Antenatal depression, defined as clinically significant depressive symptoms occurring during pregnancy, has been suggested to increase the risk of gestational diabetes mellitus (GDM), a glucose intolerance disorder with onset or first recognition during pregnancy. However, evidence regarding its relationship with GDM remains inconsistent. This meta-analysis aimed to quantitatively assess the association between antenatal depression and the risk of GDM.

We systematically searched PubMed, Embase, Wanfang, and the Cochrane Library from inception to June 12, 2025, for observational studies reporting the association between depression during pregnancy and GDM. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a random-effects model.

A total of eight studies were included in the meta-analysis. The pooled analysis showed that maternal depression during pregnancy was significantly associated with an increased risk of GDM (OR = 1.37, 95% CI: 1.20-1.54). Subgroup analyses based on country, depression assessment tool, and study design showed consistent results. Sensitivity analyses confirmed the stability of the results. No significant publication bias was detected.

This meta-analysis suggests that maternal depression during pregnancy is associated with a significantly increased risk of developing GDM. Screening for depression in early pregnancy may represent a potential strategy to reduce the risk of GDM and improve maternal health outcomes.

Diabetes mellitus has become a highly common noncommunicable condition. An in-depth understanding of the disease condition, its influencing factors, and effective management practices can provide numerous benefits to the diabetic population. Not only does it prevent the complications associated with diabetes, but it also helps people make informed decisions regarding the management choices and ensures better compliance. The study aims to determine the knowledge and practices regarding diabetes mellitus among the type 2 diabetic population.  Methods: The study design was cross-sectional, involving a total of 200 participants who attended the outpatient department (OPD) of the urban and rural field practice areas under ESIC Hospital, Hyderabad, Telangana. The study was conducted over two months. A semistructured, interviewer-administered questionnaire was prepared in the patient's preferred languages to evaluate the knowledge and practices of diabetes mellitus among the type 2 diabetic population. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 21 (Released 2012; IBM Corp., Armonk, New York, United States), with the statistical significance fixed at p-values less than 0.05.  Results: Our study found that the average age of the study population was 56.72, and most of them were males. The urban participants outnumbered the rural participants. The data obtained were compared between the urban and rural settings. We observed a statistically significant (p < 0.0001) relationship in the educational status of diabetic patients in the urban versus rural areas. The proportion of educated participants was higher in the urban areas. Similarly, the illiteracy rate was found in 10 (18.86%) and eight (5.44%) rural and urban participants, respectively, showing a higher proportion in the rural setting. In addition to this, the results revealed a significant (p = 0.0214) link between the perceived risk factors of diabetes, with 124 of the 470 (26.38%) responses given by the participants citing a stressful lifestyle as the major contributor. Among the most common organs affected by diabetes, the kidneys emerged as the most common response. Furthermore, the most popular management strategies adopted for diabetes were exercise by 103 (32.08%), diet by 101 (31.46%), and medications by 97 (30.21%) participants, with the urban and rural populations relying more on medicines and diet plus exercise, respectively. However, this was not statistically significant.  Conclusion: The study thus successfully narrates the current level of awareness about type 2 diabetes in Telangana. It highlights some valid differences in the data collected from the urban and rural participants as well. This adds to the existing knowledge database, which is already limited, and facilitates further policy development.

Preeclampsia (PE) and gestational diabetes mellitus (GDM) are major contributors to maternal and neonatal morbidity and mortality. Early detection is critical, yet current approaches, such as clinical risk scores for PE and glucose challenge/oral glucose tolerance test (OGTT) screening for GDM, often show limited sensitivity and variable predictive accuracy. Artificial intelligence (AI) and machine learning (ML) offer promising avenues for enhancing early prediction and diagnosis. This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, synthesized evidence from five databases (PubMed, Scopus, Embase, IEEE Xplore, ACM Digital Library) covering January 2020-July 2025. Eligible studies included both model development and validation efforts in pregnant populations. Data were extracted on study characteristics, AI model types, and diagnostic performance metrics. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Nine studies met the inclusion criteria, reflecting strict eligibility requirements and limited high-quality research in this area. AI models frequently achieved strong performance, with area under the curve (AUC) values often >0.85. For PE, a neural network model externally validated in Spain achieved AUCs of 0.920 and 0.913 for early and preterm PE, with sensitivity up to 84%. For GDM, an XGBoost model achieved an AUC of 0.946 with an accuracy of 87.5%, while a Random Forest model reached a sensitivity of 75-85% and a specificity of 88-91%. Ensemble methods generally outperformed logistic regression. Seven studies were judged low risk of bias, while two were high risk, particularly in participant selection and analysis domains. Several models also demonstrated good calibration and positive net benefit on decision curve analysis, comparable to established clinical tools. AI models show substantial potential for early detection of PE and GDM, though heterogeneity and limited external validation remain barriers. Future research should prioritize multicenter, prospective validation, standardized reporting, and attention to equity and generalizability to ensure safe and effective translation into clinical practice.

