Burdwan Medical College & Hospital

Unfavorable experiences also present sudden changes in the distribution of clinical data streams, which tend to cause significant deterioration in the performance of traditional clinical decision support algorithms. The models of artificial intelligence used to date are primarily missing the ability to be generalized across the acute perturbations of physiology or system because most of them are not algorithmically resilient. This paper presents a Causal Foundation Model, which combines causal representation learning with large pre trained multimodal foundation models and digital twin-based simulation to become better robust to adverse clinical events. The framework limits latent representations by matching them with underlying causal factors by structural causal models and interventional training and a digital twin environment is used to simulate controlled adverse events like septic shock, pulmonary embolism and equipment failure. The evaluation of model performance was done on intensive care unit outcome prediction tasks given conditions of a normal and unfavorable condition to determine that the results were all in a form of mean values with standard deviations and ninety five percent confidence intervals. The proposed model was found to have the lowest mean penalty error of organ failure score prediction of 0.214 +- 0.003 and Brier penalty mortality prediction on the first attempt of 0.078 +- 0.002 significant at a p < 0.01 compared to recurrent and transformer-based baselines. The reduction in the performance loss was found to be very significant p = 0.001 very significant paired statistical testing confirmed that the major clinical events. These findings indicate that within a context of causal constraints, foundation models, and training on digital twins, statistically significant and clinically significant increases in resilience, accuracy, and capability in early warning are achieved, which can be used to further make clinical-based artificial intelligence systems more reliable and trustworthy.

Early diagnosis of mental vulnerability is still a problem since most clinical evaluations are based on the subjective aspects of evaluation like self-report and not on the objective physiological indications. The idea of psychological resilience which is a fundamental protective factor against stress related disorders has been connected to the autonomic regulation and daily behavioral patterns, but the objective method of calculating on such a scale is yet to be established. This paper presents a multimodal graph neural network, which combines the wearable derived physiological signals to forecast trait level resilience and stress as well as state level. The ongoing heart activity, electrodermal activity, and motion data of adult participants were gathered and matched with resilient score proven validated scores. Participant specific graphs were created whereby physiological modalities were the nodes and the inter signal dependencies were the edges. Statistical analysis showed that there were significant physiological disparities that were related to resilience. High resilience group members had much better values of root mean square of successive differences and physical activity per day (mean difference on 1000 extra steps per day, p = 0.003). The proposed graph neural network (GNN) performed well in classification tasks in terms of area under the receiver operating characteristic curve (0.80 +- 0.02) to differentiate high and low resilience, which was significantly better than that of logistic regression (0.60), random forest (0.65), and long short-term memory models (0.72), with the difference being supported by DeLong test (p < 0.01). Graph based learning also offers added benefits of discrimination and physiologically explainable output and thus can facilitate its future as a scalable and objective digital mental health monitoring and early risk stratification tool.

Classical anatomic sites are implicated in most diseases. They help the clinician to make a substantial diagnosis. However there exists indistinct chances of occurrence of diseases at sites that are unusual and unexpected. The same is true for the disease Rhinosporidiosis as well. We have attempted to illustrate cases of Rhinosporidiosis with presentations at infrequent sites in this compilation of cases. Tissue diagnosis has ultimately culminated in formulating the conclusive diagnosis here.

This study evaluates the overall risk exposure of manufacturing small- and medium-sized enterprises’ supply chains (SMESC) during plausible disruption scenarios, particularly in emerging economies where disruptions can have severe consequences. It develops a holistic, systematic, and quantitative framework to empower SMEs to assess and manage supply chain risks (SCR) effectively, thereby enhancing resilience and ensuring business continuity. A comprehensive literature review and expert consultations were undertaken to identify potential hazards. An integrated Analytic Hierarchy Process (AHP)-Hazard Identification and Risk Assessment (HIRA) methodology was employed, where AHP determined hazard weights and HIRA evaluated overall risk levels. The framework was illustrated through a case study of a manufacturing SME in India. The results revealed that SMESC are highly vulnerable to disruptions, reflected by an overall risk score of 63.476%. Key hazards included the scarcity of raw materials, distribution network breakdowns, and inventory stockouts, with procurement and production activities being particularly susceptible. The findings offer critical insights for managers and policymakers to proactively manage risks and bolster the resilience of SMESC in emerging economies. This research addresses existing gaps by proposing a structured and problem-driven integration of AHP and HIRA, enabling SMEs to quantify and prioritize both internal and external SCR. The combination of expert-driven weighting (AHP) and scenario-based risk scoring (HIRA) offers a practical decision-support framework suiting contexts with limited historical data and high uncertainty.

