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Pancreatic adenocarcinoma remains one of the deadliest malignancies affecting the older population. We are experiencing a paradigm shift in the treatment of pancreatic cancer in the era of coronavirus disease 2019 (COVID-19) pandemic. Utilizing neoadjuvant treatment and further conducting a safe surgery while protecting patients in a controlled environment can improve oncological outcomes. On the other hand, an optimal oncologic procedure performed in a hazardous setting could shorten patient survival if recovery is complicated by COVID-19 infection. We believe that oncological treatment protocols must adapt to this new health threat, and pancreatic cancer is not unique in this regard. Although survival may not be as optimistic as most other malignancies, as caregivers and researchers, we are committed to innovating and reshaping the treatment algorithms to minimize morbidity and maximize survival as caregivers and researchers.Although the whole world has been suffering under the Coronavirus Disease 2019 epidemic, the World Journal of Clinical Oncology (WJCO) was able to productively direct our efforts in the field through 2020. This was only accomplished through the collective efforts and support of our Editorial Board members, peer reviewers, authors, readers and editorial office staff. In 2020, the WJCO published 12 issues encompassing 84 papers, a 115.4% increase over 2019 (39 papers). In December of 2020, we strategized a plan to solicit even more high-quality contributions for WJCO in 2021. So far, we have registered and accepted 188 manuscript titles, including 132 reviews and 56 original articles. In the New Year, we will work with our internal and external colleagues who care about and support the development of WJCO with the express aim of improving the academic rank of WJCO in the field of oncology. Ultimately, our goal is to merit and gain inclusion in the Science Citation Index Expanded, with the first impact factor being awarded as soon as possible.The number of treatment options for metastatic hormone-sensitive prostate cancer has increased substantially in recent years. The classic treatment approach for these patients-androgen-deprivation therapy alone-is now considered suboptimal. Several randomized phase III clinical trials have demonstrated significant clinical benefits-including significantly better overall survival and quality of life-for treatments that combine androgen-deprivation therapy with docetaxel, abiraterone acetate, enzalutamide, apalutamide, and/or radiotherapy to the primary tumour. As a result, these approaches are now included in treatment guidelines and considered standard of care. However, the different treatment strategies have not been directly compared, and thus treatment selection remains at the discretion of the individual physician or, ideally, a multidisciplinary team. Given the range of available treatment approaches with varying toxicity profiles, treatment selection should be individualized based on the patient's clinical characteristics and preferences, which implies active patient participation in the decision-making process. In the present document, we discuss the changing landscape of the management of patients with metastatic hormone-sensitive prostate cancer in the context of several recently-published landmark randomized trials. Selleckchem YK-4-279 In addition, we discuss several unresolved issues, including the optimal sequencing of systemic treatments and the incorporation of local treatment of the primary tumour and metastases.
Of women diagnosed with endometriosis, 3.8-37% have bowel endometriosis. The cecum is the least common site for endometriotic implants affecting the bowel, accounting for only 3.6-6% of cases. We present a case of intrauterine fetal demise at term in which the patient was found to have gastrointestinal bleeding caused by endometriosis of the cecum.

A 35-year-old woman, gravida 4, para 1, at 37weeks and 3days of gestation, without a known history of endometriosis but with two prior miscarriages, presented with severe anemia and intrauterine fetal demise. During delivery, melanotic stool was noted. Colonoscopic biopsy noted the source of bleeding to be a 2cm endometriotic implant in the patient's cecum. Suppression therapy was started. Postpartum, the patient underwent laparoscopic cecectomy and pathology confirmed the diagnosis of endometriosis.

Hemorrhage from endometriotic implants may occur during pregnancy due to changes in the hormonal milieu. Bowel endometriosis may increase the risk of maternal hemorrhage during pregnancy, thereby increasing the risk of unfavorable pregnancy outcomes, including intrauterine fetal demise.
Hemorrhage from endometriotic implants may occur during pregnancy due to changes in the hormonal milieu. Bowel endometriosis may increase the risk of maternal hemorrhage during pregnancy, thereby increasing the risk of unfavorable pregnancy outcomes, including intrauterine fetal demise.Covid 19 pandemic has placed the entire world in a precarious condition. Earlier it was a serious issue in china whereas now it is being witnessed by citizens all over the world. Scientists are working hard to find treatment and vaccines for the coronavirus, also termed as covid. With the growing literature, it has become a major challenge for the medical community to find answers to questions related to covid-19. We have proposed a machine learning-based system that uses text classification applications of NLP to extract information from the scientific literature. Classification of large textual data makes the searching process easier thus useful for scientists. The main aim of our system is to classify the abstracts related to covid with their respective journals so that a researcher can refer to articles of his interest from the required journals instead of searching all the articles. In this paper, we describe our methodology needed to build such a system. Our system experiments on the COVID-19 open research dataset and the performance is evaluated using classifiers like KNN, MLP, etc. An explainer was also built using XGBoost to show the model predictions.
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