To the Editor: Total body skin examination (TBSE) is an essential tool for identification of dermatologic conditions; however, it is rarely taught in a standardized manner. 1 Cahn B.A. Harper H.E. Halverstam C.P. Lipoff J.B. Current status of dermatologic education in US medical schools. JAMA Dermatol. 2020; 156: 468-470https://doi.org/10.1001/jamadermatol.2020.0006 Crossref PubMed Scopus (11) Google Scholar , 2 Blakely K. Bahrani B. Doiron P. Dahlke E. Early introduction of dermatology clinical skills in medical training. J Cutan Med Surg. 2020; 24: 47-54https://doi.org/10.1177/1203475419882341 Crossref PubMed Scopus (8) Google Scholar , 3 McCleskey P.E. Gilson R.T. DeVillez R.L. Medical student core curriculum in dermatology survey. J Am Acad Dermatol. 2009; 61: 30-35https://doi.org/10.1016/j.jaad.2008.10.066 Abstract Full Text Full Text PDF PubMed Scopus (69) Google Scholar In our previous study, we identified an effective process to perform efficient and comprehensive TBSE. 4 Helm M.F. Hallock K.K. Bisbee E. Miller J.J. Optimizing the total-body skin exam: an observational cohort study. J Am Acad Dermatol. 2019; 81: 1115-1119https://doi.org/10.1016/j.jaad.2019.02.028 Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar Our goal in this study was to use visual tracking software to objectively evaluate gaze patterns to evaluate the benefits of teaching the TBSE to medical students and primary care providers.

Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms rely on sample selection strategies wherein, essentially, a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds, and it is difficult to fix or learn these thresholds. Often, these algorithms also require information such as label noise rates which are typically unavailable in practice. In this paper, we propose an adaptive sample selection strategy that relies only on batch statistics of a given mini-batch to provide robustness against label noise. The algorithm does not have any additional hyperparameters for sample selection, does not need any information on noise rates and does not need access to separate data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.

To the Editor: Psoriasis affects >125 million people worldwide and inflicts substantial morbidity.1 Biologics are efficacious in moderate to severe disease,1 but the cost is a barrier to appropriate care. 2 Complexities of health care economics can uncouple costs from traditional market forces.3 As such, we investigated the costs of biologics over time to determine if changes are proportional to medical inflation rates.

Prophylaxis against infusion-related reactions (IRR) from paclitaxel with steroids and antihistamines is a standard of care due to high rates of IRR. This systematic review and meta-analysis aimed to comprehensively summarize the evidence behind various prophylaxis strategies. EMBASE, MEDLINE, PubMed, and the Cochrane Register of Controlled Trials were searched (1946 to May 14, 2021). The primary outcomes were Grade 3/4 IRR and any-grade IRR. Secondary outcomes included treatment delay or discontinuation and adverse events secondary to pre-medications. Of the 1285 unique citations, 26 studies were selected: 11 studies for quantitative analysis and 15 studies for qualitative analysis. Studies included randomized controlled trials and observational studies (n = 25–281). There was a non-significant benefit in favour of oral steroids starting 12 h prior to paclitaxel administration versus intravenous steroids immediately prior to paclitaxel administration for grade 3/4 IRRs, with a risk difference (RD) of 2% [95%CI 0 to 5%], any-grade IRR with a RD of 4% [95%CI: -1% to 9%] and treatment discontinuation with a RD of 1% [95%CI -1% to 2%]. For de-escalation strategies, a point-estimate for any-grade IRR was 0.44% [95% CI, 0 to 0.02, p = 0.98] and for grade 3/4 IRR was 3.1% (95% CI, 0.02 to 0.07, p = 0.11). Although studies have high risk of bias and risk, differences between steroid routes of administration were small, there was a non-significant trend in favour of oral steroids. De-escalation strategies after two previous successful paclitaxel infusions have an overall low incidence rate of severe IRR and warrant further prospective clinical trials. Insufficient evidence remains to recommend for or against other interventions for the prevention of paclitaxel IRR.

The use of biologics for inflammatory skin disease is increasing. Although manufacturers recommend pneumococcal, influenza and varicella zoster vaccines in patients treated with tumor necrosis factor inhibitors to mitigate the risk of infection, data regarding adherence in this group of patients is limited. The European League Against Rheumatism and American College of Rheumatology also issued the recommendations to advocating for influenza, pneumococcal pneumonia and Zoster for this high-risk patient group. We queried the MarketScan data base (which includes about 47 million people) to determine rates of vaccination and infection. In 2014, we identified 41,607 patients, aged 18–60 on adalimumab, etanercept or infliximab (TNFi) for 6 months or more. Of these patients, only 157 received a pneumococcal vaccine. We will present similar data describing utilization of influenza and zoster vaccination in psoriatic utilizing TNFi, from 2014–2016. Rates of in relevant infection (influenza, pneumonia and varicella) will be compared between vaccinated and unvaccinated biologic users. We hypothesize that immunosuppression due to biologic therapy (specifically TNF-inhibitors) incurs a higher risk of infection. Our preliminary data likely highlight an important practice gap in the use of TNFi within dermatology. Teaching points: biologics are associated with a higher risk of influenza, pneumococcal pneumonia, and varicella zoster. Although infection can lead to significant morbidity and mortality, vaccination prior to the initiation of biologic therapy can mitigate risk. Vaccination is probably underutilized in these patients.

