Hislop College

Tuberculosis (TB) continues to pose a significant public health challenge globally, with India accounting for approximately 26% of the global TB burden. Beyond its biomedical implications, TB is also associated with profound psychosocial consequences that can impede treatment adherence, affect patient well-being, and delay recovery. This study examines the psychosocial, clinical, and sociodemographic factors influencing TB patients to identify priority groups for targeted psychosocial interventions. A cross-sectional study was conducted on 90 patients registered at the DOTS Centre of Indira Gandhi Government Medical College, Nagpur. Standardized instruments, including the Social Anxiety Questionnaire for Adults, the Van Rie TB Stigma Scale, the Arcencio TB Stigma Scale, Rosenberg Self-esteem scale, and Visual Analogue Scales, were administered. Data analysis was performed using SPSS version 21. Data were analyzed using frequency, percentage, and the independent samples t-test. Findings indicate that over 50% of participants reported high levels of social anxiety and perceived stigma, both self-directed and from family members. More than 60% reported a significant disease burden and fear of social exclusion and 70% of the patients displayed low self-esteem. Social anxiety, in domains such as public speaking and interpersonal interactions, was more prevalent among married individuals, those aged between 30 and 45 years and those in the early stages of the disease. Older adults reported greater social anxiety in situations involving interactions with the opposite sex, assertive communication, and fear of criticism or embarrassment. Self-stigma was more pronounced during the early stages of illness, whereas stigma perceived by family members was higher in later stages. The findings highlight the need for psychosocial interventions and family-centered approaches tailored to specific patient profiles and to mitigate stigma within the household and society.

Introduction: Psoriasis is an autoimmune, progressive, and chronic inflammatory condition predominantly caused due to various inflammatory and immunological mediators. Phytochemicals, such as plumbagin (PL) and resveratrol (RSV), have anti-inflammatory, antioxidant, and immunomodulatory properties. Methods: The induction of psoriasis-like conditions in rats was standardized by exposing them to ultraviolet (UV) radiation for 30 minutes on their dorsal skin surfaces. The psoriasis area severity index (PASI) scoring was utilized for confirming the psoriasis induction. Biochemical measurement of the hydroxyproline (HP) content was performed to measure collagen rupture, accompanied by histological evaluation of the exposed area. The Swiss Dock web server was used for the in silico docking of mammalian targets of rapamycin (mTOR), fibro-collagenase, and nuclear factor kappa B proteins. Results: A 30-minute UV exposure was standardized and created as a model for inducing psoriasis-like conditions. Administration of PL and RSV alone or in combination restored the increased PASI score within a week and hastened recovery. These medications also limited the increase in the HP content and altered the morphology. The combination of PL with RSV is more effective than either medicine alone. In silico docking, the analysis demonstrated that PL and RSV had a good binding relationship with mTOR, NF-κB, and fibro-collagenase. Conclusion: UV exposure for 30 minutes causes a psoriasis-like state, whereas PL and RSV prevent the production and progression of psoriasis-like conditions via their multi-target modulatory mechanisms. Target-specific investigations require molecular-level research.

The formulation of targeted HA-PF127 micelles overcomes the poor solubility of tofacitinib, provides direct delivery to breast cancer cells and significantly increases the anticancer efficacy, oral bioavailability and safety of the drug.

The smart, multi-layered system in this work uses multimodal data fusion, IoT-enabled contextual feedback, and flexible reinforcement learning to improve language acquisition. The system records speech, motion, and text inputs to tailor learning. These are converted to appropriate feature representations and assembled using attention-based weighting. These coupled vectors assess student skill and recommend learning tasks. Based on input quality and learner response, these exercises are altered live. The system uses IoT sensors to measure skin temperature, heart rate variability, and background sounds in addition to multimodal interpretation. These indications instruct context-aware feedback to provide it, which adapts to the learner’s mood and state. When the system senses outside distractions or internal fatigue, it modifies input and difficulty to make things clearer and more intriguing. A third level uses reinforcement learning. Learning success is tracked across sessions and stored in a memory bank. This information is used to adjust how the system reinforces skills for long-term memory and skill progress. Past trends inform future feedback. These trends combine rapid change and long-term growth. In this closed-loop approach, the system may adapt to provide more tailored assistance. The suggested method is easier, more flexible, aware of the context, and immerses the learner better than smart language learning tools. The technology is expandable, mobile-friendly, and understands emotions. It advances computer-assisted language learning for many sorts of students in structured and unstructured settings.

