Digital healthcare systems data could provide insights into the global prevalence of chronic kidney disease (CKD). We designed the CaReMe CKD study to estimate the prevalence, key clinical adverse outcomes and costs of CKD across 11 countries. Individual-level data of a cohort of 2·4 million contemporaneous CKD patients was obtained from digital healthcare systems in participating countries using a pre-specified common protocol; summarized using random effects meta-analysis. CKD and its stages were defined in accordance with current Kidney Disease: Improving Global Outcomes (KDIGO) criteria. CKD was defined by laboratory values or by a diagnosis code. The pooled prevalence of possible CKD was 10·0% (95% confidence interval 8.5‒11.4; mean pooled age 75, 53% women, 38% diabetes, 60% using renin-angiotensin-aldosterone system inhibitors). Two out of three CKD patients identified by laboratory criteria did not have a corresponding CKD-specific diagnostic code. Among CKD patients identified by laboratory values, the majority (42%) were in KDIGO stage 3A; and this fraction was fairly consistent across countries. The share with CKD based on urine albumin-creatinine ratio (UACR) alone (KDIGO stages one and two) was 29%, with a substantial heterogeneity between countries. Adverse events were common; 6·5% were hospitalized for CKD or heart failure, and 6·2% died, annually. Costs for renal events and heart failure were consistently higher than costs for atherosclerotic events in CKD patients across all countries. We estimate that CKD is present in one out of ten adults. These individuals experience significant adverse outcomes with associated costs. The prevalence of CKD is underestimated when using diagnostic codes alone. There is considerable public health potential in diagnosing CKD and providing treatments to those currently undiagnosed. The study was sponsored by AstraZeneca.

Generative Artificial Intelligence can be an important asset in the drug discovery process to meet the demand for novel medicines. This work outlines the optimization and fine-tuning steps of MedGAN, a deep learning model based on Wasserstein Generative Adversarial Networks and Graph Convolutional Networks, developed to generate new quinoline-scaffold molecules from complex molecular graphs, including hyperparameter adjustments and evaluations of drug-likeness attributes such as pharmacokinetics, toxicity, and synthetic accessibility. The best model was capable of generating 25% valid molecules, 62% fully connected, from which 92% were quinolines, 93% were novel, and 95% unique, preserving chirality, atom charge, and favorable drug-like properties while generating 4831 novel quinolines. These results provide valuable insights into how activation functions, optimizers, learning rates, neuron units, molecule size and constitution, and scaffold structure affect the performance of generative models and their potential to create new molecular structures, enhancing deep learning applications in computational drug design.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Intimate partner violence is characterized by violent actions against a person perpetrated by his or her former or current partner, regardless of cohabitation. It most frequently affects women, and one of its most relevant outcomes is the health problems associated with the experience of repeated violence. Thus, the main objective of this study is to analyse the prevalence of health problems among women for whom there was a medical suspicion of being victims of intimate partner violence. The specific objectives are to analyse the prevalence of (a) health risk behaviours; (b) traumatic injuries and intoxications; (c) mental health conditions; and (d) somatic diseases. We conducted a real-world, retrospective, observational, cross-sectional and multicentric study based on secondary data analyses of electronic health records and health care register data in patients of the Local Healthcare Unit of Matosinhos (between 2001 and 2021). The identified data were extracted from electronic health records corresponding to the Health Insurance Portability and Accountability Act Safe Harbor Standard. Information was obtained considering the International Classification of Diseases, the International Classification of Primary Care, and the Anatomical Therapeutic Chemical Classification System, as well as clinical notes (according to previously defined keywords). Considering all information sources, 1676 cases were obtained. This number means that just 2% of the women observed at this health care unit were suspected of being victims of intimate partner violence, which is far from the known statistics. However, we found much higher rates of all health risk behaviours, trauma and intoxication cases, mental health conditions, and somatic disorders we looked for, when compared to the general population. Early detection of these cases is mandatory to prevent or minimize their related health outcomes.