Diagnostic value of HRCT-Thorax for pandemic COVID-19 pneumonia in Pakistan

Background: In the scenario of, inadequate testing, the low sensitivity of the COVID-19-PCR test, limited availability of testing kits, and low detection rate, we aimed to investigate the usefulness of high-resolution computed tomography of chest (HRCT) for diagnosing pandemic coronavirus (COVID-19) pneumonia. Objective: To determine the diagnostic efficacy of HRCT thorax in Covid-19 pandemic pneumonia. Materials and Methods: This prospective, cross-sectional study was conducted in the Pulmonology–OPD of Gulab Devi Teaching Hospital, Lahore-Pakistan from 01-04-2020 to 15-07-2020.   121 patients with dry cough, fever, and dyspnea of sudden onset were included while patients with Bronchial Asthma, ILD, Tuberculosis, Bronchiectasis, COPD, and overt heart failure were excluded. Patients were investigated with chest x-ray, HRCT, COVID-PCR, and hematological tests. HRCT films were evaluated by a qualified and experienced radiologist. Findings were summarized, organized and statistical analysis was done by using SPSS-26 software to make an inference. Results: Five patients were diagnosed as non-covid. Out of 116-diagnosed covid-19 patients, 38(32.75%) showed sub-pleural consolidation, 19(16.37%) consolidation with air-bronchogram, 29(25.0%) crazy paving sign, one pleural effusion (0.86%) and 18 cases (15.51%) displayed reticulations. 11cases(9.48%) had isolated ground glass appearances, while all categories showed it to variable extent. 65 patients (56.03%) were PCR-positive while  51(43.96) patients with positive-HRCT findings for COVID-19 pneumonia had negative nasopharyngeal-PCR. HRCT-Thorax revealed sensitivity: 97.41 %, specificity: 80%, PPV: 99.12%, NPV: 57.14%, and diagnostic accuracy of 96.69% for COVID-19 pneumonia. Conclusion: HRCT-Thorax, having high sensitivity and adequate specificity, can provide foundations for evidence-based early diagnosis and quantification of coronavirus pneumonia.  It can be tremendously useful for decision making in   PCR-negative patients and anticipating respiratory improvement or decline by serial scans

Effects of Growing Media and Irrigation Interval on Growth of Amaryllis (Amaryllis Belladonna)

Four combinations of various growing media i.e. garden soil, canal silt, farm yard manure,  mushroom compost,  leaf mold and  poultry manure and four irrigation intervals (i.e. 5, 10, 15 and 20 days) were  trailed to investigate their effects on growth and flowers production of Amaryllis belladonna, at Horticulture Nursery of University of Agriculture, Peshawar during 2012.Growing medium composed of garden soil, canal silt, and mushroom compost resulted in early emergence (18.66 days), maximum leaf length (47.87cm), leaf width (2.44 cm), number of leaves (13.55). Maximum leaf length (48.16cm), leaf width (2.36 cm), number of leaves per plant (13.55), was noted at irrigation interval of 10 days. Since Mushroom compost growing media and 10 days irrigation interval interaction showed significant result among most of the parameters observed hence for better growth growing media garden soil + canal silt + mushroom compost with 10 days irrigation interval is recommended. Keywords: Emergence, leaf length & width

Understanding biological mechanisms underlying adverse birth outcomes in developing countries: Protocol for a prospective cohort (AMANHI bio-banking) study

Objectives: The AMANHI study aims to seek for biomarkers as predictors of important pregnancy-related outcomes, and establish a biobank in developing countries for future research as new methods and technologies become available.Methods: AMANHI is using harmonised protocols to enrol 3000 women in early pregnancies (8-19 weeks of gestation) for population-based follow-up in pregnancy up to 42 days postpartum in Bangladesh, Pakistan and Tanzania, with collection taking place between August 2014 and June 2016. Urine pregnancy tests will be used to confirm reported or suspected pregnancies for screening ultrasound by trained sonographers to accurately date the pregnancy. Trained study field workers will collect very detailed phenotypic and epidemiological data from the pregnant woman and her family at scheduled home visits during pregnancy (enrolment, 24-28 weeks, 32-36 weeks & 38+ weeks) and postpartum (days 0-6 or 42-60). Trained phlebotomists will collect maternal and umbilical blood samples, centrifuge and obtain aliquots of serum, plasma and the buffy coat for storage. They will also measure HbA1C and collect a dried spot sample of whole blood. Maternal urine samples will also be collected and stored, alongside placenta, umbilical cord tissue and membrane samples, which will both be frozen and prepared for histology examination. Maternal and newborn stool (for microbiota) as well as paternal and newborn saliva samples (for DNA extraction) will also be collected. All samples will be stored at -80°C in the biobank in each of the three sites. These samples will be linked to numerous epidemiological and phenotypic data with unique study identification numbers.Importance of the study: AMANHI biobank proves that biobanking is feasible to implement in LMICs, but recognises that biobank creation is only the first step in addressing current global challenges

Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of nois

Application of glucose oxidase for the production of metal gluconates by fermentation

The present study deals with the application of glucose oxidase (GOX) for the production of metal gluconates by fermentation method. It provides a method for the conversion of glucose into gluconic acid and its derivatives using the enzyme glucose oxidase (GOX). Due to the presence of calcium carbonate in fermentation medium the gluconic acid is converted into calcium gluconate. Conditions like concentration of substrate, temperature, pH, fermentation period and different phosphate sources were optimized during fermentation. The maximum GOX activity was observed at 35°C (pH 5.5) after 44 h of incubation at 100 rpm. At the maximum enzyme activity, the percentage yield of gluconates are also maximum; both go side by side. Sulphuric and oxalic acids method were employed for the production of gluconic acid. Derivatives of gluconic acid that is, calcium lactate gluconate, sodium gluconate, potassium gluconate, zinc gluconate and copper gluconate were formed by using double displacement and direct methods. The direct method gave the better yield. The percentage yields were 73, 89.63, 81.93, 92.86 and 81.53%, respectively. Keywords: Glucose oxidase (GOX), metal gluconate, double displacement

Efficacy of Insecticides against Fall Armyworm, Spodoptera frugiperda (Lepidoptera, Noctuidae) in Maize

Fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae) is most destructive specie of genus Spodoptera for several agricultural crops. In Pakistan\u27s Sindh province, the invasive fall armyworm Spodoptera frugiperda was first documented causing serious maize damage in 2019. There is need to develop management strategies against this pest in the country. The current study was conducted to check the toxicity of different insecticides against FAW in maize field. The results showed among tested insecticides, deltamethrin was recorded most toxic insecticide followed by chlorantraniliprole and emamectin benzoate. At 1d days after first spray, least number of larvae were recorded with deltamethrin (0.07 larvae/plant), chlorantraniliprole (0.11d larvae/plant) and emamectin benzoate (0.13 larvae/plant). After three days application of first spray, significantly a minimum number of larvae were recorded with deltamethrin (1.11bcd larvae/plant) chlorantraniliprole (1.13d larvae/plant) and emamectin benzoate (1.17d larvae/plant). The maximum and minimum population of larvae was recorded at 1st day of first spray and 14 days of spray, respectively. The least number of larvae were recorded at 14 days of second spray. At 14 days after 2nd spray, 0.07ab, 0.10e and 0.10de larvae per plant were recorded with deltamethrin, chlorantraniliprole and emamectin benzoate, respectively