Co-secretion of aldosterone and cortisol from an adrenal adenoma is considered a clinically significant condition that can negatively affect blood pressure (BP) control and metabolic status. Studies in cohorts with primary aldosteronism (PA) have identified autonomous cortisol secretion (ACS) in a significant proportion of these patients. However, the frequency and clinical characteristics of this so-called "Connshing syndrome" among patients with adrenal incidentalomas (AIs) remain not sufficiently evaluated.

The main aim of the study was to investigate, in a prospective design, the prevalence and the clinical characteristics of mixed aldosterone and cortisol secretion among patients with AIs referred to our expert center. According to our hypothesis, we expected a low prevalence and an atypical clinical presentation in these patients.

PA was diagnosed based on the aldosterone/renin ratio (ARR) as a screening test, followed by two confirmatory tests: the captopril challenge test (CCT) and the dexamethasone-captopril-valsartan test (DCVT). Unilateral aldosterone-cortisol oversecretion was confirmed by imaging studies (computed tomography (CT) or magnetic resonance imaging (MRI)) in all patients and adrenal venous sampling (AVS) in the single patient with bilateral adenomas. ACS was confirmed via the overnight 1 mg dexamethasone suppression test (DST).

We conducted a prospective study on 399 patients with AIs referred to our expert center during the last three years (from May 10, 2022, to May 10, 2025). Aldosterone and cortisol co-secretion (A/C-CoS) was identified in four patients (≈1%), presenting different clinical manifestations. Three patients had well-controlled hypertension on conventional antihypertensive therapy, and one was normotensive. Three of the four patients had dyslipidemia. Carbohydrate disturbances were found in two patients with normal body mass index (BMI) (one woman with impaired fasting glucose and one man with overt diabetes mellitus (DM)). The other two subjects were slightly overweight with normal glucose tolerance.

Mixed aldosterone and cortisol hypersecretion is usually associated with poor BP control and metabolic disturbances, but in real clinical practice, a wide range of disorders is observed, from severe resistant hypertension to rare cases of normotension and from pronounced metabolic syndrome to a complete absence of metabolic abnormalities. Atypical presentation of A/C-CoS is more likely to be found among patients with AIs. This observation confirms the need for a comprehensive hormonal evaluation of all patients with AIs, regardless of their clinical presentation.

During the COVID-19 pandemic, primary health care systems worldwide adapted to manage cases in outpatient settings, including those involving children. The aim of this study was to investigate the epidemiological characteristics of 27,205 outpatient COVID-19 cases among children (0-17 years) in Almaty, Kazakhstan, from 1 January 2021 to 31 December 2022, compared with major epidemiological events and public health measures.

A cross-sectional analysis was conducted to assess the likelihood of hospitalization regarding demographic characteristics, concomitant diseases, the severity of COVID-19 course, as well as the dynamic of cases.

The majority of children (99.3%) were asymptomatic or mild. Children in the younger age group (0-4) had a higher risk of severe course and hospitalization compared with adolescents aged 15-17 years. Sex and chronic diseases (diabetes mellitus, obesity and chronic obstructive pulmonary disease) did not demonstrate statistical significance. The longest spike in outpatient COVID-19 cases in children coincided with the circulation of Delta and Eta strains, the highest with Omicron.

Among outpatient COVID-19 cases in children, the likelihood of severe forms and hospitalization is higher if the child is under 5 years of age.

This study aimed to determine the prevalence of anaemia and associated factors among type 2 diabetic patients.

This research utilised a hospital-based cross-sectional study design.

The research was conducted at the Diabetic Clinic of Ho Municipal Hospital.

