Role of gut microbiome in COVID-19: An insight into pathogenesis and therapeutic potential

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in an unprecedented global crisis. Although primarily a respiratory illness, dysregulated immune responses may lead to multi-organ dysfunction. Prior data showed that the resident microbial communities of gastrointestinal and respiratory tracts act as modulators of local and systemic inflammatory activity (the gut-lung axis). Evolving evidence now signals an alteration in the gut microbiome, brought upon either by cytokines from the infected respiratory tract or from direct infection of the gut, or both. Dysbiosis leads to a leaky gut . The intestinal permeability then allows access to bacterial products and toxins into the circulatory system and further exacerbates the systemic inflammatory response. In this review, we discuss the available data related to the role of the gut microbiome in the development and progression of COVID-19. We provide mechanistic insights into early data with a focus on immunological crosstalk and the microbiome\u27s potential as a biomarker and therapeutic target

EFFICACY OF ORAL ZINC SULPHATE VERSUS TOPICAL APPLICATION OF SALICYLIC ACID (16.7%) & LACTIC ACID (16.7%) COMBINATION IN THE TREATMENT OF PLANTAR WARTS

Background; Warts are common epidermal growths caused by human papillomavirus that often cause significant discomfort and embarrassment. Current treatment options include topical therapies, cryotherapy, laser vaporization, and surgical excision. Objective: To compare the efficacy of oral zinc sulphate versus topical application of salicylic acid (16.7%) & lactic acid (16.7%) combination in the treatment of plantar warts. Material and Methods; Group A, having 78 patients, was given oral zinc sulphate (10 mg/kg/day) in 2 or 3 divided doses. In group B, 78 patients were advised to apply a combination of salicylic (16.7%) & lactic acid (16.7%) once at night daily. In group A, using oral zinc sulphate, the treatment was given for two months and follow up continued for next 1 month. In group B, the topical preparation was also continued for 2 months or till the complete removal of warts. Results; Of these 156 study cases, 94 (60.3 %) were male patients while 62 (39.7 %) were female patients. Mean age of our study cases was 34.42 ± 9.87 years. Monthly family income up to Rs. 30000 was noted in 68 (43.6%) while 88 (56.4%) had monthly family income more than Rs. 30000. Mean weight of our study cases was 61.96 ± 10.29 kilograms. Mean height of our study cases 161.24 ± 12.23 centimeters. Mean BMI of our study cases was 26.52± 1.87 kg/m2 and obesity was present in 31 (19.9%) of our study cases. Mean no. of lesions was noted to be 5.30 ± 2.45 lesions (with minimum no. of lesions was 4 and maximum no. of lesions was 10) and 131 (84%) had up to 5 lesions. Mean duration of disease was 4.69 ± 1.61 months and 112 (71.8 %) had disease duration up to 6 months. Efficacy was noted in 110 (70.5 %). Efficacy in group A was noted to be 83.3 % and in group B was 57.7 % (p= 0.001). Conclusion; Our study results support the use of oral Zinc Sulphate in the treatment of plantar warts as compared to the salicylic acid (16.7%) and lactic acid (16.7%) combination. Efficacy of Zinc Sulphate was significantly higher when compared with combination therapy and it was found to be safe and reliable mode of treatment having no side effects were observed in our study. Use of Zinc Sulphate can help to reduce chance of recurrence and disease morbidity. Being cost effective, it provides cheaper mode of treatment as compared with cryotherapy which is quite expensive which will provide economic relief to our large poor population. Early treatment can help patients to maintain routine daily lives and physical activities such as sports without any pain and side effects. Keywords; Plantar warts, Zinc Sulphate, Salicylic acid, lactic acid

FREQUENCY OF MICROALBUMINURIA IN PATIENTS WITH TYPE-2 DIABETES MELLITUS HAVING DIABETIC RETINOPATHY

Objective; To determine the frequency of microalbuminuria in patients with type 2 diabetes mellitus with retinopathy. Methodology:- This descriptive study was carried out in the Out Patient Department Nishtar Hospital Multan from June 2017 to December 2018. Sample size of 296 was calculated according to formula. Results:- Of these 300 study cases, 179 (59.7%) were male while 121 (40.3%) were female patients. Mean age of our study cases was 52.10 ± 5.65 years (with minimum age was 42 years while maximum age was 60 years).  Majority of our study cases had low educational level such as illiterate i.e. 60 (20%), primary education in 71 (23.7%) secondary education53% while only 3 % had bachelors degree or above. Mean duration of diabetes was 14.18 ± 2.73 years (with minimum duration of disease 10 years and 19 years was maximum duration of the disease). Among these patients Grade III diabetic retinopathy was more prevalent i.e. 149 (49.7%) followed by grade IV retinopathy i.e. 100 (33.3%) and grade11 retinopathy (17%). Diabetes was controlled (HbA1c<7%) in only 81 (27%) of our study cases and obesity(BMI>30kg/m2) was present in 101 (33.7%) of our study cases. Microalbuminuria was present in 121(40.3% of our study cases.  Conclusion:- Very high frequency of microalbuminuria was noted in patients with diabetic retinopathy in our study. Keywords; Microalbuminuria, Diabetic Retinopathy, Frequency

Nutritional Composition of Meat

Meat ranks among one of the most significant, nutritious and favored food item available to masses, which aids in fulfilling most of their body requirements. It has played a vital role in human evolution and is an imperative constituent of a well-balanced diet. It is a good source of proteins, zinc, iron, selenium, and phosphorus followed by vitamin A and B-complex vitamins. Average value of meat protein is about 23% that varies from higher to lower value according to the type of meat source. Meat fat and its fatty acid profile is point to worry, with respect to its consumption, but its moderate usage is always advised by doctors and nutritionists, in order to lead a healthy life. Fat content of animal carcasses ranges between 8 and 20%. Quality traits of meat along with its nutritional composition become dependent upon animal breed type, feeding source (grains, pasture and grass), genetics of animal and post mortem techniques. This chapter will mainly focus on the variant aspects of nutritional constituents of meat including proteins and essential amino acids, fats and fatty acid profile, carbohydrates, vitamins and minerals along with their health benefits to human health

