Classification of Breast Cancer Histopathological Images Using Semi-Supervised GANs

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new CNN. Deep neural networks are trained in a generative adversarial fashion in a semi-supervised environment by extracting low-level features that improve classification accuracy. This paper proposes an eloquent approach to classifying histopathological images accurately using Semi-Supervised GANs with a classification accuracy greater than 93%

Exploring the Effectiveness of Hydrophobic Glass Surface on Touch-Enabled Digital Device to Reduce Microbial Adhesion and Propagation

This study investigates the effectiveness of hydrophobic glass surfaces in reducing microbial populations on touch-enabled digital devices. Hydrophobic coatings have been proposed as a potential solution to minimize microbial adhesion and growth on device surfaces. Here, we intended to investigate the effect of hydrophobic spray on microbial load. The results were quantitatively analyzed using microbiological techniques. the nonhydrophobic surface harbors gradual microbial buildup upon time, such as threefold increase from 2 to 4 h and fivefold increase to 6 h post initial sampling with 143.6 ± 33.89 cfu/ml increase up to 264.7 ± 28.53 cfu/ml, whereas the hydrophobic surface had an overall build-up from 16.6 ± 1.2 to 50.45 ± 11.12 cfu/ml with P < 0.0001 significance. This research provides valuable insights into the potential application of hydrophobic glass coatings to mitigate microbial contamination on touch-enabled digital devices, enhancing their hygienic properties and minimizing the risk of infectious disease transmission

Classification of Breast Cancer Histopathological Images Using Semi-Supervised GANs

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new CNN. Deep neural networks are trained in a generative adversarial fashion in a semi-supervised environment by extracting low-level features that improve classification accuracy. This paper proposes an eloquent approach to classifying histopathological images accurately using Semi-Supervised GANs with a classification accuracy greater than 93%

Cost of care and impact on quality of life of upper urinary tract infections in South India with a focus on diabetics and extended-spectrum beta-lactam producing organisms

Background and Objectives: Upper urinary tract infections (UTIs) that require in-patient care can be expensive. Comorbid conditions such as diabetes as well as UTI due to extended-spectrum beta-lactamase (ESBL) producing bacteria may affect costs. The quality of life of patients with this condition has not been described. Methods: This was a cost of illness study that prospectively evaluated patients admitted with upper UTI to a medical ward in a tertiary care hospital. Direct medical and nonmedical costs, indirect costs were collected to make the total cost per admission. Quality of life was assessed using the World Health Organization Quality of Life-BREF score. We also compared costs between those with and without diabetes or ESBL infection. Results: Between March 2016 and July 2017, 92 eligible patients were included in the study. The average age was 55.8 years; two thirds were diabetics. The mean overall cost of a single admission for upper UTI was INR.88, 330.2 (1370.4 USD). This was INR.96, 193.0 (1492.6 USD) and INR.1, 03,154.9 (1600.4 USD) among those with diabetes mellitus and ESBL infection, respectively. The cost was higher among those with diabetes and ESBL than those without; this difference reached statistically significance for the ESBL group. The quality of life was affected in all domains; the psychological being most affected among diabetics and ESBL infected. Conclusions: The mean total cost of admission for an upper UTI in a tertiary care hospital in South India was INR 88,330 (1370.4 USD). This is higher if the patient has diabetes or ESBL organism causing the UTI. Quality of life is clearly reduced especially in the psychological domain

Abstracts of National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology

This book contains the abstracts of the papers presented at the National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology (NCB4EBT-2021) Organized by the Department of Biotechnology, National Institute of Technology Warangal, India held on 29–30 January 2021. This conference is the first of its kind organized by NIT-W which covered an array of interesting topics in biotechnology. This makes it a bit special as it brings together researchers from different disciplines of biotechnology, which in turn will also open new research and cooperation fields for them. Conference Title: National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental BiotechnologyConference Acronym: NCB4EBT-2021Conference Date: 29–30 January 2021Conference Location: Online (Virtual Mode)Conference Organizer: Department of Biotechnology, National Institute of Technology Warangal, Indi