Explore the vast range of titles available.

Housing stability is a key health determinant and there is a need for early screening for instability with existing electronic health record (EHR) data to improve health outcomes. We aim to establish recorded address changes as a screening variable for housing instability and homelessness and to attempt to define the threshold of high churn. Our study is a single-center cross-sectional study of EHR data (2018-2024) conducted at a US academic center with eleven sites across Chicago. We include patients 18 years or older with at least three hospital encounters over three different years. We define address churn as the number of address changes recorded in the EHR corrected to three-year intervals. We compare demographic and clinical characteristics of individuals with varying address churn with the student T-test to look at distribution of address churn for patients with and without record of homelessness, ANOVA to evaluate the distribution of ages for different levels of churn, and the chi-square test to evaluate for association between churn and clinical diagnoses. We perform multivariable logistic regression to measure the association between people with a record of homelessness and address changes. The study includes 1,068,311 patients with 756,222 having zero address changes, 156,911 having one address change, 137,491 with two address changes, 9,558 with three address changes, and 8,129 with four or more address changes. People with no record of homelessness in the EHR have mean address changes of 0.6 (SD 0.7) whereas people with record of homelessness have mean address changes of 1.8 (SD 1.3). Diagnostic profiles of the varying address change groups show increased prevalence of psychiatric diagnoses (65.2% in the 4 or more-address change group) compared to lower address change (27.7% in the 0-address change group). Address churn is significantly associated with homelessness with an odds ratio (OR) of 1.44 (95% CI =  [1.42-1.47], P < 0.001). Our results support a role for residential address churn in screening for housing instability in healthcare systems and reinforce the association between psychiatric disorders and housing instability. Our findings can help public health policy makers in targeting vulnerable populations at risk of homelessness with multiple health comorbidities for housing interventions.

Complete genome sequencing has identified millions of DNA changes that differ between humans and chimpanzees. Although a subset of these changes likely underlies important phenotypic differences between humans and chimpanzees, it is currently difficult to distinguish causal from incidental changes and to map specific phenotypes to particular genome locations. To facilitate further genetic study of human–chimpanzee divergence, we have generated human and chimpanzee autotetraploids and allotetraploids by fusing induced pluripotent stem cells (iPSCs) of each species. The resulting tetraploid iPSCs can be stably maintained and retain the ability to differentiate along ectoderm, mesoderm, and endoderm lineages. RNA sequencing identifies thousands of genes whose expression differs between humans and chimpanzees when assessed in single-species diploid or autotetraploid iPSCs. Analysis of gene expression patterns in interspecific allotetraploid iPSCs shows that human–chimpanzee expression differences arise from substantial contributions of both cis-acting changes linked to the genes themselves and trans-acting changes elsewhere in the genome. To enable further genetic mapping of species differences, we tested chemical treatments for stimulating genome-wide mitotic recombination between human and chimpanzee chromosomes, and CRISPR methods for inducing species-specific changes on particular chromosomes in allotetraploid cells. We successfully generated derivative cells with nested deletions or interspecific recombination on the X chromosome. These studies confirm an important role for the X chromosome in trans regulation of expression differences between species and illustrate the potential of this system for more detailed cis and trans mapping of the molecular basis of human and chimpanzee evolution.

The obstetrical conditions placenta accreta spectrum (PAS) and placenta previa are a significant source of pregnancy-associated morbidity and mortality, yet the specific molecular and cellular underpinnings of these conditions are not known. In this study, we identified misregulated gene expression patterns in tissues from placenta previa and percreta (the most extreme form of PAS) compared with control cases. By comparing this gene set with existing placental single-cell and bulk RNA-Seq datasets, we show that the upregulated genes predominantly mark extravillous trophoblasts. We performed immunofluorescence on several candidate molecules and found that PRG2 and AQPEP protein levels are upregulated in both the fetal membranes and the placental disk in both conditions. While this increased AQPEP expression remains restricted to trophoblasts, PRG2 is mislocalized and is found throughout the fetal membranes. Using a larger patient cohort with a diverse set of gestationally aged-matched controls, we validated PRG2 as a marker for both previa and PAS and AQPEP as a marker for only previa in the fetal membranes. Our findings suggest that the extraembryonic tissues surrounding the conceptus, including both the fetal membranes and the placental disk, harbor a signature of previa and PAS that is characteristic of EVTs and that may reflect increased trophoblast invasiveness. Summary sentence 3SEQ and immunofluorescence reveal that extravillous trophoblast factors, most notably PRG2 and AQPEP, define the diseases placenta previa and placenta accreta spectrum in both the chorioamniotic membranes and the placental disk.

