The personality trait of neuroticism has been recognized as a risk factor for depression. Research into the interplay between brain activity, neuroticism, and subclinical depressive symptoms could potentially uncover biomarkers capable of predicting the onset of depression. Brain intrinsic signal variability, a measure of system flexibility, is related to lifespan development, cognitive performance, and mental health disorders. Despite its importance, the link between signal variability and both neuroticism and depression has not been previously explored. This study, therefore, sought to investigate the correlation between brain signal variability, neuroticism, and depressive symptoms in individuals without clinical depression. The resting-state functional magnetic resonance imaging data were collected from 103 healthy right-handed individuals (63 males, 40 females; mean age 24.73 ± 8.34 years). Neuroticism and subclinical depressive symptoms were assessed by Eysenck Personality Questionnaire and Zung Self-Rating Depression Scale, respectively. To quantify brain signal variability, voxel-wise mean-squared successive difference (MSSD) was calculated. A mediation analysis was subsequently performed to explore the interrelationships among signal variability, neuroticism, and depressive symptoms. Neuroticism showed a significant negative correlation with MSSD in several brain regions, including the medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex (dlPFC), precentral gyrus, angular gyrus, and middle temporal gyrus. Additionally, MSSD in the MPFC and dlPFC were negatively associated with depressive symptoms, with neuroticism mediating this relationship. In sum, neuroticism and depressive symptoms were associated with reduced signal variability in the PFC regions. These effects suggested that dysfunctional activity within the PFC regions is linked to the etiology of depression, mediated by individual differences in neuroticism.

Trait social anxiety (SA) is continuous and can manifest across the entire spectrum of mental health, from subclinical levels in healthy controls to burdensome manifestations in major depressive disorder and finally full-blown disorders like social anxiety disorder (SAD). Although it has been proposed that SA is associated with deviations in the structural connectome, there is little research on the SA spectrum and its relation to abnormalities in brain structural networks. In this study, we aim to identify a structural brain network for SA across diagnostic groups using a transdiagnostic sample and connectome-based predictive modeling (CPM) to provide insight on neurostructural underpinnings of the SA spectrum. We collected magnetic resonance imaging (MRI) and clinical data of N = 160 participants across an SA spectrum. Diffusion-weighted and T1-weighted structural images were used to reconstruct each participant's structural connectome. We applied CPM to identify a structural brain network that predicts SA across diagnostic groups. We further analyzed the structural connectome to investigate hubs and differences in global graph metrics between participants with and without SAD (noSAD n = 96, SAD n = 64). CPM revealed a brain network with 46 edges predicting SA across the SA spectrum with mean r = 0.141 (CI = [0.12, 0.2], p = 0.003). Hubs were located in the left pericalcarine, inferior-parietal, superior-parietal, right lateral-occipital cortex and pars orbitalis. This study provides first evidence for a shared neurobiological foundation of SA across diagnostic groups and points towards a transdiagnostic relevance of SA and the related brain network.

Accurately predicting postoperative pulmonary function is essential for surgical decision-making in patients with pulmonary nodules. Here, we developed a hybrid model using preoperative computed tomography (CT) images and evaluated its accuracy and interpretability for predicting postoperative pulmonary function.

This retrospective study included 136 patients who underwent preoperative chest CT and postoperative pulmonary function tests. A pre-trained Inflated 3D ConvNet (I3D) model was fine-tuned for transfer learning to predict the postoperative pulmonary function, and the resulting model served as the feature extractor. Extracted deep learning features were combined with an elastic net regression for prediction. Models established using clinical features or radiomics with elastic net, end-to-end I3D transfer learning, and 3D ResNet18 trained from scratch were compared, with additional evaluation against a conventional segment-counting method. Performance was evaluated against spirometry, and interpretability was assessed using Grad-CAM.

The hybrid model exhibited the best external test performance. For postoperative forced vital capacity (FVC) prediction, the concordance correlation coefficient (CCC) was 0.707, the Pearson correlation coefficient (Pearson r) was 0.796, and the R-squared (R²) was 0.499. For postoperative forced expiratory volume in 1 s (FEV₁) prediction, the CCC was 0.729, Pearson r was 0.772, and R² was 0.418. Grad-CAM revealed the inferior and paravertebral lung regions in the FVC model and bilateral lung bases and diaphragmatic areas in the FEV₁ model.

The hybrid elastic net model based on fine-tuned I3D features predicted postoperative pulmonary function without requiring preoperative spirometry or detailed surgical planning and may guide the development of future predictive models.

Question CT-only prediction of postoperative pulmonary function remains an unmet clinical need due to the non-routine use of pulmonary function testing. Findings A hybrid CT-based model enables prediction of postoperative pulmonary function and outperforms conventional segment-counting, clinical, radiomics, and end-to-end deep learning approaches. Relevance Statement This hybrid CT-based model can estimate postoperative pulmonary function from routine preoperative non-contrast CT images, potentially assisting surgical decision-making for lung cancer.

