Browse Articles

Impact of group antenatal care on lactational amenorrhea method awareness and knowledge: A cluster randomized control trial

High fertility rates in low and middle-income countries (LMICs), especially in sub-Saharan Africa and Ghana, lead to closely spaced pregnancies and contribute to high maternal and infant morbidity and mortality. Family planning remains critical for reducing rapid repeat and unwanted pregnancies, thus improving the health and survival outcomes. Unfortunately, many women face significant unmet needs due to limited knowledge, misinformation, and limited access to modern family planning methods in particular. Breastfeeding plays a substantial role, and the lactational amenorrhea method (LAM) offers a practical, natural, readily available, and cost-efficient postpartum option when practiced correctly. However, many mothers lack sufficient awareness of LAM and the conditions necessary for its effectiveness. Antenatal care provides a strategic opportunity for targeted education and counselling on contraceptive choices, empowering women with the knowledge to adopt safe, informed, and sustainable practices. Group antenatal care (G-ANC), recommended by WHO for research in LMICs, offers a comprehensive and participatory platform for health education and behavioral change. This study examined G-ANC and its impact on creating awareness and improving knowledge of the lactational amenorrhea method among mothers. A cluster randomized control trial, registered at ClinicalTrials.gov on 25/07/2019 with RCT number NCT04033003, was conducted in Ghana at 14 health facilities with a total of 1761 participants. The study included pregnant women who were at least 15 years old, able to speak English or one of four local languages, and less than 20 weeks of gestation at enrollment. Women classified as high-risk by the midwife were excluded from participation in the study. Facilities were randomized using a matched pair method. Structured interviews were conducted at baseline and subsequent timepoints. The results found that group antenatal care increases maternal awareness and knowledge of the postpartum lactational amenorrhea method of family planning compared to individualized care. Subgroup analysis revealed that a mother’s level of education and parity strongly predict maternal awareness of the lactational amenorrhea method. These findings support group antenatal care as an effective strategy to improve knowledge on the use of lactational amenorrhea as a family planning method.

Efficient hybrid fuzzy weighted 3D FCNN with TSO PSO optimization for accurate multi modal MRI brain tumor classification

Abstract Detecting and segmenting brain tumors from 3D MRI images is a challenging and time-intensive task for clinicians. This research introduces an innovative hybrid architecture for deep learning, comprising a 3D fully convolutional neural network (3D-FCNN), an interval type-2 fuzzy weighting system, and a hybrid transit search optimization-particle swarm optimization (Hybrid-TSO-PSO) algorithm. The proposed models, 3D-FCNN-Hybrid-TSO-PSO and 3D-FCNN-SVM, employ type-2 fuzzy weighting to diminish the quantity of trainable parameters and expedite training on MRI volumetric data. The Hybrid-TSO-PSO optimization approach integrates the heuristic strengths of TSO with the rapid convergence attributes of PSO, enhancing learning stability and augmenting the precision of segmentation and classification. Assessments were conducted on the BraTS 2019, BraTS 2020, and a portion of the BraTS 2021 datasets, comprising 300 3D MRI images (230 high-grade HGG and 70 low-grade LGG glioma specimens). During the testing phase, the 3D-FCNN-Hybrid-TSO-PSO model attained an accuracy of 98.1%, sensitivity of 98.9%, specificity of 95.0%, and a Dice score of 0.987, whereas the 3D-FCNN-SVM model earned an accuracy of 95.2%. This method not only enhances accuracy but also decreases training duration by as much as sixfold relative to traditional architectures, serving as an efficient and precise diagnostic aid for the identification and classification of brain cancers.

