The burden of stroke can be reduced by controlling its mortality risk factors. We aimed to identify the predictors of mortality within six months after ischemic stroke.

This prospective cohort study was performed on 703 ischemic stroke patients in Tehran, Iran, during 2018-2019. Data on demographic and clinical characteristics were collected through interviews and hospital records. The patients' survival status was followed up by telephone interviews at 28 days, 3 months, and 6 months after stroke. Cox proportional hazard model and extended Cox model were used to determine the predictors of mortality after stroke.

The 6-month mortality rate was 19.50% (95% CI: 16.70-22.67). Age (HR=1.01; 95% CI: 1.001-1.03), higher educational levels (HR=1.05; 95% CI: 1.01-1.10), and blood sugar levels on admission (HR=1.04; 95% CI: 1.01-1.08) were significantly associated with an increase in 6-month mortality. However, alcohol consumption (HR=0.09; 95% CI: 0.02-0.38), alteplase administration (HR=0.65; 95% CI: 0.43-0.98), and higher hemoglobin values (HR=0.80; 95% CI: 0.72-0.88) were associated with a decrease in 6-month mortality. The hazard ratio of death for diastolic blood pressure, socioeconomic status, cholesterol levels, and stroke severity varied over time.

Some characteristics significantly increased or decreased the risk of mortality after stroke. Additionally, the effect of some variables changed over time, suggesting that stroke prognosis may be associated with dynamic risk factors. Identifying and addressing these factors can inform targeted strategies to improve post-stroke survival outcomes.

The oral cavity acts as the anatomical gateway to the respiratory tract, sharing both microbiological and pathophysiological links with the lower airways. Although radiotherapy is a cornerstone treatment for lung cancer, reliable oral microbiome biomarkers for predicting patient outcomes remain lacking. We analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls across discovery and two validation cohorts via 16S rRNA sequencing. Healthy controls exhibited a significantly higher abundance of Streptococcus compared to patients (p = 0.049, p < 0.001, p < 0.001, respectively). The structure of the microbial community exhibited substantial dynamic changes during treatment. Responders showed enrichment of Rothia aeria (p = 0.027) and Prevotella salivae (p = 0.043), associated with prolonged overall survival (OS) and progression-free survival (PFS), whereas non-responders exhibited elevated Porphyromonas endodontalis (p = 0.037) correlating with shorter OS and PFS. According to Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) analysis, Akkermansia and Alistipes were nearly absent in non-responders, while Desulfovibrio and Moraxella were virtually absent in responders. A diagnostic model based on Streptococcus achieved area under the curve (AUC) values of 0.85 (95% CI: 0.78-0.91) and 0.99 (95% CI: 0.98-1) in the validation cohorts, and a response prediction model incorporating Prevotella salivae and Neisseria oralis yielded an AUC of 0.74 (95% CI: 0.58-0.90). Furthermore, in small cell lung cancer, microbiota richness and diversity were inversely correlated with Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.008, p < 0.001, respectively) and pro-gastrin-releasing peptide (ProGRP) levels (p = 0.065, p = 0.084, respectively). These results demonstrate that lung cancer-associated oral microbiota signatures dynamically reflect therapeutic response and survival outcomes, supporting their potential role as non-invasive biomarkers for diagnosis and prognosis.

The escalating prevalence of obesity and diabetes mellitus (DM) worldwide poses significant challenges to global health systems, particularly in countries such as Libya, where public health infrastructure faces various constraints. This study explores the levels of awareness and knowledge regarding obesity and DM among the Libyan population, aiming to identify factors influencing awareness and to assess the impact of this awareness on preventive healthcare practices.

Between August and September 2023, a cross-sectional survey was conducted among Libyan citizens aged 18-75 years. A bilingual online questionnaire, validated in prior research, was employed to assess participants' knowledge of obesity and DM, along with demographic variables.