The majority of people with HIV live in sub-Saharan Africa, where epidemics are generalized. For these epidemics to develop, populations need to be mobile. However, the role of population-level mobility in the development of generalized HIV epidemics has not been studied. Here we do so by studying historical migration data from Botswana, which has one of the most severe generalized HIV epidemics worldwide; HIV prevalence was 21% in 2021. The country reported its first AIDS case in 1985 when it began to rapidly urbanize. We hypothesize that, during the development of Botswana’s epidemic, the population was extremely mobile and the country was highly connected by substantial migratory flows. We test this mobility hypothesis by conducting a network analysis using a historical time series (1981–2011) of micro-census data from Botswana. Our results support our hypothesis. We found complex migration networks with very high rates of rural-to-urban, and urban-to-rural, migration: 10% of the population moved annually. Mining towns (where AIDS cases were first reported, and risk behavior was high) were important in-flow and out-flow migration hubs, suggesting that they functioned as ‘core groups’ for HIV transmission and dissemination. Migration networks could have dispersed HIV throughout Botswana and generated the current hyperendemic epidemic. Over 25 million people in sub-Saharan Africa live with HIV. After reporting its first AIDS case in 1985, Botswana is one of the most severely affected countries in the region, with one in five adults now living with HIV. Movement of the population is likely to have contributed to a geographically dispersed, and high-prevalence, HIV epidemic in Botswana. Since 1985, urbanization, rapid economic and population growth, and migration have transformed Botswana. Yet, few studies have analyzed the role of population-level movement patterns in the spread of HIV during this time. By studying micro-census data from Botswana between 1981 and 2011, Song et al. found that the country’s population was highly mobile during this period. Reconstructions of internal migration patterns show very high rates of rural-to-urban and urban-to-rural migration, with 10% of Botswana’s population moving each year. The first reported AIDS cases in Botswana occurred in mining towns and cities where high-risk behavior was prevalent. These areas were also migration hubs during this period and could have contributed to the rapid spread of HIV throughout the country as infected individuals moved back to rural districts. Understanding human migration patterns and how they affect the spread of infectious diseases using current data could help public health authorities in Botswana and additional sub-Saharan African countries design control strategies for HIV and other important infections that occur in the region.

Background Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas. Methods We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters. Results We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p  = 5.67E-05). We find nine clusters/hotspots of inpatients. Conclusions Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots. Plain language summary The frequencies of mental disorders vary by geographic region. Investigating such variations may lead to more equitable access to mental healthcare and to scientific discoveries that reveal specific localized factors that contribute to the causes of mental illness. This study examined the frequency of three disorders with a major impact on public health – schizophrenia, bipolar disorder, and major depressive disorder – by analyzing electronic health records from a hospital providing comprehensive mental health care for a large region in Colombia. We show that individuals receiving outpatient care mainly live relatively near the facility. Those receiving inpatient care live throughout the region, but cluster in a few scattered locations. Future research could lead to strategies for more equitable provision of mental healthcare in Colombia and identify environmental or genetic factors that affect the likelihood that someone will develop one of these disorders. Song et al. conduct a geospatial analysis of variations in incidence of mood and psychotic disorders and access to care in Caldas, Colombia. They show that many patients requiring only outpatient care live too far away to access it and identify hotspots of more severely ill patients, highlighting the need for targeted interventions.

Measures of regional brain volumes, which can be derived from magnetic resonance imaging (MRI) images by dividing a brain into its constituent parts, can be used as structural indicators of many different neuroanatomical diseases and disorders, including alcoholism. Reducing the time and cost required for brain segmentation would greatly facilitate both clinical and research endeavors. In the present study, we compared two segmentation methods to measure brain volumes in alcoholic and nonalcoholic control subjects: 1) an automated system (FreeSurfer) and 2) a semi-automated, supervised system (Cardviews, developed by the Center for Morphometric Analysis [CMA] at Massachusetts General Hospital), which requires extensive staff and oversight. The participants included 32 abstinent alcoholics (19 women) and 37 demographically matched, nonalcoholic controls (17 women). Brain scans were acquired in a 3 Tesla MRI scanner. The FreeSurfer and CMA methods showed good agreement for the lateral ventricles, cerebral white matter, caudate, and thalamus. In general, the larger the brain structure, the closer the agreement between the methods, except for the cerebral cortex, which showed large between-method differences. However, several other discrepancies existed between the FreeSurfer and CMA volume measures of alcoholics' brains. The CMA volumes, but not FreeSurfer, demonstrated that the thalamus, caudate, and putamen were significantly smaller in male alcoholics as compared with male controls. Additionally, the hippocampus was significantly smaller in alcoholic women compared with women controls. In general, correlation between methods was lowest in male alcoholic subjects, who also showed the greatest abnormalities. These results suggest that although many brain structures can be segmented reliably by CMA and FreeSurfer, low correlations between methods in some regions may be due to morphological changes in the brains of alcoholics.

