Modal wet processing – A novel approach

In this study, various pretreatment parameters have been optimized for greige woven modal fabric in an industrial set-up of semi-continuous and continuous pretreatment range in order to overcome its processing issues without altering the inherent fabric softness. Various recipes of pad-batch desizing, pad-steam bleaching and cold causticization have been attempted and the results are compared with exhaust pretreatment. The pretreated fabric is then subjected to dyeing with. a reactive dye. The performance of the processed fabric has been evaluated in terms of water absorbency, tensile strength, CIE whiteness index, and tegewa rating. Wash and crock fastness of the subsequently dyed fabric are also evaluated. The XRD spectrum indicates marginal increase in the crystallinity of modal fabric post causticization. The results are found encouraging in terms of good and uniform depth of colour with very little deterioration in the desired fabric properties

Characterisation and In Vitro Antimicrobial Activity of Biosynthetic Silver-loaded Bacterial Cellulose Hydrogels

Wounds that remain in the inflammatory phase for a prolonged period of time are likely to be colonised and infected by a range of commensal and pathogenic microorganisms. Treatment associated with these types of wounds mainly focuses on controlling infection and providing an optimum environment capable of facilitating re-epithelialisation, thus promoting wound healing. Hydrogels have attracted vast interest as moist wound-responsive dressing materials. In the current study, biosynthetic bacterial cellulose hydrogels synthesised by Gluconacetobacter xylinus and subsequently loaded with silver were characterised and investigated for their antimicrobial activity against two representative wound infecting pathogens, namely S. aureus and P. aeruginosa. Silver nitrate and silver zeolite provided the source of silver and loading parameters were optimised based on experimental findings. The results indicate that both AgNO3 and AgZ loaded biosynthetic hydrogels possess antimicrobial activity (p < .05) against both S. aureus and P. aeruginosa and may therefore be suitable for wound management applications

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Modal wet processing – A novel approach

98-103In this study, various pretreatment parameters have been optimized for greige woven modal fabric in an industrial set-up of semi-continuous and continuous pretreatment range in order to overcome its processing issues without altering the inherent fabric softness. Various recipes of pad-batch desizing, pad-steam bleaching and cold causticization have been attempted and the results are compared with exhaust pretreatment. The pretreated fabric is then subjected to dyeing with a reactive dye. The performance of the processed fabric has been evaluated in terms of water absorbency, tensile strength, CIE whiteness index, and tegewa rating. Wash and crock fastness of the subsequently dyed fabric are also evaluated. The XRD spectrum indicates marginal increase in the crystallinity of modal fabric post causticization. The results are found encouraging in terms of good and uniform depth of colour with very little deterioration in the desired fabric properties

STUDY ON IMPROVING PERFORMANCE OF SOLAR COOKER BY USING DIFFERENT COATING MATERIAL

Day by day demand of the energy increasing in all over the world. Solar energy is very large, in exhaustible source of energy. The use of renewable energy is receiving growing interest worldwide. Everybody demand clean and safe energy devices with cost effective. One of the most essential energy needs for human living is for cooking. In India mostly rural sector uses Biogas, Kerosene, and LPG for cooking. According to the World Health Organization comparative risk study, exposure to smoke from household use of solid fuels is responsible for the premature deaths of approximate 400000 women in India every year.Also in solar cooker device if black material coating is done for receiver it improves the efficiency of system and it also increases the temperature of cooker for cooking. Black coating improves the absorbptance of the receiver surface. This project paper is Improving performance of solar cooker by using different coating material

Impact of COVID-19 on rheumatic diseases in india: Determinants of mortality and adverse outcome: A retrospective, cross-sectional cohort study

Introduction: There is varying impact of COVID19 on world population depending on ethnicity, age and underlying co-morbidities. However, the lack of data regarding the effect of COVID on patients with rheumatological disorders (RDs) from different nations adds to uncertainty on disease outcome. Across the world, many rheumatology associations have joined hands to collate-related information. A national database under Indian Rheumatology Associations (IRAs) was developed to understand the impact of underlying RD and immunosuppressants during the COVID pandemic on its severity and outcome in our country. Methods: All registered members of IRA were invited to participate in this registry and provide information of reverse transcription–polymerase chain reaction confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-infected RD patients using an online platform https://iradatabaseard.in/iracovid/index.php. The results of the data were analyzed using the appropriate statistics. Multivariate logistic regression was used to analyze the impact of different variables on mortality. Odds ratio and 95% confidence interval were used to define risk of death. Results: In this retrospective cross-sectional study, data for 447 RD patients who were infected with SARS-CoV2 data were available as of December 1, 2020. The mean age was 47.9 ± 14.4 years, including two children and 93 (20.8%) geriatric age group patients, male: female ratio was 0.4:1 and mean disease duration was 79.3 ± 77.1 months. Rheumatoid arthritis was the most common RD. Underlying disease was quiescent in 54.7% and active in 18.4% patients. Most common medications at the time of COVID diagnosis were steroids (57.76%) and hydroxychloroquine (67.34%). Fever and cough were the most common symptoms. More than half of the patients (54.4%) needed hospitalization. Oxygen requirement was noted in 18.8%, intensive care unit admission, and invasive ventilation was needed in 6.0%, and 2.9% patients, respectively. Complete recovery was seen in 95.5% of patients and 4.47% (n = 20) expired due to COVID. The presence of comorbidity, dyspnea, and a higher neutrophil count was statistically significantly associated with death (P < 0.05). None of the other factors affected COVID-19 outcome. Conclusion: This is the largest cohort from a single nation looking at the interface between RD and COVID. The results indicate that patients with RD do not show increased mortality despite current use of disease-modifying anti-rheumatic drugs/immunosuppressants