Browse Articles

Parental major life events before or during pregnancy and autistic behaviors among preschool children

Abstract In this study, we aimed to examine whether parental major life events before or during pregnancy were associated with autistic behaviors in preschoolers, and whether alcohol use or smoking modified these associations. This study included 18,664 children aged 3–6 years in southern China. Parents reported seven types of major life events before or during pregnancy. Autistic behaviors were assessed by the Clancy Autism Behavior Scale. Logistic regression was applied to evaluate the associations of parental major life events with the risk of autistic behaviors, along with the modifying effects of alcohol consumption and smoking. This study found that paternal exposure to major life events before or during pregnancy was associated with an increased risk of autistic behaviors. Maternal exposure to major life events during pregnancy was associated with a higher risk of autistic behaviors. The associations between paternal pre-pregnancy major life events and autistic behaviors were stronger when either parent consumed alcohol before pregnancy. No modifying effect was shown for smoking. Findings from this study highlighted the importance of monitoring parental stress and alcohol use before and during pregnancy to improve children’s neurodevelopmental outcomes.

Exploring the impact of COVID-19 pandemic on nurses: A cross-sectional study on job burnout and quality of work life

Background The COVID-19 pandemic has posed unprecedented challenges for healthcare professionals, especially nurses, affecting their occupational health, burnout levels, and overall quality of working life. Limited research exists in the local context regarding these issues. Objectives To evaluate burnout and quality of working life among nurses during COVID-19, and to explore related factors such as exposure level, work shifts, and organizational conditions. Methods A cross-sectional study was conducted from January to March 2022 at Velayat Hospital in Damghan, Iran. All 217 active nurses were surveyed using the Maslach Burnout Inventory and Walton’s quality of working life questionnaire. Response rate was 58.5%. Data analysis was performed with SPSS. Results The nurses had an average age of 36.3 years; most were female (78.7%) and married (81.8%). The majority held bachelor’s degrees. Regarding quality of working life, 21.3% experienced poor quality, 71.3% moderate, and only 7.4% rated it as good. The lowest scoring domain was “fair and appropriate compensation,” while “development of human capabilities” scored highest. Over 70% experienced moderate to high emotional exhaustion, with higher exposure to COVID-19 correlating with increased burnout. Significant inverse relationships were found between emotional exhaustion and all quality of working life domains. Additionally, the number of shifts was associated with higher levels of depersonalization and perceptions of unfairness, safety, and growth opportunities. Nurses with moderate to severe exposure to COVID-19 reported higher burnout levels. Exposure to COVID-19 also negatively impacted perceptions of organizational legality and social environment. Conclusions The COVID-19 crisis has heightened occupational burnout and reduced quality of working life among nurses, risking the quality of healthcare services. Strategies such as workforce reorganization, improved working conditions, and mental health interventions are essential. Policymakers should prioritize supportive measures to enhance resilience and prepare for future crises.

First report and diversity analysis of endophytic fungi associated with Ulva sp. from Iran

Robust adaptive control with lumped model uncertainty and wind disturbance estimation for airship trajectory tracking

The robotic airship can be used as an aerostatic platform for many potential applications, for example, communication, hovering payload deliveries, data-gathering for research studies, etc. These applications require a fully autonomous perspective of an airship. One of the important aspects of airship autonomy is trajectory tracking control. An airship has complex and uncertain nonlinear dynamics which pose a major challenge for designing a precise trajectory tracking control. This paper addresses the airship trajectory tracking control problem under model uncertainties and wind disturbance. We propose a lumped model uncertainties and wind disturbance estimation approach based on an unscented Kalman filter. The estimated lumped uncertainty is used by the Sliding Mode Controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, Unscented Kalman filter-based Sliding Mode Controller (USMC), is used as a robust adaptive control solution to track the desired trajectory. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. Simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC without the bound constraint of model uncertainties and wind disturbance.

