Spectrum of endometrial lesions observed on histopathological examination of endometrial samples in women with abnormal uterine bleeding

Background: Abnormal uterine bleeding is one of the common gynecological complaints of women of all age groups. Histopathological study of endometrial biopsy and curettage samples is an effective diagnostic modality that can be used to identify cause of AUB at its earliest. This study was done to investigate the various endometrial causes of AUB that frequently come to our hospital and their incidence in various age groups i.e. reproductive, perimenopausal and postmenopausal.Methods: This study was conducted on 108 patients who clinically presented with AUB and had their endometrial biopsy and curettage specimens sent to the histopathology department of our tertiary care hospital and teaching centre, located in Uttar Pradesh from June 2018 to May 2019. The endometrial patterns were observed, and their frequencies and percentages were computed and classified age group wise.Results: These studies included patients with age range from 19 to 77 years. The predominant age group with AUB was reproductive age group (<40 years). The most common histopathological finding in this study was normal menstrual pattern (48.15 %). The endometrial pathologies observed were hormonal imbalance and pill effect (22.22%), atrophic endometrium (10.19%), chronic endometritis (5.56%), benign endometrial polyp (4.63%), gestation products (3.70%), endometrial hyperplasia (3.70%), and endometrial carcinoma (1.85%). Conclusion: The most commonly known cause of AUB in reproductive age group is due to hormonal imbalance. Endometrial hyperplasia and carcinoma are usually more common in the perimenopausal and postmenopausal age groups. Overall, in patients with no organic cause of AUB, normal cyclical endometrial pattern is the most prevalent endometrial pattern observed.Conclusions: The most commonly known cause of AUB in reproductive age group is due to hormonal imbalance. Endometrial hyperplasia and carcinoma are usually more common in the perimenopausal and postmenopausal age groups. Overall, in patients with no organic cause of AUB, normal cyclical endometrial pattern is the most prevalent endometrial pattern observed

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

A clinicopathologic study of triple negative breast cancer

Background: Triple negative breast cancers (TNBC) are defined by absence of estrogen and progesterone receptors (ER and PR) and absence of overexpression of human epidermal growth factor receptor 2 (Her2). They are associated with poor prognosis. The purpose of this study is to study the clinicopathologic parameters of TNBC such as age, tumor size, stage, grade, and lymph node involvement and compare them with nonTNBC tumors. There are many studies which have shown that TNBC are similar to basal-like breast cancers (BBC). We have found the proportion of BBC in the TNBC group using surrogate immunohistochemical (IHC) markers cytokeratin5 (CK5) and epidermal growth factor receptor (EGFR). Materials and Methods : This is a retrospective study of 102 cases of carcinoma breast. Clinical records of the cases were retrieved. Histopathology slides and the IHC slides (ER, PR, Her2) were reviewed. Thus, two groups of patients were made TNBC and nonTNBC. Using the software SPSS version 16 statistical significance of the difference between clinicopathologic variables of the two groups was calculated. TNBC group was later studied for the presence of basal markers CK5 and EGFR using tissue microarray. Results: Statistically significant difference was found between the two groups in the variables such as mean age at diagnosis, mean tumor size, tumor grade, and the presence of lymphovascular invasion. Conclusions: TNBC formed 23.5% of total cases. Overall, TNBC were high grade tumors with larger size at diagnosis, presenting in younger women and showing lymphovascular invasion in a higher number of cases. 87.5% of TNBC were BBC

Proceedings of the International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development

This proceeding contains articles on the various ideas of the academic community presented at the International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development (FEEMSSD-2023) &amp; Annual Congress of InDA (InDACON-2023) jointly organized by the Madan Mohan Malaviya University of Technology Gorakhpur, KIPM-College of Engineering and Technology Gida Gorakhpur, and Indian Desalination Association, India on 16th-17th March 2023.  FEEMSSD-2023 &amp; InDACON-2023 focuses on addressing issues and concerns related to sustainability in all domains of Energy, Environment, Desalination, and Material Science and attempts to present the research and innovative outputs in a global platform. The conference aims to bring together leading academicians, researchers, technocrats, practitioners, and students to exchange and share their experiences and research outputs in Energy, Environment, Desalination, and Material Science.  Conference Title: International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development &amp; Annual Congress of InDAConference Acronyms: FEEMSSD-2023 &amp; InDACON-2023Conference Date: 16th-17th March 2023Conference Location: Madan Mohan Malaviya University of Technology, GorakhpurConference Organizers: Madan Mohan Malaviya University of Technology Gorakhpur, KIPM-College of Engineering and Technology Gida Gorakhpur, and Indian Desalination Association, Indi