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.

Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely because of challenges in early diagnosis and the limited sensitivity of conventional biomarkers. Therefore, reliable molecular tools for early detection, prognostic stratification, and individualized treatment predictions are urgently required. Methods: This retrospective study analyzed publicly available gene expression datasets. Candidate biomarkers were identified from the GSE14520 cohort using a multistep screening workflow that integrated differential expression analysis, diagnostic performance, and prognostic relevance. A 10-gene diagnostic model was constructed using least absolute shrinkage and selection operator logistic regression and subsequently validated across multiple independent cohorts. Survival outcomes were evaluated using the Kaplan-Meier analysis and treatment responses to sorafenib and transarterial chemoembolization (TACE) were assessed using receiver operating characteristic analysis. Results: A 10-gene signature (TOP2A, CDK1, CYP3A4, MASP2, EPHX2, HAO1, RACGAP1, GLYAT, ADH1B, and CYP4A11) was established. The model demonstrated robust internal performance and consistent accuracy across external validation cohorts (area under the curve [AUC], >0.9). This signature effectively identified early-stage HCC and distinguished malignancy from cirrhosis. High-risk scores were significantly associated with poor overall survival and recurrence-free survival (p<0.05). Furthermore, the model could predict treatment sensitivity, with higher risk scores associated with better outcomes for sorafenib (AUC, 0.791), whereas lower risk scores correlated with an improved response to TACE (AUC, 0.768). Conclusion Our gene expression-based machine learning model provides a robust tool for HCC diagnosis, prognosis, and treatment response prediction, with potential as a supportive system for personalized clinical decision-making.

Dong Nai province in southern Viet Nam was among the hardest hit in a measles outbreak in 2024–2025 with over 7000 cases. Our analysis outlines major takeaways for measles control in recommending the need for strengthened surveillance, digital communication and intersectoral collaboration to address immunization gaps and build health system resilience in Viet Nam.

Streptocaulon juventas, a medicinal plant traditionally used in Vietnam and other Southeast Asian countries, has been previously investigated for certain biological activities of its roots; however, studies on its leaves remain limited. In this study, leaf extracts were evaluated for their in vitro anti-proliferative activity against B16F10 (murine melanoma) and HepG2 (human hepatocellular carcinoma) cell lines, as well as for their αglucosidase inhibitory potential. MTT assays revealed that the hexane (Sj-Hx) and dichloromethane (Sj-DCM) fractions exhibited the strongest cytotoxic effects on B16F10 cells, with IC₅₀ values of 36.09 µg/mL and 45.91 µg/mL, respectively. The hexane fraction (Sj-Hx) also demonstrated potent activity against HepG2 cells, with an IC50 value of 36.65 µg/mL. Notably, none of the extracts exhibited cytotoxicity toward Hs68 normal human fibroblasts, indicating favorable selectivity. DAPI staining further confirmed that both Sj-Hx and Sj-DCM induced nuclear fragmentation and chromatin condensation, consistent with apoptosis induction. Additionally, in vitro assays of anti-diabetic activity revealed that the hexane (Sj-Hx) and ethyl acetate (Sj-EA) extracts displayed the strongest α-glucosidase inhibition, with IC₅₀ values of 221.59 µg/mL and 265.73 µg/mL, respectively. Collectively, these findings suggest that S. juventas leaf extracts possess selective pro-apoptotic activity against cancer cells and represent a promising natural source for the discovery of anti-diabetic agents.

Objective: To investigate the cause of a foodborne outbreak that occurred in Dong Nai province, Viet Nam, in 2024, and implement control measures. Methods: An initial investigation was conducted to confirm the outbreak, which was followed by epidemiological and environmental investigations to find the plausible causative food item. Clinical specimens and food samples were tested to identify the pathogen. Results: A total of 547 symptomatic cases were recorded, of whom two were in severe condition requiring extracorporeal membrane oxygenation and ventilation, one of whom died. Among 99 interviewed cases, the mean incubation time was 9 hours (range 2–24 hours), with the main symptoms being fever, abdominal pain, diarrhoea and vomiting. All patients had eaten banh mi from a local bakery. Salmonella spp. were identified in food samples and clinical specimens. The bakery halted production, and the outbreak ended after 1 week. Discussion: All the patients were exposed to only one food in common, which facilitated the investigation process. This outbreak is a reminder to small retailers and take-away shops of the importance of food safety management in preventing similar future outbreaks. All food handlers must comply with food hygiene principles, especially in hot temperatures, which boosts bacterial growth.

