Impact of National Rural Health Mission: A Public Welfare Programme of the Government on Indian Health Sector

The study reveals circumstances under which the National Rural Health Mission NRHM program was launched by the Government of India to fulfill the target set out by the United Nation s Millennium Development Goals MDG s It examines the role and functioning of NRHM in delivering basic health care services to rural India It further delineates the role of NRHM in reducing Infant Mortality Rate IMR Maternal Mortality Rate MMR Total Fertility Rate TFR Dengue Mortality Reduction Rate etc The study also highlights the character of NRHM in providing equitable affordable and quality health care services to the rural population particularly the vulnerable groups It was instrumental in creating new institutions decentralizing of services and providing new ideas and resources for health system The study mainly focused on the approaches major planks achievements and evaluation of the NRHM progra

Collective Asynchronous Remote Invocation (CARI): A High-Level and Effcient Communication API for Irregular Applications

The Message Passing Interface (MPI) standard continues to dominate the landscape of parallel computing as the de facto API for writing large-scale scientific applications. But the critics argue that it is a low-level API and harder to practice than shared memory approaches. This paper addresses the issue of programming productivity by proposing a high-level, easy-to-use, and effcient programming API that hides and segregates complex low-level message passing code from the application specific code. Our proposed API is inspired by communication patterns found in Gadget-2, which is an MPI-based parallel production code for cosmological N-body and hydrodynamic simulations. In this paper—we analyze Gadget-2 with a view to understanding what high-level Single Program Multiple Data (SPMD) communication abstractions might be developed to replace the intricate use of MPI in such an irregular application—and do so without compromising the effciency. Our analysis revealed that the use of low-level MPI primitives—bundled with the computation code—makes Gadget-2 diffcult to understand and probably hard to maintain. In addition, we found out that the original Gadget-2 code contains a small handful of—complex and recurring—patterns of message passing. We also noted that these complex patterns can be reorganized into a higherlevel communication library with some modifications to the Gadget-2 code. We present the implementation and evaluation of one such message passing pattern (or schedule) that we term Collective Asynchronous Remote Invocation (CARI). As the name suggests, CARI is a collective variant of Remote Method Invocation (RMI), which is an attractive, high-level, and established paradigm in distributed systems programming. The CARI API might be implemented in several ways—we develop and evaluate two versions of this API on a compute cluster. The performance evaluation reveals that CARI versions of the Gadget-2 code perform as well as the original Gadget-2 code but the level of abstraction is raised considerably

Cattaneo-Christov heat flux model for second grade nanofluid flow with Hall effect through entropy generation over stretchable rotating disk

The second grade nanofluid flow with Cattaneo-Christov heat flux model by a stretching disk is examined in this paper. The nanofluid flow is characterized with Hall current, Brownian motion and thermophoresis influences. Entropy optimization with nonlinear thermal radiation, Joule heating and heat absorption/generation is also presented. The convergence of an analytical approach (HAM) is shown. Variation in the nanofluid flow profiles (velocities, thermal, concentration, total entropy, Bejan number) via influential parameters and number are also presented. Radial velocity, axial velocity and total entropy are enhanced with the Weissenberg number. Axial velocity, tangential velocity and Bejan number are heightened with the Hall parameter. The total entropy profile is enhanced with the Brinkman number, diffusion parameter, magnetic parameter and temperature difference. The Bejan number profile is heightened with the diffusion parameter and temperature difference. Arithmetical values of physical quantities are illustrated in Tables

Efficacy of Arthrocentesis in Internal Derangement of Temporomandibular Joint

Abstrac

A design strategy to generate a SARS‐CoV‐2 RBD vaccine that abrogates ACE2 binding and improves neutralizing antibody responses

The structure-based design of antigens holds promise for developing vaccines with higher efficacy and improved safety profiles. We postulate that abrogation of host receptor interaction bears potential for the improvement of vaccines by preventing antigen-induced modification of receptor function as well as the displacement or masking of the immunogen. Antigen modifications may yet destroy epitopes crucial for antibody neutralization. Here, we present a methodology that integrates deep mutational scans to identify and score SARS-CoV-2 receptor binding domain variants that maintain immunogenicity, but lack interaction with the widely expressed host receptor. Single point mutations were scored in silico, validated in vitro, and applied in vivo. Our top-scoring variant receptor binding domain-G502E prevented spike-induced cell-to-cell fusion, receptor internalization, and improved neutralizing antibody responses by 3.3-fold in rabbit immunizations. We name our strategy BIBAX for body-inert, B-cell-activating vaccines, which in the future may be applied beyond SARS-CoV-2 for the improvement of vaccines by design

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Reviving Natural Rubber Synthesis via Native/Large Nanodiscs