Among 980 participants, knowledge of DM was significantly influenced by age (p = 0.04), socioeconomic status (p = 0.009), and education level (p = 0.03). Participants aged 20-40 years had the highest median score (14, IQR: 12-17), while having DM or a family history of DM was associated with lower scores (p < 0.0005). Multiple linear regression identified these factors, along with gender, as significant predictors (p < 0.05).

This study highlights critical gaps in the awareness and knowledge of DM and obesity among Libyan adults. Although overall awareness levels are relatively high, significant deficits in comprehensive understanding persist, particularly among older adults, individuals with lower socioeconomic status, and those with limited access to digital resources. Addressing these disparities requires multifaceted public health strategies, including tailored community-based initiatives, the integration of digital tools and traditional communication channels, enhanced training for healthcare providers, and culturally sensitive interventions.

Managing type 1 diabetes mellitus (T1DM) in elderly patients with multiple comorbidities presents significant challenges, particularly when complicated by persistent glycemic variability, severe hypoglycemia, and hypoglycemia unawareness. We present a 72-year-old Saudi male with T1DM, ischemic heart disease, hypertension, hypothyroidism, and peripheral neuropathy who was transitioned from multiple daily injections to insulin pump therapy. Prior to intervention, he had frequent severe hypoglycemia and elevated glycated hemoglobin (HbA1c) levels (9.4%, 8.8%). Following the initiation of hybrid closed-loop insulin pump therapy, glycemic control improved (HbA1c 7.7%), and hypoglycemia episodes decreased. However, a language barrier led to a critical insulin dosing error and hospitalization. This case highlights both the benefits and potential risks of insulin pump use in elderly patients. It underscores the importance of addressing language barriers, providing individualized education, and involving caregivers to reduce adverse outcomes. The case adds to the limited data on advanced diabetes technology in the elderly with complex comorbidities. Insulin pump therapy can be effective in elderly patients with T1DM and multiple comorbidities when personalized support and appropriate safeguards are implemented.

Background Thyroid dysfunction during pregnancy has emerged as an important yet under-recognized contributor to adverse maternal and fetal outcomes. Both gestational diabetes mellitus (GDM) and thyroid disease share overlapping metabolic and hormonal pathways that may synergistically amplify obstetric risk. In developing countries such as India, where the dual burden of iodine deficiency and gestational dysglycemia persists, understanding this association is essential for optimizing antenatal care. This study aimed to evaluate the prevalence, determinants, and perinatal consequences of thyroid dysfunction among women with GDM in the second trimester. Methods A hospital-based cross-sectional analytical study was conducted among 105 pregnant women aged 18-40 years diagnosed with GDM using the DIPSI single-step test (2-hour plasma glucose ≥140 mg/dL after 75 g glucose). Women with pre-existing thyroid or systemic illness were excluded. Serum thyroid-stimulating hormone (TSH), free thyroxine (FT4), and anti-thyroid peroxidase (anti-TPO) antibodies were measured using the electrochemiluminescence immunoassay method. Thyroid dysfunction was classified using ATA pregnancy-specific reference ranges. Maternal demographic, biochemical, and obstetric parameters were analyzed using SPSS version 26. Logistic regression identified predictors of thyroid dysfunction, with p < 0.05 considered significant. Results Thyroid dysfunction was detected in 11.4% of GDM women, comprising mainly subclinical hypothyroidism. Women with thyroid dysfunction were significantly older (30.2 ± 4.8 years) and had higher BMI (28.5 ± 4.2 kg/m²) compared to euthyroid women (p = 0.042 and p = 0.008, respectively). Family history of thyroid disorder (41.7% vs 12.9%; p = 0.012) and anti-TPO positivity (33.3% vs 5.4%; p = 0.003) were strongly associated. On multivariate analysis, anti-TPO positivity (aOR 6.78; p = 0.016) and familial thyroid history (aOR 4.12; p = 0.047) independently predicted dysfunction. Insulin therapy was required more often (50% vs 24.7%), indicating greater metabolic derangement. Adverse neonatal outcomes were higher - macrosomia (41.7% vs 19.4%; p = 0.048) and NICU admission (33.3% vs 12.9%; p = 0.045) - among women with thyroid dysfunction. Conclusion The coexistence of thyroid dysfunction and GDM in mid-pregnancy is clinically significant and frequently autoimmune in nature. Routine screening for thyroid function and anti-TPO antibodies in GDM women can facilitate early diagnosis, prevent perinatal complications, and improve maternal-fetal outcomes through timely intervention.