This study presents the microwave-assisted sol-gel autocombustion synthesis of soft magnetic Mg0.6Zn0.4Fe2O4(MZF) and polyvinyl alcohol (PVA)-coated Mg0.6Zn0.4Fe2O4 (MZF@PVA) spinel nanoferrites. The structural, functional group, magnetic, and dielectric properties of these materials were thoroughly investigated using X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Vibrating Sample Magnetometry (VSM), and an Impedance Analyzer. The structural analysis revealed crystallite sizes ranging from (31 to 35 nm) and lattice dimensions of (8.3769 to 8.3831 Å). These characteristics were influenced by induced lattice strain, measured at 3.581 × 10−3 to 3.584 × 10−3, and dislocations ranging from 0.99 × 10−3 1/nm2 to 0.80 × 10−3 1/nm2. FTIR analysis confirmed the presence of the Fe-O functional group, with a strong vibration band observed at 450 to 600 cm−1. Field Emission Scanning Electron Microscopy (FESEM) images showed spherical nanoparticles with slight agglomeration. Magnetic characterization via VSM indicated that these nanoferrites exhibit a soft magnetic pseudo-single domain nature, with coercivity values ranging from 77.29 to 81.66 Oe, magnetic saturation from 28.16 to 33.99 emu g−1, retentivity from 2.58 to 3.50 emu g−1, and a squareness ratio between 0.09 and 0.1. Furthermore, the MZF nanoferrite demonstrated significant biological activity. It effectively inhibited the growth of Escherichia coli and Pseudomonas aeruginosa in agar diffusion tests. Additionally, when applied to Vigna radiata (mung bean) plants, the nanoferrite promoted growth compared to untreated controls. These findings suggest that the synthesized MZF nanoferrite has promising potential for applications in bacterial growth inhibition and as a plant growth promoter.

Organ transplant rejection is still a big problem in transplant medicine. It can make the graft not work right or even fail. Being able to predict rejection early on could greatly improve patient results by allowing for quick measures and personalized immune treatments. The main topic of this study is on how clinical, biological, and genetic data can be used with machine learning (ML) methods to predict organ donation refusal. Several machine learning models, such as controlled and unstructured learning, were tested to see if they could be used to identify acute and chronic rejection events in transplant patients. Key algorithms like decision trees, support vector machines, random forests, and neural networks were tested to see how well they could predict rejection events and how sensitive and detailed they were.Medical records of patients, immune profiles (like cytokine levels and HLA mismatches), and genetic factors linked to donor refusal were used as data sources. We used feature selection techniques to find the most important factors and cross-validation techniques to see how well the model could be used in other situations. Instead of just using standard clinical signs, the study shows how important it is to use multidimensional data to make predictions more accurate. The results show that machine learning models can be a very useful tool for predicting organ donation rejection if they are properly taught with large datasets. These models can help doctors find people who are at high risk, so they can help them in a more personalized and fast way. Adding machine learning to clinical workflows could also lead to smarter transplant management, which would increase the survival rates of both short-term and long-term grafts.

Construction equipment has long been recognized as a crucial factor in enhancing efficiency and productivity on construction sites, thus facilitating economic growth by reducing overall construction and operational costs. This study specifically examined the productivity of power shovels to provide a comprehensive analysis based on both theoretical and practical evaluations. The methodology involved an in-depth analysis of various factors affecting the productivity of power shovels. These factors include cycle time, bucket capacity, angle of swing, soil type, soil condition, and environmental influences. The productivity was assessed at six distinct construction sites, considering seasonal variations that could impact equipment performance. Data were collected during three different seasons: the rainy season, the winter season, and the summer season. Subsequently, we conducted a comparative analysis of the ideal productivity versus the theoretical productivity for the power shovels. In this investigation, we presented our findings using graphs to make them easier to understand. Our study showed that changes in cycle time and the surrounding environment had a notable impact on the excavating equipment's productivity. This analysis underscores the importance of considering these factors when evaluating and optimizing the use of power shovels in construction operations. Keywords: Construction equipment, Efficiency, Productivity, Power shovels, Cycle time, Bucket capacity, Angle of swing, Soil type, Soil condition, Environmental influences, Comparative analysis, Seasonal variations, Excavating equipment, Theoretical productivity, Optimization