are organised in LMICs, using a task-shifted and decentralised model, results in high quality services that should be considered for adoption in the UK. Collaboration with professional medical bodies and governmental departments is necessary to expand services from secondary to primary care.
 The way services are organised in LMICs, using a task-shifted and decentralised model, results in high quality services that should be considered for adoption in the UK. Collaboration with professional medical bodies and governmental departments is necessary to expand services from secondary to primary care.
  The development of pretreatment drug resistance (PDR) is becoming an obstacle to the success of antiretroviral therapy (ART). Besides, data from developing settings including Ethiopia is still limited. Therefore, this study was aimed to assess HIV-1 genetic diversity and PDR mutations among ART-naive recently diagnosed HIV-1 infected individuals in Addis Ababa, Ethiopia.
 
  Institutional based cross-sectional study was conducted from June to December 2018 in Addis Ababa among ART-naive recently diagnosed individuals. Partial HIV-1 pol region covering the entire protease (PR) and partial reverse transcriptase (RT) regions of 51 samples were amplified and sequenced using an in-house assay. Drug resistance mutations were examined using calibrated population resistance (CPR) tool version 6.0 from the Stanford HIV drug resistance database and the International Antiviral Society-USA (IAS-USA) 2019 mutation list.
 
  According to both algorithms used, 9.8% (5/51) of analyzed samples had at least one PDR Mutation. PDRenotypic drug resistance testing for all newly diagnosed HIV infected patients before initiating treatment. This will aid the selection of appropriate therapy in achieving the 90% of patients having an undetectable viral load in consonance with the UN target.
 This study showed an increased level of PDR and persistence HIV-1C clade homogeneity after 15 years of the rollout of ART and 3 decades of HIV-1C circulation in Ethiopia, respectively. Therefore, we recommend routine baseline genotypic drug resistance testing for all newly diagnosed HIV infected patients before initiating treatment. This will aid the selection of appropriate therapy in achieving the 90% of patients having an undetectable viral load in consonance with the UN target.
  Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models.
 
  This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that receihe ANN model was 81.25% and the AUC-ROC was 0.77.
 
  Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.
 Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.
  Tuberculosis (TB) continues to be an important cause of fatal and non-fatal burden in Brazil. In this study, we present estimates for TB burden in Brazil from 1990 to 2017 using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017).
 
  This descriptive study used GBD 2017 findings to report years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) of TB in Brazil by sex, age group, HIV status, and Brazilian states, from 1990 to 2017. We also present the TB burden attributable to independent risk factors such as smoking, alcohol use, and diabetes. Results are reported in absolute number and age-standardized rates (per 100,000 inhabitants) with 95% uncertainty intervals (UIs).
 
  In 2017, the number of DALYs due to TB (HIV-negative and HIV-positive combined) in Brazil was 284,323 (95% UI 240,269-349,265). Among HIV-negative individuals, the number of DALYs was 196,366 (95% UI 189,645-202,394), while 87,957 DALYs (95% UI 50,624-1ectoral actions that enable the access to prevention, early diagnosis, and timely treatment, with emphasis on high-risk groups and populations most vulnerable to the disease in the country.
 GBD 2017 results show that, despite the remarkable progress in reducing the DALY rates during the period, TB remains as an important and preventable cause of health lost to due premature death and disability in Brazil.  https://www.selleckchem.com/products/sp2509.html  The findings reinforce the importance of strengthening TB control strategies in Brazil through integrated and multisectoral actions that enable the access to prevention, early diagnosis, and timely treatment, with emphasis on high-risk groups and populations most vulnerable to the disease in the country.
  COVID-19 in Italy has led to the need to reorganize hospital protocols with a significant risk of interruption to cancer treatment programs. In this report, we will focus on a management model covering the two phases of the COVID-19 emergency, namely lockdown-phase I and post-lockdown-phase II.
 
  The following steps were taken in the two phases workload during visits and radiotherapy planning, use of dedicated routes, measures for triage areas, management of suspected and positive COVID-19 cases, personal protective equipment, hospital environments and intra-institutional meetings and tumor board management. Due to the guidelines set out by the Ministry of Health, oncological follow-up visits were interrupted during the lockdown-phase I; consequently, we set about contacting patients by telephone, with laboratory and instrumental exams being viewed via telematics. During the post-lockdown-phase II, the oncological follow-up clinic reopened, with two shifts operating daily.
 
  By comparing our radiotherapy activity from March 9 to May 4 2019 with the same period in 2020 during full phase I of the COVID-19 emergency, similar results were achieved.