Assessment the relationship between Testis Cancer incidence and mortality rate with human development index in the European countries in 2012

Testis Cancer (TC) is the most common cancer in 15-39 year-old men and with the white Caucasian race. This cancer consists of 0.7 percent of men's cancer all over the world. The aim of this study is to investigate the relationship between the Age-Standardised Incidence Rates (ASIR) and Age-Standardised Mortality Rates (ASMR) of TC with Human Development Index (HDI) and its components at the European countries in 2012. This study was an ecologic study in European countries for assessment the correlation between ASIR and ASMR with HDI and its details including: Life expectancy at birth, Mean years of schooling and Gross National Income (GNI) per capita. We use correlation bivariate method for assessment the correlation between SIR and SMR with HDI and its components. Data of study was analyzed by SPSS15statistical analysis software; the significance level of the tests was considered P<0.05. The results of the data investigation showed that the maximum ASIR of TC was observed respectively in the countries of Norway, Switzerland, and Denmark. Moreover findings showed the highest ASMR was in the countries of Hungary, Bulgaria and FYR Macedonia. The ASIR was positively correlated with HDI equal to 0.623( p <= 0.001), with Life expectancy at birth equal to 0.602( p <= 0.001), with the average years of schooling equal to 0.339( p= 0.032) and with country's Gross National Income ( GNI) per capita equal to 0.466( p= 0.002). The ASMR was negatively correlated with HDI equal to 0.537( p <= 0.001), life expectancy at birth equal t

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Current practice of histopathology in Pakistan: difficulties, challenges, and solutions

Histopathology is the gold standard for diagnosis of cancers as well as many non- neoplastic diseases. Pakistan is a country of more than 220 million people and the fifth most populated country of the world. Unfortunately, it has a weak healthcare system in general and poor pathology services in particular. Till date, only 338 histopathologists have passed their fellowship examination in Pakistan; this has led to a very alarming situation considering the marked increase in the prevalence of cancer cases and other diseases which need histopathological interpretation. There are only 18 big histopathological labs in the country, the majority of which are located in major cities which further delays the diagnosis of patients who live in rural areas. Immediate steps are required for better histopathology services in the country. Adoption of digital tools may bridge the gaps of histopathology-practice and ensure consistency across the country

Current practice of histopathology in Pakistan: Difficulties, challenges, and solutions

Histopathology is the gold standard for diagnosis of cancers as well as many non 14 neoplastic diseases. Pakistan is a country of more than 220 million people and the fifth most populated country of the world. Unfortunately, it has a weak healthcare system in general and poor pathology services in particular. Till date, only 338 histopathologists have passed their fellowship examination in Pakistan; this has led to a very alarming situation considering the marked increase in the prevalence of cancer cases and other diseases which need histopathological interpretation. There are only 18 big histopathological labs in the country, the majority of which are located in major cities which further delays the diagnosis of patients who live in rural areas. Immediate steps are required for better histopathology services in the country. Adoption of digital tools may bridge the gaps of histopathology-practice and ensure consistency across the country

Ki-67 quantification in breast cancer by digital imaging AI software and its concordance with manual method

Objective: To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method.Study design: Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.ZMethodology: Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome.Results: The manual and automated scoring methods showed strong positive concordance (p \u3c0.001).Conclusion: Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available