Background: Over the last 25 years, digital health interventions in low- and middle-income countries have undergone substantial transformations propelled by technological advancements, increased internet accessibility, and a deeper appreciation of the benefits of digital tools in enhancing health care availability. Objective: This study aims to examine the evolution, impact, and prospects of digital health interventions in low- and middle-income countries, highlighting their role in improving health care accessibility and equity. Methods: A retrospective analysis of digital health initiatives scanning the past two and a half decades focused on the progression from basic SMS platforms to sophisticated mobile health apps and other health digital interventions. Relevant literature and case studies were reviewed to elucidate key milestones, successes, challenges, and opportunities in advancing digital health initiatives in low- and middle-income regions. Results: Digital health initiatives in low- and middle-income countries initially targeted specific health concerns, such as malaria diagnosis and treatment, through text-based platforms, demonstrating their efficacy in reaching remote and marginalized communities. With the proliferation of mobile phone ownership and internet access, these interventions evolved into comprehensive mobile health apps, facilitating self-care support, patient education, chronic disease monitoring, and remote consultations. The COVID-19 pandemic further accelerated the adoption of digital health interventions, particularly in disseminating health information, supporting contact tracing efforts, and enabling virtual consultations to alleviate strain on health care systems. Conclusions: The future of digital health interventions in low- and middle-income countries holds immense promise, fueled by emerging technologies such as artificial intelligence, machine learning, and blockchain. However, challenges persist in ensuring equitable access to digital health technologies, addressing disparities in digital literacy, and establishing robust health care infrastructure. Collaboration among governments, health care providers, technology innovators, and communities is essential to overcome these challenges and harness the full potential of digital health to improve health care outcomes in low- and middle-income countries.

Objectives The aim of this research was to automate the search of publications concerning adverse drug reactions (ADR) by defining the queries used t

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions. Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.