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Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction
The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p < 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd -
Hybrid Mobile-Spinalnet with feature extraction for brain tumor detection using MRI images
Brain tumors are deadly and can hinder the normal functioning of the human body. Generally, surgical methods are preferred for treating brain tumors. Early and accurate detection remains a problem, due to the complexity of tumor shapes and poor generalization to diverse tumor types. To address this, a hybrid Mobile-SpinalNet is established in this paper for the detection of brain tumors with Magnetic Resonance Imaging (MRI) images. This system involves seven stages, including input image acquisition, image preprocessing, skill stripping, tumor segmentation, data augmentation, feature extraction, and brain tumor detection. Initially, image acquisition is carried out, and then the input image is preprocessed by using a Mean filter. Subsequently, the skull stripping is performed using Fuzzy C-Means (FCM). After that, by using the TransUNet, the tumor region is isolated in the segmentation module. Furthermore, the data augmentation is carried out, and then the feature mining takes place in the feature extraction phase to excerpt features such as Speeded Up Robust Features (SURF), Oriented Fast and Rotated BRIEF (ORB), Fuzzy Local Binary Pattern (FLBP) and statistical features. At last, the brain tumor is identified by employing a hybrid Mobile-SpinalNet. This framework fuses the MobileNet and SpinalNet depending on regression modeling with applied Fractional Calculus (FC). The Mobile-SpinalNet is validated for its efficacy by comparing it to other techniques, and it showed better performance with a precision of 0.953, accuracy of 0.943, and recall of 0.970. 2025 Elsevier Ltd -
FaithfulNet: An explainable deep learning framework for autism diagnosis using structural MRI
Explainable Artificial Intelligence (XAI) can decode the black box models, enhancing trust in clinical decision-making. XAI makes the predictions of deep learning models interpretable, transparent, and trustworthy. This study employed XAI techniques to explain the predictions made by a deep learning-based model for diagnosing autism and identifying the memory regions responsible for children's academic performance. This study utilized publicly available sMRI data from the ABIDE-II repository. First, a deep learning model, FaithfulNet, was developed to aid in the diagnosis of autism. Next, gradient-based class activation maps and the SHAP gradient explainer were employed to generate explanations for the model's predictions. These explanations were integrated to develop a novel and faithful visual explanation, Faith_CAM. Finally, this faithful explanation was quantified using the pointing game score and analyzed with cortical and subcortical structure masks to identify the impaired brain regions in the autistic brain. This study achieved a classification accuracy of 99.74% with an AUC value of 1. In addition to facilitating autism diagnosis, this study assesses the degree of impairment in memory regions responsible for the children's academic performance, thus contributing to the development of personalized treatment plans. 2025 Elsevier B.V. -
Chlorella vulgaris-mediated sustainable biogenic synthesis of silver nanoparticles for wastewater remediation and antibacterial applications
This investigation examines the green synthesis of silver nanoparticles (SNPs) using Chlorella vulgaris as a reducing and stabilizing agent. Algae-mediated SNPs (ASNPs) were tested for the potential application in sewage water remediation and as an antibacterial agent. Biogenic ASNPs demonstrated excellent stability and a surface plasmon resonance (SPR) peak at 440 nm. Energy dispersive X-ray (EDAX) spectroscopy analysis and Fourier transform infrared (FTIR) spectroscopy investigation indicated the role of biomolecules originating from the algal extract, which play a crucial role in the green synthesis process of ASNPs. Dynamic light scattering analysis yielded a hydrodynamic mean particle size of 200 nm and a zeta potential of around 18 mV. Observation under electron microscopy presented the morphological diversity with a prominent signature of elemental silver in ASNPs. A domestic waste sewage sample collected from a sewage treatment plant presented elevated levels of alkalinity, salinity, and biological oxygen demand (BOD). ASNP treatment normalises most of the water parameters, while algal extract alone could produce minimal effects. The antibacterial evaluations against Staphylococcus aureus and Escherichia coli, well-known opportunistic pathogens responsible for a wide range of hospital and community-acquired infections, showed dose-dependent effects. These findings highlight the dual functional role of C. vulgaris-mediated SNPs as an effective, eco-friendly solution for both wastewater remediation and antibacterial application. 2025 Elsevier Ltd -