Natural rubber (NR) is utilized in more than 40,000 products, and the demand for NR is projected to reach $68.5 billion by 2026. The primary commercial source of NR is the latex of Hevea brasiliensis. NR is produced by the sequential cis-condensation of isopentenyl diphosphate (IPP) through a complex known as the rubber transferase (RTase) complex. This complex is associated with rubber particles, specialized organelles for NR synthesis. Despite numerous attempts to isolate, characterize, and study the RTase complex, definitive results have not yet been achieved. This review proposes an innovative approach to overcome this longstanding challenge. The suggested method involves isolating the RTase complex without using detergents, instead utilizing the native membrane lipids, referred to as “natural nanodiscs”, and subsequently reconstituting the complex on liposomes. Additionally, we recommend the adaptation of large nanodiscs for the incorporation and reconstitution of the RTase complex, whether it is in vitro transcribed or present within the natural nanodiscs. These techniques show promise as a viable solution to the current obstacles. Based on our experimental experience and insights from published literature, we believe these refined methodologies can significantly enhance our understanding of the RTase complex and its role in in vitro NR synthesis

Leveraging CIELAB Segmentation and CNN for Wheat Fungi Disease Classification

Wheat is the third most harvested and consumed grain globally, but a significant portion of its production is wasted due to diseases. Fungal infections caused by pathogenic fungi are particularly harmful, greatly reducing crop yields. Manual visual inspection of large fields is slow, exhausting, and requires specialized expertise. This research introduces a novel combination of image augmentation, CIELAB segmentation, and a fine-tuned pre-trained CNN, achieving an unprecedented 98.43% accuracy in wheat fungal disease classification, addressing gaps in current detection methods and promoting sustainable agriculture. To conduct this research, datasets from Kaggle were merged and meticulously validated to create a comprehensive set with five classes: healthy wheat and four fungal diseases. Preprocessing steps included resizing, contrast enhancement and noise removal to ensure uniform and high-quality images followed by rigorous image augmentation techniques to expand and diversify the dataset ultimately enhancing the deep learning model\u27s robustness and accuracy. The CNN model, trained over 80 epochs achieved an impressive 98.43% accuracy in classifying wheat fungal diseases. With a precision of 98.47% and an F1 score of 98.43% the model demonstrated strong positive classification accuracy. Additionally, a recall of 98.43% and specificity of 98.47% indicated its effectiveness in identifying true positive cases and accurately detecting disease presence or absence

Leveraging CIELAB Segmentation and CNN for Wheat Fungi Disease Classification

Wheat is the third most harvested and consumed grain globally, but a significant portion of its production is wasted due to diseases. Fungal infections caused by pathogenic fungi are particularly harmful, greatly reducing crop yields. Manual visual inspection of large fields is slow, exhausting, and requires specialized expertise. This research introduces a novel combination of image augmentation, CIELAB segmentation, and a fine-tuned pre-trained CNN, achieving an unprecedented 98.43% accuracy in wheat fungal disease classification, addressing gaps in current detection methods and promoting sustainable agriculture. To conduct this research, datasets from Kaggle were merged and meticulously validated to create a comprehensive set with five classes: healthy wheat and four fungal diseases. Preprocessing steps included resizing, contrast enhancement and noise removal to ensure uniform and high-quality images followed by rigorous image augmentation techniques to expand and diversify the dataset ultimately enhancing the deep learning model\u27s robustness and accuracy. The CNN model, trained over 80 epochs achieved an impressive 98.43% accuracy in classifying wheat fungal diseases. With a precision of 98.47% and an F1 score of 98.43% the model demonstrated strong positive classification accuracy. Additionally, a recall of 98.43% and specificity of 98.47% indicated its effectiveness in identifying true positive cases and accurately detecting disease presence or absence

Deep Faces: Advancing Age and Gender Classification using Facial Images with Deep Features

In the realm of identity recognition and social interactions, human facial features play a pivotal role. Accurate age estimation and gender classification from facial images have practical implications across various fields, including biometrics, surveillance, and personalized services. This study presents a novel approach that harnesses deep features extracted by the VGG-19 architecture for age and gender prediction, employing a custom convolutional neural network (CNN) for classification. Leveraging the UTKFace dataset, encompassing a diverse collection of facial images with annotated age and gender labels spanning various ages, ethnicities, and gender representations, provides a robust foundation for model training and evaluation. Deep features extracted from the VGG-19 architecture serve as rich representations of facial patterns, enabling our model to discern discriminative cues for age and gender. These deep features are input to CNN model, which is fine-tuned specifically for age and gender classification. The model comprises input layer, Dense layers, incorporating dropout and batch normalization to mitigate overfitting, and Activation Functions Sigmoid for gender classification and SoftMax for Age group classification. The dataset is divided into training and validation sets (70% and 30%, respectively), enabling the model to learn to map VGG-19 features to age and gender labels. To evaluate the performance of the model, metrics like accuracy, precision, recall, and F1-score are employed. The proposed model achieves an impressive 78.67% accuracy in predicting age and 97.02% accuracy in gender classification on the UTKFace dataset, outperforming traditional methods despite challenges posed by variations in lighting, pose, and expression. The robustness of our approach is evidenced by its capability to handle diverse gender representations