In a technologically advanced world like this, no one is isolated from the effects of smartphones and social media. Everyone nowadays uses social media, even parents who know very little about it as compared to younger generations; still, they use it, and it somehow hampers their way of living and family dynamics in general. Social media has led to the rise of a new phenomenon named "phubbing," which refers to focusing on a phone rather than having face-to-face conversations with the person in front of them. Both excessive social media usage and phubbing have been linked to harmful effects on multiple domains of human life, including academics, career, interpersonal, social, etc. While significant research has been done on phubbing and its effect on interpersonal and peer relationships, very little is known about how it impacts parenting and even less about the comparative patterns between mothers and fathers. This research aims to compare maternal and paternal phubbing to highlight gendered patterns of digitalisation. The study provides insights into the concepts of social media and phubbing behaviours. By highlighting gendered patterns of digital use and how it varies across gender, the study may offer essential insights to policymakers and awareness programs. Additionally, the study helps us understand how using digital platforms in a mindful manner is crucial for parents. Moreover, the study offers necessary implications to concerned institutions in order to promote digital awareness.

Diabetic foot ulcer (DFU) is a serious complication of poorly controlled diabetes, commonly affecting the plantar surface of the foot. Nearly 15% of diabetic individuals develop DFU, and 14–24% may require amputation. It results from neuropathy, vascular insufficiency, and infection. Prevention through foot care education is essential to reduce complications. Diabetic foot ulcers are a major cause of morbidity and amputation among diabetic patients, especially in rural areas, due to poor awareness, delayed treatment, and inadequate self-care practices. This review is needed to evaluate existing evidence on foot care education, identify knowledge gaps, and guide effective interventions to improve self-care practices and prevent diabetic foot ulcers. To review and synthesize existing evidence on the effectiveness of foot care education in improving knowledge and self-care practices for the prevention of diabetic foot ulcers among diabetic patients, with special emphasis on rural populations. A comprehensive literature review was conducted using PubMed and Google Scholar. MeSH terms like 'diabetic foot' and 'self-care' and keywords such as 'practice' and 'behaviour' were used to identify relevant English-language studies. Findings showed varied assessment tools including Modified Diabetic Foot Care Knowledge (MDFCK), Modified Diabetic Foot Care Behaviours (MDFCB), Nottingham Assessment of Functional Foot Care (NAFFC), Diabetes Knowledge Test (DKT), and Diabetes Foot Care Questionnaire (DFQ). Most studies used questionnaires focusing on hygiene, foot inspection, footwear, and self-care practices. However, inconsistency in tool usage and validation was noted. Overall, patients demonstrated inadequate knowledge and poor self-care practices. The review highlights the need for effective educational interventions to improve foot care knowledge and practices, especially among high-risk groups such as rural populations and individuals with low literacy levels.

Background Hypertension is a major risk factor for atrial fibrillation (AF), largely mediated through progressive left atrial (LA) remodeling. Conventional echocardiographic markers such as LA size reflect relatively late structural changes and may fail to detect early functional atrial abnormalities. Left atrial strain assessed by two-dimensional speckle-tracking echocardiography has emerged as a sensitive marker of atrial myocardial dysfunction and may improve risk stratification for AF in hypertensive patients. Objectives To evaluate left atrial strain parameters using two-dimensional speckle-tracking echocardiography in hypertensive patients and to determine their association with atrial fibrillation risk. Methods This hospital-based observational cross-sectional study was conducted over six months in a tertiary care center. A total of 120 adult patients with essential hypertension and preserved left ventricular systolic function were included. Comprehensive transthoracic echocardiography was performed, including assessment of left ventricular diastolic function, left atrial volume index, and left atrial strain parameters (reservoir, conduit, and contractile strain). Patients were categorized based on the presence or absence of documented paroxysmal atrial fibrillation. Statistical analyses included group comparisons, correlation analysis, and multivariable logistic regression. Results Paroxysmal atrial fibrillation was present in 34 patients (28.3%). Patients with AF demonstrated significantly higher E/e′ ratios and larger left atrial volume index compared to those without AF (p < 0.001). Left atrial reservoir strain was markedly reduced in the AF group (19.6 ± 4.9% vs. 30.4 ± 5.7%, p < 0.001). Left atrial reservoir strain showed significant inverse correlations with left atrial volume index (r = −0.62) and E/e′ ratio (r = −0.58). On multivariable analysis, left atrial reservoir strain emerged as an independent predictor of atrial fibrillation (OR 0.82 per 1% increase, p < 0.001), providing incremental predictive value beyond conventional echocardiographic parameters. Conclusion Left atrial strain assessed by two-dimensional speckle-tracking echocardiography is significantly impaired in hypertensive patients with atrial fibrillation and independently predicts AF risk. Incorporation of left atrial strain into routine echocardiographic evaluation may facilitate earlier identification of high-risk hypertensive patients and improve atrial fibrillation risk stratification.