Humanized monoacylglycerol acyltransferase 2 mice on a high-fat diet exhibit impaired liver detoxification during metabolic dysfunction-associated steatotic liver disease

Obesity significantly increases the risk of hyperlipidemia, type 2 diabetes, and liver disease. This study examined humanized monoacylglycerol acyltransferase 2 mice (HuMgat2) and their response to a high fat diet (HFD) while investigating hepatocyte dysfunction during obesity development. HuMgat2 mice fed a HFD exhibited hyperlipidemia, hyperglycemia, insulin resistance, and metabolic dysfunction-associated steatotic liver disease (MASLD). Elevated levels of cholesterol and triglycerides were associated with increased expression of lipogenic genes and accumulation of nuclear Srebp1/Srebp2. Mice fed a HFD demonstrated impaired insulin signaling and increased glucose production through the expression of gluconeogenesis genes. Liver fibrosis was characterized by collagen deposition and activation of Jak2-Stat3 signaling, resulting in hepatocyte apoptosis. RNA sequencing identified extracellular matrix degradation and apolipoprotein metabolism as being altered. Levels of cytochrome P450 enzymes were downregulated, as indicated by decreased Cyp2b10 and Cyb3a11 levels, alongside reduced expression of the di- and tri-carboxylic acid transporter Slc13a2, correlating with elevated Krebs cycle intermediates. Notably, HuMgat2 mice exhibited responses to a high-fat diet that were comparable to those observed in mMgat2 mice. These findings suggest that HFD consumption and concomitant obesity disrupts metabolite homeostasis, contributing to liver damage and cell death. They also further validate HuMgat2 mice as an excellent preclinical model for testing human MOGAT2 inhibitors as therapeutics for treating obesity.

FNS as a more satisfactory surgical option for young and middle-aged patients with femoral neck fractures

Association between food insecurity, ethnicity, and mental health in the UK: An analysis of the Family Resource Survey

The study aimed to assess the relationship between food insecurity and ethnicity in the United Kingdom (UK), and to explore how the relationship between food insecurity and mental health varies by ethnic group. Data from the 2019/20 Family Resource Survey provided information on ethnicity, presence of long-standing illnesses affecting mental health, and food security assessed using 10-item Adult Food Security module. Logistic regression was used to assess the relationship between food security status and degree of anxiety and presence of long-standing illness affecting mental health. Analyses were adjusted for covariates and stratified by ethnicity. Participants were a representative sample of private UK households (N = 19,210), using the Household Reference Person as the main respondent. The majority of the sample were food secure (87%), identified as White (90.7%), and 22% reported a long-standing illness affecting mental health. Food insecurity was associated with longstanding illness affecting mental health (adjusted OR 2.01 (1.70, 2.39)) among all ethnic groups; Asian/Asian British respondents reported the highest odds of having a longstanding illness affecting their mental health (OR=2.63 (1.05, 6.56)). The study finding of an association between food insecurity and mental health for all UK ethnic groups, but one which is stronger for ethnic minority groups, necessitates a population-wide response alongside targeted interventions.

Effect of listening to preferred music at different frequencies during warmup on physical performance and psychophysiological responses in male athletes

Suppression of kinesin family member-18A diminishes progression and induces apoptotic cell death of gemcitabine-resistant cholangiocarcinoma cells by modulating PI3K/Akt/mTOR and NF-κB pathways

Cholangiocarcinoma (CCA), particularly when associated with Opisthorchis viverrini infection, is often diagnosed at a late stage and exhibits high resistance to chemotherapy, notably gemcitabine. While kinesin family member 18A (KIF18A) is upregulated in opisthorchiasis-associated CCA, its precise function, especially in gemcitabine-resistant CCA, remains largely unexplored. Herein, expression of KIF18A in relation to survival and progression was assayed by TCGA database mining and immunohistochemistry of a tissue microarray derived from 84 CCA patients. For functional study, KIF18A was suppressed in gemcitabine-resistant CCA cells (KKU-213BGemR) using a CRISPR/Cas9 technique, followed by cellular and molecular analyses. Our results showed that KIF18A was highly expressed in CCA tissues compared to normal counterparts. Its expression was significantly correlated with tumor size and histological type of CCA but not with overall survival time. In vitro, KIF18A expression levels were increased in CCA cell lines, particularly KKU-213BGemR. Suppression of KIF18A significantly inhibited colony formation, migration and invasion by KKU-213BGemR cells. In addition, KIF18A knockdown led to a significant increase in the sub-G1 population, indicating the occurrence of cellular apoptosis. Flow cytometry confirmed that suppression of KIF18A significantly induced early apoptotic cell death of KKU-213BGemR cells. Suppression of KIF18A dramatically downregulated the expression of key oncogenic and survival signaling proteins, including PI3K (total and p-PI3K), Akt (total and p-Akt), mTOR (total and p-mTOR), NF-κB (total and p-NF-κB) and Bcl-2 in KKU-213BGemR cells. Taken together, our findings suggest that KIF18A plays crucial roles in promoting the progression and survival of gemcitabine-resistant CCA cells, partly by modulating PI3K/Akt/mTOR and NF-κB pathways. Therefore, despite its lack of prognostic utility, KIF18A represents a promising therapeutic target for improving treatment outcomes in CCA patients, especially those who do not respond to gemcitabine treatment.

