A Laboratory Manual for the Preparation of Chemical Reagents, Solutions and Special Indicators

A Laboratory Manual for the Preparation of Chemical Reagents, Solutions and Special Indicators. ETHIOPIAN BIODIVERSITY INSTITUTE, Microbial Biodiversity Directorate. This manual includes detailed view of the following: Laboratory Safety Precautions, Chemical Reagent Preparation, Buffer and Chemical Solution Preparation, Staining Solution Preparation, Indicators and Dye Solution Preparation, Preparation of Special Reagents, Known and Suspected List of Carcinogens and References.

Laboratory diagnosis and management of COVID-19 cases: creating a safe testing environment

Background COVID-19 disease has had a profound impact worldwide since it was discovered in Wuhan, China, in December 2019. Laboratory testing is crucial to prompt identification of positive cases, initiation of treatment and management strategies. However, medical scientists are vulnerable to infection due to the risk of exposure in the laboratory and the community. This study sought to determine the awareness of laboratory safety measures, assess the personal efforts of medical scientists in creating a safe laboratory environment for testing and examine the laboratory safety enabling factors. Methods The data used for the study were generated among medical scientists in Nigeria through an internet-broadcasted questionnaire and were analyzed using IBM SPSS Statistics (version 25). Results The majority of the respondents had a high awareness of laboratory safety measures (60.3%) and demonstrated good personal efforts in creating a safe laboratory testing environment (63%). The level of awareness of laboratory safety measures was significantly associated with respondents’ level of education (χ2 = 6.143; p = 0.046) and influences respondents’ efforts in creating a safe laboratory testing environment (p = 0.007). However, just a few respondents could convincingly attest to the availability of adequate and appropriate PPE with proper utilization training (45.1%), adequate rest and other welfare packages (45.8%) as well as access to appropriate Biological Safety Cabinets (BSCs) and other essential equipment in their laboratories (48.8%). Furthermore, a significant association existed between the availability of laboratory safety enabling factors and respondents’ efforts in creating a safe environment for testing with the p-value ranging between < 0.0001 and 0.003. Conclusion This study revealed that despite the high awareness of safety measures and good personal efforts of the study participants in creating a safe laboratory-testing environment, there was poor availability of safety facilities, equipment, support and welfare packages required to enhance their safety. It is, therefore, crucial to provide necessary laboratory biosafety equipment and PPE in order not to compromise medical scientists’ safety as they perform their duties in COVID-19 pandemic response.

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial

Background In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. Results Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: − 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: − 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. Conclusion We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration ClinicalTrials.gov: NCT00852423