Antibiotic Indexing and Heavy Metal Reduction Potential of Four Multi-metal Tolerant Bacterial Strains in Real-Time Sanitary Landfill Leachate Matrix.

Sanitary leachate from urban landfills is known to be contaminated with multi-metals and residual antibiotics. Current research edges on exploring the multi-metal and antibiotic sensitivity profile of four indigenous strains, "Brevibacillus spp. Leclercia spp. Pseudescherichia spp., and Brucella spp." isolated from the leachate of a sanitary landfill in a tropical region. Indigenous isolates were observed to be antibiotic-resistant and have high tolerance against eight of the ten tested metals except Cu & Co. It was observed that interaction with multi-metals in laboratory conditions significantly altered the cell morphology of bacterial strains, as depicted by Scanning Electron Microscope. Metal adsorption onto the microbial surface was deciphered through Electron Dispersive Spectrometer analysis and elemental mapping. Application of isolated strains into real-time leachate matrix exhibits a complete reduction of Ag and Zn and for other tested metals. Their response to these toxicants may facilitate their application in bioremediation-based treatment technologies for urban landfill leachate.

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections.

Virus diseases are a continued threat to human health in both community and healthcare settings. The current virus disease COVID-19 outbreak raises an unparalleled public health issue for the world at large. Wuhan is the city in China from where this virus came first and, after some time the whole world was affected by this severe disease. It is a challenge for every country's people and higher authorities to fight with this battle due to the insufficient number of resources. On-going assessment of the epidemiological features and future impacts of the COVID-19 disease is required to stay up-to-date of any changes to its spread dynamics and foresee needed resources and consequences in different aspects as social or economic ones. This paper proposes a prediction model of confirmed and death cases of COVID-19. The model is based on a deep learning algorithm with two long short-term memory (LSTM) layers. We consider the available infection cases of COVID-19 in India from January 22, 2020, till October 9, 2020, and parameterize the model. The proposed model is an inference to obtain predicted coronavirus cases and deaths for the next 30 days, taking the data of the previous 260 days of duration of the pandemic. The proposed deep learning model has been compared with other popular prediction methods (Support Vector Machine, Decision Tree and Random Forest) showing a lower normalized RMSE. This work also compares COVID-19 with other previous diseases (SARS, MERS, h1n1, Ebola, and 2019-nCoV). Based on the mortality rate and virus spread, this study concludes that the novel coronavirus (COVID-19) is more dangerous than other diseases.

Deep learning based detection and analysis of COVID-19 on chest X-ray images.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

Burden & pattern of illnesses among the tribal communities in central India : A report from a community health programme.

Tribals are the most marginalised social category in the country and there is little and scattered information on the actual burden and pattern of illnesses they suffer from. This study provides information on burden and pattern of diseases among tribals, and whether these can be linked to their nutritional status, especially in particularly vulnerable tribal groups (PVTG) seen at a community health programme being run in the tribal areas of Chhattisgarh and Madhya Pradesh States of India. This community based programme, known as Jan Swasthya Sahyog (JSS) has been serving people in over 2500 villages in rural central India. It was found that the tribals had significantly higher proportion of all tuberculosis, sputum positive tuberculosis, severe hypertension, illnesses that require major surgery as a primary therapeutic intervention and cancers than non tribals. The proportions of people with rheumatic heart disease, sickle cell disease and epilepsy were not significantly different between different social groups. Nutritional levels of tribals were poor. Tribals in central India suffer a disproportionate burden of both communicable and non communicable diseases amidst worrisome levels of undernutrition. There is a need for universal health coverage with preferential care for the tribals, especially those belonging to the PVTG. Further, the high level of undernutrition demands a more augmented and universal Public Distribution System.

In vitro comparative analysis of monocrotophos degrading potential of Aspergillus flavus, Fusarium pallidoroseum and Macrophomina sp.