The study involved 180 type 2 diabetic patients, aged 20 years or older, who had been on anti-diabetic medications for a minimum of three months before the study. Premenopausal women who had not menstruated in the two weeks before recruitment were also included in the study. Participants were excluded if they were receiving haematinics, had undergone a blood transfusion in the preceding month, were undergoing treatment for malaria or helminthiasis, or had any other chronic complications such as renal failure, liver disease, or stroke. Individuals with type 1 diabetes and pregnant women were excluded from the study.

Approximately a quarter [44 (24.4%)] of the study population had anaemia, with a slight male preponderance [15(25.0%)]. Mild and moderate anaemia were 31 (70.5%) and 13(29.5%), respectively. Microcytic hypochromic anaemia [16 (36.4%)] was the most frequent morphological type of anaemia, followed by normocytic normochromic anaemia [12(27.3%)]. High BMI and low platelet counts were independently associated with reduced odds of developing anaemia in patients with type 2 diabetes mellitus.

Anaemia is a common finding in patients with type 2 diabetes mellitus in the Ho municipality. Mild anaemia and microcytic hypochromic anaemia were predominant among the anaemic patients. High BMI and low platelet count were significant predictors of reduced probability of anaemia.

None declared.

The burden of sarcopenia is increasing but studies on sarcopenia at the population level are limited in Malaysia. This study was conducted to identify the prevalence, risk factors and effect of sarcopenia on functional status and falls among the elderly in Selangor state.

Anthropometry, body composition measurements and face-to-face interview using questionnaires on functional status and falls were conducted on 469 respondents. Prior to the interview, written informed consent was obtained from the respondents. The inclusion criteria for this study is being 60-years old and above, able to understand, read and speak Bahasa Malaysia or English language and voluntarily consents to participates in the study. Multifrequency bioelectrical impedance analysis (BIA) was used to measure the body composition. Sarcopenia assessment was done using the guideline from Asian Working Group for Sarcopenia (AWGS) 2019.

The prevalence of possible sarcopenia, confirmed sarcopenia and severe sarcopenia were 38.4%, 10.0% and 24.5%, respectively. Prevalence of activities of daily living (ADL) and instrumental activities of daily living (IADL) dependence were 26.0% and 25.4%, respectively and 42.2% of the respondents experienced falls in the last 12 months. Multinomial logistic regression model showed that locality (AOR = 2.90; p < 0.001), type-2 diabetes mellitus (adjusted odds ratio (AOR) = 1.87; p = 0.031) and female gender (AOR = 2.58; p < 0.001) were significantly associated with possible sarcopenia. Female gender (AOR = 3.04; p = 0.005) and depression (AOR = 3.27; p = 0.048) were significantly associated with confirmed sarcopenia, where else hypertension (AOR = 0.45; p = 0.039) were found to be a protective factor for confirmed sarcopenia. Age (AOR = 4.52; p < 0.001), female gender (AOR = 1.84; p = 0.045), race (AOR = 3.82; p = 0.001), locality (AOR = 3.82; p < 0.001), level of education (AOR = 5.32; p = 0.010) and physical activity (AOR = 2.28; p = 0.029) were significantly associated with severe sarcopenia. The respondents with confirmed sarcopenia and severe sarcopenia were significantly associated with ADL (AOR = 10.54; p < 0.001) and IADL (AOR = 8.55; p < 0.001) dependence after adjustment for the covariates. In addition, after adjusting for covariates, respondents with possible sarcopenia (AOR = 3.34; p < 0.001) and respondents with confirmed sarcopenia and severe sarcopenia (AOR = 10.62; p < 0.001) were significantly associated with falls incidents.

The findings from this study highlights the detrimental effects of sarcopenia and the importance of early detection at the community level.

This study was conducted to compare postpartum depression, anxiety, sleep quality, and neonatal outcomes in mothers with pregestational and gestational diabetes mellitus.

Designed as a descriptive, comparative, and prospective study, the sample consisted of 80 mothers (40 with pregestational diabetes mellitus (PGDM) and 40 with gestational diabetes mellitus (GDM)), who were monitored by the Pediatric Outpatient Clinic of Balıkesir University Training and Research Hospital in Türkiye. The data were collected using the "Descriptive Information Form," "Pittsburgh Sleep Quality Index (PSQI)," "Beck Anxiety Inventory (BAI)," and "Edinburgh Postnatal Depression Scale (EPDS)."