Investigating the distributional response of the rare and endangered plant Fritillaria przewalskii to climate change based on optimized MaxEnt model

VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans

Brain tumors are an increasing global health concern, and accurate diagnosis is essential for improving patient outcomes. Although existing Magnetic Resonance Imaging (MRI)-based machine learning utilizes computer vision for tumor diagnosis, these methods are limited. They either focus solely on segmentation, which does not facilitate tumor detection, or on classification, which fails to identify tumor boundaries. To overcome these limitations, we propose the “View-specific Integrated Segmentation-Classification” (VISION) framework, designed for more accurate brain tumor diagnosis by integrating both segmentation and classification processes. The VISION framework introduces two novel components: (1) a View Classifier that determines MRI orientation (axial, coronal, or sagittal), and (2) a view-specific integrated network combining a customized segmentation model with a classification header. This architecture simultaneously identifies tumor boundaries (segmentation) and detects tumor presence (classification). We evaluated our approach using publicly available data and compared it against state-of-the-art MRI-based tumor diagnosis techniques. The VISION framework outperformed existing methods, achieving a Dice score of 0.89, an IoU of 0.87, and an F1 score of 0.98 while maintaining competitive computational efficiency. The proposed VISION framework offers a robust solution for brain tumor diagnosis by integrating view classification, segmentation, and detection into a unified system. Its high accuracy and efficiency demonstrate significant potential for clinical applications in improving tumor diagnosis and treatment planning.

A fine-tuned foundational model SurgiSAM2 for surgical video anatomy segmentation and detection

Abstract The foundational segmentation models, segmenting anything model (SAM) and SAM 2, have transformed segmentation by enabling remarkable zero-shot performance across diverse domains. In this study, we evaluate SAM 2 for surgical scene understanding by examining its semantic segmentation capabilities for organs/tissues both in zero-shot scenarios and after fine-tuning. We utilized five public datasets to evaluate and fine-tune SAM 2 for segmenting anatomical tissues in surgical videos/images. Fine-tuning was applied to the image encoder and mask decoder. We limited training subsets from 50 to 400 samples per class to better model real-world constraints with data acquisition. The impact of dataset size on fine-tuning performance was evaluated with weighted mean dice coefficient (WMDC), and the results were also compared against previously reported state-of-the-art (SOTA) results. SurgiSAM 2, a fine-tuned SAM 2 model, demonstrated significant improvements in segmentation performance, achieving a 17.9% relative WMDC gain compared to the baseline SAM 2. Increasing prompt points from 1 to 10 and training data scale from 50/class to 400/class enhanced performance; the best WMDC of 0.92 on the validation subset was achieved with 10 prompt points and 400 samples per class. On the test subset, this model outperformed prior SOTA methods in 24/30 (80%) of the classes with a WMDC of 0.91 using 10-point prompts. Notably, SurgiSAM 2 generalized effectively to unseen organ classes, achieving SOTA on 7/9 (77.8%) of them. Heavily dissected tissues and similar appearing organs such as small and large intestines remained challenging. SAM 2 achieves remarkable zero-shot and fine-tuned performance for surgical scene segmentation, surpassing prior SOTA models across several organ classes of diverse datasets. This suggests immense potential for enabling automated/semi-automated annotation pipelines, thereby decreasing the burden of annotations facilitating several surgical applications.