Fungal degradation is emerging as a new powerful tool for the removal of potent neurotoxin pesticide, monocrotophos. Therefore, the present study is aimed at comparative characterization of monocrotophos degrading ability of three different fungal strains. Fungal strains were isolated from local agricultural soil by enrichment culture method, screened by gradient culture and identified as Aspergillus flavus, Fusarium pallidoroseum and Macrophomina sp. Growth kinetics revealed a direct positive influence of monocrotophos on the viability of fungal isolates. Fungal degradation was studied in phosphorus free liquid culture medium supplemented with 150 mg L(-1) concentration of monocrotophos for a period of 15 days under optimized culture conditions. Degradation of MCP followed first order kinetics with kdeg of 0.007, 0.002 and 0.005 day(-1) and half life (t1/2) of 4.21, 12.64 and 6.32 days for A. flavus, F. pallidoroseum and Macrophomina sp. respectively. To the best of our knowledge, it is the first report signifying the potential of monocrotophos degradation by Fusarium and Macrophomina sp. The results were further confirmed by HPTLC and FTIR which indicates disappearance of monocrotophos by hydrolytic cleavage of vinyl phosphate bond. Degradation of monocrotophos by fungal isolates was accompanied by the release of extracellular alkaline phosphatases, inorganic phosphates and ammonia. The overall comparative analysis followed the order of A. flavus > Macrophomina sp. > F. pallidoroseum. Therefore, it could be concluded from the study that these three different fungal strains could be effectively used as a potential candidate for the removal of monocrotophos from contaminated sites.

Comparative purification and characterization of two distinct extracellular monocrotophos hydrolases secreted by Penicillium aculeatum and Fusarium pallidoroseum isolated from agricultural fields.

The present study aimed at a comparative characterization of two distinct extracellular monocrotophos hydrolases, from Penicillium aculeatum ITCC 7980.10 (M3) and Fusarium pallidoroseum ITCC 7785.10 (M4), isolated from agricultural fields. The MCP hydrolases were purified by Sephadex G-100 column and DEAE-Sepharose CL-6B ion-exchange column followed by SDS-PAGE analysis, which showed the presence of two hydrolases, of 33 and 67 kDa respectively. Both enzymes were most active at alkaline pH and were stable over a wide range of temperatures (60-70 °C). Between the strains, the MCP hydrolases from M3 were 2-fold more active than that from M4. Enzyme kinetic studies showed lowest Km (33.52 mM) and highest Vmax (5.18 U/mg protein) for OPH67 of M3 in comparison to the Km and Vmax of the other hydrolases purified from M3 and M4, suggesting that M3 OPH67 was the most efficient MCP hydrolase. To the best of our knowledge, this is the first report of the purification of two distinct extracellular thermostable MCP hydrolases from fungal strains Penicillium aculeatum ITCC 7980.10 and Fusarium pallidoroseum ITCC 7785.10. Owing to its potential MCP hydrolyzing activity, M3 OPH67 can perhaps used directly or in the encapsulated form for remediation of MCP contaminated sites.

Microbial Treatment of Raw and Primary Treated Sanitary Landfill Leachate by Indigenous Strain Brevibacillus agri.

Landfill leachate is a potential environmental threat. Sanitary landfills are model sites which contains a leachate collection pool and a processing facility to treat it up to environmental standards before discharge. The present study is the very first endeavor to establish leachate treatment efficiency of indigenous microbial strain Brevibacillus agri. Leachate samples were inoculated with isolated strain and incubated for 41 days in an orbital shaker. Percent reduction in major water quality parameters was assessed after 0, 7, 21, and 41 days of incubation, for understanding the degradation kinetics. Results of the study demonstrate that Brevibacillus agri was effective in improving the wastewater quality of both raw and primary treated leachate. Overall reduction for different water quality parameters was found to be 50% higher for primary treated leachate than that for raw leachate within 21 days of incubation. Microbial degradation followed first-order kinetics with rate constants in the range of 0.0047-0.03 and 0.0061-0.074 day-1 for raw and primary treated leachate respectively. Calculated half-life of each pollutant parameter was significantly higher in the raw sample (23-147 days) as compared to the primary treated one (27-112 days). The leachate pollution index (LPI) value of the raw leachate was also found to be > 25% higher than primary treated leachate sample after microbial treatment. Hence, it can be concluded that on site application of primary treatment technology followed by secondary microbial degradation by indigenous microflora, viz., Brevibacillus sp., may prove effective to achieve desirable water quality for safe environmental discharge.

Explaining sentiment analysis results on social media texts through visualization.