Valorization of lignin to produce nanofibers of industrial importance
Lignin nanofibers (LNFs) have emerged as promising materials for various environmental applications due to their unique properties, abundance, and sustainability. This review examines recent advances in LNF synthesis and their environmental applications, lignin types are discussed in relation to nanofiber production. Synthesis techniques are evaluated, with electrospinning emerging as a versatile method for producing LNFs with diameters typically in the nanometer range. The intrinsic properties including molecular weight, polydispersity, and glass transition temperature, significantly influence nanofiber formation and performance. Environmental applications of LNFs are extensively reviewed, highlighting their potential in adsorption of pollutants, air filtration, energy storage devices, and as catalyst supports. Despite significant progress, challenges remain in large-scale production, consistency of properties, and economic viability. This review provides a comprehensive overview of the current state of LNFs technology, addressing both opportunities and challenges in leveraging this sustainable material for environmental solutions. 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Oxygen surface-functionalized carbon dots derived from waste cassava peel for UV shielding applications
UV radiation, falling in the wavelength range between 290nm and 400nm, which reaches the Earth's surface, is capable of causing potential damage to human cells, especially the skin. Sun protection products, which were earlier treated as skincare utilities, have now become indispensable and fall under the category of healthcare commodities. The requirement for skin- and environment-friendly UV absorbers that are reliable enough to substitute synthetic ones is spiking day by day. In this work, we report the conversion of waste cassava peels into UV-absorbing carbon dots through a facile one-step microwave-assisted solvothermal route. The as-synthesized carbon dots, when dispersed in NMP, show intense absorption in the UVA and UVB region, which can be effectively used for UV shielding applications. In-vitro studies based on transmittance data show that dispersion is capable of blocking 90% of the UV rays at a concentration of 0.2mg/mL, and at 0.5mg/mL, an SPF of 35+ was obtained, corresponding to a shielding capability of more than 97%. The conversion of cassava peel waste into UV-absorbing carbon dots adds to the value of this agricultural waste and, on crossing the compatibility standards, would provide a suitable alternative for existing synthetic UV shielding materials. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
FeCl3/KOH two steps activated biocarbon with hierarchical porosity and oxygen-rich for enhanced supercapacitor applications
Biomass waste derived from jackfruit (Artocarpus heterophyllus) cores is used to fabricate hierarchical porous activated carbon through chemical activation with Iron(III) chloride (FeCl3) and potassium hydroxide (KOH). Jackfruit is an abundant agricultural by-product in tropical regions, including India, Bangladesh, and Sri Lanka. The activated carbon derived from jackfruit provides a sustainable, low-cost, and high-performance alternative to conventional carbon materials for supercapacitors, thereby aligning with waste valorisation strategies. The prepared carbon displays hierarchical porous structures of both micro and mesopore architectures. They are amorphous and contain functional oxygen groups, as confirmed by X-Ray photoelectron spectroscopy (XPS) and Fourier Transform Infrared Spectroscopy (FTIR). A high surface area (1251m2g?1) was obtained via Brunauer-Emmett-Teller (BET) analysis. The electrochemical performances, via cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and galvanostatic charge/discharge (GCD) show high specific capacitance of 310Fg?1 at 0.8Ag?1 from GCD, 331Fg?1 at 10mVs?1 from CV, and a charge transfer resistance of 0.1410?cm2, in three electrode configuration and showing good cycling stability of 87% over 2500 cycles. These results suggest that the activated carbon offers potential application in low-cost and renewable production of carbon materials for supercapacitors applications. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Bioprospecting of fish scale waste as a cost effective substrate for crude protease production using Stenotrophomonas koreensis SH32 and its multifunctional applications: A sustainable strategy for circular bioeconomy