Introduction Diabetes is a major public health challenge in India, and simple, low-cost tools are needed to detect early microvascular complications. We explored whether the neutrophil-to-lymphocyte ratio (NLR) can help predict such complications in people with type 2 diabetes. Methods We conducted a cross-sectional study in 2022-23, enrolling 150 patients aged 40-80 years with type 2 diabetes. Each participant underwent a detailed clinical evaluation, including blood sugar, HbA1c, NLR, and screening for diabetic neuropathy, nephropathy, and retinopathy. Results The mean age of participants was 58.9 ± 9.3 years. Comorbidities were present in 84% of participants (n=126), mainly dyslipidaemia (n=102, 68%) and hypertension (n=86, 57.3%). Almost half of the patients (n=73, 48.6%) had neuropathy, 42.6% (n=64) had nephropathy, and about one-quarter (n=39, 26%) had retinopathy. The mean NLR was higher in patients with microvascular complications than in those without (p < 0.001). The NLR correlated strongly with neuropathy (r = 0.72, p < 0.001) and moderately with nephropathy (r = 0.39, p < 0.001) and retinopathy (p < 0.001). A higher NLR was also associated with poor glycaemic control (HbA1c) (r = 0.41, p < 0.001) and central obesity (waist-to-hip ratio) (r = 0.31, p < 0.001). NLR showed high prediction for neuropathy (cut-off = 1.75, AUC = 0.926, sensitivity 93.2%, specificity 74%) and retinopathy (cut-off = 2.15, AUC = 0.942, sensitivity 94.9%, specificity 78.4%), but not for nephropathy. Conclusion Although the NLR correlated significantly with all microvascular complications in T2DM, it showed strong predictive ability only for neuropathy and retinopathy. Thus, it may serve as a cost-effective marker for predicting microvascular complications.

Diabetic foot ulcer (DFU) is a common and serious complication in patients with diabetes, which affects the quality of life greatly as well as brings high risk for mortality. Identification of high-risk individuals, as early as possible is important for efficient intervention and prevention. This study systematically evaluates and summarizes the diagnostic accuracy of machine learning approaches for predicting DFU risk in diabetic patients.

This study adhered to the TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Extended for Artificial Intelligence) guidelines. Using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004 to determine diagnosis of DFU related clinical characteristics, laboratory indicators and lifestyle-related variables. The diagnostic performance of its models trained using Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classifiers were compared. An independent testing dataset collected from the Second Affiliated Hospital of Zunyi Medical University was used to conduct external validation.

This study included 1, 857 participants from NHANES and 807 individuals recruited at the testing dataset. Key predictors identified in NHANES were numbness in extremities, direct HDL cholesterol, lymphocyte, white blood cell, segmented neutrophils, and BMI. Among them, the RF was identified as having the highest area under receiver operating characteristic curve (AUC) for NHANES at 0.81. The RF model also had the highest discriminative performance in external validation (as measured by an AUC of 0.79). Other models also provided good results in external validation: XGBoost had an AUC of 0.76, SVM reached 0.72, KNN reached 0.70, and LR received a score of 0.69.

The ability of machine learning models to predict DFU risk was good in a combined population cohort when measured using common metrics but varied across distinct regions. These results support future clinical evaluation of these models and underscore the need to select algorithms a priori based on the target patient population.