Macroeconomic Covariates of Default Risk: Case of Pakistani Non-Financial Firms

Empirical estimation of default probability through structural approach in the context of macroeconomic dynamics turn out be an emerging idea. However, various aspects of these studies are still needs to be explored to make these models more reliable. This study explored the structural model of default risk (Moody’s KMV) application with macroeconomic dynamics in Pakistani non-financial firm’s context and confirm whether Moody’s KMV model of default prediction could be applicable in Pakistan where the markets are highly speculative and stock markets are highly volatile. The study approximate about the expected default frequency (hereafter EDF) of 307 Pakistani non-financial firms, categorized in 12 industries for a span of 8 years from 2004 to 2011. It further check the macroeconomic variables effects on EDF with the use of generalized method of moments (hereafter GMM). Empirical results compared with the real life scenarios over the said years and on the basis of results we infer that Moody’s KMV model can predict default probability in a much better way than traditional ratio based approach

Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. Stateof-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that pre-processes raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of things environment

In vitro shoot regeneration of fenugreek (Trigonella foenum-graecum L.) using different cytokinins

Fenugreek (Trigonella foenum-graecum L.) is an important vegetable, spice and medicinal legume plantused as fresh and dried leaves and seeds in many parts of the world. Fenugreek seeds contain polysaccharide galactomannan and different saponins and have anti-microbial activity. The study presents an efficient shoot regeneration protocols from 8 - 10 days old in vitro grown cotyledon node explants cultured on Murashige and Skoog (MS) medium supplemented with 0.05 - 0.80 mg/l kinetin, 0.25 - 1.0 mg/l 6 benzyladenine (BA) with 0 and 0.20 mg/l α naphtalene acetic acid (NAA) and 0.05 - 0.80 mg/l thidiazuran (TDZ) with 0 and 0.10 mg/l indole 3 butyric acid (IBA). All culture mediums were solidified with 0.22% gelrite. Maximum shoot regeneration and number of shoots per explant were recorded on MS medium supplemented with TDZ with or without IBA. Maximum of 22.21 shoots per explant were recorded on MS medium containing 0.40 mg/l TDZ. Presence of auxins in the culture medium positively increased the mean shoot length. Regenerated shoots were transferred to rooting media containing 0.1 - 1.0 mg/l IBA or NAA.Key words: Fenugreek , cotyledon node, hypocotyl, shoot regeneration

Lípidos totales y perfil de ácidos grasos de hígado de pescado Catla catla salvajes y criados

This experimental work was aimed to study the moisture content, total lipids and fatty acid profile in the liver of wild and farmed freshwater major carp Catla catla of three different weight categories designated as W1 (601-900g), W2 (901- 1200)g and W3 (1201-1500g). Seven fish specimens of each of the three weight categories of wild and farmed Catla catla were obtained from Trimu Head, Jhang and Fish Hatchery, Satiana Road and Faisalabad, respectively. The fish were dissected to remove the liver and after weighing, liver samples were prepared and subjected to chemical analysis. Wild Catla catla liver had a significantly (p < 0.05) higher moisture content as compared to the farmed species. Farmed Catla catla deposited significantly (p < 0.05) higher lipid contents in liver. Proportions of saturated fatty acids varied irregularly in the lipids of the liver from both wild and farmed Catla catla. Saturated fatty acids C12:0, C14:0, C16:0, C18:0, C20:0 and C22:0 were identified with considerable percentages in the liver of Catla catla from both habitats and monounsaturated fatty acid C18:1 was found in considerable amounts in the liver of both major carp. Polyunsaturated fatty acids such as C18:3 (n-6) and C20: 2 (n-6) were detected in the liver of the wild fish of W2 and W3 and was similar in the W3 weight category of the farmed species.Este trabajo experimental tiene como objetivo estudiar el contenido de humedad, lípidos totales y perfil de ácidos grasos de hígado de carpa de agua dulce Catla catla de tres categorías con peso diferentes designadas como W1 (601- 900g), W2 (901-1200)g and W3 (1201-1500g). Siete muestras de pescados por cada una de las tres categorías de peso para Catla catla salvajes y criados fueron obtenidos de Trimu Head, Jhang and Fish Hatchery, Satiana Road y Faisalabad, respectivamente. Los pescados fueron diseccionados para obtener el hígado y las muestras de hígado fueron preparadas y sujetas a análisis químico. Catla catla salvaje tuvo un contenido de humedad significativamente mayor (p < 0.05) en comparación con las especies criadas. En Catla catla criadas se depositaron mayores contenidos de lípidos en el hígado. La proporción de ácidos grasos saturados vario irregularmente en los lípidos del hígado de Catla catla salvajes y criados. Los ácidos grasos saturados C12:0, C14:0, C16:0, C18:0, C20:0 y C22:0 fueron identificados en un alto porcentaje en hígados de Catla catla procedentes de ambos hábitat y el ácido graso monoinsaturado C18:1 fue encontrado en una cantidad considerable en el hígado de ambas carpas. Los ácidos grasos poliinsaturados tales como C18:3 (n-6) y C20: 2 (n-6) fueron detectados en el hígado de pescados salvajes W2 y W3, así como en la misma categoría de peso W3 de especies criadas

Mobile edge computing-based data-driven deep learning framework for anomaly detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector