BioMed

Background: Breast cancer is the most common malignancy and the leading cause of cancer-related death for women globally. There is currently no tumor-specific targeted therapy for TNBC, in contrast to other clinical subtypes of breast cancer. Nervonic acid (NA), a very long-chain omega-9 fatty acid, has been reported to have neuroprotective and anti-inflammatory properties, but the anticancer effect of NA is poorly understood. The aim of this study is to investigate the anticancer potential of NA. Methods: In order to investigate the anticancer effect of nervonic acid on the MDA-MB-231 breast cancer cell line, we performed a cell viability assay, assessed redox balance, performed nuclear morphological staining, evaluated gene expression, and carried out immunoblotting. Results: The results showed that NA treatment markedly decreased cell viability, increased ROS production, and increased protein expression levels of the apoptotic markers Caspase-3, -8, and -9 and p53 compared to untreated cells. Conclusions: From a mechanistic viewpoint, NA treatment altered the redox balance and changed the expression pattern of p53 and Caspase-3, -8, and -9 proteins to initiate apoptosis. This investigation has shown that NA has potential anticancer activity against the MDA-MB-231 breast cancer cell line.

Background/Objectives: The COVID-19 pandemic has resulted in more than 7.11 million deaths worldwide and has been associated with numerous extrapulmonary complications, including cardiovascular dysfunction. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets the respiratory system, increasing evidence suggests that viral components may directly affect cardiac cells and contribute to long-term cardiovascular sequelae. In this study, we investigated the effects of recombinant spike proteins from the Alpha, Delta, and Omicron SARS-CoV-2 variants on the cytoskeletal organization and proteomic landscape of human AC16 cardiomyocytes. Methods: Confocal microscopy and quantitative image analysis were employed to evaluate cytoskeletal organization, including F-actin filament count and length, phalloidin fluorescence intensity, and cell solidity, following spike-protein exposure at 24, 48, and 72 h, while quantitative tandem mass tag (TMT)-based proteomics at the 72 h exposure time point was used to characterize variant-associated changes in host protein abundance. Results: Quantitative analysis revealed time- and variant-dependent F-actin and morphological remodeling. Mean filament length was significantly reduced by all three spike variants at 48 h, whereas significant reductions in filament count emerged at 72 h across all variant-exposed groups. Phalloidin fluorescence intensity and cell solidity further revealed variant-specific responses, particularly at 72 h, with Delta and Omicron exhibiting distinct quantitative remodeling patterns. Morphologically, Delta spike-exposed cells exhibited the most pronounced disruption of overall cellular architecture at 72 h. Proteomic analyses revealed distinct abundance patterns among proteins involved in cytoskeletal regulation, cell adhesion, membrane dynamics, mechanotransduction, and intracellular trafficking. Notably, Omicron was associated with broader changes in protein abundance, whereas Delta exhibited fewer protein-abundance changes despite showing the most pronounced morphological alterations. Collectively, these findings demonstrate time- and variant-dependent patterns of cytoskeletal and morphological remodeling, together with distinct variant-associated proteomic abundance patterns at the 72 h exposure time point. The divergence between the pronounced morphological alterations associated with Delta and the broader proteomic response induced by Omicron suggests that cytoskeletal, morphological, and molecular remodeling represent distinct but interconnected dimensions of the cardiomyocyte response to SARS-CoV-2 spike proteins. Conclusions: These findings identify cytoskeletal regulation and associated signaling pathways as potential contributors to SARS-CoV-2-associated cardiac cellular dysfunction.

Background: Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Few approaches combine population-level surveillance with individual-level monitoring within a single analytical framework. Methods: This work developed a dual machine learning framework. Study 1 modeled the daily count of respiratory admissions (chapter X of the ICD-10) in São Paulo, Brazil, from 2017 to 2022 as a nowcasting task, combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated by walk-forward over 30 bimonthly windows. Study 2 applied an XGBoost and Random Forest pipeline to 913 diaphragmatic-breathing sessions from 17 patients aged 9 to 16 years in the Respire Bem system, with Asthma Control Test and salivary cortisol represented using evidence-based synthetic simulation. Results: Study 1 achieved a mean MAE of 18.22, RMSE of 23.99, and R² of 0.675, exceeding the seasonal baseline by 41.5%, with a significant advantage over all three baselines (Wilcoxon and Diebold–Mariano, p ≤ 0.038). Ablation showed each single-component configuration to be significantly worse than the full hybrid, but removing the environmental block cost only 0.27 admissions per day, an effect indistinguishable from zero. SHAP rankings were stable across windows (Kendall W = 0.640), led by NO₂, PM2.5, and temperature. In Study 2 the pipeline ran end-to-end on real behavioral data, but because the outcomes were simulated, no predictive-accuracy metric is reported. Conclusions: Study 1 delivers a validated population-level nowcasting model whose accuracy rests mainly on the temporal structure of the series. Study 2 contributes a real behavioral dataset and a reproducible pipeline; the clinical validity of the digital biomarkers remains open and requires prospective work with directly measured outcomes.