Most applications of WSNs (Wireless Sensor Networks) demand an extended life expectancy, implementing improved and effective energy efficient measures. Clustering techniques are normally implemented to ensure improved energy utility and life-expectancy of sensor network. Multiple algorithms have been proposed with optimization techniques implemented for better energy efficiency and enhanced life-span of sensor networks. These algorithms arrange the sensing nodes into clusters and elect one of them as its leader. The data routing from the sensing nodes towards the sink may adopt either one-hop routing technique or multihop data routing technique. The functions of cluster head in a cluster are high energy consuming activities and hence its choice and the even distribution of cluster-head's (CH's) work-load among cluster members determines performance level of clustering algorithm. Similarly in multi-hop algorithms the probability of over burdening of routing nodes located near the sink is very high which hampers the performance level of routing algorithm. Hence clustering, CH selection and data flow path selection presents a computationally hard problem, which motivates us to explore metaheuristic algorithms to address the above issues for improved energy efficiency and network lifespan. Existing approaches use improved versions of standard Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for clustering purposes and separate approach for data routing. Here we have proposed a spatial-correlation based clustering and data routing approach, wherein the computational complexities are reduced and only those cluster-head nodes are allowed to participate in the data-routing process which have an energy reserve higher than a pre-determined threshold. Here we have analysed the performance level of proposed algorithm against LEACH and its advanced variant in context of the throughput, network-Lifetime and the energy utility. Simulation results showcase enhancement of these quality-of-service metrics using our proposed algorithm as against the two standard algorithms. We have deeply investigated our multi-hop algorithm with varying parameters like node energy of the routing agent, spatial correlation constant considered for cluster formation. In addition, the dynamic nature of primary cluster-head and secondary cluster-head election also employs an effective data redundancy technique.

Artemether, a potent lipophilic antimalarial drug, suffers from poor aqueous solubility and low, variable oral bioavailability, which can compromise its therapeutic efficacy and contribute to the emergence of drug-resistant malaria strains. The present study aimed to develop and evaluate a Self-Emulsifying Drug Delivery System (SMEDDS) to enhance the solubility, dissolution, and intestinal permeability of artemether. Solubility studies were conducted to identify suitable excipients, and Capryol 90 (oil), Cremophor EL (surfactant), and Transcutol P (co-surfactant) were selected based on their superior drug solubilizing capacity. The optimized SMEDDS formulation was subjected to in vitro dissolution and ex vivo permeability studies using the Caco-2 cell model. The in vitro dissolution study demonstrated a significant improvement in drug release from the SMEDDS formulation, with more than 95% of artemether released within 30 minutes, compared to less than 20% release from the conventional suspension. This enhancement is attributed to the spontaneous formation of a fine oil-inwater nanoemulsion, providing a large surface area and maintaining the drug in a solubilized state. Furthermore, ex vivo permeability studies revealed a marked increase in drug transport across intestinal cell monolayers, with the SMEDDS formulation showing approximately 3.8-fold higher apparent permeability compared to the suspension. These findings confirm that the SMEDDS formulation effectively overcomes the dissolution and permeability limitations of artemether. The developed system offers a promising strategy to enhance oral bioavailability and therapeutic performance of lipophilic drugs. This approach may significantly contribute to improving malaria treatment outcomes and reducing variability in drug absorption.

Artificial intelligence (AI) is increasingly being integrated into healthcare systems to improve decision-making, communication, awareness and patient care. This narrative review explores the application of AI in palliative care enhancement. Literature from 2022–2025 was reviewed using databases such as PubMed, CINAHL,MEDLINE and Cochrane databases. Findings indicates that AI supports early identification of patients, improves communication, enhances emotional care, and aids professional training. However, ethical concerns and lack of human empathy remain challenges. AI should be used as a supportive tool alongside human care. Total 10 articles were included in under the study, and after data analysis under Jean Watson's Theory of Transpersonal Caring, four categories were defined that respond to the proposed objective: person-centred care and authentic relationships, decision support based on individualized knowledge, facilitation of transparent communication and advanced care planning, promotion of a healing environment and emotional well-being and education of health professionals and critical reflection. As a result, we identified the need for a multifaceted approach, involving the continuous validation of models, proper training of healthcare professionals and engagement of individuals in decisionmaking process. This ensures that decisions are grounded in robust evidence and ethical principles, ensuring that AI acts as a true aid rather than a source of additional risks. Concluding the study, AI can effectively be a valuable support tool in decision-making, but it is crucial that professionals remain aware of its limitations and can apply critical judgment in each situation.

