Vishwakarma University

Physical or sexual abuse in childhood is a risk factor for violence and harassment in future intimate partner, which is a critical health concern globally. Several studies have been proposed to address the influence of attachment on violence in general, but few studies are available highlighting the role of attachment on dating violence. We explore how attachment is linked to dating violence among youth. We predict that gender would have a moderating role between attachment and dating violence among youth. The present research was Online-Questionnaire-based and employed a predictive correlational research design. Our sample consisted of 1,246 participants aged 18 to 22 years, selected using a convenience sampling method. Moderation analysis was used to analyze the data. Moderation analysis shows partial acceptance of the hypotheses. We found a moderating influence of gender among participants who exhibited an ambivalent attachment style, which is significant for males. Gender did not moderate the relationship for secure and avoidant attachment styles toward dating violence among youth. These findings offer novel insights into prevention programs aimed at reducing dating and domestic violence, emphasizing the pivotal role of attachment security and insecurity in mitigating the risk of violence in dating relationships.

Objective:Detecting 5-hydroxyindoleacetic acid (5-HIAA), a vital metabolite in urine, is crucial for diagnosing and monitoring carcinoid tumors. Traditional methods such as spectrophotometric analysis and high-performance liquid chromatography provide high selectivity and sensitivity but are costly and unsuitable for on-site testing.Materials and Methods:Recently, paper-based analytical devices have emerged as innovative solutions for detecting various substances, including chemicals, environmental pollutants, and microorganisms. This study investigated the use of Whatman filter papers No. 1, No. 2, and No. 4, along with Whatman chromatography paper, to develop a paper-based assay for 5-HIAA using Ehrlich's reagent.Results:Optimization showed that an immersion time of 180 min and a drying time of 30 min at room temperature were most effective.Conclusion:Among the tested filter papers, Whatman CF6 paper demonstrated the best performance for 5-HIAA detection.

A series of novel pyrazole-coumarin-linked 1,2,3-triazole conjugates 9a-g and 10a-e were synthesized by using click chemistry approach. The synthesised conjugates were characterized by IR, 1H NMR, 13C NMR spectroscopy and mass spectrometry. The synthesised conjugates have been screened for their anticancer and antioxidant activity. Among all the conjugates, compound 9d exhibited excellent anticancer activity against HeLa (IC50 = 13 mu M), and MCF7 (IC50 = 35 mu M) cell lines. All the conjugates could efficiently bind to the active site of beta-tubulin receptor, forming strong bonded and non-bonded interactions with specific residues within the active site. The per-residue interaction analysis could provide insights into the specific thermodynamic interactions governing the binding affinity, thus could serve as guiding principles for fragment-based optimization in the quest for new anticancer drug candidates.

The growing prevalence of psychological distress worldwide has drawn attention to the limitations of conventional mental health care systems, particularly in terms of accessibility, affordability and social stigma. Artificial intelligence (AI)–driven chatbots are emerging as digital tools capable of extending mental health support beyond traditional clinical environments. This review examines existing literature on AI‐powered mental health chatbots, focusing on systems that utilise natural language processing and established psychological frameworks such as cognitive behavioral therapy (CBT), dialectical behavior therapy (DBT) and mindfulness‐based approaches. AI‐driven chatbots facilitate emotional expression, provide round‐the‐clock availability and enable anonymous interactions, making them especially useful for individuals who are reluctant or unable to seek conventional therapy. Despite these advantages, concerns remain regarding limitations in empathy, data privacy, contextual awareness and clinical accountability. AI‐powered mental health chatbots hold significant potential to enhance access to mental health support; however, their meaningful and responsible integration into mental health care requires interdisciplinary collaboration, ethical design practices and careful alignment with human‐centred care to ensure safe, inclusive and trustworthy mental health ecosystems.

IntroductionEarly diagnosis of endometrial cancer is critical for improving survival. However, most AI-based methods are developed using single-modality data, and most of them are uninterpretable and do not offer privacy protection, or they completely ignore privacy issues. We propose a multimodal AI framework that utilizes histopathology whole-slide images (WSIs) and clinical data and incorporates both explainability and privacy-aware learning.MethodsFive hundred and twenty-nine patients’ data (354 early-stage and 175 advanced-stage) with 794 WSIs and 208,000 image patches were collected for the study. By using a convolutional neural network (CNN), morphological features were extracted from WSIs, and clinical variables were encoded by a multi-layer perceptron, respectively. These two modalities of information (the learned representations) were combined to make the final category prediction. Interpretability was enabled through Grad-CAM and clinical feature attribution, and privacy-aware training was supported by secure parameter aggregation.ResultsThe multimodal model obtained an accuracy of 0.91 and an AUC of 0.95, thus it exceeded the performance of clinical-only (accuracy = 0.78, AUC = 0.81) and histopathology-only (accuracy = 0.85, AUC = 0.89) models with a considerable increase in sensitivity (0.89) and specificity (0.93). The performance was preserved by privacy-aware learning as well. The net clinical benefit was maximum as per the Decision Curve Analysis.ConclusionThis framework offers a solution that is accurate, interpretable, and privacy-preserving, thus it can act as a diagnostic aid for early endometrial cancer. However, since the model was developed and evaluated using only the TCGA-UCEC cohort, external multi-center validation is required to confirm generalizability across diverse clinical populations, imaging protocols, and laboratory conditions.