In terms of neonatal outcomes, the rate of newborn intervention was 40.0%, exclusive breastfeeding 22.5%, and combined breastfeeding and formula feeding 52.5% in the GDM group. These rates were 37.5%, 25.0%, and 60.0%, respectively, in the PGDM group. The most common neonatal complications among infants of PGDM mothers were hypoglycemia (42.5%), hyperbilirubinemia (37.5%), and large for gestational age (LGA) (27.5%), while in the GDM group, hypoglycemia (42.5%), respiratory distress syndrome (RDS) (27.5%), and hyperbilirubinemia (22.5%) were most frequently observed. In our study, mothers in the PGDM group had significantly higher scores on both the Edinburgh Postnatal Depression Scale and the Beck Anxiety Inventory compared to those in the GDM group.

These results suggest that healthcare professionals should address not only diabetes treatment and monitoring, nutrition, weight management, and exercise during the prenatal and postpartum periods but also monitor emotional issues such as sleep disturbances, anxiety, and depression in mothers with pregestational and gestational diabetes to improve maternal and neonatal health outcomes.

Type 2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD) may exacerbate atherosclerosis through mechanisms such as chronic inflammation, oxidative stress, and lipid redistribution. This study aims to investigate the relationship between atherogenic index of plasma (AIP) and cardiovascular disease (CVD) mortality in patients with T2DM and DKD.

COX regression models and restricted cubic spline (RCS) curves were utilized to analyze the survival outcomes and non-linear associations among patients with T2DM and DKD. Threshold effects were observed in both global and inflection point models, confirming the predictive value of AIP for participants. Subgroup analysis further investigated the interaction of other variables within the AIP index on T2DM and patients with DKD with CVD mortality.

A total of 5,108 patients diagnosed with T2DM, along with 1,598 patients suffering from DKD, were included in this study. In patients with DKD, the AIP index has a "U"-shaped association with CVD mortality. The hazard ratio (HR) and 95% confidence interval of the third quartile of CVD mortality in patients with DKD is Model3: 0.76 (0.61-0.96) (P < 0.023). The inflection point of the threshold effect analysis is 0.14. When AIP is less than 0.14, the effect size and 95% confidence interval of CVD mortality risk in patients with DKD is 0.62 (0.40-0.96) (P < 0.033). When AIP is greater than or equal to 0.14, the effect size and 95% confidence interval of CVD mortality risk in patients with DKD is 1.68 (1.15-2.45) (P < 0.008).

The relationship between AIP and CVD mortality in patients with DKD exhibits a "U" shaped curve, with a turning point value of 0.14. When AIP is below 0.14, the risk significantly decreases (HR = 0.62); conversely, when AIP is equal to or exceeds 0.14, the risk markedly increases (HR = 1.68).

Sparse numerical datasets are dominant in fields such as applied mathematics, astronomy, finance, and healthcare, presenting challenges due to their high dimensionality and sparse distribution. The predominance of zero values complicates optimal feature selection, making data analysis and model performance more complex. To overcome this challenge, this study introduces a deep learning-based algorithm, Hybrid Stacked Sparse Autoencoder (HSSAE), which integrates [Formula: see text] and [Formula: see text] regularization with binary cross-entropy loss to improve feature selection efficiency, where [Formula: see text] regularization penalizes large weights, simplifying data representations, while [Formula: see text] regularization prevents overfitting by limiting the total weight size. Additionally, the dropout technique enhances the algorithm's performance by randomly deactivating neurons during training, avoiding over-reliance on specific features. Meanwhile, batch normalization stabilizes weight distributions, reducing computational complexity and accelerating the convergence. The proposed algorithm, HSSAE, was evaluated against traditional classifiers, including Decision Tree, Random Forest, K-Nearest Neighbors, and Naïve Bayes, as well as deep learning-based models, such as Convolutional Neural Network, Long Short-Term Memory, and Stacked Sparse Autoencoder, in terms of Precision, Recall, Accuracy, F1-score, AUC, and Hamming Loss. Quantitatively, the proposed algorithm, HSSAE, was tested on two different sparse datasets, demonstrating superior performance with the highest accuracy of 89% on the health indicator dataset and 93% on the EHRs diabetes prediction dataset, respectively, and outperforming competing classifiers. The proposed algorithm, HSSAE, extracts features effectively and enhances robustness, making it well-suited for sparse data applications, particularly in healthcare, where high prediction accuracy is crucial.