Patient satisfaction is a central measure of high-performing healthcare systems, yet real-world evaluations at scale remain challenging. In this study, we analyzed 122,194 de-identified patient reviews from 45 Ontario general hospitals between January 2015 and July 2022. We applied a natural language processing (NLP) pipeline using a clinical named entity recognition (NER) model fine-tuned on biomedical literature to extract references to diseases, symptoms, and medical procedures from patient reviews. Geospatial analysis was conducted to examine sentiment patterns based on regional census data related to low-income status and visible-minority composition. Our primary objective was to investigate how the COVID-19 pandemic influenced patient satisfaction trends, with a specific focus on clinical units and hospitals serving marginalized populations. We assessed changes in the proportion of positive comments across time periods and socioeconomic groups using multivariate logistic regression. Our findings show that over 80% of the hospitals studied had fewer than 50% positive reviews, highlighting possible systemic gaps in patient needs. Interestingly, the proportion of negative reviews decreased during the COVID-19 pandemic, suggesting possible changes in patient expectations or increased appreciation for healthcare workers. However, certain units, such as dentistry and radiology, experienced more negative ratings as a proportion of their total reviews. 'Anxiety' emerged as a recurrent concern in negative reviews, especially during the start of the pandemic, pointing to the growing awareness of mental health needs. Based on our geospatial analysis, hospitals located in regions with higher percentages of visible minority and low-income populations initially saw higher positive review proportions before COVID-19, but this trend reversed after 2020. Our statistical models confirmed that these shifts were significant, particularly for low-income-serving hospitals. Collectively, these results demonstrate how large-scale unstructured data can identify fundamental drivers of patient satisfaction, while underscoring the urgent need for adaptive strategies to address anxiety and combat systemic inequities.

Juvenile neuronal ceroid lipofuscinosis, linked to mutations in the ceroid lipofuscinosis, neuronal 3 (CLN3) gene, is a childhood-onset neurodegenerative disorder. Although mood and behavioral symptoms are described in this group, it's unclear whether these meet diagnostic criteria. We investigated the occurrence of neurodevelopmental and psychiatric disorders in a nationally representative sample, using a semistructured psychiatric interview, as well as the use of psychotropic medication. Ten of 20 individuals met the criteria for one or more current diagnoses, with an additional 5 having past diagnoses, resulting in a lifetime occurrence in 15 individuals. Anxiety disorders were the most frequent diagnostic group, followed by neurodevelopmental disorders. Attention-deficit hyperactivity disorder was the most common single diagnosis. Subthreshold psychiatric symptoms were present in all individuals. Although psychiatric disorders were frequent, few used psychotropic medication. These findings underscore the need for routine monitoring of neurodevelopmental and psychiatric disorders in individuals with CLN3 and the provision of evidence-based treatments.

The study aimed to investigate whether involvement in a stable romantic partnership is associated with differences in peripheral brain-derived neurotrophic factor (BDNF) levels.

In a cross-sectional study, 60 healthy adults (32 women; mean age 27.4 ± 4.1 years) were classified as in a stable relationship (n = 31) or not (n = 29) after a clinical interview. Morning fasting blood was collected to quantify serum and platelet BDNF using a mature-BDNF sandwich ELISA. Data were analysed with t-tests and two-way ANOVA (group, sex), reporting effect sizes.

Participants in a relationship showed higher PLT-BDNF (4.36 ± 1.22 vs 2.85 ± 0.67 ng/mg; t(58) = 5.90, p < 0.001, d = 1.52) and higher serum BDNF (36.83 ± 6.95 vs 25.47 ± 5.23 ng/ml; t(58) = 7.12, p < 0.001, d = 1.84) than participants without a current stable romantic relationship. Two-way ANOVA confirmed a main effect of group for PLT-BDNF (F = 35.35, p < 0.001, η² = 0.39) and serum BDNF (F ≈ 50, p < 0.001, η²≈0.47), while the sex and group × sex interactions were not statistically significant for both PLT-BDNF and serum BDNF.