Comparing the effects of interactive and conventional video education on activation, treatment adherence, and weight changes in dialysis patients: A randomized clinical trial protocol

Background Patients with end-stage renal disease undergoing hemodialysis face substantial challenges in adhering to complex therapeutic regimens, significantly impacting morbidity, mortality, and quality of life. While conventional educational methods offer some benefit, interactive digital tools may yield deeper engagement and sustained behavioral change. Objective This study assesses and compares the impact of interactive and conventional video-based education, as well as usual care, on patient activation, treatment adherence, and inter-dialytic weight gain in individuals undergoing hemodialysis. Methods A three-arm, parallel-group, randomized clinical trial will be conducted in three academic hospitals in Tehran, Iran. A sample of patients will be enrolled and distributed into one of three categories: (1) interactive video education, (2) conventional video education, or (3) typical nurse-led education. The 13-item Patient Activation Measure will be used to measure the primary outcome of patient activation. Secondary outcomes will encompass treatment adherence, as measured by the End Stage Renal Disease Adherence Questionnaire, and inter-dialytic weight gain. Evaluations will occur at baseline, immediately post-intervention, and at 1- and 3-month follow-up intervals. Data will be analyzed using intention-to-treat principles with mixed-effects modeling. Discussion This trial is among the first to rigorously compare interactive and conventional video education in a dialysis population. Findings may inform scalable, cost-effective strategies for improving self-management and adherence in patients with end-stage renal disease. This protocol was registered prospectively at ClinicalTrials.gov (Registration No. NCT07099326) on July 31, 2025. The National Research Ethics Committee also approved the study with the ethics code: IR.SBMU.PHARMACY.REC.1404.067.

Cerium oxide-embedded gold nanoparticles loaded with astragaloside IV for bladder cancer therapy

Learning to detect AI texts and learning the limits

This study investigates whether individuals can learn to accurately discriminate between human-written and AI-produced texts when provided with immediate feedback, and if they can use this feedback to recalibrate their self-perceived competence. We also explore the specific criteria individuals rely upon when making these decisions, focusing on textual style and perceived readability. We used GPT-4o to generate several hundred texts across various genres and text types comparable to Koditex, a multi-register corpus of human-written texts. We then presented randomized text pairs to 254 Czech native speakers who identified which text was human-written and which was AI-generated. Participants were randomly assigned to two conditions: one receiving immediate feedback after each trial, the other receiving no feedback until experiment completion. We recorded accuracy in identification, confidence levels, response times, and judgments about text readability along with demographic data and participants’ engagement with AI technologies prior to the experiment. Participants receiving immediate feedback showed significant improvement in accuracy and confidence calibration. Participants initially held incorrect assumptions about AI-generated text features, including expectations about stylistic rigidity and readability. Notably, without feedback, participants made the most errors precisely when feeling most confident—an issue largely resolved among the feedback group. The ability to differentiate between human and AI-generated texts can be effectively learned through targeted training with explicit feedback, which helps correct misconceptions about AI stylistic features and readability, as well as potential other variables that were not explored, while facilitating more accurate self-assessment. This finding might be particularly important in educational contexts, since the ability to identify AI-generated content is highly desirable and, more importantly, false confidence in this domain can be harmful.