In this study Stenotrophomonas koreensis SH32 bacterial strain previously isolated from waste dumped soil was screened for protease production through qualitative and quantitative methods. The protease enzyme production in based media was optimised using response surface methodology (RSM). Under optimised conditions 295.11 IU/mL of protease activity was recorded from the crude enzyme that marked a 5.76 fold increase in protease activity as compared to unoptimised condition (51.21 IU/mL). The crude enzyme was further explored for its dehairing activity using goat skin, destaining activity using chicken blood stained cloth and metal recovery property from old X-ray films. Further the plant growth promoting characteristics and antagonistic activity of the proteolytic strain against common phytopathogenic fungi was evaluated. The results suggested that the bacteria was positive for indole acetic acid, siderophore and ammonia production. The bacterial culture also showed antagonism against Cladosporium tennuisimum, Talaromyces albobiverticillius and Fusarium solani. The plant growth promotion activity of the bacteria was further confirmed by plant growth test using Solanum lycopersicum amended with fish scale hydrolysate produced through bacterial fermentation. A significant increase in length (8.3 cm) was observed in plants treated with fish scale hydrolysate as compared to the control (6.5 cm). 2025 -
Cancer-associated mutation at glycine 400 in TIP60 disrupt its phase separation property and catalytic activity resulting in compromised DNA damage repair function of the cell
TIP60 is a tumor suppressor with histone acetyltransferase (HAT) activity, playing a crucial role in regulating chromatin architecture by acetylating histones to enhance accessibility for other regulatory factors. Its function is vital for several key cellular processes, including DNA damage repair, apoptosis, and autophagy. While the downregulation of TIP60 has been associated with various cancers, the effects of naturally occurring mutations in TIP60 on its function in malignancies remain poorly understood. In this study, we explored how cancer-related mutations in TIP60 impact its structure and function. Several deleterious and destabilizing missense mutations were identified and analyzed for structural changes. Molecular dynamics simulations revealed alterations in protein conformational stability and radius of gyration due to these mutations, supported by significant changes in TIP60's solvent accessibility and intramolecular hydrogen bonding. Biochemical assays with recombinant proteins showed a loss of catalytic activity in the G400W mutant. Live cell imaging indicated abnormal localization of the G400W mutant within the nucleus. Additionally, we observed aberrant phase separation of TIP60 caused by the G400W mutation. Notably, the G400W mutation impairs TIP60's catalytic function, preventing effective DNA repair and leaving the genome vulnerable to further mutations. Our findings highlight cancer-associated mutations in TIP60 that may contribute to the molecular mechanisms underlying cancer initiation and progression. 2025 Elsevier Inc. -
Enhancing environmental sound classification with weighted attention-based spectrogram fusion and overlapping pre-patching
Environmental Sound Classification (ESC) remains challenging due to the diverse and overlapping acoustic characteristics of real-world environments. Traditional models relying on single-feature representations such as Mel spectrograms often fail to capture the full range of spectral and temporal details. This paper introduces a novel algorithm Weighted Attention-based Spectrogram Fusion (WASF) that adaptively integrates Mel spectrograms, Cochleograms, and Correlograms using a hierarchical attention mechanism across channel, temporal, and frequency dimensions. Compared to traditional fusion techniques, WASF uses a learnable attention mechanism to dynamically weight each feature's importance over time and frequency, improving the model's capacity to focus on important acoustic cues. In addition, an overlapping pre-patching strategy is proposed to preserve local temporal continuity, enhancing transformer-based modeling. Proposed model demonstrates superior performance with 95.71 % accuracy on UrbanSound8K, 93.97 % on ESC-50, and 94.91 % on ESC-10 datasets. Extensive ablation studies and interpretability analysis validate the effectiveness of each component, demonstrating robustness across diverse acoustic environments and noise conditions. The computational efficiency and interpretable attention patterns make our approach suitable for real-time deployment in smart city applications, surveillance systems, and assistive technologies. 2025 Elsevier B.V. -
An enhanced hybrid framework for IoT healthcare security using blockchain-driven multimedia data analysis and cybersecurity techniques
