BACKGROUND Till 31 March 2020, 105,792 COVID-19 cases were confirmed in Italy including 15,726 deaths which explains how worst the epidemic has affected the country. After the announcement of lockdown in Italy on 9 March 2020, situation was becoming stable since last days of March. In view of this, it is important to forecast the COVID-19 evaluation of Italy condition and the possible effects, if this lock down could continue for another 60 days. METHODS COVID-19 infected patient data has extracted from the Italian Health Ministry website includes registered and recovered cases from mid February to end March. Adoption of seasonal ARIMA forecasting package with R statistical model was done. RESULTS Predictions were done with 93.75% of accuracy for registered case models and 84.4% of accuracy for recovered case models. The forecasting of infected patients could be reach the value of 182,757, and recovered cases could be registered value of 81,635 at end of May. CONCLUSIONS This study highlights the importance of country lockdown and self isolation in control the disease transmissibility among Italian population through data driven model analysis. Our findings suggest that nearly 35% decrement of registered cases and 66% growth of recovered cases will be possible. V.Zimbabwe is among the countries that have been identified to be at risk of the COVID-19 pandemic. As of the 15th of March 2020, there was no confirmed case of the virus. Official reports of suspected cases were used to appraise the general screening, case management, and the emergency preparedness and response of the country towards the COVID-19 pandemic. In terms of the surveillance and capacity to screen at the ports of entry, the country seems to be faring well. The country might not be screening optimally, considering the number of COVID-19 tests conducted to date and the suspected cases who missed testing. Three of the suspected cases faced mental, social, and psychological consequences due to them being suspected cases of COVID-19. There is a need to enhance the screening process and infrastructure at all the ports of entry. More COVID-19 diagnostic tests should be procured to increase the testing capacity. Training and awareness on mental, social, and psychological consequences of COVID-19 should be offered to the health care workers and the general public. More financial resources should be sourced to enable the country control the pandemic. V.BACKGROUND Therapeutic decision-making is a core element of pharmacy practice, however, little has been documented about how it is enacted in practice and how it can be theorised. OBJECTIVE(S) This study aims to contribute to pharmacy education and practice theory by investigating the correspondence between explanations from primary care pharmacists in clinical practice roles about how they make decisions related to medicines therapy and a theoretical model of therapeutic decision-making. METHODS In this qualitative study, interview data from 10 pharmacists in primary care settings were analysed using a general inductive approach. The emergent themes were compared to a theoretical model of therapeutic decision-making. RESULTS Eight themes were identified from the explanations of how participants were making therapeutic decisions in practice. The themes were found to correspond to at least one of the four steps of therapeutic decision-making in the model. Themes corresponding to the information gathering step were described most vividly, whereas, the themes corresponding to the reasoning, judgement, and decision steps were less well-articulated. CONCLUSIONS These findings suggest that the theoretical model can be useful to interpret empirical data about therapeutic decision-making in practice. These findings might provide a means for pharmacists to adopt language to better describe the steps in their therapeutic decision-making process to others, and especially, their colleagues and patients. Findings can be used by pharmacy educators to design learning opportunities for students about therapeutic decision-making. INTRODUCTION For marker-negative clinical stage (CS) IIA nonseminomatous germ cell tumor (NSGCT), National Comprehensive Cancer Network and American Urological Association guidelines recommend either retroperitoneal lymph node dissection (RPLND) or induction chemotherapy. The goal is cure with one form of therapy. We evaluated national practice patterns in the management of CSIIA NSGCT and utilization of secondary therapies. METHODS The National Cancer Data Base was used to identify 400 men diagnosed with marker negative CSIIA NSGCT between 2004 and 2014 treated with RPLND or chemotherapy. Trends in the utilization of initial and adjuvant treatment (chemotherapy only, RPLND only, RPLND with adjuvant chemotherapy, and postchemotherapy RPLND) were analyzed. Apcin cell line RESULTS Of the 400 cases, 233 (58%) underwent induction chemotherapy with surveillance, 51 (20%) underwent RPLND with surveillance, 89 (22%) underwent RPLND followed by adjuvant chemotherapy, and 14 (4%) underwent induction chemotherapy followed by RPLND. Thirty percent of patients received dual therapy. After RPLND with pN1 staging, 43 (61%) underwent adjuvant chemotherapy. The pN0 rate after primary RPLND was 22%. Five year overall survival ranged from 95% to 100% based on initial treatment choice. CONCLUSIONS For marker negative CS IIA nonseminoma, dual, therapy, and treatment with chemotherapy is common. With low volume retroperitoneal disease resected at RPLND, adjuvant chemotherapy was frequently administered but has debatable therapeutic value. These data highlight opportunities to decrease treatment burden in patients with CS IIA nonseminoma. BACKGROUND A clinical need exists for a biomarker test to accurately delineate aggressive prostate cancer (AgCaP), and thus better assist clinicians and patients decision-making on whether to proceed to prostate biopsy. OBJECTIVES To develop a blood test for AgCaP and compare to PSA, %free PSA, proPSA, and prostate health index (PHI) tests. DESIGN, SETTINGS AND PARTICIPANTS Patient samples from the MiCheck-01 trial were used for development of the MiCheck test. METHODS Serum analyte concentrations for cellular growth factors were determined using a custom-made Luminex-based R&D Systems multianalyte kit. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Bayesian model averaging and random forest approaches were used to identify clinical factors and growth factors able to distinguish between men with AgCaP (Gleason Score [GS] ≥3+4) from those with non-AgCaP (GS 3+3). Logistic regression and Monte Carlo cross-validation identified variable combinations in order to able to maximize differentiation of AgCaP from non-AgCaP.