Alterations of the amygdala in post-COVID olfactory dysfunction

Abstract Olfactory dysfunction (OD) as a symptom of COVID-19 has received significant attention in research due to its high prevalence. While it is transient in the majority of individuals, post-COVID OD persists in a notable subset of patients even months to years after the acute infection. A deeper understanding of the underlying factors driving this phenomenon is essential. There is increasing evidence for an involvement of the central nervous system in this deficit. The objective of this study was to investigate the structural connectivity and integrity of white matter pathways in brain regions associated with olfactory processing using MRI with diffusion tensor imaging (DTI) in patients with persistent post-COVID OD. The study involved 61 patients, divided into two groups: 31 participants with post-COVID OD (PC-OlfDys) and 30 post-COVID normosmic controls (PC-N). For MRI analyses, a region of interest (ROI)-based approach and voxelwise statistical comparisons between the groups with age as a covariate was used. Fractional anisotropy (FA) in the left amygdala was higher in the PC-OlfDys than in the PC-N group, and radial diffusivity (RD) in the right amygdala was higher in the PC-OlfDys group than in PC-N. The PC-OlfDys group exhibited higher depression and anxiety scores, as measured by the eight-item Patient Health Questionnaire depression scale and the Generalized Anxiety Disorder 7 questionnaire, respectively. This study shows that post-COVID OD is associated with significant changes in the myelination or axonal diameter of olfactory-related brain regions. As the amygdala, putamen and piriform cortex (all involved in olfactory function and emotional well-being) showed associations with depression and anxiety scores, we hypothesise that post-COVID OD and depression and anxiety are interrelated, although the direction of this relationship remains to be elucidated.

Land surface temperature evolution in rapidly urbanizing areas of Southeast Asia: Studies from Vietnam and Cambodia

Land surface temperature (LST) is one of the crucial variables in urban microclimate studies. Satellite-based thermal data and vegetation indices, like the normalized difference vegetation index (NDVI), help to understand changes in LST and the development of urban heat islands (UHI). We analyzed the variations in LST and vegetation coverage in two rapidly urbanizing provinces, located in southern Vietnam and Cambodia, respectively, over the 10 years from 2013 to 2025. Additionally, complementary ERA5 Interim air temperature data were also utilized. The satellite and in situ data analysis have been used to understand the impacts of urbanization on LSTs. Spatiotemporal changes in NDVI showed rapid urbanization in the eastern region of Battambang city (39.2 km2 to 47.8 km2) and throughout the southern areas of Binh Duong Province (387 km2 to 464.3 km2). Time-series analysis indicated a consistent increase in LST in both study sites. There has been a notable increase in minimum LST since 2017 in the entire city of Battambang, whereas the central area of Battambang has become consistently warmer after 2020. The minimum estimated LST in Battambang varied between 16.1 °C and 28.58 °C (and increased 0.35 °C per year), whereas the maximum LST varied between 29.2 °C to 40.23 °C (and increased 0.36 °C per year). The LST in southern Binh Duong increased gradually during the study period, primarily due to rapid urbanization and vegetation loss. The minimum estimated LST in Binh Duong varied between 13.2 °C to 24.73 °C (and increased 0.26 °C per year), whereas the maximum LST varied between 34.6 °C to 41.3 °C (and increased 0.024 °C per year). The outcome of this study holds considerable importance, as the phenomenon of UHI formation has been documented in rapidly expanding cities and impervious surfaces globally, especially in Southeast Asia.

Strain-controlled superconductivity in epitaxially grown thin films of 1T-TaS2

Abstract 1T-TaS2 is a prototype layered material with a rich phase diagram that includes multiple charge density wave (CDW) transitions and technologically important metastable states. It also supports a superconducting phase induced by hydrostatic pressure, cation substitution, intercalation, or doping. Thin 1T-TaS2 crystals deposited on various substrates exhibit transition temperatures that are strongly dependent on the substrate-induced strain, and depart from bulk transition temperatures in a way that is not clearly understood at present. Here we show that thin polycrystalline films of 1T-TaS2 grown by molecular beam epitaxy on (LaAlO3)0.3(Sr2TaAlO6)0.7 (LSAT) substrates have a suppressed CDW transition to a commensurate phase. Instead, resistivity, magnetoresistance, and critical current measurements reveal metallic behavior with an onset to a superconducting state below $$\:{T}_{c}=3.8$$ K. The appearance of superconductivity is suggested to be driven by the in-plane tensile differential strain exerted on the 1T-TaS2 film by the LSAT substrate during cooling, which in turn results in a strongly amplified out-of-plane compressive strain triggered by the Poisson effect, combined with traceable signs of intercalation with La and Sr atoms from the substrate. The experiments suggest that tensile substrate strain may be usefully applied for achieving desirable Functional properties that are otherwise accessible through hydrostatic pressure, and generally for investigating of the effects of anisotropic strain in 2D materials and monolayer stacks or heterostructures.

