Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as...

Ovarian cancer is the leading cause of death among gynecological malignancies

Introduction: COVID-19, caused by severe acure respiratory syndrome coronavirus 2, has posed unprecedented challenges globally, with diverse clinical manifestations ranging from asymptomatic and mild symptoms to severe and fatal illness. Identifying patient subgroups with distinct clinical profiles could enhance individualized treatment strategies. Clustering mixed clinical data offers a promising avenue for uncovering meaningful patterns; however, few algorithms effectively manage heterogeneous datasets. This study applied evidence-based clustering algorithms, that is, KAMILA and K-prototypes, to categorize COVID-19 patients on the basis of medical history and biochemical and radiological data. Methods: A retrospective cohort study was conducted on 556 COVID-19 patients admitted to Hôtel Dieu de France Hospital in Beirut between March 2020 and October 2021. Only data collected within the first 24 hours of admission were used for clustering to ensure early prognostic relevance. After data cleaning, the missing values were imputed into 30 datasets. KAMILA and K-prototype algorithms were applied to these datasets, generating clusters ranging from two to six groups. The optimal clustering solution was determined via the silhouette, Calinski–Harabasz, and Dunn indices, followed by statistical analyses to characterize cluster-specific patient profiles and outcomes. Results: Clustering identified three distinct patient groups, with the KAMILA algorithm providing the best fit. Cluster 1 primarily included middle-aged male patients exhibiting elevated inflammatory markers, consistent oxygen requirements, and extended hospital stays. Cluster 2 included elderly patients with multiple comorbidities and high intensive care unit (ICU) admission rates, requiring cautious anticoagulation and early antibiotic intervention. Cluster 3 included younger, generally healthier individuals who required minimal interventions and experienced low mortality. Conclusions: Mixed-data clustering revealed three COVID-19 patient clusters indicating the clinical meaningfulness and global reproducibility with prognostic and therapeutic implications. This unsupervised approach may inform early triage and resource allocation. Further prospective validation in diverse, vaccinated populations is warranted.

Background/Objectives: The prognostic and predictive values of biomarkers in fertility-sparing management of atypical hyperplasia (AH) and endometrial cancer (EC) remain ill-defined. We aimed to identify the impact of tumour profiles on oncologic and reproductive outcomes to inform clinical decision-making. Methods: We conducted a systematic review and meta-analysis by searching MEDLINE, PubMed, Embase, Cochrane Library, Scopus, Google Scholar, and ClinicalTrials.gov, up to July 2026. We intended to include comparative studies or clinical trials, in English or French, assessing outcomes according to molecular or hormonal profiles in reproductive-aged women diagnosed with AH or EC. The primary outcome was the best overall complete remission (CR). Pooled odds ratios (ORs) were calculated using a random-effects model with logit transformation and restricted maximum likelihood estimation. Risk of bias was assessed using the Newcastle–Ottawa scale (NOS). The study protocol was registered in PROSPERO (CRD42025632885). Results: Eighteen retrospective studies comprising 965 patients were included. No specific molecular profile (NSMP) tumours demonstrated significantly higher odds of CR (OR 2.04, 95% CI 1.33–3.11). p53-abnormal (p53abn) and deficient mismatch repair (dMMR) tumours were significantly less likely to achieve CR compared to NSMP (OR 3.87, 95% CI 1.80–8.29 and OR 2.48, 95% CI 1.44–4.29, respectively). POLE-mutated (POLEmut) tumours showed CR comparable to NSMP (OR 1.39, 95% CI 0.60–3.24). Progesterone receptor (PR) positivity was strongly associated with CR (OR 7.73, 95% CI 2.77–21.63). Conclusions: NSMP and PR-positivity represented a favourable prognosis and potential prediction of CR. POLEmut tumours demonstrated CR rates comparable to NSMP, whereas p53abn and dMMR demonstrated unfavourable outcomes. These findings support a biologically tailored approach to patient selection for fertility-sparing management.

Cancer cells have unique metabolic activity in the glycolysis pathway compared to normal cells, which allows them to sustain their growth and proliferation. Therefore, inhibiting glycolytic pathways may provide a promising therapeutic approach to cancer treatment. In this first-of-its-kind study, we analyzed the genetic responses of cancer cells to stressors, particularly drugs that target the glycolysis pathway. Gene expression data for experiments on different types of cancer cells were retrieved from the Gene Expression Omnibus and expression fold-change was then clustered after dimensionality reduction. We identified four response clusters, the first and third are affected the most by anti-glycolytic drugs, consisting mainly of squamous and mesenchymal tissues, showing higher mitotic inhibition and apoptosis. Drugs acting on several glycolytic targets at once resulted in such responses. The second and fourth clusters were relatively unaffected by the treatments, succumbing the least to glycolysis inhibitors. These clusters are mainly gynecological and hormone-sensitive, with drugs acting on hexokinases mainly inducing this response. This study highlights the importance of analyzing the molecular states of cancer cells to identify potential targets for personalized cancer treatments and to improve our understanding of the disease.

Background The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has profoundly affected economies and healthcare systems around the world, including Lebanon. While numerous meta-analyses have explored the systemic manifestations of COVID-19, few have linked them to patient history. Our study aims to fill this gap by using cluster analysis to identify distinct clinical patterns among patients, which could aid prognosis and guide tailored treatments. Methods We conducted a retrospective cohort study at Beirut’s largest teaching hospital on 556 patients with SARS-CoV-2. We performed cluster analyses using K-prototypes, KAMILA and LCM algorithms based on 26 variables, including laboratory results, demographics and imaging findings. Silhouette scores, concordance index and signature variables helped determine the optimal number of clusters. Subsequent comparisons and regression analyses assessed survival rates and treatment efficacy according to clusters. Results Our analysis revealed three distinct clusters: “resilient recoverees” with varying disease severity and low mortality rates, “vulnerable veterans” with severe to critical disease and high mortality rates, and “paradoxical patients” with a late presentation but eventual recovery. Conclusions These clusters offer insights for prognosis and treatment selection. Future studies should include vaccination data and various COVID-19 strains for a comprehensive understanding of the disease’s dynamics.