Immunotherapy with Immune Checkpoint Inhibitors (ICIs) has demonstrated a profitable performance for Non-Small Cell Lung Cancer (NSCLC) cancer treatment in some patients; however, there is still a percentage of patients in whom immunotherapy does not provide the desired results regarding beneficial outcomes. Therefore, obtaining predictive biomarkers for ICI response will improve the treatment management in clinical practice. In this sense, liquid biopsy appears as a promising method to obtain samples in a minimally invasive and non-biased way. In spite of its evident potential, the use of these circulating biomarkers is still very limited in the real clinical practice, mainly due to the huge heterogeneity among the techniques, the lack of consensus, and the limited number of patients included in these previous studies. In this work, we review the pros and cons of the different proposed biomarkers, such as soluble PD-L1, circulating non-coding RNA, circulating immune cells, peripheral blood cytokines, and ctDNA, obtained from liquid biopsy to predict response to ICI treatment at baseline and to monitor changes in tumor and tumor microenvironment during the course of the treatment in NSCLC patients.

The aim of this study was to evaluate the efficacy of a treat-and-extend (T&E) regimen of ranibizumab as the first-choice treatment in macular oedema (MO) secondary to branch retinal vein occlusion (BRVO). We conducted a retrospective study of 20 patients who developed MO due to BRVO treated with intravitreal ranibizumab in a T&E regimen between 2016 and 2017 with a minimum follow-up of two years. Patients were classified as complete responders if treated with ranibizumab alone or incomplete responders if salvage treatment with other medications or laser was needed. Data on best corrected visual acuity (BCVA) and central macular thickness (CMT) every 6 months were recorded. The mean BCVA (logMAR) improved from 0.60 ± 0.36 to 0.29 ± 0.44 and the CMT decreased from 559.85 ± 198.61 to 305.85 ± 11.78 μm. We found statistically significant differences between complete and incomplete responders on the average number of injections during the second year (2.46 ± 2.18 compared to 5.43 ± 1.27; p = 0.007) and change of the BCVA and CMT between both groups (p < 0.001) at 6, 12, 18 and 24 months. T&E seems to be effective in MO secondary to BRVO, improving visual function and decreasing CMT, with less need for injections.

Context: Hepatocellular carcinoma is the third leading cause of cancer death. Currently, sorafenib is the treatment of choice in advanced hepatocarcinoma. Aims: Assessing the effectiveness and toxicity of sorafenib in real-word clinical practice in patients with hepatocarcinoma. Settings and Design: Single-centered observational retrospective study. Methods and Material: We included patients with hepatocarcinoma who began treatment with sorafenib between 2008 and 2018. We evaluated overall survival, time to progression, and response using RECIST (Response Evaluation Criteria in Solid Tumors) criteria. Toxicity was assessed according to the Common Terminology Criteria for Adverse Events version 5. 2020. Statistical Analysis Used: Kaplan–Meier curves and the log-rank test were used to determine the survival time and estimate factors associated with these events. Data were analyzed with SPSS 19.0 software. Results: We included 36 patients (88.9% male) with an average age of 64 ± 3.4 years. The tumor stage was advanced (C) in 21 patients (61.8%). We obtained a median overall survival of 8.5 months (IQR 3.14–18.9) and a time to progression of 4.5 months (IQR 2.4–8.8). The main degree of response was progression in 19 patients (36.1%), followed by stable disease in 13 (52.8%). The most commonly reported adverse reactions were: constitutional (83.3%), gastrointestinal (55%) and dermatological symptoms (50.0%). The development of grades 3 or 4 toxicity was not associated with increased overall survival (P = 0.719). Conclusions: The findings of the survival analysis obtained in real practice are similar to those obtained in pivotal clinical trials. Adverse reactions were different from those expected.