Sodium-glucose cotransporter 2 (SGLT2) is a low-affinity, high-capacity transporter expressed predominantly in the renal proximal tubule, where it mediates the sodium-coupled reabsorption of most filtered glucose. SGLT2 inhibitors have evolved from glucose-lowering agents into disease-modifying therapies across the cardio-renal-metabolic continuum. Blockade of proximal tubular transport induces glycosuria, osmotic diuresis, restoration of tubuloglomerular feedback, resetting of renal haemodynamics, and altered fluid compartmentalisation. These early renal and circulatory effects interact with systemic metabolic adaptations, including pseudo-starvation signalling, erythropoiesis, altered uric acid handling, and changes in substrate availability. At the cellular level, SGLT2 inhibition has been linked to improved ionic homeostasis and mitochondrial quality control, reduced oxidative stress, modulation of inflammation, and attenuation of maladaptive tissue remodelling. Endothelial injury may connect these cellular effects with microvascular dysfunction and fibrosis. Endothelial dysfunction and endothelial-to-mesenchymal transition (EndMT) offer a mechanistic framework for understanding the relationship between microvascular injury and fibrosis. However, EndMT is best viewed as one component of a broader endothelial-stromal stress response rather than as a single proven mediator of clinical benefit. These pharmacodynamic and cellular effects help explain the consistent clinical efficacy of SGLT2 inhibitors across type 2 diabetes, chronic kidney disease, and heart failure (HF), while also informing emerging applications in acute HF, after myocardial infarction, and in patients with arrhythmia or metabolic dysfunction-associated steatohepatitis. This Review integrates mechanistic, pharmacological, and clinical evidence to identify unresolved translational gaps and future directions, including mechanism-informed biomarkers, imaging, spatial multi-omics, phenotype-guided patient stratification, dual sodium-glucose cotransporter 1/2 inhibition, and rational combination therapy.

In recent times, non-communicable diseases (NCDs) have emerged as a significant global public health challenge. Particularly impact in middle- and low-income countries, NCDs is responsible for 74.0% of global deaths, with South Asia, including Bangladesh, experiencing a mortality rate of 77.0%. Amidst an epidemiological transition, Bangladesh is witnessing a shift from infectious to chronic diseases. This study aims to explore the burden of NCDs in a tertiary care setting, offering insights to inform public health interventions and policies. A cross-sectional descriptive study was conducted at Sir Salimullah Medical College Mitford Hospital, Dhaka, involving 6698 patients admitted in 2022 (1st January 2022 to 31st December 2022). Data were collected retrospectively from medical records, focusing on patients above 14 years with NCDs. Statistical analyses were performed using SPSS 24.0. Among 6698 admissions, 42.05% were attributed to NCDs. Females exhibited a higher prevalence (56.37%) than males (43.63%). The age group 41-60 years demonstrated the highest NCD prevalence (37.20%). Major NCDs included hypertension (33.22%), diabetes (25.34%), stroke (17.85%) and cardiovascular diseases (12.14%). NCDs contributed to 88.41% of 164 total deaths, with cardiovascular diseases, stroke, chronic respiratory diseases and malignancies as primary causes. In brief, this study sheds light on the critical challenge posed by non-communicable diseases (NCDs) in Bangladesh. With a significant proportion of hospital admissions attributed to NCDs, urgent and targeted interventions are warranted. The observed prominence of NCDs and tha alarming rate of attributed deaths necessitates a shift in healthcare focus. This study's findings emphasize the imperative for innovative approaches, increased resource allocation and heightened awareness campaigns to effectively address the escalating impact of NCDs on both individuals and communities.

Heart failure (HF) is a common condition with substantial geographical variation in its prevalence and underlying etiologies. Consistent and comparable data on the burden of HF within populations are needed to inform health system priorities.

To estimate the global burden of HF.

This cross-sectional study estimates disease burden using Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) methodologies to integrate multiple information sources. Data come from 204 countries and territories from 1990 to 2023 representing the global population with prevalent HF.

HF and underlying etiologies.

Estimates with 95% uncertainty intervals (UIs) for the prevalence and years lived with disability (YLDs) for HF by age, sex, location, year, and underlying etiology.