Genetic diversity and population structure of the vulnerable medicinal tree Saraca asoca in the Western Ghats India

Myopia control efficacy of second-generation defocus incorporated multiple segments spectacle lenses on fast progressing myopes: Study protocol of a randomised controlled trial

Background Spectacle-based interventions for myopia control are appealing to parents and children due to their non-invasive nature. However, long-term efficacy results remain modest and do not account for high-risk children with early-onset myopia and fast progression. This paper presents the protocol of a trial designed to evaluate the efficacy of the new-generation Defocus Incorporated Multiple Segments (DIMS) spectacle lenses in slowing the progression of myopia in children with early-onset and fast myopia progression. Methods and design This is a prospective, double-masked, active-controlled, randomised trial (ClinicalTrials.gov identifiers: NCT05888792 and NCT05888805). Participants are Chinese schoolchildren aged 4–12 years with myopia of at least −0.75 diopter (D) in both eyes and with fast progression (≥ 0.50 D per year) or fast axial growth (≥ 0.27 mm per year) in either or both eyes. They are age-stratified and randomly assigned to an experimental arm, a control arm or an auxiliary arm in a 1:1:1 ratio. The experimental arm receives new-generation DIMS spectacle lenses, while the control arm receives single-vision spectacle lenses. The control subjects will crossover to experimental lenses at the end of the first year, and all subjects will continue wearing experimental lenses in the second year. The auxiliary arm receives marketed DIMS spectacle lenses for two years. The primary and secondary outcome measures are the changes in cycloplegic objective refraction and axial length at 12 months from baseline. Peripheral refraction and choroidal thickness will also be monitored, and their relationships with myopia control efficacy will be explored. Discussion This study will provide insights into the efficacy of a new generation of DIMS technology for controlling myopia in children with early-onset and fast myopia progression, offering evidence-based practice for myopia management. Trial registration ClinicalTrials.gov identifiers: NCT05888792 and NCT05888805

Cultivation of Wolffia globosa and its application in functional food development

Dignity-based care and infertility treatment: A qualitative study

Introduction The treatment of infertile people is generally time-consuming and requires frequent and long-term visits and providing dignity-based services. Due to the different perceptions and experiences of people and the lack of a specific study to explain the concept of dignity-based care, this study aimed to explain the concept and dimensions of dignity-based care in infertility treatment services. Methods This was a qualitative study with a conventional content analysis approach. Fifty participants (20 infertile women, 16 infertile men, and 14 key informants) were recruited using a purposive sampling method from an educational center of Mazandaran University of Medical Sciences and a private infertility center in Mazandaran –Iran in 2023. Sampling was continued until data saturation. Data were collected using in-depth and semi-structured individual interviews. The data were also analyzed using the conventional content analysis method and the steps suggested by Grandheim and Lundman. Also, Lincoln and Guba’s criteria were used to check the trustworthiness of the data. Results The content analysis demonstrated 43 codes in 11 sub-categories and 4 categories. These categories are “conserving dignity in providing care”, “making the information accessible and obtaining informed consent for care procedure”, “providing professional care and standard services”, and considering cultural and social aspects of infertility. The categories were used to explain the concept and dimensions of dignity-based care in infertility treatment services which are showing the multidimensional aspects of this concept. Conclusion Dignity-based care in infertility treatment services means “conserving dignity in providing care services; making the information accessible and obtaining informed consent for care procedure; providing professional care and standard services; and considering cultural and social aspects of infertility.” This concept can be used in future policy-making and planning, and appropriate support should be taken into account to improve the quality of infertility treatment services.

