Deccan College of Medical Sciences

In preterm neonates of ≥ 30weeks' gestation with RDS, NHFOV delivered through NM at equivalent pressures is non-inferior to CPAP when used as primary NRS. The finding of shorter NRS duration with NHFOV needs to be proven in adequately powered trials. Use of NM interface with equivalent pressures addresses the lacunae in the current literature on NHFOV and provides a rigorous comparison between the two NRS modes. www.ctri.nic.in , id CTRI/2024/10/074939, registered on 8 October 2024. • CPAP as initial respiratory support for preterm neonates with RDS is the standard of care in LMICs. • NHFOV is more efficacious than CPAP when used as a post-extubation respiratory support modality, evidence for the same being uncertain when used as primary support. • NHFOV is non-inferior to CPAP as primary support in preterm neonates ≥ 30weeks with RDS for the outcomes of treatment failure and IMV requirement with equivalent pressures after lung recruitment and nasal mask interface. • A superiority design RCT comparing these two non-invasive respiratory support modalities in this subgroup of preterm neonates may not be feasible. The low baseline event rate of IMV necessitates an impractically large sample size to achieve adequate power.

In the case of chronic management diseases like diabetes, cardiovascular disorders, and cancer, there is a significant challenge of predicting the reaction of patients to medical treatment. Patient health status, medical history, genetic and treatment arrangements create difficulty in the health practitioners determining the most effective treatment to be offered to each individual. The latest developments in Artificial Intelligence (AI) can open new possibilities in terms of using the large amounts of medical data and helping in the clinical decision-making. The proposed project aims at the creation of a smart system where Generative Artificial Intelligence and Large Language Models (LLMs) are applied to forecast patient treatment reactions and help in customized care. The suggested system will challenge structured and unstructured healthcare data, such as establishment health records, lab reports, treatment history, and clinical notes. Identifying treatment outcome patterns based on patient characteristics is done using machine learning and deep learning techniques. Large Language Models are also integrated to analyze and comprehend written medical data including doctor notes and patient reports so that clinical data could be interpreted more accurately. Python is used to implement the system along with deep learning systems such as PyTorch or TensorFlow, and healthcare datasets made publicly available are used in training and evaluation. Some of the standard metrics that are used to evaluate the performance of the system are accuracy, precision, recall, and F1-score in order to determine the effectiveness of prediction. The last system gives insights on whether a patient is likely to respond a given treatment in a positive way to assist medical practitioners give more effective and personalized treatment. The proposed system helps to reveal the potential of intelligent healthcare technologies by allowing them to enhance the planning of the treatment and supporting the personalized medicine through the combination of Generative AI, predictive analytics, and natural language processing.

Background: Acute watery diarrhea episodes are mostly caused by viruses, with rotaviruses being the most common. Probiotics are living microorganisms that improve the quality of gut flora and offer health benefits. Hence, quality and quantity of probiotic yogurt play vital roles. Method: Out of 80 (eighty) adult patients with acute watery diarrhea, 40 (group I) were given regular yogurt, and 40 (group II) were treated with probiotic yogurt. The response was checked after 72 hours of treatment. Results: Comparison of malnutrition, dehydration, and frequency of stool per day has a significant p value (p<0.001). The comparative study of outcomes of regular yogurt and probiotic yogurt also had a significant p value (p<0.001). Conclusion: Group II had diarrhea for a shorter period than group I. Stool frequency and composition returned to normalcy more quickly in group-II patients. More research and placebo-controlled clinical investigations are required to confirm these significant comparative studies.