BACKGROUND AND OBJECTIVES :- The present study was attempted to nd out the role of conventional and newer modalities for the treatment and rehabilitation and prevention of complication of diabetic foot patients. MATERIALAND METHOD:-. 50 patients of diabetic foot admitted in civil Hospital,ahmedabad were studied within two years from 2018 to 2020 and careful assessment of history, clinical ndings, investigation, management and follow-up of these patients done. RESULT:-According to my study, Diabetic foot is common in males & 51-60 years of age group,in smokers, in lower socio-economic class, with average duration of 8 to 10 years of diabetes melitus, most common type of lesion was abscess,most common site was forefoot, mostly was of neuropathic in nature & mostly managed by debridement. Mean hospital stay was 1 week to 1 month. CONCLUSION:- Patient education and awareness regarding good sugar control of diabetes, use of proper antibiotics, adequate debridement and proper dressing ;with eusol, betadine hydrogen peroxide along with newer dressing methods like vacuum dressing found to be effective. Amputation done only for gangrene and proper rehabilitation method carried out for these patients.

During this course of international emergency, diagnosing patients infected with COVID-19 at an early stage with the help of deep learning models is a crucial development. The paper aims to evaluate the deep learning models available for the image classification task for detecting COVID-19. The dataset containing 956 X-ray images of three classes, namely COVID-19, viral pneumonia and normal, is used. Standard deep learning models like AlexNet, ResNets and Inception v3 along with various custom models of convolution neural networks (CNNs) have been trained and tested on the dataset. The Inception v3 model gave the best training accuracy of 99.22%, while custom made CNN3 gave a promising training accuracy of 96.61%. Both models gave a similar validation accuracy of 97.89%. Sensitivity and specificity for COVID-19 were (100% and 98.5%) and (100% and 100%) for Inception v3 and CNN3, respectively.

An enterocutaneous stula (ECF) is an aberrant connection between intra-abdominal gastrointestinal tract and the skin. While great majority are iatrogenic, between 15-25% occur spontaneously. Common causes of spontaneous stula are congenital, infections, inammation, tumour, radiation and ischemia. Mortality associated with ECF has decreased from 40-60% to 15-20% largely attributed to advances in uid, electrolyte, acid-base balance knowledge, administration of blood products, critical care, antibiotic regimen and nutritional management – both enteral and parenteral.

Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Empirical studies have also shown that none of the standard regularization techniques mitigate such overfitting. We investigate whether choice of loss function can affect this memorization. We empirically show, with benchmark data sets MNIST and CIFAR-10, that a symmetric loss function as opposed to either cross entropy or squared error loss results in significant improvement in the ability of the network to resist such overfitting. We then provide a formal definition for robustness to memorization and provide theoretical explanation as to why the symmetric losses provide this robustness. Our results clearly bring out the role loss functions alone can play in this phenomenon of memorization.

Most herbal products are the chief source of medicinal compounds that are known to exhibit various therapeutic properties. Many researchers have ensured the efficacy of traditional medicines at large. The evolving nature of the viruses cause major barriers in the fundamental blockage for illness, instead viruses show complexion which refers to genetic change which lead to accumulate during their lifespan. The immense works have been carried out for the probable best medications to deal with it. For decades, synthetic organic compounds have played a tremendous role in today's medications; however the possibilities for their adverse effects are scaring the human life. The perception to use herbal remedies as complementary vogue can reduce toxicities, has a minimum amount of side effects and can be easily available from nature. The identification of natural products as medicinal drugs is of critical importance and is an excellent source of protease inhibitor. In this review, we focused mainly on the herbal products that give a wide range of anti-viral, anti-cancer, antimicrobial and antioxidant properties which can be used in medications instead of synthetic once.

