HIV-exposed but uninfected (HEU) children represent a growing population and show a significantly higher number of infectious diseases, several immune alterations, compromised growth, and increased mortality rates when compared to HIV-unexposed children. Considering the impact that the gut microbiota has on general host homeostasis and immune system development and modulation, we hypothesized that HEU children present altered gut microbiota that is linked to the increased morbidity and the immune system disorders faced by them. Our experiments revealed no differences in beta and alpha diversity of the gut microbiota between HEU and unexposed children or between HIV-infected and uninfected mothers. However, there were differences in the abundance of several taxa from the gut microbiota between HEU and unexposed children and between HIV-infected and uninfected mothers. Functional prediction based on 16S rRNA sequences also indicated differences between HEU and unexposed children and between infected and uninfected mothers. In addition, we detected no differences between HEU and unexposed children in relation to weight, weight-for-age z scores, albumin serum levels, or microbial translocation and inflammation markers. In summary, HIV-infected mothers and their HIV-exposed children present alterations in the abundance of several taxa in the gut microbiome and the predicted functional metagenome when compared to uninfected mothers and unexposed children. Knowledge about the gut microbiome of HEU children in different settings is essential in order to determine better treatments for this susceptible population.

The normal composition of the intestinal microbiota is a key factor for maintaining healthy homeostasis, and accordingly, dysbiosis is well known to be present in HIV-1 patients. This article investigates the gut microbiota profile of antiretroviral therapy-naive HIV-1 patients and healthy donors living in Latin America in a cohort of 13 HIV positive patients (six elite controllers, EC, and seven non-controllers, NC) and nine healthy donors (HD). Microbiota compositions in stool samples were determined by sequencing the V3-V4 region of the bacterial 16S rRNA, and functional prediction was inferred using PICRUSt. Several taxa were enriched in EC compared to NC or HD groups, including Acidaminococcus, Clostridium methylpentosum, Barnesiella, Eubacterium coprostanoligenes, and Lachnospiraceae UCG-004. In addition, our data indicate that the route of infection is an important factor associated with changes in gut microbiome composition, and we extend these results by identifying several metabolic pathways associated with each route of infection. Importantly, we observed several bacterial taxa that might be associated with different viral subtypes, such as Succinivibrio, which were more abundant in patients infected by HIV subtype B, and Streptococcus enrichment in patients infected by subtype C. In conclusion, our data brings a significant contribution to the understanding of dysbiosis-associated changes in HIV infection and describes, for the first time, differences in microbiota composition according to HIV subtypes. These results warrant further confirmation in a larger cohort of patients.

Background: Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. Objective: The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients’ adherence behavior. Methods: This prospective, randomized controlled study is nested in a large-scale international project named “Enhancing therapy adherence among metastatic breast cancer patients” (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients’ engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning–based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). Results: The recruitment process started in May 2023 and is expected to conclude on December 2023. Conclusions: The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. Trial Registration: ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181 International Registered Report Identifier (IRRID): DERR1-10.2196/48852

Purpose: Adherence to oral anticancer treatments (OATs) is a critical issue in metastatic breast cancer (MBC) to enhance survivorship and quality of life. The study is aimed to analyze the main themes and attributes related to OATs in MBC patients. This research is part of a project titled “Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients" designed to produce a predictive model of non-adherence, a decision support system, and guidelines to improve adherence to OATs. Methods: The study consists of an exploratory observational and qualitative analysis using a focus group method. A semi-structured interview guide was developed to handle relevant OAT themes. Wordcloud plots, network analysis, and sentiment analysis were performed. Results: Nineteen female MBC patients participated in the protocol (age mean 55.95, SD = 6.87). Four main themes emerged: (theme 1) individual clinical pathway; (theme 2) barriers to adherence; (theme 3) resources to adherence; (theme 4) patients’ perception of new technologies. The Wordcloud and network analysis highlighted the important role of treatment side effects and the relationship with the clinician in the modulation of adherence behavior. This result is consistent with the sentiment analysis underscoring patients experience fear of issues related to clinical values and ineffective communication and discontinuity of the doctor in charge of the patient care. Conclusion: The study highlighted the key role of the individual, relational variables, and side effects as internal and external determinants influencing adherence to MBC. Finally, the opportunity offered by eHealth technology to connect with other patients with similar conditions and share experiences could be a relief for MBC patients.

[This corrects the article DOI: 10.2196/48852.].