In the era of digital healthcare, safeguarding sensitive patient information while ensuring real-time access and decision-making is paramount. This study presents a novel Hybrid Blockchain-IoT Framework for secure healthcare data management, integrating Elman neural networkbased Blowfish encryption with blockchain and deep anomaly detection. The framework leverages IoT sensor data and utilizes a Proof-of-Authority (PoA) consensus mechanism to ensure tamper-proof transaction recording across decentralized nodes. A Long Short-Term Memory (LSTM) autoencoder combined with a Support Vector Machine (SVM) classifier enables accurate anomaly detection, while cryptographic functions ensure privacy and data integrity. The proposed system is evaluated using a healthcare dataset comprising over 1000 patient records across three network configurations (195, 585, and 1171 nodes). Results demonstrate a Wormhole Attack Probability (%) as low as 1.1%, Product Drop Ratio (%) between 1.2 and 2.7%, and Authentication Delay under 111 msoutperforming existing systems. Although the anomaly detection accuracy (98.98%) and F1-Score (0.90) are slightly below leading deep learning models, our framework uniquely combines encrypted transmission, distributed validation, and intelligent threat detection in a practical healthcare setting. The architecture ensures security, scalability, and efficiency, positioning it as a robust solution for next-generation smart healthcare ecosystems. 2026 The Authors -
Synergistic interfacial passivation of dye-sensitized solar cell photoanodes using Myristica fragrans pulp-derived carbon dots and Ag nanoparticles
Interfacial charge recombination at the photoanode remains a critical factor in limiting the performance of Dye-sensitized solar cells. The photoanodes play a crucial role in efficient electron injection and transport with minimised recombination reactions, directly influencing the efficiency of the cell. This work investigates the use of carbon dots (CDs) derived from Myristica fragrans fruit pulp via a one-step hydrothermal process as a component in photoanodes. The TiO2 photoanode is modified by in situ deposition of silver nanoparticles (ANP) and CDs directly onto the metal-oxide semiconductor matrix via photoreduction. The passivated cell exhibited ?28 % increase in efficiency over the pristine cell, attaining 6.39 0.20 %, with a current density of 14.74 0.28mAcm?2, and an open-circuit voltage of 0.76 0.02 V. Herein, the passivation of TiO2 has altered the optical bandgap of the photoanode from 3.23 eV to 1.75 eV, thereby enhancing light absorption towards the visible and near-IR region. Furthermore, the localized surface plasmon resonance (LSPR) effect of ANP and the charge-transfer mediator properties of CDs synergistically enhance charge mobility, thereby improving photoanode performance by reducing recombination. With a charge-transfer resistance of 6.87 ? and an electron lifetime of 47.19 s, the work explored here serves as an appropriate passivation layer, providing a clean interface between the layers. 2026 Elsevier B.V. -
Ultrasmall cobalt boride-decorated P, K-doped gC3N4 for plasmon-driven degradation of high concentration tetracycline
The widespread contamination of aquatic ecosystems by pharmaceutical pollutants, particularly tetracycline (TC) antibiotics, poses significant environmental risks. gC3N4 is widely studied as a photocatalyst for environmental remediation, yet its practical use remains limited. To overcome these limitations, we developed gC3N4 by co-doping phosphorus (P) and potassium (K), and further decorating with ultrasmall cobalt boride (CoB) nanoparticles. Elemental co-doping with P and K modulates the electronic structure of gC3N4 by narrowing the bandgap, introducing shallow impurity bands, and enhancing charge separation through directional charge redistribution, supported by spectroscopic analysis and DFT simulations. The introduction of plasmonic CoB nanoparticles leads to the formation of Schottky junctions, while also inducing localized surface plasmon resonance (LSPR) that significantly amplifies the photocatalytic activity. CoB/P-K-gC3N4 exhibited a 21-fold increase in TC degradation rate compared to pristine gC3N4, where 84.8 % of 100 ppm TC was degraded using 10 mg of photocatalyst in one hour, achieving high removal activity of 7.1 mgpollutant/gcatalyst/min. The catalyst also demonstrated excellent structural stability and sustained photocatalytic efficiency over multiple reuse cycles. Electrochemical studies showed a higher charge carrier density along with a noticeable decline in charge transfer resistance, while photoluminescence and time-resolved fluorescence analysis confirmed a suppressed electron-hole recombination rate. This study demonstrates the synergistic interplay of co-doping and plasmonic enhancement in advancing next-generation photocatalysts for sustainable water purification. 2025 Elsevier B.V. -
Radiation attenuation parameters and intrinsic efficiency of a few semiconductor crystals for radiation detection applications