1. Background of the Study: Digital health technologies are increasingly transforming physiotherapy practice by enhancing clinical decision-making, patient engagement, and care delivery. The integration of tools such as telerehabilitation, wearable sensors, artificial intelligence, and electronic health records has reshaped rehabilitation models. These technologies support real-time data exchange and inter-professional communication within multidisciplinary healthcare teams. However, evidence regarding their systematic integration into physiotherapy practice and education remains limited. Understanding their impact on collaboration and patient outcomes is essential for sustainable healthcare delivery. 2. Aims of the Study: This study aims to evaluate the role of digital health technologies in enhancing multidisciplinary collaboration in physiotherapy care. It seeks to examine the impact of technology-assisted rehabilitation on patient functional outcomes and engagement. The study also aims to assess the influence of digital innovations on physiotherapy education and professional competencies. Additionally, it explores facilitators and barriers to effective technological integration. Finally, the study aims to provide evidence-based recommendations for clinical and educational implementation. 3. Setting: The study will be conducted across hospital-based physiotherapy departments, outpatient rehabilitation centres, and academic physiotherapy institutions. 4. Design, Subjects, and Methods: A mixed-methods research design will be employed. Subjects will include practicing physiotherapists, multidisciplinary healthcare professionals, physiotherapy students, and patients undergoing rehabilitation. Quantitative data will be collected using outcome measures, surveys, and digital health usage metrics, while qualitative data will be gathered through interviews and focus group discussions. Data will be analysed to assess the effectiveness of digital health technologies in improving collaboration, education, and patient outcomes.

The present study reports the green synthesis of iron nanoparticles (INPs) using Ocimum tenuiflorum (Tulsi) leaf extract in 20% ethanol and their evaluation for anti-inflammatory activity. The characterization was done by UV-Vis spectroscopy, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-Ray diffraction (XRD). The anti-inflammatory activity of INPs was assessed through both in vitro and in vivo models. In vitro HRBC assays demonstrated significant inhibition, comparable to that of the standard anti-inflammatory drug diclofenac. In vivo anti-inflammatory efficacy was evaluated in mice using EPP and carrageenan-induced paw edema assays. The INP-treated groups showed a significant reduction in ear and paw edema, with efficacy comparable to that of diclofenac. Additionally, in vitro and in vivo biocompatibility studies confirmed the non-toxic nature and biological safety of the synthesized INPs. The results of this study suggest that O. tenuiflorummediated iron nanoparticles exhibit promising anti-inflammatory properties and good biocompatibility, indicating their potential as a novel and effective therapeutic agent for inflammatory disorders.

Liver disease is a huge global health burden and the need for accurate and interpretable analysis of routine bioanalytical data for effective screening and risk stratification. This study proposes a computational machine learning framework for analysing biochemical markers for assessment of liver disease based on routinely collected laboratory measurements. A hepatitis bioanalytical dataset with 605 samples and thirteen clinical features was analysed with binary and multiclass classification setting. Logistic regression, support vector machine using radial basis function kernel and extreme gradient boosting were evaluated using nested stratified cross-validation with class-weighted learning. In the binary classification, the F1 score was 0.95 and the area under the receiver operating characteristic curve was greater than 0.88, according to extreme gradient boosting model, which was very effective in discriminating healthy and diseased individuals. Multiclass classification was less effective with the support vector machine scoring a macro-averaged F1 of 0.68, which indicates some overlap of intermediate disease stages. The results of statistical testing confirmed the lack of significant pairwise differences between the best performing models. Interpretability analysis identified aspartate aminotransferase, bilirubin, alanine aminotransferase, total protein, and cholinesterase as the best predictors of the primary clinical knowledge. The results underscore the importance of statistical validated and interpretable machine learning methods for bioanalytical liver disease screening while showing the difficulties of fine-grained disease staging.

Objective: Non-communicable diseases rapidly growing in india due to rapid industrialization, urbanization, and changing lifestyles. Diabetes is a predominant concern, with 77 million affected individuals, proclaiming india the second most impacted country globally. Evaluating adverse drug reactions (adrs) to anti-diabetic medications is critical for optimizing patient care and ensuring drug safety. This study uses standardized causality assessment methods to assess adrs associated with commonly prescribed anti-diabetic drugs in a tertiary care hospital. Methodology: A cross-sectional was conducted over six months, involving 307 diabetic patients from the outpatient department of the college of medicine, jnm hospital, kalyani, west bengal. Data on demographic profiles, prescribed medications, and adrs were collected and analyzed. Results: Metformin was the most frequently prescribed drug and was primarily associated with gastric disturbances, mouth ulcers, and folic acid deficiency. A small subset of male patients reported erectile dysfunction. Teneligliptin was linked to adverse effects, including gastric discomfort, skin rashes, headaches, and nasopharyngitis. Overall, the female population represented the majority of diabetic patients visiting the hospital. Discussion: The study highlights the predictable adrs of metformin and teneligliptin, including gastrointestinal and mild systemic effects, with no novel events reported. A higher prevalence of diabetes in females suggests a need for targeted interventions addressing socio-economic and gender-specific factors to improve treatment outcomes and enhance diabetes management in diverse populations.