Engineering of an Fc-specific monovalent protein G for the light-controlled affinity purification of antibodies

Abstract Like other widely applied bacterial surface receptor proteins for immunoglobulins (Igs), such as protein A and protein L, the Ig-binding domain of protein G (ProtG) has dual binding activity. ProtG can independently associate both with the Fc region of an antibody (mAb) and with its Fab and, thus, provoke cross-linking if applied in solution. Indeed, we observed pronounced precipitation activity when using ProtG equipped with the Azo-tag as a small adapter molecule for the light-controlled affinity purification of mAbs. We demonstrate that this undesired precipitation phenomenon follows the classical Heidelberger-Kendall curve. Furthermore, we describe a mutant of ProtG in which Asn478 at the interface with the Fab is replaced by Arg, which results in the effective loss of this secondary binding activity while maintaining high affinity towards the Ig Fc region. ProtG N478R no longer induces precipitation when mixed with a series of medically relevant mAbs. Hence, Azo-ProtG N478R can be applied as a convenient molecular tool to isolate antibodies from cell culture medium—even with a high content of albumin—in a single step via Excitography. In this technique, elution is triggered by trans  →  cis isomerisation of the Azo-tag upon illumination with mild UV-A light and a harsh pH shift is avoided.

Micro-Computed Tomography as a complementary tool for histopathological diagnosis of oral soft tissue lesions – Proof of concept

Background Accurate diagnosis of oral soft tissue lesions is critical for effective treatment, yet conventional histopathological examination, the gold standard, faces limitations. These include two-dimensional (2D) visualization and malorientation, which can obscure critical diagnostic features, like epithelial-connective tissue interfaces. Micro-computed tomography (µCT) offers a non-destructive, high-resolution three-dimensional (3D) imaging alternative to address these challenges. Still, its use for soft tissue visualization is limited. We tested a method with specific radio-opaque staining and µCT scanning settings to visualize oral soft tissue biopsies as a proof of concept. Methods Biopsies from 12 patients with different oral mucosa lesions were stained with Lugol’s iodine, scanned at 3µm resolution with 70kV energy, and the resulting volumes were compared to histopathological sections by specialists in oral radiology and oral pathology. Results µCT produced 2D images with tissue architecture comparable to hematoxylin and eosin (H&E)-stained sections, distinguishing epithelium, connective tissue, and keratin, while 3D reconstructions revealed topographic details, such as ulceration depth and vascular patterns, unattainable in histopathology. Conclusions These findings highlight µCT potential as a complementary diagnostic tool, enhancing topographic rendering while preserving tissue integrity. Standardized protocols and broader validation, particularly for precancerous and malignant lesions, are essential for clinical adoption, promising improved diagnostic accuracy in oral pathology.

Deep learning for motion classification in ankle exoskeletons using surface EMG and IMU signals

Abstract Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility, support rehabilitation, and reduce fall risks, particularly among the aging population. Their effectiveness depends on accurate, real-time prediction of user intentions from wearable sensor data, as even small delays or errors can compromise stability and safety. Here, we present a motion classification framework that integrates three Inertial Measurement Units (IMUs) with eight surface Electromyography (sEMG) sensors fabricated as towel-based textile electrodes, which improve comfort, durability, and usability for long-term deployment compared to traditional gel electrodes. The dataset comprises multichannel time-series recordings of five functional daily motions, enabling a realistic evaluation of exoskeleton use in everyday environments. Using this framework, Convolutional Neural Networks (CNNs) achieved an accuracy of $$99.263 \pm 0.26\%$$ 99.263 ± 0.26 % , substantially surpassing previously reported results in the field. Beyond overall accuracy, we address deployment-critical requirements: transfer learning enables reliable adaptation to new users with as few as ten calibration samples per motion, while robustness testing demonstrates that the system continues to provide stable classification even when individual sensors are disrupted. Together, these results highlight the feasibility of safe, high-accuracy, and real-world-ready exoskeleton control through deep learning combined with wearable textile electrodes and IMUs.

