The prevalence of HBV infection in the cohort of IDPs of war against terrorism in Malakand Division of Northern Pakistan

<p>Abstract</p> <p>Background</p> <p>Hepatitis B is an important public health problem in the Pakistani population and is the major cause of chronic hepatitis, cirrhosis, fibrosis and hepatocellular carcinoma. High prevalence of HBV infections has been observed especially in areas of low economic status. In spite of effective immunization programs, no significant change has been observed in the epidemiology of HBV in the rural areas of Pakistan (~67.5% of the total population) mainly due to lack of interest from government authorities and poor hygienic measures. The current study was aimed at estimating the prevalence and risk factors associated with HBV infection within internally displaced persons (IDPs) due to war against terrorism in the Malakand Division of Northern Pakistan.</p> <p>Methods</p> <p>Blood samples from 950 IDPs suspected with HBV infection (including both males and females) were collected and processed with commercial ELISA kits for HBsAg, Anti HBs, HBeAg, Anti HBe antibodies. The samples positive by ELISA were confirmed for HBV DNA by real-time PCR analysis.</p> <p>Results</p> <p>The overall prevalence of HBV observed was 21.05% of which 78.5% were males and 21.5% were females. Most confirmed HBV patients belong to the Malakand and Dir (lower) district. High-risk of infection was found in the older subjects 29.13% (46-60 years), while a lower incidence (11.97%) was observed in children aged <15 years. Lack of awareness, socioecomic conditions, sexual activities and sharing of razor blades, syringes and tattooing needles were the most common risk factors of HBV infection observed during the cohort of patients.</p> <p>Conclusion</p> <p>The present study, revealed for the first time a high degree of prevalence of HBV infection in rural areas of Northern Pakistan. The noticed prevalence is gender- and age-dependent that might be due to their high exposures to the common risk factors. To avoid the transmission of HBV infection proper awareness about the possible risk factors and extension of immunization to the rural areas are recommended.</p

Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 &times; 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region&rsquo;s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab

Clinical characteristics, mortality and associated risk factors in COVID-19 patients reported in ten major hospitals of Khyber Pakhtunkhwa, Pakistan

COVID-19 is an ongoing public health issue across the world. Several risk factors associated with mortality in COVID-19 have been reported. The present study aims to describe clinical and epidemiological characteristics and predictors of mortality in hospitalized patients from Khyber Pakhtunkhwa, a province in Pakistan with highest COVID-19 associated case fatality rate. This multicentre, retrospective study was conducted in hospitalized COVID-19 patients who died or discharged alive until 1st May 2020. Data about sociodemographic characteristics, clinical and laboratory findings, treatment and outcome were obtained from hospital records and compared between survivors and non-survivors. Statistical tests were applied to determine the risk factors associated with mortality in hospitalized patients. Of the total 179 patients from the 10 designated hospitals, 127 (70.9%) were discharged alive while 52 (29.1%) died in the hospital. Overall, 109 (60.9%) patients had an underlying comorbidity with hypertension being the commonest. Multivariate logistics regression analysis showed significantly higher odds of in-hospital death from COVID-19 in patients with multiple morbidities (OR 3.2, 95% CI 1.1, 9.1, p-value=0.03), length of hospital stay (OR 0.8, 95% CI 0.7, 0.9, p-value &lt;0.001), those presenting with dyspnoea (OR 4.0, 95% CI 1.1, 14.0, p-value=0.03) and oxygen saturation below 90 (OR 9.6, 95% CI: 3.1, 29.2, p-value &lt;0.001). Comorbidity, oxygen saturation and dyspnoea on arrival and length of stay in hospital (late admission) are associated with COVID-19 mortality. The demographic, clinical and lab characteristics could potentially help clinician and policy makers before potential second wave in the country