This study investigates the effectiveness of nine inorganic semiconductor crystals ? LiGaSe2, LiInSe2, CsHgInS3, SnS, GaTe, BiI3, Sb2Te3, Tl4CdI6, and TlBr ? for radiation detection applications based on photon and charged particle (electrons, protons, and heavy ions) interaction parameters. Mass attenuation coefficient (?/?), half value layer (HVL), relaxation length (?), effective atomic number (Zeff), electron density (Neff), equivalent atomic number (Zeq), and exposure buildup factor (EBF) were computed using PAGEX software. These results, along with their intrinsic efficiencies calculated, were compared with that of standard materials (NaI(Tl), CdZnTe, and CdTe). The ?/? values of the studied semiconducting materials are ranked in the decreasing order as: TlBr, Tl4CdI6, BiI3, CsHgInS3, Sb2Te3, GaTe, SnS, LiInSe2, and LiGaSe2. TlBr, Tl4CdI6, BiI3, and Sb2Te3 show superior photon detection capabilities compared to the reference materials. TlBr and Tl4CdI6 have the highest intrinsic efficiency across nearly all energy regions, while LiGaSe2 has the lowest. Interaction parameters like range and Zeff for charged particles were also computed using standard databases, with SnS and Sb2Te3 showing the least range for all the charged particles studied throughout the entire energy region. The study indicates that TlBr and Tl4CdI6 have strong potential for developing next-generation radiation detectors with enhanced sensitivity, addressing needs in healthcare and national security. 2025 Elsevier Ltd -
Resilience in the crucible: Compassion fatigue among Indian clinical psychologists and the need for mental health policy reform
Objectives: Compassion fatigue (CF) significantly affects mental health professionals, especially in high-stress, resource-limited contexts. Despite its impact on therapeutic outcomes, little is known about how Indian clinical psychologists experience and manage CF. This study explored the lived experiences of CF among RCI-licensed Indian clinical psychologists, focusing on protective and risk factors in the post-pandemic context. Methods: A qualitative phenomenological design using Interpretative Phenomenological Analysis (IPA) was employed. Ten clinical psychologists from urban and semi-urban India were purposively sampled. Semi-structured interviews explored experiences of CF, resilience, and self-efficacy. Thematic analysis was informed by Figley's Compassion Fatigue Model and the Conservation of Resources Theory. Results: Four major themes emerged: (1) Professional Competence and Growth; (2) Therapeutic Relationship; (3) Professional Challenges, including vicarious trauma and boundary-setting; and (4) Self-Care and Support. Participants frequently reported emotional exhaustion and vicarious trauma, but also described post-traumatic growth and reflective practice as buffers. Conclusions: The findings underscore the need for policy-level interventions to address CF among Indian clinical psychologists. Enhancing clinical supervision, integrating trauma-informed curricula, and strengthening institutional support systems are critical for sustaining practitioner resilience and ethical therapeutic care in India's mental health landscape. Key practitioner message: Addressing compassion fatigue through supervision, policy reform, and resilience-building is vital for therapist well-being and service sustainability. 2026 Elsevier Inc. -
Exploring boronate-appended hyperbranched amino-functionalized dendrimer-empowered sensors for the potential recognition of FSH in age-categorized human plasma samples
Boronic acids can act as ideal saccharide receptors as they possess a high affinity for diols and readily form cyclic-boronate esters when reacting in an aqueous medium. Here, we present hydrophilic amino-functionalized boronic acid dendrimer (Af-BAD) for the first time, with significantly enhanced sensitivity towards Follicle Stimulating Hormone (FSH) detection. In this study, newly synthesized Af-BAD was dip-coated on a gold substrate to create an impedance-type sensing working electrode. The effects of Af-BAD coating on the gold chip, the sensing properties for FSH recognition, sensitivity, and stability were measured by the charge transfer resistance across the electrochemical setup. The impedimetric measurements were conducted in the presence of [Fe(CN)6]3-/[Fe(CN)6]4- redox reporter at pH 7.4. The increments in the charge-transfer resistance were monitored upon increasing the FSH concentrations from 25 fg/mL to 100 pg/mL. The device achieved good sensitivity with a calculated detection limit of 4.01 fg/mL and acceptable linearity. The observed behavior was linear concerning the tested concentrations. An attempt at a real application to serum samples was also successfully conducted. Meanwhile, the level of tolerance of boronic acid dendrimer with other competing glycoproteins and monosaccharides was also tested. In this study, we also compared human plasma FSH levels in female oral cancer patients and normal controls using the Af-BAD modified device and the clinically used ELISA method. With a sound understanding of boronate materials and their affinity, amino functionalized multi-boronic acid dendrimer was developed as a highly selective conjugate toward glycoprotein FSH detection. Copyright 2025. Published by Elsevier Ltd. -