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition

To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases; however, achieving high accuracy remains a challenge. Basic convolutional neural networks (CNNs) in deep learning struggle with issues such as the abnormal orientation of images, rotation, and others, resulting in subpar performance. CNNs also need extensive data covering a wide range of variations to deliver strong performance. CapsNet is an innovative deep-learning architecture designed to address the limitations of CNNs. It performs well without needing a vast amount of data in various variations. CapsNets have their limitations, such as the encoder network considering every element in the image and the crowding issue. Due to this, they perform well on simple image recognition tasks but struggle with more complex images. To address these challenges, we introduced a new CapsNet model known as CCFM-CapsNet. This model incorporates CLAHE to reduce image noise and CDH to extract crucial features. Also, max-pooling and dropout layers are incorporated in the original CapsNet model for identifying and classifying diseases in apples, bananas, grapes, corn, mangoes, pepper, potatoes, rice, tomato and also for classifying fashion-MNIST and CIFAR-10 datasets. The proposed CCFM-CapsNet demonstrates significantly high validation accuracies, achieving 99.53%, 95.24%, 99.75%, 97.40%, 99.13%, 100%, 99.77%, 100%, 98.54%, 93.48%, and 82.34% with corresponding parameters in millions(M) 4.68M, 4.68M, 4.68M, 4.68M, 4.79M, 4.63M, 4.66M, 4.68M, 4.84M, 2.39M, and 4.84M for the datasets aforementioned respectively, outperforming the traditional CapsNet and other advanced CapsNet models. Consequently, the CCFM-CapsNet model can be utilized effectively as a smart tool for identifying plant diseases and also in achieving Sustainable Development Goal 2 (Zero Hunger), which aims to end global hunger by the year 2030.

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors

Abstract Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learning technologies, it is now possible to simulate interactions with high precision between EGFR and small molecules to predict inhibitory/ modulatory activity at an unprecedented scale. In this work, we propose a novel machine-learning method to fast and precise classification of small compounds that are active, intermediate or inactive in inhibiting/modulating EGFR activity. We developed DeepEGFR, a novel multi-class graph neural network (GNN) model, to classify compounds into Active, Inactive, and Intermediate functional categories. DeepEGFR leverages complementary molecular representations, combining SMILES strings and molecular fingerprint matrices (Klekota-Roth and PubChem) to capture both structural and property-based features of compounds. The model constructs an advanced molecular graph representing atom type, formal charge, bond type, and bond order, through nodes and edges. DeepEGFR achieved superior performance compared to baseline machine learning algorithms (e.g., SVM, Random Forest, ANN), with approximately 94% F1-scores across training and test datasets for all activity classes. To ensure interpretability, the top 20 features identified by DeepEGFR were validated against the five key characteristics of FDA-approved EGFR inhibitors (Afatinib, Gefitinib, Osimertinib, Dacomitinib, Erlotinib), confirming the biological relevance of the features. Moreover, DeepEGFR successfully identified 300 underexplored EGFR-targeting compounds, demonstrating its potential to accelerate the discovery of therapeutic agents. These results highlight the effectiveness of graph neural networks in advancing molecular activity classification, setting a potential new benchmark for EGFR inhibitor prediction. These findings demonstrate the DeepEGFR’s ability to highlight the promising EGFR inhibitors, that have received limited prior investigation, thereby supporting its role in facilitating the rational development of targeted therapies for precision oncology.

Ethnobotanical secrets of the Baiga tribe in Chhattisgarh Central India

Abstract The ethnobotanical knowledge of indigenous communities represents a vital yet under documented resource for sustainable healthcare and biodiversity conservation. This study explores the traditional medicinal practices of the Baiga tribe and the phytosociological structure of forests surrounding their villages in Bilaspur district, central India. Fieldwork was conducted between January and December 2024 across six villages, using semi-structured interviews with 74 informants (63 males and 11 females) and quadrat-based vegetation sampling. A total of 80 plant species belonging to 75 genera and 42 families were recorded. Fabaceae was the most dominant family with 11 species. Herbs and trees were the most common life forms (36% each), and open land was the primary habitat (46%). Bark was the most frequently used plant part (24%), with paste preparation (43%) and oral administration (77%) being the most preferred methods. Use value (UV) ranged from 0.08 ( Jatropha curcas L.) to 0.97 ( Azadirachta indica A.Juss.), while family use values ranged from 0.12 to 0.95 Informant Consensus Factor (ICF) values ranged from 0.92 to 0.97, indicating strong agreement among informants regarding plant usage. Phytosociological analysis revealed Diospyros melanoxylon Roxb. as the most ecologically dominant species (IVI = 44.89), followed by Shorea robusta C.F.Gaertn. (IVI = 26.33), both of which also hold significant cultural and medicinal value. Aegle marmelos (L.) Corrêa and Azadirachta indica A.Juss. also showed high IVI values, reflecting their dual ecological and therapeutic roles. Despite the Baiga tribe’s rich medicinal heritage, their knowledge remains underrepresented in academic literature. This study fills a critical gap by documenting their ethnomedicinal practices and highlighting ecologically important species. To support long-term sustainability, we propose conservation strategies such as the establishment of Medicinal Plant Conservation and Development Areas (MPCDAs), community-based training on sustainable harvesting, and inclusion of ethnobotanical knowledge in local education and healthcare systems. These efforts can help preserve both biodiversity and traditional wisdom for future generations.