Introduction Aging in people with HIV (PWH) is accompanied by an increased burden of multimorbidity and persistent inflammation. Identifying biomarkers that reflect comorbidity risk can help improve long-term care. This study evaluated the association of multimorbidity with GDF-15, sICAM-1, sVCAM-1, and sP-selectin in PWH. Methods A cross-sectional study was performed in two cohorts of adults receiving antiretroviral therapy: a discovery cohort (n = 74) and a validation cohort (Spanish CoRIS network) (n = 150). Median age was 53 years in both cohorts (IQR 44–60 and 45–58), and women represented 19 (25.7%) and 75 (50.0%), respectively. Multimorbidity was defined as ≥ 2 comorbidities, including but not limited to cardiovascular, metabolic, renal, and non-AIDS-defining cancers. Plasma GDF-15, sICAM-1, sVCAM-1, and sP-selectin were quantified by multiplex immunoassay. Associations with log-transformed GDF-15 were assessed using multivariable linear regression including age-multimorbidity ordinal categories, tobacco smoking, and CD4+ nadir. Results Multimorbidity prevalence was 48.6% (36) in the hospital cohort and 54.7% (82) in CoRIS. In both cohorts, participants with multimorbidity had significantly higher GDF-15 levels (hospital: 771.5 vs. 390.0 pg/ml; CoRIS: 485.2 vs. 360.1 pg/ml; both p < 0.001). In the hospital cohort, smoking and age-multimorbidity were independently associated with elevated GDF-15, with 26.1% and 16.0% increases per category, respectively (p < 0.05). These associations were confirmed in CoRIS, with 5.44% and 19.0% increases (p < 0.01). CD4+ nadir showed no significant association with GDF-15. No significant associations were observed between multimorbidity and sICAM-1, sVCAM-1, or sP-selectin (all p > 0.05). Conclusions Elevated GDF-15 was consistently associated with multimorbidity in PWH, primarily driven by aging and tobacco smoking. GDF-15 appears to reflect a broader state of multisystem physiological stress than traditional endothelial activation markers, supporting its utility as a biomarker to identify PWH at higher risk of age-related comorbidities and to monitor the impact of modifiable risk factors in clinical care.

Polypharmacy is increasingly prevalent among people living with HIV (PLWH), especially as they age and manage multiple comorbidities. This cross-sectional study analyzed data from 268 PLWH in Vigo, Spain (2020–2023), revealing an aging cohort (mean age 49.8 years) and a 51.9% prevalence of multimorbidity. Descriptive, bivariate, and multivariable logistic regression analyses were performed. Polypharmacy, defined as the chronic use of ≥ 5 non-antiretroviral drugs, was observed in 35.7% of participants, increasing among older adults (≥ 50 years, 50.7%; p < 0.001) and those living with HIV for > 10 years (43.0%; p = 0.004). Nervous system medications (47.0%), alimentary tract/metabolism drugs (36.2%), and cardiovascular drugs (34.3%) were the most common. Psychotropic drugs were frequent, particularly anxiolytics (24.8%) and antidepressants (22.9%). In multivariable analysis, anxiolytic use was associated with older age (OR = 1.03; p = 0.038), female sex (OR = 1.97; p = 0.042), current smoking (OR = 3.74; p = 0.002), and past cocaine use (OR = 2.52; p = 0.008); antidepressant use with past (OR = 3.46; p = 0.015) and current smoking (OR = 4.46; p = 0.001). These findings highlight the complexity of managing polypharmacy in aging PLWH and underscore the need for strategies to optimize medication use.

Long COVID, a complex condition characterized by persistent symptoms following SARS-CoV-2 infection, has become a significant public health concern. Early identification of individuals at risk for long COVID is crucial for effective management. This study explores the potential of biochemical and clinical markers, alongside machine learning (ML) models, to predict long COVID development using data from the first 72 h post-admission. We analyzed clinical and laboratory data from 394 individuals diagnosed with SARS-CoV-2. A predictive model for long COVID was developed using machine learning algorithms, particularly XGBoost, to identify key biomarkers associated with the development of long COVID symptoms at 3 months post-infection. The model hyperparameters were optimized using Bayesian optimization techniques, and variable importance was assessed using SHAP values. The predictive model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.732, indicating moderate discriminatory power. Key variables identified included hemoglobin levels, oxygen saturation, weight, C-reactive protein (CRP), activated partial thromboplastin time (APTT), sodium, type of pulmonary infiltrates, and sex. Hemoglobin levels were the only statistically significant variable (p = 0.015) between those who developed long COVID and those who did not. Despite these findings, the model showed moderate overall accuracy (63.9 %) but excelled in recall (78.6 %), highlighting its potential in clinical settings for early identification of high-risk patients. Our study demonstrates the feasibility of using machine learning to predict long COVID based on early clinical and laboratory data. While individual biomarkers alone may have limited predictive value, their combination enhances risk assessment for long COVID. Future studies should focus on refining the model and validating it in broader populations, including those with milder COVID-19 forms, to improve its accuracy and clinical utility.