The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a “score” obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward ’s method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.

Background: Plants have long been recognized for their potential to influence neurological health, with both neuroprotective and neurotoxic properties. This review explores the dual nature of plant-derived compounds and their impact on the human brain. Discussion: Numerous studies have highlighted the neuroprotective effects of various phytoconstituents, such as those found in Ginkgo biloba, Centella asiatica, Panax ginseng, Withania somnifera, and Curcuma longa. The neuroprotective compounds have demonstrated antioxidant, anti-inflammatory, and cognitive-enhancing properties, making them promising candidates for combating neurodegenerative diseases and improving brain function. Polyphenolic compounds, triterpenic acids, and specific phytocompounds like the ones from EGb 761 extract have shown interactions with key enzymes and receptors in the brain, leading to neuroprotective outcomes. However, this review also acknowledges the neurotoxic potential of certain plants, such as the Veratrum species, which contains steroidal alkaloids that can cause DNA damage and disrupt neurological function, or Atropa belladonna, which interfere with the normal functioning of the cholinergic system in the body, leading to a range of symptoms associated with anticholinergic toxicity. Conslusions: This review also emphasizes the need for further research to elucidate the complex mechanisms underlying the neuroprotective and neurotoxic effects of plant-derived compounds, as well as to identify novel phytoconstituents with therapeutic potential. Understanding the complex relationship between plants and the human brain is crucial for harnessing the benefits of neuroprotective compounds while mitigating the risks associated with neurotoxic substances. This review provides a comprehensive overview of the knowledge on the neurological properties of plants and highlights the importance of continued research in this field for the development of novel therapeutic strategies targeting brain health and neurological disorders.

Background: Breast cancer is the most frequently diagnosed cancer among women, responsible for the highest number of cancer-related deaths worldwide. There is limited data available related to serum tumor markers in breast cancer and other blood parameters or other glandular laboratory parameters. This study aims to evaluate the correlation of tumor-specific markers for breast cancer with other blood parameters and how these correlations could impact clinical management. Material and Method: This retrospective study represents a data analysis from 1 January 2020 to 31 May 2023, in the County Hospital of Timisoara, Romania. We reviewed all the cases where, in the laboratory analyses, the serum tumor specific biomarkers for breast cancer were analyzed. Results: A statistical analysis was performed in order to identify a possible relationship between CA 15-3 and the various biomarkers and blood parameters included in the present study. Values were classified according to reference ranges. The tests revealed no statistically significant associations between CA 15-3 values and the levels of CA125 (χ²(1) = 1.852, p = 0.174), CEA (χ²(1) = 1.139, p = 0.286), AFP (Fisher’s exact test, p = 0.341), fT4 (Fisher’s exact test, p = 0.310), TSH (Fisher’s exact test, p = 0.177), or PTH (Fisher’s exact test, p = 0.650). Conclusion: The findings indicate a lack of strong correlation between CA 15-3 and CA125, CEA, AFP, thyroid function markers, or PTH within this cohort.

Mitochondrial dysfunction is an important mechanism contributing to the development and progression of diabetic kidney disease (DKD). Mitochondrial DNA (mtDNA) levels in blood and urine were evaluated in relation to podocyte injury and proximal tubule (PT) dysfunction, as well as to a specific inflammatory response in normoalbuminuric DKD. A total of 150 type 2 diabetes mellitus (DM) patients (52 normoalbuminuric, 48 microalbuminuric, and 50 macroalbuminuric ones, respectively) and 30 healthy controls were assessed concerning the urinary albumin/creatinine ratio (UACR), biomarkers of podocyte damage (synaptopodin and podocalyxin), PT dysfunction (kidney injury molecule-1 (KIM-1) and N-acetyl-β-(D)-glucosaminidase (NAG)), and inflammation (serum and urinary interleukins (IL-17A, IL-18, and IL-10)). MtDNA-CN and nuclear DNA (nDNA) were quantified in peripheral blood and urine via qRT-PCR. MtDNA-CN was defined as the ratio of the number of mtDNA/nDNA copies via analysis of the CYTB/B2M and ND2/B2M ratio. Multivariable regression analysis provided models in which serum mtDNA directly correlated with IL-10 and indirectly correlated with UACR, IL-17A, and KIM-1 (R² = 0.626; p < 0.0001). Urinary mtDNA directly correlated with UACR, podocalyxin, IL-18, and NAG, and negatively correlated with eGFR and IL-10 (R² = 0.631; p < 0.0001). Mitochondrial DNA changes in serum and urine display a specific signature in relation to inflammation both at the podocyte and tubular levels in normoalbuminuric type 2 DM patients.

Breast cancer is one of the most common cancers and the leading cause of cancer death in women. Less than 1% of breast cancer cases are male breast cancers. Although there has been significant progress made in the management of breast cancer, due to its rarity among men, the question of whether men and women with breast cancer have the same treatment response and survival rate still needs to be answered. The primary goal of this study is to compare survival outcomes between male and female breast cancer patients. Material and Method: This cohort study represents a retrospective and anonymized data analysis of 2162 breast cancer cases (19 males and 2143 females), registered over a period of 12 years, from 1 January 2010 to 31 December 2021, in the Clinic of Obstetrics and Gynecology, Diakoneo Diak Klinikum Schwäbisch Hall, Germany. Results: According to the Kaplan–Meier survival analysis, the estimated overall 3-year survival rate was 91.1% for women and 88.9% for men. The log-rank test of equality of survival distributions indicated a statistically significant difference in survival times between the two groups (p = 0.009). In the subsequent age-matched Kaplan–Meier analysis, the p-value was below the significance threshold (p = 0.068). Conclusions: Male breast cancer is a rare disease that may show some particularities in terms of survival compared to female breast cancer.

Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care.