a large, representative Taiwanese cohort. Leveraging Taiwan's National Health Insurance Research Database (LHID2010), this nationwide case-control study identified 4,681 individuals aged&#x2009;&#x2265;&#x2009;65 years with a first-time AD diagnosis (cases) and 14,043 propensity-score-matched controls. A rigorous definition of prior AR required at least two clinical diagnoses, including one by an otolaryngology specialist. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for potential confounders. The prevalence of prior AR was significantly higher in AD patients than in controls (25.29% vs. 21.01%, p&#x2009;<&#x2009;0.001). Following meticulous adjustment for demographic variables, socioeconomic status, geographic factors, and medical comorbidities (including hyperlipidemia, diabetes, coronary heart disease, hearing loss, and hypertension), prior AR was robustly associated with elevated odds of AD (adjusted OR&#x2009;=&#x2009;1.279, 95% CI&#x2009;=&#x2009;1.182&#x2009;~&#x2009;1.384). This association remained significant for both males (adjusted OR&#x2009;=&#x2009;1.196, 95% CI&#x2009;=&#x2009;1.053&#x2009;~&#x2009;1.358) and females (adjusted OR&#x2009;=&#x2009;1.339, 95% CI&#x2009;=&#x2009;1.210&#x2009;~&#x2009;1.482). This study suggests a significant association between prior AR and an increased odds of developing AD in an elderly Taiwanese population. These findings highlight chronic peripheral inflammation as a factor potentially associated with neurodegeneration.

The integration of artificial intelligence-generated content (AIGC) tools into academic research offers transformative potential for enhancing productivity and innovation. However, within the highly regulated and ethically sensitive medical context, the use of AIGC is accompanied by significant challenges. Medical postgraduates, as the future vanguard of medical science, play a crucial role in the advancement of digital health, and their intention to use AIGC tools will significantly influence the use of these emerging technologies in medical research. Despite the growing popularity of AIGC tools, there remains a paucity of in-depth understanding of the factors driving or hindering medical postgraduates' intention to use these tools in academic research. A clear comprehension of these influencing factors is essential to foster the responsible, effective, and sustainable integration of AIGC into medical research. This study aimed to systematically explore the key factors influencing medical postgraduates' intention to use AIGC tools in academic research, with the goal of informing strategies to promote their ethical use and enhance scholarly research capabilities. We used a qualitative research design based on grounded theory. Semistructured interviews were conducted with 30 medical postgraduates across diverse specialties, all of whom had prior research experience and familiarity with AIGC tools. Participants were recruited purposively to ensure diverse perspectives. Data analysis followed a systematic coding process to inductively develop a conceptual model, which was further structured and interpreted through the theoretical lens of the Unified Theory of Acceptance and Use of Technology. Our analysis identified 7 core factors directly shaping usage intention: performance expectancy, effort expectancy, social influence, facilitating conditions, individual characteristics, task characteristics, and technology characteristics. Further analysis revealed that performance expectancy acted as a mediating variable in the relationships between both task characteristics and technology characteristics and usage intention. Additionally, social influence moderated the relationship between task characteristics and performance expectancy. The research findings underscore that, while AIGC tools are valued for assisting daily research tasks, medical postgraduates' intention to use them in academic research is influenced by technical deficiencies, high cognitive load, and the strict ethical risks and data governance requirements in the medical field. This study constructs a conceptual model aimed at elucidating the influencing factors of medical graduate students' intention to use AIGC in academic research. Recommendations derived from the findings include (1) fostering artificial intelligence literacy and critical competency among medical postgraduates; (2) optimizing AIGC tools to better address domain-specific needs, accuracy, and security concerns prevalent in health research; and (3) establishing clear academic supervision and ethical governance mechanisms to ensure responsible use. These measures are essential to harness the potential of AIGC while safeguarding the rigor and integrity of medical academic research.

Allergic asthma (AA) is a heterogeneous chronic inflammatory airway disorder. In this study, we performed a retrospective bioinformatics analysis based on public transcriptome datasets to identify critical genes associated with immune cell infiltration in AA and to establish a novel predictive model. Two transcriptome datasets (GSE73482 and GSE40889) were analyzed to explore key genes implicated in AA. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, were performed using Metascape. Least absolute shrinkage and selection operator regression was applied to screen feature genes and construct a diagnostic prediction model. Weighted gene co-expression network analysis (WGCNA) was conducted to identify AA-related gene modules. The fractions of infiltrating immune cells were estimated using single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis and gene set enrichment analysis (GSEA) were performed to explore the biological functions and related signaling pathways of the key genes. The Cistrome Data Browser database was used to predict transcription factors that potentially regulate these key genes. We identified 4 highly significant genes in the brown module: membrane associated O acetyltransferase 1 (MBOAT1), leucine rich repeats and immunoglobulin-like domains 1 (LRIG1), LOC401357, and G protein regulated inducer of neurite outgrowth 3 (GPRIN3). GSEA results revealed that these key genes were significantly enriched in multiple immune-related signaling pathways. To further explore the regulatory network of these genes, transcription factors were predicted using the Cistrome Data Browser database, and the regulatory network was visualized using Cytoscape software. MBOAT1, LRIG1, LOC401357, and GPRIN3 are candidate AA-associated genes identified through retrospective modeling. The identification of these genes offers potential opportunities to utilize them as biomarkers and targets for immunotherapy in AA.

