Southern African Treatment Resistance Network (SATuRN) RegaDB HIV drug resistance and clinical management database: supporting patient management, surveillance and research in southern Africa

Substantial amounts of data have been generated from patient management and academic exercises designed to better understand the human immunodeficiency virus (HIV) epidemic and design interventions to control it. A number of specialized databases have been designed to manage huge data sets from HIV cohort, vaccine, host genomic and drug resistance studies. Besides databases from cohort studies, most of the online databases contain limited curated data and are thus sequence repositories. HIV drug resistance has been shown to have a great potential to derail the progress made thus far through antiretroviral therapy. Thus, a lot of resources have been invested in generating drug resistance data for patient management and surveillance purposes. Unfortunately, most of the data currently available relate to subtype B even though >60% of the epidemic is caused by HIV-1 subtype C. A consortium of clinicians, scientists, public health experts and policy markers working in southern Africa came together and formed a network, the Southern African Treatment and Resistance Network (SATuRN), with the aim of increasing curated HIV-1 subtype C and tuberculosis drug resistance data. This article describes the HIV-1 data curation process using the SATuRN Rega database. The data curation is a manual and time-consuming process done by clinical, laboratory and data curation specialists. Access to the highly curated data sets is through applications that are reviewed by the SATuRN executive committee. Examples of research outputs from the analysis of the curated data include trends in the level of transmitted drug resistance in South Africa, analysis of the levels of acquired resistance among patients failing therapy and factors associated with the absence of genotypic evidence of drug resistance among patients failing therapy. All these studies have been important for informing first- and second-line therapy. This database is a free password-protected open source database available on www.bioafrica.net

Identification and Classification of Pulmonary Nodule in Lung Modality Using Digital Computer

This paper proposes an intelligent approach for the development of a new support system to improve the performance of Computer Aided Diagnosis for automated pulmonary nodule identification on Computed Tomography images which is Digital Imaging and Communications in Medicine format. The first step in diagnosis of any abnormality in lung region, is to acquire a Computer Tomography image, a non-invasive procedure. The digital format of the image is highly portable, hence the extraction and sharing of valuable knowledge. The large number of related images pose a challenge in coherence and consequently arriving at conclusion. The CAD system has been designed and developed to segment the lung tumour region and extract the features which is of region of interest. The Detection process consists of two steps, namely Lung segmentation and Feature extraction. In segmentation of lung region K-means, Watershed and Histogram based algorithms is implemented. The extracted features and the label of the corresponding ROI were used to train a neural network . Finally , these properties are used to classify lung tumour as benign or malignant. The main objective of this method is to reduce false positive rate and to improve the access time and reduce inter-observer variability

Sustainable Crop Recommendation System Using Soil NPK Sensor

The effective management of nutrient resources in agricultural practices is crucial for optimizing crop yields and ensuring sustainable farming. Traditionally, farmers have relied on manual methods or expert knowledge to determine the appropriate amount and type of nutrients required by crops. However, these methods often lack precision and can lead to suboptimal fertilization, resulting in reduced productivity and environmental degradation. In recent years, advancements in sensor technology have paved the way for more accurate and efficient crop management systems. One such innovation is the NPK sensor, which enables real-time monitoring of soil nutrient levels. Our proposed system utilizes NPK sensor data to offer personalized fertilization recommendations to farmers. The system integrates sensor technology, machine learning algorithms, and agronomic expertise to provide precise and tailored nutrient recommendations based on the specific requirements of different crops and soil conditions. The system collects data from NPK sensors deployed in the field that includes soil nutrient levels. Machine learning algorithms analyze this data to identify patterns and correlation between nutrient levels and crop performance. By leveraging historical data and agronomic knowledge, the system can generate accurate and timely recommendations for nutrient application. In conclusion, the crop recommendation system presented here offers a novel approach to crop management by leveraging NPK sensor technology and machine learning. By providing accurate and personalized nutrient recommendations, the system has the potential to revolutionize modern agriculture, enhancing productivity while promoting environmental stewardship. Further research and field trials are needed to validate and refine the system’s performance and usability, but the preliminary results show promising potential for the adoption of such system in real-world agricultural settings