Exploring the efficiency and scalability of using algae as a biomass feedstock for biofuel production
Sustainability is paramount to preserving essential resources for future generations. The widespread use of fossil fuels generates significant pollution, severely impacting both terrestrial and aquatic ecosystems through phenomena such as acid rain. Despite their rapid growth, high photosynthetic efficiency, and ability to thrive in a variety of conditions, algae have become a viable alternative biomass feedstock for biofuel production. This review explores the efficiency and scalability of algae-based biofuels, focusing on key factors such as biomass yield, lipid content, and conversion technologies. Algae have a higher lipid yield compared to traditional biofuel feedstocks such as corn or soybeans, making them an attractive option for large-scale fuel production. However, several obstacles hinder the widespread adoption of algae-based biofuels, including high production costs, energy-intensive cultivation, and water consumption. This paper also examines the efficiency and suitability of various cultivation technologies, including open ponds and photobioreactors, for large-scale production. Algal biofuel production could become more economically viable and environmentally sustainable through the integration of carbon capture technology and wastewater treatment. Advances in genetic engineering and metabolic optimization are further increasing lipid productivity, offering promising prospects for large-scale applications. This review additionally provides an analysis of genetic engineering techniques aimed at increasing biofuel yields. The study emphasizes the potential of algae-based biofuels to serve as environmentally friendly alternatives to traditional fossil fuels, highlighting these innovative approaches. While the evaluation acknowledges that algae-based biofuels can reduce dependency on fossil fuels and help mitigate climate change, it also notes that further research and development are necessary to overcome current financial and technological challenges. This review explores the recent advancements in algae cultivation, harvesting techniques, and biofuel extraction processes. Its goal is to present a comprehensive understanding of the current state of algae as a sustainable and effective feedstock for biofuel production, along with future prospects. 2025 Elsevier B.V. -
The competencycontrol paradox in parenting adolescents: Evidence from an Indian mixed-methods study
Background: Adolescence is a critical developmental period during which parenting practices interact with temperament and sociocultural context to shape mental health and adaptation. Most parenting models are derived from Western settings, with limited evidence from India. Methods: This simultaneous mixed methods study drew on cross sectional data from the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, including adolescents aged 1217 years (parent report n?=?931; child report n?=?836). Exploratory factor analysis was conducted on parent and child versions of the Alabama Parenting Questionnaire. Qualitative data were obtained through in-depth interviews with 31 adolescents and their parents and analysed using thematic analysis. Findings were integrated at the interpretation stage. Findings: The original APQ structure did not replicate. Parent reports yielded three dimensionsInvolvement/Positive Parenting, Poor Monitoring, and Corporal Punishmentwhile child reports yielded five, distinguishing fathers and mothers involvement. Inconsistent disciplining did not emerge as a distinct construct. Qualitative findings indicated high involvement and behavioural and psychological control, largely driven by academic goals. Adolescents experienced these practices as both supportive and restrictive, with parental openness shaping communication. Contextual pressures, including resource constraints and urban stressors, contributed to a competencycontrol paradox. Interpretations: Parenting of adolescents in India must be understood within its relational and sociocultural ecology. While involvement and control function as primary supports, excessive control may constrain broader competency development. Integrating parent and adolescent perspectives is essential for culturally grounded research and intervention. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
The effect of spatial and intensity level augmentation of structural magnetic resonance images on autism diagnosis model
