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Cucumbers (Cucumis sativus) are known for their high water content and nutritional value, making them a valuable addition to a balanced diet. The objective of this study was to evaluate the physicochemical quality (color, moisture, water activity, and texture), and antioxidant/antiradical properties of freshly packed greenhouse-cultivated cucumbers over a 28-day storage period, utilizing a combination of high-throughput analytical techniques, including attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, instrumental analysis, image analysis, and chemometrics. The key findings indicated that the cucumbers underwent a significant loss of moisture during the storage period, with a concomitant decrease in firmness. Color analysis revealed an increase in the yellowness of both the peel and flesh, while image analysis demonstrated increasing dissimilarity and textural disorder, particularly evident by the 13th day of storage. The spectrophotometric and spectroscopic findings demonstrated that the quality of the cucumbers was maintained during the storage period. Meanwhile, regression and discriminant analyses were employed to interpret the collected data. Predictive models were developed with high accuracy to estimate the actual storage days and flesh quality attributes based on textural and color features derived from image analysis. Overall, this study highlights the importance of controlled storage conditions in maintaining cucumber quality and provides a comprehensive approach to monitoring post-harvest changes. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate Marisol strawberries' (Fragaria × ananassa) physicochemical quality and shelf-life during storage, using an integrated analytical approach. More specifically, the research aimed to assess the strawberries' color, texture, and nutritional quality, over an 11-day storage period, employing physicochemical analyses, spectrophotometric assays, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy, image analysis, and statistical tools. The results revealed significant changes in the outer surface texture and color characteristics, indicating spoilage progression. Physicochemical parameters such as water activity, moisture content, soluble solids, titratable acidity, and ascorbic acid content exhibited significant alterations, influencing the taste profile and freshness perception. Antioxidant and antiradical activities showed fluctuations, suggesting a potential decrease in phenolic content during storage. Moreover, the ATR-FTIR spectra findings confirmed the results regarding the moisture content, soluble solids, and total phenolic content. The integration of physicochemical and image analysis-derived features through a principal component analysis (PCA) enabled the accurate classification of samples based on storage days. Regression analysis, using these features, successfully predicted the storage day with high accuracy. Overall, this integrated analytical approach provided valuable information on the estimation of Marisol strawberries' shelf-life and the prediction of their quality deterioration, contributing to better fruit management and the minimization of discards. [ABSTRACT FROM AUTHOR]

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines and empowers chemical space enrichment. Herein, we propose a data-driven approach towards drug repurposing in diabetes mellitus by integrating heterogeneous biomedical data in a unified knowledge graph. Through extensive data mining in public repositories, diabetes-related multimodal data have been retrieved. Several data analysis techniques were employed to extract information and define semantic associations, followed by data parsing and, next, descriptive statistics, regression, and cluster analysis. Biomedical entity recognition and negation detection were performed by natural language processing. Predefined biological ontologies served as reference endpoints for class definition upon data integration. Graph analytics were performed, and drug–drug, protein–protein, drug–protein, and drug–disease interactions were established. A majority vote-based machine learning framework for the prediction of human cytochrome P450 inhibitors was also integrated into the proposed enhanced knowledge graph analysis that facilitates data-driven ranking for drug repurposing candidates in diabetes mellitus. The presented method yields a ranked list of repurposing candidates. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiation therapy (RT) is an established therapeutic regimen for prostate cancer patients which aims for the direct elimination of tumor cells in the prostate gland and occasionally at distant anatomic sites. In this study, we performed next-generation sequencing-based gene expression analysis in peripheral blood from prostate cancer patients obtained pre- and post-radiotherapy and found six independently down-regulated genes including CCR7, FCGR2B, BTLA, CD6, CD3D, and CD3E. The analysis of the expression of the 6-genes as a signature also revealed significantly lower levels post- vs. pre-radiotherapy. Data extracted from the PRAD (PRostate ADenocarcinomas) dataset linked low levels of the 6-gene signature to better survival. More importantly, this 6-gene signature strongly correlated with a favorable prognosis regardless of poor standard clinicopathological parameters (i.e., Gleason score ≥ 8 and T3), thus highlighting its potential predictive value. Radiotherapy for localized prostate cancer has increased the cure and survival rates of patients. Besides its local tumoricidal effects, ionizing radiation has been linked to mechanisms leading to systemic immune activation, a phenomenon called the abscopal effect. In this study, we performed gene expression analysis on peripheral blood from prostate cancer patients obtained post- radiotherapy and showed that 6 genes, including CCR7, FCGR2B, BTLA, CD6, CD3D, and CD3E, were down-regulated by a range of 1.5–2.5-fold as compared to pre-radiotherapy samples. The expression of the signature consisting of these six genes was also significantly lower post- vs. pre-radiotherapy. These genes are involved in various tumor-promoting immune pathways and their down-regulation post-radiotherapy could be considered beneficial for patients. This is supported by the fact that low mRNA expression levels for the 6-gene signature in the prostate tumor tissue was linked to better survival. Importantly, we report that this 6-gene signature strongly correlated with a favorable prognosis regardless of poor standard clinicopathological parameters (i.e., Gleason score ≥ 8 and T3 (including T3a and T3b). Our pioneering data open the possibility that the 6-gene signature identified herein may have a predictive value, but this requires further long-term studies. [ABSTRACT FROM AUTHOR]