Empowering health personnel for decentralized health planning in India: The Public Health Resource Network

The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services

Appropriate initial antibiotic therapy in hospitalized patients with gram-negative infections: systematic review and meta-analysis

Study characteristics and results of economic outcomes. Table S4. Study characteristics and results of length of stay (DOCX 74 kb

How appropriately are adults being prescribed proton pump inhibitors - experience of a tertiary care centre

Background and Objectives: Proton pump inhibitors (PPIs) are one of the commonest medicines prescribed in recent years as they are highly effective and remarkably safe. However, there is a growing concern that PPIs are being overprescribed and used for poorly defined reasons or for conditions where they are not beneficial. This study was conducted to study the type, duration, indication and appropriateness of PPI use.Methodology: This prospective observational study was conducted in medicine department of K.M.C.H and LSK Hospital, Kishanganj, Bihar, India over1 year from January 2020 to December 2020 including adults visiting our medicine OPD for the first time who were already on PPI therapy. Data was collected by direct interviewing as well as review of previous prescriptions.Results: Total 393 patients were enrolled and assessed for use of PPIs. Mean duration of PPI use was 11.7 ± 6.1 months. More than half (52.7%) had no clear indication, 37.7% had valid indication and 9.7% had a borderline indication. Patients with valid indication were given PPIs for dyspepsia (27.7%), GERD (24.3%), stress ulcer prophylaxis (19.6%) and peptic ulcer (16.2%). Patients without valid indication were given PPIs for anemia (24.6%), NSAIDs (14%) and corticosteroids therapy (12.6%). Similarly, patients with borderline acceptable indication were given PPIs for post endoscopic procedure (39.5%), use of double antiplatelet agents (18.4%) and uninvestigated dyspepsia (18.4%). Only 61.3% were receiving recommended maintenance dose and the rest 38.7% were using high dose. Only 22.9% had undergone upper G.I endoscopy and the rest 77.1% were prescribed long term PPI without a convincing evidence. Conclusion: Doctors should be more thoughtful while prescribing PPIs to provide an appropriate, safe and cost effective advice. Prescription should follow evidence based practice as unnecessary and inappropriate prescribing isn’t cost effective and potentially harmful too

Design and implementation of an optimized mask RCNN model for liver tumour prediction and segmentation

Liver tumour segmentation is a challenging task due to the wide diversity in size, position, depth, and proximity to surrounding organs. This research uses the state-of-the-art model of Mask R-CNN model with the ResNet-50 architecture as the backbone. The suggested methodology leverages the Mask Region-Convolutional Neural Network approach to accurately identify liver tumors by identifying tumour location. To address variations of the liver and CT scan images with different parameters. The normalized CT images are then fed into the RESNET-50 model to extract relevant features. Subsequently, the liver tumor are segmented using the Mask R-CNN algorithm. The experimental dataset used in this study consists of one hundred and thirty CT scans obtained from various hospitals and nursing homes, which are freely accessible on the LiTS web page. The suggested algorithm is trained on transformed CT image slices. The results demonstrate that the proposed Mask RCNN system, with its innovative connections, surpasses state-of-the-art methods in identifying liver tumor, achieving a remarkable DSC value of 0.97%. This technique has the potential to significantly contribute to early and precise diagnosis of liver tumor in the field of biotechnology, potentially saving many patients' lives

Design and implementation of an optimized mask RCNN model for liver tumour prediction and segmentation

Liver tumour segmentation is a challenging taskdue to the wide diversity in size, position, depth, and proximityto surrounding organs. This research uses the state-of-the-artmodel of Mask R-CNN model with the ResNet-50 architectureas the backbone. The suggested methodology leverages the MaskRegion-Convolutional Neural Network approach to accuratelyidentify liver tumors by identifying tumour location. To addressvariations of the liver and CT scan images with differentparameters. The normalized CT images are then fed into theRESNET-50 model to extract relevant features. Subsequently,the liver tumor are segmented using the Mask R-CNNalgorithm. The experimental dataset used in this study consistsof one hundred and thirty CT scans obtained from varioushospitals and nursing homes, which are freely accessible on theLiTS web page. The suggested algorithm is trained ontransformed CT image slices. The results demonstrate that theproposed Mask RCNN system, with its innovative connections,surpasses state-of-the-art methods in identifying liver tumor,achieving a remarkable DSC value of 0.97%. This technique hasthe potential to significantly contribute to early and precisediagnosis of liver tumor in the field of biotechnology, potentiallysaving many patients' lives. <br/