Artificial intelligence (AI)-driven clinical decision support (CDS) tools offer promising solutions for health care delivery by optimizing resource allocation, detecting deterioration, and enabling early interventions. However, adoption remains limited due to insufficient validation and a lack of transparency and trust. Explainable AI (XAI) seeks to improve user understanding of AI outputs; however, how clinicians interpret and integrate these explanations into their decision-making remains underexplored. Furthermore, discrepancies in explanations, known as the "disagreement problem," can undermine trust and, at worst, lead to poor clinical decisions. This study examines clinicians' perspectives on the role and value of explainability in AI-driven CDS tools within Australian critical care settings and the impact of discrepancies in AI-generated explanations on clinical decision-making. Qualitative data were collected using semistructured interviews with 14 clinical experts, incorporating scenario-based exercises, and were analyzed using inductive thematic analysis. Clinicians valued explainability, particularly in complex or unfamiliar situations, when explanations were clear, plausible, and actionable. Trust and perceived usefulness extended beyond explanation quality, encompassing factors such as system accuracy, alignment with clinicians' reasoning, workflow integration, and perceived reliability. Discrepancies in explanations generated by different XAI methods were not a major concern, provided that the AI-generated predictive alerts were accurate. This study provides design recommendations for developing trustworthy, user-centric CDS tools that incorporate XAI. Findings highlight that explainability is critical for establishing initial trust in AI-driven tools by supporting perceived usefulness, but its importance diminishes over time and with user expertise and familiarity, as learned usefulness takes precedence. Recommendations highlight the importance of aligning the design and implementation of AI tools with clinicians' needs to enhance trust, mitigate risks, and promote successful adoption for improved patient outcomes.

Inflammaging refers to the chronic, low-grade, sterile inflammatory state that emerges as a hallmark of biological aging and is increasingly recognized as a contributor to functional decline, frailty, and the progression of multiple age-associated diseases. While acute inflammation supports host defense and tissue repair, persistent and unresolved inflammatory signaling promotes tissue damage, metabolic dysregulation, and impaired immune homeostasis. Inflammaging reflects a dysregulated physiological state associated with elevated damage-associated molecular patterns (DAMPs), pro-inflammatory cytokines, altered immune cell composition, metabolic imbalance, and the accumulation of senescent cells exhibiting a senescence-associated secretory phenotype (SASP). Together, these processes impair immune surveillance, increase oxidative stress, and tissue vulnerability, potentially accelerating functional decline and amplifying disease trajectories that may originate earlier in life. Despite ongoing challenges in precisely defining and measuring inflammaging, evidence suggests that its development is shaped not only by chronological aging but also by behavioral, environmental, psychosocial, and genetic factors, highlighting its dynamic and potentially modifiable nature. In this review, we distinguish inflammaging from general chronic inflammation, synthesize current understanding of its biological origins and mechanistic drivers, and examine its role in clinical outcomes including sarcopenia, neurodegeneration, and cardiovascular disease. We propose a conceptual translational framework linking biological mechanisms of inflammaging to multilayer biomarker signatures, AI-based risk stratification, and precision interventions. Additionally, we discuss the opportunities and limitations of these approaches for identifying individuals at risk for chronic disease and informing multi-dimensional strategies to promote resilience and extend health-span.

Land plants underpin civilization and planetary health, yet their genomic diversity remains largely uncharted. Current resources are unstandardized and scarce, lacking reference genomes for 95% of genera, 70% of families, and 51% of orders, impeding evolutionary and functional insight. We thus propose the PLANeT initiative, an international effort to generate high-quality, standardized genomes across the plant tree of life. Integrating artificial intelligence (AI) with genomics, we will decode conserved principles to advance fundamental plant biology, biodiversity conservation, crop improvement, and natural product discovery. Engaging around 100 labs to train 1,000 scientists, we will tackle pivotal questions for a sustainable future.