Globally, there were 56.0 million (95% UI, 49.9-62.2 million) estimated cases of HF in 2023. The estimated prevalence of HF generally increased with age for all locations. While ischemic heart disease and hypertensive heart disease were the 2 most common etiologies globally among older adults, there was substantial variation in etiological pattern by location and age. Congenital heart anomalies and rheumatic heart disease (RHD) were common etiologies in children and young adults, while the proportion of HF due to alcohol use peaked in middle adulthood. Location-specific estimates highlighted regions with higher burden of HF due to specific etiologies, including RHD (south Asia, east Asia, Oceania, southeast Asia, central sub-Saharan Africa, eastern sub-Saharan Africa, southern sub-Saharan Africa, and western sub-Saharan Africa), alcohol use (eastern Europe, Australasia, the Caribbean, and central Europe), and Chagas disease (southern, Andean, tropical, and central Latin America).

Heart failure is a significant global public health burden, and the impact is likely to increase as populations age. However, most HF etiologies can be prevented by interventions that reduce exposure to modifiable risk factors, such as elevated blood pressure, excessive alcohol consumption, and tobacco use. Etiology- and location-specific estimates can provide necessary information for public health officials to determine how best to prioritize interventions to reduce HF disease burden.

Dilated cardiomyopathy (DCM) is a heterogeneous myocardial disease with variable clinical outcomes. Although both genetic variants and cardiac magnetic resonance (CMR)-derived fibrosis markers are associated with prognosis, their interactions and relative contributions to disease progression remain incompletely understood.

This study aims to explore the associations among genetic variants, CMR characteristics, and long-term prognosis in DCM.

The authors prospectively enrolled patients diagnosed with DCM who underwent whole-genome sequencing and CMR examination. DCM-associated genetic status was classified as TTN variant, non-TTN variant, and genotype-negative. CMR evaluation included cardiac structure and function, late gadolinium enhancement (LGE), and T1 mapping. Clinical outcomes included cardiovascular death/heart transplantation, sudden cardiac death, and arrhythmic events.

Of the 837 enrolled patients, 163 (19.5%) carried pathogenic/likely pathogenic variants, including 78 (9.3%) non-TTN and 85 (10.2%) TTN variants. Patients with non-TTN variants demonstrated the greatest fibrosis burden, with higher LGE prevalence (69.2% vs 24.7% in patients with TTN variants and 46.6% in genotype-negative patients, P < 0.001), greater LGE extent, more frequent ring-like and subepicardial patterns, and higher native T1 and extracellular volume fraction (ECV) values. In multivariable Cox regression analysis, non-TTN variants independently predicted cardiovascular death/heart transplantation (adjusted HR: 2.39; 95% CI: 1.46-3.92; P < 0.001) and sudden cardiac death (adjusted HR: 3.26; 95% CI: 1.59-6.65; P = 0.001), together with left ventricular end-diastolic volume index, LGE, and ECV. Patients with non-TTN variants consistently had worse outcomes among patients with LGE presence, greater LGE extent, elevated ECV, and in both left ventricular ejection fraction strata (≤35% and >35%) (log-rank P < 0.05). Mediation analysis showed LGE (indirect HR: 1.26; 95% CI: 1.11-1.47; P = 0.008) and ECV (indirect HR: 1.29; 95% CI: 1.12-1.53; P = 0.006) partly mediated the association between non-TTN variants and adverse outcomes, whereas left ventricular end-diastolic volume index and left ventricular ejection fraction did not show significant mediating effects.

Genetic background was associated with distinct myocardial fibrosis phenotypes and provided prognostic information beyond conventional CMR-derived fibrosis and functional phenotypes in DCM. Myocardial fibrosis partly mediates the association between non-TTN variants and adverse outcomes.

Suboptimal glycemic control remains a significant public health challenge among adults with type 2 diabetes and prediabetes, with 47.4% of US adults with diagnosed diabetes having hemoglobin A_1c (HbA_1c) ≥7%. Remote glucose monitoring programs have shown promise for supporting self-management, but real-world evidence on the causal impact of varying patient engagement levels on glycemic outcomes remains limited.

This study aimed to estimate the causal dose-response relationship between patient engagement, operationalized as weekly glucose monitoring frequency, and glycemic control measured by HbA_1c among adults enrolled in a comprehensive primary care-integrated remote monitoring program.

We conducted a retrospective cohort study of 1436 adults with type 2 diabetes or prediabetes enrolled in the Unika Health program between 2019 and 2024. The program integrated Bluetooth-connected glucose meters, a mobile app, structured lifestyle coaching, and primary care coordination across 74 physician practices. Engagement was defined as mean weekly glucose monitoring frequency during the first 6 months. The causal effect of monitoring frequency on 6-month HbA_1c was estimated using marginal structural models (MSMs) with inverse probability weighting to address time-varying confounding. Covariates included age, sex, BMI, baseline HbA_1c, comorbidities, medication status, and physical activity level.