In deep learning, the robustness and generalizability of models significantly depend on diverse and heterogeneous training data. Acquiring such an extensive dataset is challenging in fields like disorder prediction due to data scarcity, which can be attributed to factors such as privacy concerns, limited patient population, or inadequate facilities. Data augmentation can be an ideal solution to this problem, particularly in the field of disorder prediction, like autism, using medical imaging. Data augmentation can expand and balance datasets by generating high-quality and varied data, thereby improving the generalizability of deep learning models. This study proposed two types of augmentation methods: 1. Spatial level 2. Intensity level augmentation techniques. Eight different levels of augmentations were experimented with across these categories. This study found that the combination of spatial and intensity level augmentations enhanced the model's generalizability and robustness, achieving an AUC value of 0.7433. Additionally, it was observed that the Left to Right flip method, under spatial augmentation, diminished the model's performance, whereas random noise injection, under intensity level augmentation, improved prediction accuracy. 2026 Elsevier B.V. -
New biomarkers for the detection of fetal death derived from large-scale proteomic analysis of maternal plasma
Background Normal pregnancy involves the modulation of thousands of maternal plasma proteins, and protein values not within the normal range may indicate the development of adverse pregnancy outcomes. A decrease in placental growth factor and an increase in soluble fms-like tyrosine kinase 1 in maternal plasma were shown to be associated with fetal death at the time of diagnosis and to predict this devastating pregnancy outcome at 24 to 28 weeks of gestation. However, these proteomic dysregulations are also present in other obstetrical syndromes, and more specific and sensitive biomarkers are needed to implement preventive strategies. Objective This study aimed to identify candidate protein biomarkers that can improve the prediction of fetal death relative to placental growth factor and soluble fms-like tyrosine kinase 1. Study Design This retrospective case-control study included 38 patients who experienced fetal death (cases) and 23 patients with uncomplicated pregnancies (controls). Plasma samples were collected at the time of diagnosis (2041 weeks of gestation) from cases and during routine care from gestational agematched controls. An aptamer-based multiplex assay was used to measure the abundance of >7000 protein analytes. Differential protein abundance was assessed using linear models with adjustment for gestational age at sample collection. Significance was inferred using a moderated t test adjusted P value of <.1 and a fold change of >1.25. Hypergeometric tests were performed to identify gene ontology biological processes enriched among proteins with significant changes in abundance. Random forest models were trained and evaluated via cross-validation to distinguish between fetal death cases and controls and to pinpoint the most salient predictors. Results Among the 7146 protein assays tested, 97 assays (1.4%) corresponding to 87 unique proteins differed significantly in abundance between fetal death cases and controls: 63 of 87 proteins (72%) were less abundant in fetal death cases, and 24 of 87 proteins (26%) were more abundant in fetal death cases. Dysregulated proteins were involved in pregnancy-related processes, such as angiogenesis and lactation. Random forest models effectively differentiated fetal death cases from controls, achieving an area under the receiver operating characteristic curve of 72% for the combination of placental growth factor and soluble fms-like tyrosine kinase 1, which increased to 86% when up to 50 additional proteins were included in the models (Delong test: P =.004). In addition, the point estimate of sensitivity increased from 53% to 74% (false-positive rate of approximately 10% for both). Glycoprotein hormones alpha chain (CGA), DnaJ homolog subfamily B member 9 (DNAJB9), and DNA-directed RNA polymerase III subunit RPC10 (POLR3K) emerged as the top 3 candidates to improve discrimination relative to placental growth factor and soluble fms-like tyrosine kinase 1. The significant proteomic changes in a subset of fetal death cases diagnosed first with preeclampsia relative to controls were highly correlated ( r =0.78; P <.001) with those reported in late preeclampsia cases leading to live births. On average, for each 2-fold change in protein abundance in late preeclampsia leading to live birth, there was an 8.6-fold change in preeclampsia leading to fetal death. Despite this overall correlation, transcobalamin 2, glucose-6-phosphate 1-dehydrogenase, and hepcidin, among others, demonstrated dysregulation only in preeclampsia leading to fetal death, suggesting both shared and distinct pathways perturbed in the 2 syndromes. Conclusion Our findings suggest that new maternal plasma proteins improve the discrimination of fetal death from controls relative to known biomarkers and that, although the signatures of fetal death and of preeclampsia are correlated, fetal death not only represents a much heightened disease state but also involves distinct perturbed pathways. Future studies are needed to determine whether the biomarkers can predict fetal death. 2026 Elsevier Inc.