Rare bi-allelic variation is a major contributor to human disease risk, yet its effects are difficult to study at scale in population cohorts owing to the limited number of individuals with putatively deleterious bi-allelic genotypes and the challenges of accurately phasing low-frequency variants. Here, we present recessive, gene-based analyses of rare and low-frequency variants in up to 948,690 exome- or whole-genome-sequenced individuals across six biobanks with linked electronic health records. Through statistical phasing, we inferred putatively damaging compound-heterozygous genotypes, increasing the number of bi-allelic damaging genotypes by 19%. Restricting to predicted loss-of-function (pLoF) variants, we identified 5,563 genes harboring bi-allelic genotypes, a 19.8% increase in putative knockouts. We then considered all low-frequency variants (minor allele frequency [MAF] <5%) and performed gene-based recessive association testing using putatively damaging bi-allelic genotypes, identifying 58 significant associations (false discovery rate [FDR] &#x2264;1% or prec&#x2264;7.5 &#xd7; 10-7) after meta-analysis and Cauchy combination of nonsynonymous annotations. Comparing recessive and additive models, we found 17 instances where recessive effects were more pronounced, including several previously unreported associations, such as HBB with heart failure (prec = 2.6 &#xd7; 10-14; padd = 0.98), LECT2 with height (prec = 3.7 &#xd7; 10-14; padd = 4.1 &#xd7; 10-10), and ENSG00000267561 with height (prec = 2.9 &#xd7; 10-9; padd = 0.37). This study demonstrates the potential of federated approaches to study the effects of rare bi-allelic variation.

Acute lung injury (ALI) is a life-threatening pulmonary disorder with high morbidity and mortality, and current treatments remain limited. Mitochondrial energy metabolism plays a key role in ALI pathophysiology. This research aims to systematically explore the relationship between mitochondrial energy metabolism and ALI pathogenesis, thereby advancing our understanding of the condition and informing the establishment of improved treatment strategies. In this study, we systematically investigated its involvement through comprehensive bioinformatics analyses of publicly available Gene Expression Omnibus datasets, including differential expression analysis, functional enrichment, immune infiltration profiling, protein-protein interaction network construction, and regulatory network prediction, with the aim of elucidating disease mechanisms and identifying potential therapeutic targets. Differential expression analysis identified 575 differentially expressed genes (DEGs), comprising 431 and 144 upregulated and downregulated genes, respectively. Subsequent pathway analysis revealed that mitochondrial energy metabolism-related DEGs were significantly enriched in fatty acid oxidation and other key metabolic processes, highlighting the crucial role of mitochondrial dysfunction in ALI pathogenesis. Additionally, immune cell infiltration analysis indicated obvious differences in the composition of 11 immune cell types between ALI and control samples (P&#x2005;<&#x2005;.05), suggesting potential avenues for immunotherapeutic interventions. The protein-protein interaction network identified 12 mitochondrial energy metabolism-related DEGs with significant connectivity, from which 9 hub genes were prioritized as promising therapeutic targets. Furthermore, regulatory network analyses elucidated interactions among these hub genes, transcription factors, and miRNAs. Despite limitations, such as the absence of experimental validation and the potential influence of batch effects, this study provides new insights into the molecular mechanisms of ALI and establishes a foundation for future research on metabolic modulation and personalized therapeutic strategies to improve patient outcomes.

Both basic and clinical consciousness research aims to find objective measures that reliably distinguish conscious from unconscious brain states. Electroencephalogram (EEG) measures are widely used, although they may be affected by interference from electrical signals such as those generated by muscles. To assess this source of error, we investigated the impact of neuromuscular blockade (NMB) on proposed measures of awareness (spectral slope, Lempel-Ziv complexity (LZc), connectivity, alpha peak frequency, power in canonical EEG frequency bands) computed from spontaneous high-density EEG recorded from six healthy volunteers in three different conditions: (1) awake-unparalysed (normal wakefulness), (2) awake-paralysed (complete paralysis caused by neuromuscular blocking agent (NMBA)), and (3) sedated-paralysed (deep sedation with propofol, with paralysis by NMBA). The measures we investigated distinguished awake-unparalysed states from sedated-paralysed with close to perfect accuracy in accordance with past findings. However, our analysis revealed a serious failure of most measures to recognise the awake-paralysed condition as an aware state. Errors ranged from 7% of awake-paralysed time segments predicted as unaware (using alpha power) to 100% (using LZc). Using a unique high-density EEG data set, this study clearly demonstrates that many EEG-based measures fail to recognise awareness in awake subjects under the influence of muscle relaxants. These results highlight critical limitations of current EEG-based measures at detecting awareness.