The cohort was predominantly older (95.4%, 1370/1436 aged ≥46 years), with 82.6% (1186/1436) having hypertension and 45.9% (659/1436) classified as obese. Overall, HbA_1c decreased by a mean of 0.54 (SD 1.47; 95% CI 0.47-0.62) percentage points (P<.001) over 6 months. Cluster analysis identified 3 engagement tiers: low (n=835; mean 2.55, SD 1.35 measurements/week), medium (n=493; mean 6.19, SD 1.46 measurements/week), and high (n=108; mean 12.59, SD 3.03 measurements/week). A monotonic dose-response was observed, with mean HbA_1c reductions of 0.38 (SD 1.40; 95% CI 0.29-0.48), 0.71 (SD 1.50; 95% CI 0.58-0.85), and 1.01 (SD 1.67; 95% CI 0.69-1.32) percentage points for the low, medium, and high tiers, respectively (all P<.001 by 2-tailed paired t test). In weighted MSMs, each additional weekly measurement was associated with a 0.05 (95% CI 0.03-0.07) percentage point greater HbA_1c reduction (P<.001). Among patients with high baseline HbA_1c (≥9%), the high-engagement group achieved a mean reduction of 3.12 (SD 1.97; 95% CI 2.29-3.94) percentage points. Findings were consistent in sensitivity analyses at 3 months (β=-0.04; P<.001) and 12 months (β=-0.03; P=.003) and across alternative weighting specifications.

Higher engagement with a digitally enabled, primary care-integrated remote glucose monitoring program was causally associated with significantly greater HbA_1c reductions in adults with type 2 diabetes and prediabetes. These findings support scalable remote patient monitoring strategies that actively foster sustained patient engagement as an effective approach to improving glycemic control and reducing the burden of diabetes-related complications at a population level.

Systemic inflammation and impaired nutritional status are important drivers of adverse outcomes in peripheral artery disease (PAD). The hemoglobin-albumin-lymphocyte-platelet index (HALP) represents a multidimensional marker reflecting the interaction between hematologic, nutritional, and inflammatory status.

This study evaluated the independent prognostic value of HALP score and neutrophil-lymphocyte ratio (NLR) in determining mortality in PAD patients.

This retrospective cohort study consisted of 638 individuals with a first-time diagnosis of PAD. Patients were categorized as deceased (n = 241) and survivors (n = 397). Clinical characteristics, laboratory findings, inflammatory indices, NLR and HALP scores were analyzed. Survival probabilities were assessed using Kaplan-Meier curves, and independent determinants of mortality were examined through logistic regression analysis.

The mean follow-up period was 36.17 months for deceased patients and 71.69 months for survivors. Deceased patients exhibited significantly lower HALP scores (34.6 ± 2.1 vs. 51.9 ± 2.2, P = 0.047). While the HALP score demonstrated weak discriminative capacity in ROC analysis, Cox regression showed that lower HALP levels were independently associated with decreased survival status (Hazard ratio (HR) 0.46; 95% confidence interval (CI): 0.35-0.60; P < 0.001). The higher NLR levels had significantly elevated all-cause mortality rates (HR, 1.87; 95% CI: 1.44-2.44; P < 0.001). In multivariable analysis, HALP score, age, C-reactive protein levels, chronic renal failure, diabetes mellitus, and heart failure were independent predictors of death.

Lower HALP scores are related to increased long-term mortality in PAD. The HALP score provides complementary prognostic information and may enhance holistic risk stratification in PAD.

Peripheral neuropathy is among the complications associated with diabetes and is often accompanied by numbness, which can cause tissue damage and lead to the formation of diabetic foot ulcers (DFUs). This research aimed to investigate the prevalence and clinical outcomes of DFU among diabetic patients in Jordan.

A retrospective longitudinal cohort study was conducted between January 2013 and December 2023. All patients with DFU attending the University of Jordan Hospital and above 18 years of age were retrospectively followed up for at least one year. Mortality rates were considered as secondary outcomes.

A total of 3131 DFU patients were identified, with 1648 inpatients and 1483 outpatients. Overall, the cumulative percentage of the prevalence of DFU during the 11 years was 6.57%. DFUs were found to have increased the length of stay in hospitals (mean = 9.46 days), a high number of complicated cases of ulcers with infections: Inpatient (37.2%), outpatient (25.3%); and significant amputations: minor amputations (20.4%), major amputations (12.4%). The prevalence of infected ulcers among DFU patients was 12.2%. The mean survival time was 70.4 days (±3.3), with a 95% confidence interval of 64.0 to 76.9 days. The number of conditions, including gangrene, myocardial infarction, kidney disease, retinopathy, and neuropathy, were marginally linked to increased mortality risks.

Despite the progress that has been achieved in the management and treatment of diabetes, the frequent hospitalizations, infections, and limb amputations that occur as a result of DFUs underscore the importance of effective management and prevention of the disease.