Introduction: The human body’s ability to heal is innate. This procedure is guided by potentials found in nature. We may harness these potentials by employing various therapeutic approaches that uphold the natural order, fortify the body and encourage vitality. The aim of the study is to develop novel anti-cariogenic antibacterial compounds from nine common medicinal plants easily available in West Bengal, India. Materials and Methods: The antibacterial efficacy of ethanolic, methanolic and aqueous extracts of selected plant samples was checked against freeze-dried Streptococcus mutans by the well diffusion method and the disc diffusion method. Results: In the well diffusion method, the methanolic extract of haritaki showed maximum inhibition zones at both higher (100 mg/ml) and lower (50 mg/ml) concentrations. The antimicrobial activity of methanol extracts of haritaki was also highest at a higher (40 µL) concentration against the standard bacteria sample (Microbial Type Culture Collection and Gene Bank-890) S. mutans and at a lower concentration (40 µL) in the disc diffusion method. Similarly, the ethanolic and aqueous extracts of Haritaki exhibited maximum potency at both higher and lower concentrations in both the well diffusion and disc diffusion methods tests. Conclusion: This work has investigated the in vitro antibacterial efficacy of medicinal herbs against freeze-dried S. mutans . Alcoholic plant extracts are more effective than aqueous extracts. Among all the plant extracts, Terminalia chebula (haritaki) demonstrated the highest antimicrobial efficacy against the standard freeze-dried bacterial sample. Azadirachta indica (neem), Curcuma longa (turmeric) and Psidium guajava (guava) exhibited moderate antibacterial efficacy against the test bacterial strains in all solutions. Minimum activity was shown by Aloe barbadensis miller (aloe vera).

Introduction: Parkinson’s disease (PD) is a severe neurodegenerative disorder characterized by the loss of dopamine-producing neurons, leading to motor symptoms such as tremors, stiffness, and bradykinesia. Current therapeutic approaches primarily focus on symptom management rather than addressing the underlying neurodegeneration. Catharanthus roseus, commonly known as Madagascar periwinkle, has been widely used in traditional medicine and is known for its diverse pharmacological properties, particularly due to its rich alkaloid content. Method: This review explores the potential therapeutic role of Catharanthus roseus in treating PD. Taxonomically classified under the Apocynaceae family, the plant has been traditionally used for various ailments, including cancer, Diabetes, and cardiovascular diseases. Its pharmacological effects, such as anti-inflammatory, antioxidant, and anti-cancer activities, are mainly attributed to active compounds like vincristine and vinblastine. The study examines existing literature, focusing on preclinical models investigating its neuroprotective capabilities. Results: Preclinical studies have demonstrated that Catharanthus roseus exhibits neuroprotective effects in various models of neurodegenerative diseases. The plant's phytochemicals show promise in slowing or preventing neurodegeneration, potentially offering a novel approach to managing PD progression. Discussion: The neuroprotective effects observed in preclinical studies suggest that Catharanthus roseus could modulate key pathogenic pathways involved in PD, such as oxidative stress, inflammation, and neuronal apoptosis. The presence of potent alkaloids may contribute to these effects by enhancing neuronal survival and reducing dopaminergic neuron loss. However, variability in study models, dosage, and extraction methods indicates the need for standardized protocols. Additionally, the mechanism of action for many of its compounds remains poorly understood, necessitating further molecular studies to clarify their role in neuroprotection. Conclusion: While the initial findings are promising, further research is essential to identify the specific compounds responsible for the neuroprotective effects. Clinical trials are also necessary to evaluate the safety and efficacy of Catharanthus roseus in treating Parkinson's disease.

Background: Urothelial carcinoma is the 10th most commonly occurring and 13th most common cause of cancer-related deaths worldwide. The major prognostic factors in urothelial carcinomas are the depth of invasion into the bladder wall and the degree of differentiation of the tumour. However, there is no reliable prognostic marker, and thus molecular markers are required to estimate the individual prognosis of patients as well as for effective treatment. Methods: To evaluate HER2/Neu and β-catenin immunohistochemical (IHC) expression in urothelial carcinoma and their correlation with tumour grade and stage, thus contributing as prognostic factors. Fifty formalin-fixed TURBT specimens of urinary bladder carcinoma were collected from our hospital during the period from January 2023-June 2024. Hematoxylin and Eosin stained sections were done, along with microscopical examination, and immunohistochemistry for HER2/Neu and 8-catenin were done. A p-value of <0.05 was considered statistically significant. Results: A total of 38 (82%) out of 46 cases of high-grade urothelial carcinoma and 24 (92.3%) out of 26 cases of muscle-invasive urothelial carcinoma expressed strong HER2/Neu membranous positivity (score 3). HER2/Neu expression was found to be statistically significant with respect to histological grade and pathological stage (p< 0.001). 42/46 cases of high-grade urothelial carcinoma showed β-catenin positivity, and intensity increased with an increase in pathological stage (p<0.001). Conclusion: A positive association between HER2/Neu and 3-catenin expression with tumour grade and pathological stage of urothelial carcinoma was found, contributing to prognostic assessment and thereby guiding targeted therapy.

Titanium is an earth abundant metal with low toxicitythat is ableto form complexes that mediate a wide range of organic transformationsin both polar and radical manifolds. In context of the latter, theuse of Ti-catalysts in electrosynthesis is surprisingly underexplored,considering the great potential for electrochemical (re)­generationof low-valent and catalytically active species. To spur further innovationin the field, this Review provides an overview of the current literatureand discusses the limitations and possibilities for electrochemicallydriven Ti-catalysis.