Animal models of drug addiction simulate core behaviors such as active drug seeking, compulsive drug use, and relapse, thereby establishing a system for studying the mesolimbic reward circuit. Rats and mice serve as the most commonly used model organisms in this field. Mainstream models are developed based on the principles of operant conditioning and classical conditioning, among which the self-administration paradigm is regarded as the "gold standard" because it can directly simulate active drug seeking and compulsive drug use. These models demonstrate face validity, construct validity, and predictive validity to varying degrees, but also face limitations in fully replicating the complex cognitive and sociocultural factors of human addiction. In recent years, optogenetic and chemogenetic technologies have enabled precise spatiotemporal regulation of specific neuronal types and circuits, promoting research from phenomenological observation to causal verification. Current research trends emphasize integrating multimodal behavioral paradigms, focusing on individual differences, and striving to translate basic research findings into intervention strategies targeting specific neural circuits, providing direction for understanding addiction mechanisms and developing new treatment approaches.

Sepsis is a leading cause of child mortality worldwide, disproportionately affecting children in resource-limited settings (RLS). Effective risk-stratification tools using readily available data are urgently needed for this population. Therefore, the study objective was to evaluate the performance of point-of-care (POC) biomarkers and clinical characteristics for predicting in-hospital mortality among Tanzanian children with sepsis.

We conducted a prospective observational cohort study of children (28 days-14 years) with sepsis presenting to Muhimbili National Hospital in Dar es Salaam, Tanzania (July 2022-November 2024). POC biomarkers (procalcitonin [PCT], C-reactive protein, ferritin, and lactate) and clinical characteristics were evaluated for their association with mortality. We used the least absolute shrinkage and selection operator (LASSO) regression to construct predictive models of mortality. We evaluated model performance using the area under the receiver operating characteristic curve (AUC) and classification metrics, including sensitivity and specificity.

Among the 755 enrolled participants, 19.6% (n = 148) died during hospitalization. PCT and tested clinical characteristics were significantly associated with mortality (all p<0.001). A multivariable model incorporating PCT, malnutrition, breathing difficulty, and altered mental status demonstrated strong discrimination (AUC 0.87, 95% CI 0.84-0.90), outperforming individual biomarkers and clinical characteristics alone.

A combined POC biomarker and clinical characteristics model was highly predictive of mortality among children with sepsis in Tanzania. Integrating POC biomarkers with easy-to-measure clinical characteristics associated with severity may enable timely risk stratification and inform targeted interventions to improve pediatric sepsis outcomes in RLS.

Dysfunction of autobiographical memory (AM) is one of the core markers of psychiatric disorders such as major depressive disorder and schizophrenia. However, it remains unclear whether there is a common neural basis underlying AM impairment across psychiatric patients. In this study, a systematic review and meta-analysis using both Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) and Activation Likelihood Estimation (ALE) approaches were conducted to examine brain activation differences between psychiatric populations and healthy controls during AM. A computerized search was performed using the databases Web of Science, PubMed, APA PsycInfo and EBSCO to identify relevant studies published from inception to 31 October 2025. Twenty-four studies (1385 participants) were identified for the qualitative synthesis and 12 studies (547 participants) for the meta-analysis. The qualitative analysis revealed widespread abnormalities in psychiatric patients in both activation and functional connectivity (FC) across default mode network, salience and attentional network, control network and visual network. Meta-analysis results indicated that patients with psychiatric disorders exhibited hyperactivations in the cingulate cortex, and subsequent meta-analytic connectivity modeling (MACM) analysis demonstrated its widespread co-activation with large-scale functional networks. These findings suggest the network-level dysfunction across psychiatric disorders during AM process and provide insights for future clinical research.

Although the overall rate of adherence to postpartum glucose screening is low (less than 50%), the geographical disparities, rural versus urban, in postpartum glucose screening rates for women with gestational diabetes are relatively unknown.

The purpose of this study was to compare postpartum glucose screening rates between women living in rural versus urban areas. Secondarily, this study evaluated the impact of transfer of maternity care on adherence to postpartum care and glucose screening rates in women with gestational diabetes mellitus.

Through retrospective chart review, data were collected from records with a diagnosis of gestational diabetes between January 2015 and October 2020.

Subjects were classified as urban or rural residents. The following data were collected: number of prenatal and postpartum care visits, number of prenatal and postpartum glucose testing, infant birthweight, maternal complications, and demographic data, including race, age, and insurance status.