The diversity of genes implicated in autism spectrum disorder (ASD) creates challenges for identifying core pathophysiological mechanisms. Aggregation of seven different classes of genetic variants implicated in ASD, in a database we call Consensus-ASD, reveals shared features across distinct types of ASD variants. Functional interrogation of 19 ASD genes and 9 neighboring long non-coding RNAs (lncRNAs) using CRISPR-Cas13 strikingly revealed differential gene expression profiles that were significantly enriched for other ASD genes. Furthermore, construction of a gene regulatory network (GRN) enabled the identification of central regulators that exhibit convergently altered activity upon ASD gene disruption. Thus, this study reveals how perturbing distinct ASD-associated genes can lead to shared, broad dysregulation of GRNs with critical relevance to ASD. This provides a crucial framework for understanding how diverse genes, including lncRNAs, can play convergent roles in key neurodevelopmental processes and ultimately contribute to ASD.Competing Interest StatementThe authors have declared no competing interest.

Despite growing healthcare coverage, disparities in access to and outcomes of psychiatric care persist, even in countries with universal healthcare. How socioeconomic status (SES), travel time, and social support individually and jointly affect psychiatric clinical trajectories remains largely unexplored. We analyze electronic health records (EHRs) from patients diagnosed with bipolar disorder, major depressive disorder, or schizophrenia at Clínica San Juan de Dios Manizales. Using zero-inflated and standard negative binomial regression, we quantify the effects of SES, travel time, and family/social support on utilization, clinical outcomes, and symptoms of mania, psychosis, and suicidality. A mixed-effects model examines how care-seeking patterns affect visit-to-visit variability in outcomes. Among 21,095 patients, utilization is lower for those with low SES (rate ratio [RR] 0.92, 95% CI: 0.90-0.95,  = 1.27e-10) and longer travel times (RR 0.94, 95% CI: 0.93-0.95,  = 1.19e-53). Patients with low SES are more likely to have severe symptoms (e.g., delusions: RR 1.28, 95% CI: 1.20-1.37,  = 2.57e-15) and require hospitalization (RR 1.10, 95% CI: 1.05-1.15,  = 1.94e-04), suggesting they primarily seek care when critical. Longer travel differentially affects those with low SES. However, the relationship between SES and adverse outcomes is less pronounced when living with family (e.g., hospitalizations: LRT,  = 47.08, df = 3,  = 3.35e-10). Frequent outpatient care is associated with lower odds of hospitalization, suicidality, and other symptoms. Findings demonstrate use of EHRs to model patient outcomes, the important role of social support, and need for improved healthcare accessibility.

Autism spectrum disorder (ASD) is a common neurodevelopmental condition characterized by deficits in social communication as well as restricted and/or repetitive behaviors. ASD is highly heritable , with a complex genetic architecture: large-scale studies have identified dosage-altering copy number variants (CNV) and single nucleotide variants (SNV) that implicate hundreds of genes as individually rare causes of ASD (ASD genes) , with common variation at multiple loci also contributing substantially to risk . Understanding how disruptions to these functionally diverse genes lead to the shared core features of ASD remains a major challenge . Moreover, ASD is three- to four-fold more common in males than females , and autistic females tend to carry more autosomal risk alleles for ASD compared to autistic males , but the biological basis of this \"female protective effect\" (FPE) is unknown . Here we show that individual perturbations of 18 ASD genes converge on shared effects on gene expression, including widespread downregulation of other ASD genes. reconstruction of a gene regulatory network (GRN) enabled the identification of central transcriptional regulators, including the prominent ASD gene as well as novel candidates such as , that drive this transcriptomic convergence in ASD. Furthermore, the X-linked transcription factor , which is expressed from both the active and the \"inactive\" X chromosomes in females , emerged as a key activator of many ASD genes: we propose that the higher expression level observed in female brain can buffer damaging mutations in diverse ASD genes, contributing to the FPE. Together, these results reveal how key GRNs can become broadly and similarly dysregulated upon disruption of individual ASD genes and provide molecular insight into the female protective effect in ASD.