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Objective: Teamwork positively affects staff performance and patient outcomes in chronic disease management. However, there is limited research on the impact of specific team components on clinical outcomes. This review aims to explore the impact of teamwork components on key clinical outcomes of chronic diseases in primary care., Study Design: Systematic review and meta-analysis., Methods: This systematic review and meta-analysis conducted searching EMBASE, PubMed, Cochrane Central Register of Controlled Trials. Studies included must have at least one teamwork component, conducted in primary care for selected chronic diseases, and report an impact of teamwork on clinical outcomes. Mean differences and 95% confidence intervals were used to determine pooled effects of intervention., Results: A total of 54 studies from 1988 to 2021 were reviewed. Shared decision-making, roles sharing, and leadership were missing in most studies. Team-based intervention showed a reduction in mean systolic blood pressure (MD = 5.88, 95% CI 3.29-8.46, P= <0.001, I 2  = 95%), diastolic blood pressure (MD = 3.23, 95% CI 1.53 to 4.92, P = <0.001, I 2  = 94%), and HbA1C (MD = 0.38, 95% CI 0.21 to 0.54, P = <0.001, I 2  = 58%). More team components led to better SBP and DBP outcomes, while individual team components have no impact on HbA1C. Fewer studies limit analysis of cholesterol levels, hospitalizations, emergency visits and chronic obstructive pulmonary disease-related outcomes., Conclusion: Team-based interventions improve outcomes for chronic diseases, but more research is needed on managing cholesterol, hospitalizations, and chronic obstructive pulmonary disease. Studies with 4-5 team components were more effective in reducing systolic blood pressure and diastolic blood pressure. Heterogeneity should be considered, and additional research is needed to optimize interventions for specific patient populations., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Competing Interests: None declared

Background: The care provided in general practice to intravenous drug users (IDUs) with hepatitis C (HCV) extends beyond opioid substitution therapy. An aggregated analysis of HCV service utilization within general practice specifically related to diagnosis and treatment outcomes remains unknown from previous literature., Aims: This study aims to estimate the prevalence of HCV and analyze data related to the diagnosis and treatment-related outcomes of HCV patients with a history of intravenous drug use in the general practice setting., Design and Setting: A systematic review and meta-analysis in general practice., Methods: This review included studies published in the following databases: EMBASE, PubMed, and Cochrane Central Register of Controlled Trials. Two reviewers independently extracted data in standard forms in Covidence. A meta-analysis was done using a DerSimonian and Laird random-effects model with inverse variance weighting., Results: A total of 20,956 patients from 440 general practices participated in the 18 selected studies. A meta-analysis of 15 studies showed a 46% (95% confidence interval (CI), 26-67%) prevalence rate of hepatitis C amongst IDUs. Genotype information was available in four studies and treatment-related outcomes in 11 studies. Overall, treatment uptake was 9%, with a cure rate of 64% (95% CI, 43-83%). However, relevant information, such as specific treatment regimens, treatment duration and doses, and patient comorbidities, was poorly documented in these studies., Conclusion: The prevalence of HCV in IDUs is 46% in general practice. Only ten studies reported HCV-related treatment outcomes; however, the overall uptake rate was below 10%, with a cure rate of 64%. Likewise, the genotypic variants of HCV diagnoses, medication types, and doses were poorly reported, suggesting a need for further research into this aspect of care within this patient group to ensure optimal treatment outcomes.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.