Our final sample consisted of 82 women classified as urban and 67 classified as rural. Overall, the number of postpartum glucose tests was lower in the rural group (M = 0.30), compared to the urban group (M = 0.52) and demonstrated statistically significant differences with Poisson regression (coefficient 0.56, Wald chi-square = 4.59, p = 0.03). Women from rural areas, who were referred and transferred maternity care to the Regional Perinatal Health Care Center, had the lowest average postpartum visits (M = 1.00) and postpartum glucose tests (M = 0.22) compared to the women from urban areas who were transferred from other providers with mean postpartum visits (M = 1.46) and mean postpartum glucose tests (M = 0.29).

Rural women received lower postpartum glucose screening than those residing in urban areas. This finding illustrates greater disparities among rural women seeking postpartum care. Larger prospective studies are warranted to examine contributing factors, such as continuity of care and the transition from postpartum care to primary care. The remaining significant need is for healthcare providers to facilitate preventive care following pregnancy, including timely glucose screening and detection of women at high risk for developing type 2 diabetes, especially among rural populations.

Continuous educational programs based on the PRECEDE-PROCEED model are recommended for women with gestational diabetes to improve their knowledge and awareness of the condition. These programs should address individual, organizational, and healthcare-related factors.

This research aimed to evaluate the effect of a nursing approach based on the PRECEDE-PROCEED model on health behaviors and sleep quality among women with gestational diabetes.

This study is a quasi-experimental research (pre-posttest design for control and study groups).

This study includes a purposive sample of 90 pregnant women. This study was carried out in obstetrics and gynecology outpatient clinic at Al-Basheer hospital in Jordan. Data were collected through face-to-face interviews using a structured questionnaire, the questionnaire incorporated constructs from the PRECEDE-PROCEED model, along with the health-promoting lifestyle profile-II and Pittsburgh sleep quality index.

Following the intervention, participants in the study group achieved higher overall health-promoting lifestyle scores than those in the control group. The mean score in the intervention group was 97.02 ± 15.27, compared with 77.71 ± 11.55 in the control group, indicating a statistically significant difference. Significant improvements were also observed in key lifestyle domains, including nutrition (28.04 ± 5.52), physical activity (17.08 ± 3.34), and stress management (18.46 ± 2.69). In addition, sleep quality improved in the intervention group, as reflected by a reduction in the mean sleep disturbance score to 8.20 ± 2.65 compared with 11.24 ± 5.32 in the control group.

The PRECEDE-PROCEED model was effective in improving knowledge, attitude, sleep quality, and health behaviors among pregnant women diagnosed with gestational diabetes.

PFAS are a group of persistent organic pollutants, that bioaccumulate and are associated with negative health effects. Reviews have suggested that the most critical effects of PFAS are on the immune system, but little is known of effects on development of autoimmunity. Our objective was to map and summarize available evidence concerning exposure to any PFAS and development/presence of autoimmunity, in humans and animals. We assessed studies reporting potential associations between PFAS exposure and autoimmune disease and/or autoantibodies. We searched MEDLINE, Embase, CENTRAL, Scopus, and Web of Science (02.15.2024) and conducted complimentary searches. Results were presented descriptively and we categorized autoimmune diseases and autoantibodies into relevant outcome groups. 51 studies were included, distributed in the following groups: Autoimmune thyroid disease, inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, celiac disease, other diseases and autoantibodies. Cross-sectional studies were most common, limiting opportunities for causal inference. 33 studies showed associations between higher PFAS and increased risk of autoimmunity, while nine studies found lower PFAS associated with increased autoimmunity risk. The results suggest that PFAS have negative health impacts with strongest evidence for celiac and inflammatory bowel disease, weaker evidence for rheumatoid arthritis, systemic lupus erythematosus and type 1 diabetes mellitus. No clear indications of association with autoimmune thyroid disease. The majority of the studies showed an association between PFAS and autoimmunity. There is a need for more longitudinal and dose-response studies, to improve our understanding of individual autoimmunity outcomes in the future.Protocol registration: OSF.io ( https://doi.org/10.17605/OSF.IO/3FEVQ ).