Today, Artificial Intelligence is achieving prodigious real-time performance, thanks to growing computational data and power capacities. However, there is little knowledge about what system results convey; thus, they are at risk of being susceptible to bias, and with the roots of Artificial Intelligence ("AI") in almost every territory, even a minuscule bias can result in excessive damage. Efforts towards making AI interpretable have been made to address fairness, accountability, and transparency concerns. This paper proposes two unique methods to understand the system's decisions aided by visualizing the results. For this study, interpretability has been implemented on Natural Language Processing-based sentiment analysis using data from various social media sites like Twitter, Facebook, and Reddit. With Valence Aware Dictionary for Sentiment Reasoning ("VADER"), heatmaps are generated, which account for visual justification of the result, increasing comprehensibility. Furthermore, Locally Interpretable Model-Agnostic Explanations ("LIME") have been used to provide in-depth insight into the predictions. It has been found experimentally that the proposed system can surpass several contemporary systems designed to attempt interpretability.

Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks.

Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples. International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset was used for lung sound. Here a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture. The combination of pre-processing steps MFCC, Melspectrogram, and Chroma CENS with CNN improvise the performance of the proposed system, which helps to make an accurate diagnosis of lung sounds. The comparative analysis shows how the proposed approach performs better with previous state-of-the-art research approaches. It also shows that there is no need for a wheeze or a crackle to be present in the lung sound to carry out the classification of respiratory pathologies.

The importance of physical examination in primary health care provided by NPHW is being threatened in COVID19 times.

Physical examination has been one of the three pillars of diagnostic evaluation of illnesses. It has a larger role in the armamentarium of non-physician health workers. Due to prescriptions for social distancing in preventing COVID19, physical examination is being performed lesser than before. This poses a serious threat to the abilities of NPHW as well as to their relationship with the community.

Geriatric population in India: Demography, vulnerabilities, and healthcare challenges.

Global pandemic due to corona virus disease (COVID-19) has exposed vulnerabilities of the geriatric population all over the world. India has been adding progressively increasing number of elderly to its population. This is happening with increasing life expectancy and decreasing mortality. In comparison to children, the population as well as deaths in elderly are rising with changing demography. The elderly population has its own vulnerabilities based on education, socioeconomic condition, gender, place of residence etc. They are affected by various non-communicable diseases which form predominant cause of morbidity and mortality like cardiovascular diseases, stroke, cancer, respiratory illnesses etc. The elderly also contribute to various kinds of disabilities like movement, vision, hearing and in many cases multiple disabilities. They are also more vulnerable to mental health problems and cognitive impairment. The article also suggests a way forward in dealing with rising geriatric age group and its associated problems. The programs supporting this population are largely scattered which needs to be consolidated to include social security, pension and food security along with health benefits. The approach to health care of the elderly needs a comprehensive strategy instead of the present fragmented approach where different disease based programs for non-communicable diseases, cancer and mental health cater to specific health issues of the elderly. Greater awareness, training and skill building in geriatric health for primary care physicians need focus and energy. Prioritizing training and research in this field including the need for more geriatricians has been highlighted.

COBERT: COVID-19 Question Answering System Using BERT.

In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.

Gestational diabetes in rural central India: low prevalence but absence of typical risk factors.

INTRODUCTION: The reported prevalence of gestational diabetes mellitus (GDM) varies widely across India. Given the short-term, long-term, and multigenerational health impacts of GDM, understanding its frequency and risk factors is important for population screening strategies. We estimated the prevalence of GDM and determined associated risk factors in rural, central India, where data is sparse. METHODS: We conducted a cross-sectional study of a convenience sample of 575 pregnant women attending antenatal care (ANC) clinics at Jan Swasthya Sahyog's (JSS) outreach clinics in rural Chhattisgarh, India. Study participants underwent a non-fasting 75g oral glucose tolerance test (OGTT) between 24-28 weeks gestation. Using Diabetes in Pregnancy Study Group of India (DIPSI) criteria, a 2-hour post-OGTT glucose ≥140 mg/dL was used to diagnose GDM. RESULTS: We found 11 patients (1.9%) who met diagnostic criteria for GDM. Median age, systolic blood pressure, and diastolic blood pressure were higher in those with GDM (26 vs 23 years, p = 0.02; 117 vs 106 mmHg, p = 0.04, 77 vs 68 mmHg, p < 0.01, respectively). Pre-hypertension was associated with increased odds of GDM on multivariate analysis (OR 4.0, 95% CI: 1.1, 14.8). BMI was not associated with GDM. With appropriate management there were no differences in fetal complications between GDM and normal glucose tolerance (NGT) groups. CONCLUSIONS: In rural, central India the prevalence of GDM was 1.9% in the absence of traditional risk factors such as increased BMI. Further research is needed to define the applicability of optimal screening strategies in such settings.