Our results would indicate that a stable romantic partnership is associated with higher intraplatelet and serum BDNF levels. These findings support an association between current committed romantic relationship status and peripheral BDNF measures in healthy adults.

With the advancement of modern medicine and engineering, breast deformation analysis has become increasingly important in the fields such as cosmetic surgery, reconstructive surgery, sports biomechanics, and health-related research. The morphological changes in breast tissue not only affect an individual's aesthetic appearance and mental well-being but are also closely linked to breast health. Finite Element Method (FEM) and Machine Learning (ML) are two advanced technologies that exhibit significant potential in breast deformation analysis. FEM plays a crucial role in modeling the mechanical behavior of breast soft tissues and analyzing deformations due to its precise numerical solutions and realistic simulation capabilities. On the other hand, machine learning techniques provide new perspectives for personalized health management and disease risk assessment by processing large-scale breast morphology data to uncover patterns and correlations. This paper reviews the latest advancements in the application of FEM and ML in breast tissue morphology analysis, explores their potential and challenges in simulating both static and dynamic breast deformations in clinical practice, and summarizes the characteristics and application scenarios of both technologies. This paper discusses the novel opportunities brought by the integration of these two approaches for the clinical diagnosis and analysis of breast-related diseases. It synthesizes these developments and explores the potential benefits of methodological integration for future breast morphology-related research and clinical applications.

People on hemodialysis (HD) often report lower quality of life (QoL) compared to people not on HD. People with kidney disease have a high prevalence of health-related social needs (HRSN). The association of HRSN and QoL in people on HD remains understudied. Though some groups of patients treated with HD tend to have lower QoL, there exists minimal research investigating the mechanism by which this occurs.

We surveyed people receiving HD at five urban dialysis units using the Kidney Disease Quality of Life (KDQOL) and the Accountable Health Communities Health-Related Social Needs Screening Tool (AHC-HRSN) to assess their housing, food, transportation, utilities, and perceived safety. We calculated physical and mental component scores as well as sub scores measuring burden, symptoms, and effect of kidney disease. We analyzed scores using Python packages. We used the Shapiro-Wilk test to assess normality. For analysis we used the Wilcoxon Rank Sum test and univariate, multivariate, and LASSO regressions.

A total of 324 patients participated in the study. HRSN was common with 56% of participants reporting at least one HRSN. Food insecurity (35%) and housing instability (24%) was most common. All QoL sub scores were significantly lower in patients who had at least one HRSN. In regression models, housing and transportation insecurity most frequently emerged as significant variables associated with lower QoL sub scores even after adjusting for patient demographics. Burden scores showed the largest effect sizes (housing instability β=-17.90, P<0.001, transportation problems β=-14.03, P=0.001).

HRSN is significantly associated with lower QoL scores, with largest effect sizes seen with housing instability and transportation problems. Increased screening and intervention for HRSN may improve QoL among people on HD.

Outreach in psychiatric genetics bridges the gaps across research advancements, clinical practice, and public understanding. Effective communication with a range of audiences faces multiple challenges, including the complex nature of psychiatric disorders and of genetic findings, the chronicled and ongoing misuse of genetic data, and the rapid growth of direct-to-consumer genetic testing. This is particularly true in the internet and social media era, which has accelerated the spread of inaccurate information with serious consequences, including perpetuating stigma and increasing shame. Yet a significant gap remains in outreach: a recent survey found that only 51% of psychiatric genetics researchers participate in outreach, largely attributed to their perceived lack of skill and support. The Psychiatric Genomics Consortium (PGC) developed an Outreach Committee to organize and catalyze initiatives including: the Worldwide Lab, an online seminar series covering new and important approaches that is then posted on YouTube; the PGC Video Textbook, which has resources for clinicians, researchers, educators, and the broader public; patient and family engagement programs; and social media campaigns designed to inform both researchers and affected communities about new developments in psychiatric genetics. These initiatives were developed to demystify psychiatric genetics research and empower broad audiences with reliable, actionable information. Along with research and traditional teaching, the psychiatric genomics community should continue to prioritize and recognize engagement as a core component of our academic mission and values.

