Protective Effect of Metformin on Venous Thrombosis in Diabetic Patients: Findings From a Systematic Review

Background and Purpose. Gentamicin (GEN) is a broad-spectrum antibiotic that cannot be prescribed freely because of its toxicity. Thymoquinone (THQ), a phytochemical, has antibacterial, antioxidant, and toxicity-reducing properties. However, its hydrophobicity and light sensitivity make it challenging to utilize. This incited the idea of co-encapsulating GEN and THQ in liposomes (Lipo-GEN-THQ). Method. Lipo-GEN-THQ were characterized using the zeta-potential, dynamic light scattering, Fourier transform infrared spectroscopy, and transmission electron microscope (TEM). The liposomes’ stability was evaluated under different storage and biological conditions. Lipo-GEN-THQ’s efficacy was investigated by the minimum inhibitory/bactericidal concentrations (MICs-MBCs), time–kill curves, and antibiofilm and antiadhesion assays. Bacterial interactions with the empty and GEN-THQ-loaded liposomes were evaluated using TEM. Results. The Lipo-GEN-THQ were spherical, monodispersed, and negatively charged. The Lipo-GEN-THQ were relatively stable and released GEN sustainably over 24 h. The liposomes exhibited significantly higher antibacterial activity than free GEN, as evidenced by the four-fold lower MIC and biofilm eradication in resistant E. coli strain (EC-219). TEM images display how the empty liposomes fused closely to the tested bacteria and how the loaded liposomes caused ultrastructure damage and intracellular component release. An antiadhesion assay showed that the Lipo-GEN-THQ and free GEN (0.125 mg/L) similarly inhibited Escherichia coli (EC-157) adhesion to the A549 cells (68% vs. 64%). Conclusions. The Lipo-THQ-GEN enhanced GEN by combining it with THQ within the liposomes, reducing the effective dose. The reduction in the GEN dose after adding THQ may indirectly reduce the toxicity and aid in developing an enhanced and safer form of GEN.

Background: Skin cancer is one of the most common among white-skinned individuals. Its prevalence depends on geographic and ethnic variations. The pattern of skin cancer in Saudi patients per province is not well-established. Purpose: This retrospective study evaluated the pattern of skin cancer among Saudi patients from Riyadh area who have visited King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia, between the years of 2005 and 2015. Methods: Data from 300 cancer patients were collected from KAMC and analyzed with respect to age, sex, cancer type, and cancer location. Results: Except for mycosis fungoides (MF), all other studied skin cancer types were more common in males as compared to females (1.56:1). Among the study population, the majority of skin cancer cases were seen in patient who aged more than 40 years old (86%) with the highest rate to be among those who are between 60 and 79 years old. Among all cases, the pattern and ratio of various skin cancers were as follows: Basal cell carcinoma (BCC) (45.7%), squamous cell carcinoma (SCC) (14.7%), MF (7.3%), and malignant melanoma (MM) (7%), followed by sebaceous adenocarcinoma (SC), Kaposi sarcoma, and dermatofibrosarcoma protuberance each of 4.3%. With respect to cancer location, 141 cases were located on the face (46.95%), 46 on the hips and lower limbs (15.26%), 44 on the chest and abdomen (14.8%), 28 on the scalp and neck (9.29%), 21 on the shoulders and upper limbs (6.95%), and 17 were unspecified. Conclusion: Among Saudi patients with skin cancer in the central region of the Kingdom of Saudi Arabia (KSA), BCC and SCC were the most common cancers, followed by MF and MM. Further studies are needed to fully determine common risk factors among these and future cases.

Cancer is increasingly recognized as a metabolic disease influenced by nutritional factors, with multi-omics technologies and artificial intelligence (AI), particularly machine learning (ML), enabling integrative analyses of diet, metabolism, and tumor biology interactions. This study aimed to synthesize evidence on these approaches for understanding the nutrition–metabolism–cancer axis and assess their translational potential in oncology, especially in low-resource settings. A PRISMA-compliant systematic review and meta-analysis searched PubMed, EMBASE, and Cochrane databases from 2018 to 2025, including studies on human cancers using ≥2 omics layers integrated via AI/ML and addressing nutritional/metabolic exposures. Random-effects pooling evaluated area under the curve (AUC), odds ratios (OR), and clinical endpoints, with subgroup analyses and quality assessments via QUADAS-2, ROBINS-I, TRIPOD, and PRISMA-AI. From 4812 records, 42 studies were included, yielding a pooled AUC of 0.88 (95% CI: 0.86–0.91) and OR of 2.4 (95% CI: 1.2–3.5), demonstrating encouraging but early-stage exploratory evidence of predictive performance. Cancer-specific signatures emerged in colorectal, breast, pancreatic, liver, and hematologic malignancies. A conceptual translational framework was proposed, integrating nutrition, omics, AI/ML, and oncology to illustrate a potential implementation pathway for developing countries like Saudi Arabia. These findings represent preliminary, hypothesis-generating evidence; the proposed framework requires prospective validation before clinical deployment, particularly in resource-limited settings.