The recent area of interest for computer scientists and data analysts working on precision farming has been the use of machine learning and deep learning algorithms to recommend crops to the farmers and predict yield. Improving crop yields not only helps farmers but also seeks to address global problems such as food shortages. Predictions can be made taking into account forecasts of climate, soil and its mineral content, moisture, crop historic performance, rainfall and others. Crop Data from six states of India for five crops from 2009–2016 has been used for training and validation of different machine learning regression algorithms to produce a comprehensive study of crop yield estimate. The yields are estimated using Linear models, Support Vector Regressor, K Neighbors Regressor, Tree-based models, Ensemble models and Shallow Neural Networks with R-squared score for evaluation. Test accuracy showed promising results in ensemble models and neural networks. Extra Trees Regressor is the best model with Mean Absolute Error of 351.10 and maximum accuracy of 99.95%. This paper aims at providing state of art implementation on machine learning algorithms to facilitate farmers, governments, economists, banks to estimate the crop yields in Indian states based on specific parameters.

Data imbalance is a ubiquitous problem in machine learning. In large scale collected and annotated datasets, data imbalance is either mitigated manually by undersampling frequent classes and oversampling rare classes, or planned for with imputation and augmentation techniques. In both cases balancing data requires labels. In other words, only annotated data can be balanced. Collecting fully annotated datasets is challenging, especially for large scale satellite systems such as the unlabeled NASA's 35 PB Earth Imagery dataset. Although the NASA Earth Imagery dataset is unlabeled, there are implicit properties of the data source that we can rely on to hypothesize about its imbalance, such as distribution of land and water in the case of the Earth's imagery. We present a new iterative method to balance unlabeled data. Our method utilizes image embeddings as a proxy for image labels that can be used to balance data, and ultimately when trained increases overall accuracy.

Developing nations today face a major hurdle of excessive waste generation due to overpopulation and rapid urbanization. Also, the waste management systems in such countries are ineffective and limited. Considering this issue, an effective and efficient waste management system would be of great societal benefit. Artificial Intelligence and Deep Learning has found its way into many diverse areas in recent years. This research work proposes a Garbage Detection System using object detection models to automatically detect and locate garbage in real-world images as well as video. The work comprises of a detailed review of previous research and proposes new method with different algorithms to detect garbage. Five different models used in this paper are EfficientDet-D1, SSD ResNet-50 V1, Faster R-CNN ResNet-101 V1, CenterNet ResNet-101 V1 and YOLOv5M. After hyper-parameter tuning and evaluation, YOLOv5M achieved the best results for the proposed system by achieving a Mean Average Precision (mAP@0.5) value of 0.613. This system directly engages citizens to join a national movement to help the authorities to maintain a clean and green environment.

Modern management of liver abcess include a combination of percutaneous Needle aspiration or percutaneous Catherter drainage along with intravenous antibiotic .Liver abcess is common disease in india, if not treated properly can lead to hazardous complication. MATERIAL AND METHOD; This was comparative study of 30 patient from august 2018 to August2020 in civil hospital ahmedabad. Randomization was done and dived into two groups of 25 each and assigned two group as percutaneous Catherter drainage and needle aspiration. Both groups were given intravenous antibiotics for 7 days .Both modalities were performed under guidance of ultrasound imaging. Needle aspiration was repeated for three times and if size of abcess cavity not reduced to half consider as failure of treatment. Effectiveness of treatment measured in term of days to achieve clinical improvement, total/near total resolution of abcess cavity and duration of hospital stay. RESULT; Needle aspiration was successful in 13 out of 15,whereas percutaneous drainage was successful in 14 out of 15.Duration of hospital stay were significantly lower in percutaneous drainage.one patient with needle aspiration developed subcapsular hematoma. CONCLUSION; We can conclude that percutaneous drainage is better modality is better modality as compared to needle aspiration in medium to large size liver abcess. The duration of hospital stay is comparatively lower in percutaneous drainage and days of clinical relief were earlier in percutaneous drainage. This study also verify that both were adequately effective in the treatment of liver abcess. *AIM OF THE STUDY To compare the effectiveness of percutaneous catheter drainage and percutaneous needle aspiration in management of liver abcess.

—The Indian economy and moreover the Indian energy sector is at the doorstep of revolution in the way energy is being transmitted. The process has begun to change the conventional system of transmission to more agile and smart system depended on advanced technology. Due to increase in the variety of sources of power, grid system in India has started experiencing stress. Above all this India will need a robust charging infrastructure for charging of EVs. Blockchain has the potential to solve the problem of India of creating a charging infrastructure in the short period of time and also can tackle the issue of lack of range of electricity for such EV. Blockchain even promises to present a practical solution of maintaining the transaction privacy of the users. With its distributed ledger technology, all the transaction done can be collected together and can be assigned a specific unique code thus maintaining the privacy. As renewable sector is growing at a significant 17.33% CAGR. The transmission & distribution losses and lower power purchase agreement (PPA) price for prosumer lead to further momentum in blockchain technology. Indian should grab the opportunity with both the hands and make the most of the best practices amalgamating with the innovation by Indian companies. India has the potential to become the driver of digitally driven future of distribution and transmission of renewable energy.

