Objectives: Osteoporosis is a silent disease with low screening rates in many developing countries. This study aimed to evaluate the feasibility of using an artificial intelligence (AI)-based system to screen osteoporosis from pelvic and hip radiographs in Vietnam. Methods: We conducted a cross-sectional study at a tertiary medical center in Central Vietnam in 2023. A total of 2000 consecutive pelvic and hip radiographs from patients aged ≥ 40 years were collected. After excluding poorquality images, 1987 radiographs were analyzed using an AI-based software designed to estimate bone mineral density (BMD) from plain radiographs and derive T-scores. Osteoporosis was defined as a T-score ≤ − 2.5. Patient characteristics, radiographic findings, and risk factors for osteoporosis were analyzed. Results: Among 1987 patients (mean age 66.4 ± 15.1 years; 41.3% men), osteoporosis was identified in 872 patients (43.9%). The prevalence increased with age and was higher in women than in men (58.7% vs 22.8%, P < 0.001). Osteoporosis was associated with femoral neck (OR = 3.8, 95% CI: 2.7–5.2) and intertrochanteric fractures (OR = 7.0, 95% CI: 4.5–11.0). Patients with lower T-scores had a higher risk of hip fractures, especially those with T-scores ≤ − 3.0 (OR = 11.5, 95% CI: 5.5–24.5). Conclusions AI-based analysis of pelvic and hip radiographs is a feasible and effective tool for osteoporosis screening in Vietnam. The prevalence of osteoporosis in this hospital-based setting was high, particularly among elderly women.AI-assisted screening may offer an accessible strategy for early detection of osteoporosis in resource-limited settings.

Genus Penicillium comprising the most important and extensively studied fungi has been well-known as a rich source of secondary metabolites. Our study aimed to analyze and investigate biological activities, including in vitro anti-cancer, anti-inflammatory and anti-diabetic properties, of metabolites from a marine-derived fungus belonging to P. levitum. The chemical compounds in the culture broth of P. levitum strain N33.2 were extracted with ethyl acetate. Followingly, chemical analysis of the extract leaded to the isolation of three ergostane-type steroid components, namely cerevisterol (1), ergosterol peroxide (2), and (3β,5α,22E)-ergosta-6,8(14),22-triene-3,5-diol (3). Among these, (3) was the most potent cytotoxic against human cancer cell lines Hep-G2, A549 and MCF-7 with IC 50 values of 2.89, 18.51, and 16.47 ㎍/mL, respectively, while the compound (1) showed no significant effect against tested cancer cells. Anti-inflammatory properties of purified compounds were evaluated based on NO-production in LPS-induced murine RAW264.7 macrophages. As a result, tested compounds performed diverse inhibitory effects on NO production by the macrophages, with the most significant inhibition rate of 81.37 ± 1.35% at 25 ㎍/mL by the compound (2). Interestingly, compounds (2) and (3) exhibited inhibitory activities against pancreatic lipase and α-glucosidase enzymes in vitro assays. Our study brought out new data concerning the chemical properties and biological activities of isolated steroids from a P. levitum fungus.