Sodium-glucose cotransporter-2 (SGLT2) inhibitors improve outcomes in heart failure (HF), yet comparative effectiveness between individual agents in heart failure with mildly reduced ejection fraction (HFmrEF) remains limited.We conducted a retrospective cohort study using the TriNetX Global Collaborative Network electronic health record database. Adults (≥18 years) with HF and left ventricular ejection fraction (LVEF) 41-49% initiating empagliflozin or dapagliflozin between September 1, 2021 and September 1, 2022 were included. Propensity score matching (1:1) balanced demographics, comorbidities, medications, and laboratory values, yielding 1,386 patients per group. Outcomes were assessed over 1 year after treatment initiation and included all-cause mortality, hospitalization, HF exacerbation, and urinary tract infection (UTI). Risk analyses and Kaplan-Meier survival analyses with hazard ratios (HRs) were performed.In the matched cohort, empagliflozin was associated with significantly lower hazards of hospitalization (HR 0.54, 95% CI 0.49-0.60), HF exacerbation (HR 0.63, 95% CI 0.55-0.71) and UTI (HR 0.70, 95% CI 0.55-0.90) compared with dapagliflozin. All-cause mortality was numerically lower with empagliflozin but did not reach statistical significance (HR 0.86, 95% CI 0.67-1.10). In conclusion, in this large real-world HFmrEF population, empagliflozin was associated with lower hazards of hospitalization, HF exacerbation, and UTI compared with dapagliflozin, with no significant difference in mortality. These findings suggest potential heterogeneity in clinical effectiveness among SGLT2 inhibitors in HFmrEF and warrant confirmation in prospective comparative studies.

The number of kidney biopsies performed in patients with diabetes has increased rapidly over the last two decades. However, the overall value of the biopsy has been questioned because any coexisting non-diabetic kidney disease (NDKD) is not identified in many patients. Here, we quantify the frequency of identifying NDKD and examine clinical indications, demographic factors, and histologic parameters that increase the odds of finding NDKD and the impact on developing end stage kidney disease.

A retrospective analysis of clinical and pathologic parameters of 49,075 biopsied patients with diabetes with and without diabetic nephropathy (DN) from 2001-2024 was performed. Data from the United States Renal Data Service were examined to determine the impact of a NDKD on disease progression to end stage kidney disease.

NDKD was found in 58.8% of patients with diabetes who underwent kidney biopsy, including 35.9% without concurrent DN and 22.9% as a second diagnosis in DN. Acute kidney injury and acute nephritic syndrome had greater odds of a NDKD diagnosis in patients with DN. The youngest (under 30 years) and oldest (60 years and older) patients had a higher prevalence of NDKD. Higher chronicity on biopsy was associated with a lower prevalence of NDKD diagnosis. Patients with NDKD were 2.56-fold less likely to develop end stage kidney disease compared to patients with DN alone.

This is the largest analysis examining prevalence of a NDKD in patients with diabetes and the impact of biopsy indication on finding a second diagnosis in biopsy-proven DN. The objective is to provide nephrologists with guidance in when to perform a biopsy based on the odds of finding a NDKD related to the patient's clinical indication. Given the high prevalence of NDKD, our study shows that kidney biopsy remains a critical tool in the care of diabetic patients.

Diabetes mellitus (DM) is associated with an elevated risk of cognitive decline, though trajectories are heterogeneous. This study investigated whether a novel, clinically applicable measure of brain network integrity, the Morphometric Inverse Divergence (MIND) network, could differentiate and predict cognitive progression in DM.

We retrospectively analyzed 101 DM participants (41 cognitively normal, 60 with mild cognitive impairment) from the Alzheimer's Disease Neuroimaging Initiative, classifying them into stable (DM_S, n=64) or decline (DM_D, n=37) group based on longitudinal diagnostic conversion. MIND networks were constructed from multiple cortical morphological features derived from T1-weighted MRI and graph theory measurements were further analyzed. Using network-based statistics (NBS) and its extension NBS-predict, we tested whether subject-level connectomes were associated with long-term DM-related cognitive worsening.

At baseline, DM_D individuals exhibited significantly lower cognitive scores and a focal subnetwork of disrupted morphometric similarity, primarily involving temporal regions. Longitudinally, DM_D individuals showed a more targeted pattern of network change that significantly altered global efficiency, local efficiency, and path length exclusively, while stable individuals, the brain underwent more widespread changes. Crucially, baseline MIND networks significantly predicted long-term cognitive progression status (accuracy = 63.1%, p = 0.034). The predictive subnetwork was rich in transmodal connections involving the temporoparietal, default mode, and limbic networks.

These findings indicate that cognitive decline in DM is preceded by specific disruptions in the brain's structural connectome. The MIND method shows promise as a network-based biomarker for identifying at-risk individuals and predicting cognitive trajectory, potentially driving advanced network analyses toward real-world applicability.