The objective of this study is to describe the short-term change observed in CGM-related measures and relevant clinical variables in individuals with type 1 diabetes transitioning to Omnipod 5 insulin treatment within a real-world setting.

The study involved adults with type 1 diabetes treated with Omnipod 5, whose data were collected over a 14-days observation period prior to (Time 0) and following the (Time 1) initiation of the patch pump use.

A total of 20 adults with well-controlled glycemia were included in the study. From baseline to follow-up, Time in Range (TIR) significantly increased from 57.3% to 67.3% (P<0.001). Concurrently, there were significant decreases in Time Above Range (TAR) Level 1 (mean difference, -4.7 ± 6.1%, P = 0.003) and Level 2 (-4.2 ± 6.1%, P = 0.018), as well as in Time Below Range (TBR) Level 1 (-1.0 ± 1.1%, P<001), TBR Level 2 (-0.4 ± 0.5%, P = 0.015), and Glycemia Risk Index (-13.8 ± 15.1 P<0.001). Importantly, no significant changes in insulin doses were observed during the study period.

Omnipod 5 initiation allowed participants to improve CGM-related metrics and the quality of glucose control in the short-term, without increasing the need for insulin.

Diabetes Mellitus (DM) is a chronic metabolic disease that has become increasingly prevalent over the past few years as a result of sedentary lifestyles. Up to 70% of diabetic patients have skin lesions due to deterioration in the skin barrier and changes in the physiological pH of the skin, leading to alterations in both innate and adaptive immunity, which predisposes individuals to bacterial, fungal, and viral infections. This article focuses on the superficial mycoses associated with DM. We conducted a search in the following databases: Pubmed, EMBASE, and Web of Science. Using a date range of 15 years from 2010 to 2025 resulted in 178 articles with the following inclusion criteria: written in English, be relevant to the topic at hand, with Open or institutional access. We observe that these infections remain at the level of the stratum corneum and, in exceptional cases, affect the epidermis and dermis. The three most common types are those caused by dermatophytes (tineas), infections by yeast species of Candida (candidiasis), and non-dermatophyte mold (NDMs). DM increases the risk of these infections, highlighting the need for effective prevention.

We examined the associations between obesity-related indices and the risk of diabetes progression from prediabetes in older adults, comparing the differences in using the American Diabetes Association (ADA) and World Health Organization (WHO) criteria.

Data were obtained from the Healthy Aging Evaluation Longitudinal Study conducted in China. At baseline, prediabetes (in participants without diabetes) was classified based on fasting plasma glucose (FPG) levels using both criteria. Body mass index (BMI) and waist circumference (WC) were categorized according to data distribution and diagnostic cut-off values, respectively. Cox proportional hazards regression models were used to estimate the adjusted hazard ratios ( aHRs) with 95% confidence intervals ( CIs) for obesity-related indices and diabetes progression from prediabetes.

Among the 1,127 participants classified as prediabetic according to the ADA criteria, 474 met the WHO criteria. Under ADA-defined prediabetes, the highest WC quartile (≥ 93 cm) was significantly associated with an increased diabetes risk ( aHR 1.93 [1.06, 3.53, P < 0.05]), whereas BMI-related and cut-off-based abdominal obesity demonstrated no significant associations ( P > 0.05). Under WHO-defined prediabetes, both the high tertile of WC (≥ 90 cm) and general obesity (BMI ≥ 28.0 kg/m ²) were significantly associated with progression to diabetes ( P < 0.05), with aHR 2.13 (1.06, 4.27) and 2.44 (1.19, 5.01), respectively. However, cut-off-based abdominal obesity and the high BMI tertile (≥ 25.75 kg/m ²) were not significantly associated with diabetes progression ( P > 0.05).

Elevated WC, rather than BMI-based indices or cut-off-based abdominal obesity, was significantly associated with diabetes progression according to the ADA-defined prediabetes criteria. However, both the evaluated WC and general obesity predicted progression to diabetes according to the WHO criteria.