Climate change adversely affects adolescent health through extreme outdoor heat, causing dehydration, cognitive impairment, chronic health risks, and potentially fatal heat stroke. Adolescents' high levels of physical activity and limited awareness about the impacts of extreme outdoor heat exacerbate these risks, highlighting the need for effective adaptation strategies. However, comprehensive studies on the physical and mental effects of heat exposure, adaptation practices adopted by adolescents, psychological determinants, microclimatic perceptions, and ambient-natural conditions remain limited. This study examined outdoor heat exposure in adolescents in urban areas, focusing on Yogyakarta City, Indonesia. We combined the Social-Ecological Model and the RANAS psychological theories in the analysis. Interviews with 439 adolescents revealed mild physical and mental symptoms due to outdoor heat exposure and low engagement in heat adaptation practices. Nearly half perceived a high likelihood of experiencing health problems when exposed to heat outdoors and had good knowledge about heat exposure. However, these two factors on risk perception and knowledge were not sufficient to spur respondents to adopt adaptation practices. Young women were more likely than young men to adopt heat adaptation practices, driven by higher psychological levels towards adaptation practices. The most influential variables on adaptation practices were having experienced the physical impacts of heat exposure, followed by having experienced the mental impacts, and perceptions about ambient sunlight. Among all psychological factors, self-regulation 'remembering' was significantly associated with adopting adaptation practices. This study provides key insights into developing adaptation strategies to improve climate adaptation behavior in urban adolescents.

Postoperative delirium (POD) in elderly patients is a serious complication after surgery with an unclear molecular pathogenesis. Perioperative untargeted high-throughput proteomic profiling may provide insights into underlying mechanistic molecular patterns and help identify patients at high risk, guiding prevention and therapy. This study is a monocentric substudy of the European BioCog project, a prospective multicentre observational study involving elderly patients aged ≥ 65 undergoing elective surgery with a Mini-Mental State Examination score of ≥ 24. POD was assessed twice daily for up to 7 days using the Nursing Delirium Screening Scale (Nu-DESC) and the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). We conducted high-throughput liquid chromatography mass spectrometry on sequential pre- and postoperative plasma samples. Data are available via ProteomeXchange with the identifier PXD071265. Data were analysed using a matched case-control design, employing both cross-sectional and longitudinal approaches, along with pathway enrichment analysis. A total of 226 highly abundant proteins were investigated in 168 patients (50% POD incidence). Multiple pathways, particularly those involved in the activation of the innate immune response and the complement system, were associated with POD. Butyrylcholinesterase showed the most robust regulation, with preoperative downregulation and postoperative upregulation in patients with POD, whereas it was further downregulated postoperatively in those without POD. Catalytic enzyme activity showed significant decrease in both groups post-surgery. A combined set of 8 preoperative proteins classified patients with POD with 86% sensitivity and 79% specificity. Untargeted high-throughput proteomics is a feasible approach to characterise pathways involved in POD pathogenesis. This case-control study identified a protein signature associated with POD, emphasising the need for larger cohorts to confirm these observations and improve the mechanistic understanding of POD.

Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of electronic health records (EHR). We present MixEHR-SAGE, a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications to enhance phenotyping from large-scale EHRs. By combining expert-informed priors with probabilistic inference, MixEHR-SAGE identifies over 1000 interpretable phenotype topics from UK Biobank data. Applied to 350 000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predict incident type 2 diabetes (T2D) and leukemia diagnoses. Subsequent genome-wide association studies using these continuous risk scores uncovered novel disease-associated loci, including PPP1R15A for T2D and JMJD6/SRSF2 for leukemia, that were missed by traditional binary case definitions. These results highlight the potential of probabilistic phenotyping from multi-modal EHRs to improve genetic discovery. The MixEHR-SAGE software is publicly available at: https://github.com/li-lab-mcgill/MixEHR-SAGE.