Real-life data of immune recovery using bictegravir/emtricitabine/tenofovir alafenamide in virologically suppressed people living with HIV. Results at 48-96†weeks of RETROBIC Study.

BACKGROUND: Switching strategy with bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) has become a gold standard for people living with HIV (PLWH), achieving high efficacy and safety rates. However, data regarding immune status in long-term real-life cohorts of pretreated patients are needed. METHODS: We performed a multicentre, non-controlled, retrospective study in virologically suppressed PLWH switching to B/F/TAF. We evaluated CD4+, CD8+ and CD4+/CD8+ ratio, efficacy and safety at weeks 48 and 96. RESULTS: The study comprised 1966 PLWH from 12 hospitals in Spain, of whom 80% were men, and the median age was 51.0 [42.0-57.0] years. The median time of HIV infection was 18.0 [10.0-27.0] years. No significant changes in CD4+, CD8+ T cells, or CD4+/CD8+ were observed after 96 weeks. Nevertheless, in women at weeks 48 and 96, we found a significant increase of CD4+ T cells and a significant decrease in CD8+ T cells. In patients ≥60 years at week 96, CD4 T cells significantly increased and CD8+ T cells significantly decreased at week 48. The on-treatment analysis revealed HIV-RNA <50 copies/mL in 95.6% (1700/1779) and 96.7% (1312/1356) of patients at weeks 48 and 96, respectively. The rates increased to 99.2% (1765/1779) and 99.7% (1352/1356) when considering HIV-RNA <200 copies/mL. No resistance mutations were detected in virologic failures. B/F/TAF discontinuations accounted for 10.2% (200). Simplification was the most common reason for discontinuation in 3.8% (74) of patients. CONCLUSION: In long-term virologically controlled PLWH, B/F/TAF achieved high efficacy rates and slightly improved immune status in women and individuals aged 60 and over after 48 and 96 of switching.

Artificial intelligence model for tumoral clinical decision support systems.

BACKGROUND AND OBJECTIVE: Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. METHODS: The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. RESULTS: We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. CONCLUSIONS: This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.

Outcomes of Drug Interactions Between Antiretrovirals and Co-Medications, Including Over-the-Counter Drugs: A Real-World Study.

INTRODUCTION: The objective was to characterize real-world outcomes of drug-drug interactions (DDIs) between antiretrovirals (ARVs) and other drugs, including over-the-counter medications (OTC), and treatment outcomes in clinical practice. METHODS: www.clinicalcasesDDIs.com is an open-access website for healthcare providers to consult and briefly describe real-world clinical cases on DDI with ARVs. We reviewed all the clinical cases reported to the website between March 2019 and May 2023. RESULTS: A total of 139 cases were reported, mostly involving ritonavir or cobicistat (boosters; 74 cases), unboosted integrase inhibitors (InSTI; 29 cases), and non-nucleoside reverse transcriptase inhibitors (NNRTI; 23 cases). Central nervous system drugs (29 cases) and cardiovascular drugs (19 cases) were the most frequently described co-medications. Notably, OTC medications were implicated in 27 cases, including mineral supplements (11 cases), herbals (8 cases), weight loss drugs (4 cases), anabolic steroids (3 cases), and recreational drugs (1 case). OTC acted as the perpetrator drug in 21 cases, leading to loss of ARV efficacy in 17 instances (mineral supplements in 10 cases, weight loss drugs in 4 cases, herbals in 3 cases). Additionally, toxicity was reported in 4 out of 6 cases where OTC was considered the victim drug of the DDI (anabolic steroids in 3 cases, MDMA in 1 case). CONCLUSIONS: Frequent unwanted outcomes resulting from DDIs between ARVs and OTC medications underscore the importance of integrating non-prescription drugs into medication reconciliation. The real-world data available through www.clinicalcasesDDIs.com serves as a valuable resource for assessing the clinical relevance of DDIs.

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children.

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-α2 in 10 patients: IFN-α2 only in three, IFN-α2 plus IFN-ω in five, and IFN-α2, IFN-ω plus IFN-ß in two; IFN-ω only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-α2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-ω in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-α2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-ω only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-ω and/or IFN-α2.