To the Editor: Biologics place patients at increased risk for infection.1 The medical boards of the National Psoriasis Foundation, the American College of Rheumatology, and the European League Against Rheumatism advocate influenza, pneumococcal, and varicella zoster vaccines for this high-risk patient population.2-4 Unfortunately, low use of pneumococcal vaccine among Veterans Affairs patients prescribed tumor necrosis factor inhibitors (TNFi) has been noted.5

During the coronavirus disease (COVID-19) global pandemic, urgent strategies to alleviate shortages are required. Evaluation of the feasibility, practicality, and value of drug conservation strategies and therapeutic alternatives requires a collaborative approach at the provincial level. The Ontario COVID-19 ICU Drug Task Force was directed to create recommendations suggesting drug conservation strategies and therapeutic alternatives for essential drugs at risk of shortage in the intensive care unit during the COVID-19 pandemic. Recommendations were rapidly developed using a modified Delphi method and evaluated on their ease of implementation, feasibility, and supportive evidence. This article describes the recommendations for drug conservation strategies and therapeutic alternatives for drugs at risk of shortage that are commonly used in the care of critically ill patients. Recommendations are identified as preferred and secondary ones that might be less desirable. Although the impetus for generating this document was the COVID-19 pandemic, recommendations should also be applicable for mitigating drug shortages outside of a pandemic. Proposed provincial strategies for drug conservation and therapeutic alternatives may not all be appropriate for every institution. Local implementation will require consultation from end-users and hospital administrators. Competing equipment shortages and available resources should be considered when evaluating the appropriateness of each strategy.

INTRODUCTION: Hemostatic Powder (TC-325) is a novel hemostatic agent used for endoscopic management of gastrointestinal (GI) bleeding. It is a mineral powder thought to act by rapidly absorbing water to form a mechanical barrier over a bleeding point leading to a hemostasis. This is a non-contact technique which has a benefit of achieving immediate hemostasis in difficult to target or diffuse lesions. In this systematic review and meta-analysis of randomized controlled trials, we evaluate the efficacy of hemostatic powder in hemostasis of GI bleeding. METHODS: Electronic databases such as PubMed and the Cochrane library were used for systematic literature search. Studies with only randomized controlled trials (RCTs) for GI bleeding (upper and lower) management, and active treatment: with hemostatic powder, comparator: standard treatment (either with hemostatic clip or adrenaline injection and heater probe application) were included. Random effects model was used to calculate the summary of odds ratio (ORs) and 95% Confidence Intervals. The main outcome of the study was to assess the achievement of immediate hemostasis of actively bleeding lesion and the risk of re-bleeding after the use of hemospray. Immediate hemostasis and re-bleeding were defined based on priori validated data. RESULTS: Total four studies were included in systematic review and meta-analysis. Total number of patients in hemospray group was 82 and standard treatment group was 82 also. In each group, majority of patients had Upper GI bleeding (both variceal and non-variceal) compared to lower GI bleeding. There were no significant differences between treatment and comparator group regarding mean age and lesions locations. Immediate hemostasis was observed in 75 of 82 cases (91%) with hemospray and 59 of 82 cases (71%) with standard treatment (OR, 4.03, 95% CI: 1.36-11.91, P = 0.01), Figure 1. Cumulative risk of bleeding in 30 days was observed in 12 of 82 cases (15%) with hemospray and 16 of 82 cases (19%) with standard treatment Figure 2. There was no significant heterogenicity among the trials. CONCLUSION: Hemostatic powder significantly improves immediate endoscopic hemostasis and may even decrease the occurrence of recurrent bleeding.Figure 1Figure 2

In modern days the power demand is increasing as the industrial load is increasing.There are various types of electrical and power electronic loads.These loads are fluctuating without manual interventions.These fluctuating loads can be stable with the use of a suitable capacitor.Majority of load are inductive in nature in industries.This inductive load consumes reactive power which affect the generation of the plant.Basically inductive load means lagging of power factor.To increase power factor there is a need of APFC Panel.Many industries use a lot of power from the grid but failed to utilize in an effective way.In many cases, consumer draws access to power than their sanctioned load.Therefore, the consumer has to pay a penalty.So, this penalty can be reduced by APFC Panel.

The purpose of this research was to study the consumers' opinion of their motor bikes regarding its features like appearance, mileage, price, etc. and to identify the factor that influences consumers while purchasing of the Two-wheeler vehicles. The study is mainly focused on the buying behavior of the consumers that motivates them to purchase two-wheeler vehicles. The survey research design was employed; study was carried out with a sample of general people. A questionnaire was used as the data collection method, all questions were structure and close ended. The sample sizes of one hundred fifty (150) twowheeler users have responded the questionnaires. Data was analyzed using frequency distribution (percentage), and T-test (onesample t test).From the study it is derived that the respondents are consuming and also preferring Two-wheeler vehicle mostly.