STAG2 regulates polycomb and differentiation in urothelial precursors and bladder cancer

The STAG2 tumor suppressor gene is commonly inactivated by mutations in a wide range of common cancer types. STAG2 encodes a component of the cohesin complex, which controls sister chromatid cohesion and 3D genome organization. In bladder cancer, STAG2 mutations are most common in the earliest low-grade lesions, suggesting that mutational inactivation of STAG2 may be an initiating event. To provide insight into the mechanisms of STAG2 tumor suppression in bladder cancer, siRNA and shRNA were used to knock down STAG2 in several different human non-neoplastic bladder cancer precursor cell lines. Gene editing was used to generate cultured human cancer cell lines that differ only in the presence or absence of bladder-cancer derived STAG2 mutations. These systems were interrogated using RNA-seq, Western blot, and qRT-PCR before and after induced differentiation. We find that inactivation of STAG2 in bladder cancer cells and in bladder epithelial precursor cells resulted in concomitant inactivation of the H3K27me3 Polycomb chromatin mark. Inactivation of STAG2 also attenuated induced differentiation of bladder epithelial precursor cells. STAG2 and other components of cohesin were upregulated during this differentiation process. This study provides new insights into the role of STAG2 in the pathogenesis of bladder cancer, demonstrating roles for STAG2 in the regulation of Polycomb-mediated epigenetic regulation and in the differentiation of bladder epithelial precursor cells.

C–H Bond Activation via Photolysis of a Mn Nitride Ligand Radical Complex

Improving load frequency control in autonomous microgrid via Fick’s law-based demand optimization

Global burden of Alzheimer’s disease and other dementias attributable to smoking in 204 countries and territories, 1990–2021

Background Alzheimer’s disease and other dementias (ADD) are significant global public health challenges. Smoking is a clearly established modifiable risk factor for dementia. Objective This study aims to systematically elucidate the burden of ADD attributable to smoking from 1990 to 2021. Methods We obtained data on Disability Adjusted Life Years (DALYs) and age-standardized DALYs rate (ASDRs) associated with ADD attributable to smoking from the Global Burden of Disease (GBD) database for the years 1990–2021. These data were disaggregated by gender, age, sociodemographic index (SDI), and region. Temporal trends in the burden of smoking-induced ADD were examined by calculating the average annual percentage changes. Results From 1990−2021, global ASDR of smoking-attributed ADD declined 21.3% (EAPC = −0.88%) while DALYs increased 93% to 1.53 million. Females showed faster ASDR decline (EAPC = −1.50% vs male −0.73%). DALYs peaked at 65−85 years with accelerated crude rates post-75. Regionally, East Asia (611760.52, 262060.15–1401979.1), Western Europe (184593.67, 78898.94–420812.86), High-income North America (164283.74, 72809.79–373190.34) had highest 2021 DALYs; East Asia (29.95, 12.67–68.10), High income North America (23.32, 10.39–52.46), Tropical Latin America (20.81, 9.05–48.62) had highest ASDR. Nationally, China, United States of America and Lebanon led burdens. Steepest declines occurred in Mexico (EAPC = −3.14%), South Africa (−3.14%), and Sri Lanka (−2.84%). ASDR correlated with SDI (r = 0.44, p < 0.001) showing bimodal peaks at SDI = 0.5 and 0.8. Frontier analysis revealed peak heterogeneity in Alzheimer’s disease ASDR around SDI 0.75, with the largest effectiveness disparities observed primarily in Middle-High SDI countries, indicating urgent needs for targeted health interventions despite declining trends in high-SDI nations. Conclusion Despite declining dementia ASDR, global DALYs rose absolutely. The burden disproportionately impacted older populations, males, and high-middle-income nations. Mitigation requires context-adapted interventions including enhanced tobacco control, equitable healthcare access, and targeted health education.