Numerical investigation of coal dust migration in sealed tunneling environments: A CFD-based study

Millimeter-wave broadband dual-circularly polarized array antenna loaded with metasurface

Functional and microbiological properties of spirulina soybean tempeh flour modified by heat-moisture treatment and annealing

Abstract Spirulina, known for its high protein content, can be developed into tempeh and further processed into flour for bakery products. However, the direct use of spirulina-tempeh flour as a premix presents challenges, particularly in achieving stable volume and texture. This study aimed to determine the most effective modification method for improving the characteristics of spirulina-tempeh flour. Accordingly, two modification techniques were applied: Heat-Moisture Treatment (HMT) and annealing. Following this, statistical analysis was conducted using one-way Analysis of Variance (ANOVA) with Duncan’s Multiple Range Test (DMRT) ( p  < 0.05), and the De Garmo method was used to identify the best treatment. Overall, the results revealed that HMT was the most effective method in enhancing flour properties. In particular, the HMT-modified flour exhibited the following values: moisture content 4.82% db, fat content 27.63% db, Ash Content (AC) 2.35% db, protein content 41.40% db, Water Absorption Capacity (WAC) 1.93 g/g db, Oil Absorption Capacity (OAC) 0.38 g/g db, syneresis 77.49% db, swelling volume 4.85 mL/g db, solubility 0.20% db, antioxidant activity (IC₅₀) 49.998 ppm, starch content 1.903% db, and amylose content 0.007% db. Meanwhile, microbiological properties further indicated a Standard Plate Count (SPC) of 2.74 × 10⁴ CFU/g. In conclusion, HMT effectively improved the functional, physicochemical, and microbial characteristics of spirulina-tempeh flour, making it more suitable for use in bakery products.

Crustal constraints on the surface expression of mantle upwelling in a back-arc passive margin setting

TET1 modulates trophoblast function by regulation of ODC1 in preeclampsia

Discovery of EEG effective connectivity during visual motor imagery with multi-scale symbolic transfer entropy

Abstract Visual motor imagery (VMI) is an important component of motor imagery, with potential applications in brain-computer interfaces and motor rehabilitation due to its lower training cost compared to kinesthetic motor imagery (KMI). However, the neural mechanisms underlying VMI, particularly the effects of imagery hand and imagery perspective (first-person perspective, 1pp, vs. third-person perspective, 3pp) remain unclear. This study examines the effective connectivity of VMI EEG using multi-scale symbolic transfer entropy. Time-frequency analysis revealed prominent event-related synchronization (ERS) in the alpha and high-beta bands, while connectivity analysis emphasized strong information flow within the parieto-occipital network. Notably, hand effect dominant information flows were found between the motor and posterior parietal-occipital regions, while perspective suggested a more remarkable effect. 1pp imagery significantly enhanced top-down modulation of the occipital cortex, whereas 3pp imagery engaged the right posterior parietal region, suggesting stronger spatial localization processing. These findings provide novel insights into the distinct neural mechanisms of VMI and its potential applications in cognitive neuroscience and brain-machine engineering.