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2.

Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have been created using several algorithms and techniques. The proposed approach in this paper uses deep learning, TensorFlow, Keras, and OpenCV to detect face masks. This model can be used for safety purposes since it is very resource efficient to deploy. The SSDMNV2 approach uses Single Shot Multibox Detector as a face detector and MobilenetV2 architecture as a framework for the classifier, which is very lightweight and can even be used in embedded devices (like NVIDIA Jetson Nano, Raspberry pi) to perform real-time mask detection. The technique deployed in this paper gives us an accuracy score of 0.9264 and an F1 score of 0.93. The dataset provided in this paper, was collected from various sources, can be used by other researchers for further advanced models such as those of face recognition, facial landmarks, and facial part detection process.

Corrigendum to "SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2" [Sustain. Cities Soc. 66 (2021) 102692].

[This corrects the article DOI: 10.1016/j.scs.2020.102692.].

A 28-Day Toxicity Study of Tenofovir Alafenamide Hemifumarate by Subcutaneous Infusion in Rats and Dogs.

The toxicity of tenofovir alafenamide (TAF) hemifumarate (HF) was evaluated when administered by continuous subcutaneous (s.c.) infusion via an external infusion pump for 28 days to rats and dogs. The toxicokinetics of TAF and two metabolites, tenofovir (TFV) and tenofovir diphosphate (TFV-DP) were also evaluated. After administration of TAF HF in rats and dogs, primary systemic findings supported an inflammatory response that was considered minimal to mild. Gross pathology and histopathologic evaluation of tissue surrounding the s.c. infusion site revealed signs of inflammation, including edema, mass formation, fibrosis, and mononuclear cell inflammation in groups receiving ≥300 µg/kg/day in rats and ≥25 µg/day in dogs. Although these changes were observed in animals receiving vehicle, the severity was greater in animals receiving TAF HF. Changes in the local tissue were considered a TAF HF-mediated exacerbation of an inflammatory response to the presence of the catheter. In rats, systemic and local findings were considered not adverse due to their low severity and reversibility; therefore, the "no observed adverse effect level" (NOAEL) was set at 1,000 µg/kg/day. Because none of the systemic findings were related to systemic exposure to TAF, the systemic NOAEL was set at 250 µg/kg/day in dogs. Due to the severity of the observations noted, a NOAEL for local toxicity could not be established. Although these results might allow for exploration of tolerability and pharmacokinetics of s.c. administered TAF HF in humans, data suggest a local reaction may develop in humans at doses below a clinically relevant dose. IMPORTANCE Human immunodeficiency virus (HIV) infection continues to be a serious global human health issue, with ∼38 million people living with HIV worldwide at the end of 2019. HIV preexposure prophylaxis (PrEP) has introduced the use of antiretroviral therapies as another helpful tool for slowing the spread of HIV worldwide. One possible solution to the problem of inconsistent access and poor adherence to HIV PrEP therapies is the development of subcutaneous (s.c.) depots or s.c. implantable devices that continuously administer protective levels of an HIV PrEP therapy for weeks, months, or even years at a time. We evaluate here the toxicity of tenofovir alafenamide, a potent inhibitor or HIV replication, after continuous s.c. infusion in rats and dogs for HIV PrEP.

The dynamic interface: A review.

The implant-to-tissue interface is an extremely dynamic region of interaction. Generally, a surgical procedure is performed on a patient to insert a foreign material into the bone, and the body is called on to "heal" the wound. The time schedule crucial for a healing process that is expected to result in restitution ad integrum must be determined with respect to the condition of the individual patient and tissue to be treated. There are various factors responsible for the formation of an adequate bone-implant interface. A comprehensive review of the response of bone to implant is described.