The current work focused on the formulation and in vitro evaluation of Lemborexant oral dispersible films (ODFs) loaded with nanoparticles to improve solubility, dissolution, and patient compliance in the treatment of insomnia. Lemborexant, a dual orexin receptor antagonist, has low water solubility, reducing its oral bioavailability. To overcome this limitation, nanoparticles were prepared using the solvent displacement method and incorporated into fast-dissolving polymeric films that contained hydroxypropyl methylcellulose (HPMC) and carboxymethyl cellulose (CMC) as film-forming agents, PEG as a plasticizer, and superdisintegrants for rapid disintegration. The produced films were tested for physicochemical and mechanical properties such as thickness, weight fluctuation, folding endurance, tensile strength, surface pH, drug content homogeneity, disintegration time, and in vitro drug release.The optimized formulation demonstrated uniform thickness and weight, as well as appropriate tensile strength. The films disintegrated quickly (15-30 seconds) and had considerably higher in vitro drug release than pure Lemborexant. FTIR tests verified the absence of drug-excipient interactions, while SEM revealed homogeneous nanoparticle dispersion. The findings suggested that Lemborexant-loaded nanoparticle oral dispersible films are a viable delivery platform for quick onset of action, increased bioavailability, and enhanced patient compliance, particularly in geriatric patients with swallowing issues. Additional in vivo investigations are needed to confirm the therapeutic efficacy and pharmacokinetic advantages of the proposed formulation. Keywords: Lemborexant, Oral Dispersible Film, Nanoparticles, Solvent Casting, In vitro drug release studies.

ABSTRACT The intelligent transportation system (ITS) is enabled by the vehicular ad hoc networks (VANETs), but the security threats, such as node impersonation, node tampering, and eavesdropping, are the greatest challenges and cause security concerns within the system. The large‐scale vehicular environment is not effectively handled by the previous static and centralized security approaches, which can greatly increase the latency and data integrity problems within the network. Thus, this research proposes a deep learning–assisted blockchain approach for enabling the decentralized, reliable, and secure communication in the VANET. The main contribution of the research is to perform secure authentication and task offloading to enable secure task offloading within the VANET and to guarantee communication performance with minimum energy consumption and delays. First, the data confidentiality, privacy of the task offloading, authentication, and integrity are achieved by introducing blockchain technology. Second, the node authentication is performed using adaptive and attention‐based hybrid serial learning (AAHSL), which is developed with the combination of a deep belief network (DBN) and temporal convolution network (TCN). After authenticating the data within the nodes, the adaptive deep reinforcement learning (ADRL)–based task offloading is proposed for reducing the task completion time within the network. In both models, the parameters are tuned using the pattern improvement parameter–based poor and rich optimization algorithm (PIP‐PROA). The experimental results demonstrate that the proposed approach achieves an FNR of about 3.46% during the authentication process, and the reward score achieved by the designed model during the task offloading process is 9.22. Thus, the effectiveness of the suggested model is confirmed by the experimental analysis.

Background:Visual impairment is highly prevalent among the elderly and significantly affects independence, mobility, and quality of life. Non-pharmacological, low-cost interventions that can be implemented in community settings are of increasing interest. Bates therapy, consisting of structured eye relaxation and movement exercises, has been proposed as a complementary approach for improving visual function.Aim:To evaluate the effectiveness of Bates therapy on comprehensive visual outcomes among elderly residents of selected old age homes in Erode.Methods:A quasi-experimental time-series design was adopted among 60 elderly participants aged ≥60 years residing in two selected old age homes. Comprehensive visual outcomes—visual acuity, visual field, contrast sensitivity, and overall visual function—were assessed using standardized tools before intervention and on days 7, 15, and 30 following Bates therapy. The intervention consisted of supervised Bates exercises for 30 min twice daily for 30 days. Statistical analysis was performed.Results:Statistically significant improvements were observed in all visual outcomes by day 30. Mean visual acuity improved from 1.85 ± 0.55 to 3.60 ± 0.50 (P < 0.001), visual field from 3.70 ± 0.60 to 4.55 ± 0.45 (P < 0.001), contrast sensitivity from 1.20 ± 0.30 to 1.65 ± 0.25 (P < 0.001), and VFQ-25 score from 45.2 ± 10.5 to 72.8 ± 8.2 (P < 0.001). No significant associations were observed between baseline visual outcomes and demographic variables.Conclusion:Bates therapy improved comprehensive visual outcomes among elderly residents of old age homes and may serve as a simple, cost-effective adjunct in geriatric and community eye-care programs.