Objective: In Viet Nam, tuberculosis (TB) prevalence surveys revealed that approximately 98% of individuals with pulmonary TB have TB-presumptive abnormalities on chest radiographs, while 32% have no TB symptoms. This prompted the adoption of the “Double X” strategy, which combines chest radiographs and computer-aided detection with GeneXpert testing to screen for and diagnose TB among vulnerable populations. The aim of this study was to describe demographic, clinical and radiographic characteristics of symptomatic and asymptomatic Double X participants and to assess multilabel radiographic abnormalities on chest radiographs, interpreted by computer-aided detection software, as a possible tool for detecting TB-presumptive abnormalities, particularly for subclinical TB. Methods: Double X participants with TB-presumptive chest radiographs and/or TB symptoms and known risks were referred for confirmatory GeneXpert testing. The demographic and clinical characteristics of all Double X participants and the subset with confirmed TB were summarized. Univariate and multivariable logistic regression modelling was used to evaluate associations between participant characteristics and subclinical TB and between computer-aided detection multilabel radiographic abnormalities and TB. Results: From 2020 to 2022, 96 631 participants received chest radiographs, with 67 881 (70.2%) reporting no TB symptoms. Among 1144 individuals with Xpert-confirmed TB, 51.0% were subclinical. Subclinical TB prevalence was higher in older age groups, non-smokers, those previously treated for TB and the northern region. Among 11 computer-aided detection multilabel radiographic abnormalities, fibrosis was associated with higher odds of subclinical TB. Discussion: In Viet Nam, Double X community case finding detected pulmonary TB, including subclinical TB. Computer-aided detection software may have the potential to identify subclinical TB on chest radiographs by classifying multilabel radiographic abnormalities, but further research is needed.

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Objective: The purpose of this study was to identify factors associated with twin pregnancy following day 3 double embryo transfer (DET). Methods: This retrospective cohort study incorporated data from 16,972 day 3 DET cycles. The participants were women aged between 18 and 45 years who underwent in vitro fertilization with intracytoplasmic sperm injection (IVF/ICSI) at My Duc Assisted Reproduction Technique Unit (IVFMD), My Duc Hospital, located in Ho Chi Minh City, Vietnam. Results: Of the 16,972 day 3 DET cycles investigated, 8,812 (51.9%) resulted in pregnancy. Of these, 6,108 cycles led to clinical pregnancy, with 1,543 (25.3% of clinical pregnancies) being twin pregnancies. Factors associated with twin pregnancy included age under 35 years (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.32 to 1.71; p<0.001) and cycles involving the transfer of at least one grade I embryo. Relative to the transfer of two grade III embryos, the risk of twin pregnancy was significantly elevated following the transfer of two grade I embryos (OR, 1.40; 95% CI, 1.16 to 1.69; p<0.001) or a combination of one grade I and one grade II embryo (OR, 1.27; 95% CI, 1.05 to 1.55; p=0.001). Conclusion By analyzing a large number of IVF/ICSI cycles, we identified several predictors of twin pregnancy. These findings can assist medical professionals in tailoring treatment strategies for couples with infertility.

Differential Gene Expression Analysis of TCGA mRNA Sequencing Data from Male Patients with and without Lymph Node Metastasis in Tongue Cancer Gene expression plays a crucial role in progression, invasion, and metastasis in many groups of oral squamous cell carcinoma. Based on extensive patient data, no research has been done so far on the differences in gene expression between groups of male patients with tongue squamous cell carcinoma who drink alcohol and those who do not, about lymph node metastasis. Our study analysed differential gene expression in male patients with and without lymph node metastasis in tongue cancer, utilising messenger ribonucleic acid (mRNA) sequencing data from The Cancer Genome Atlas (TCGA) program. Analysis was performed using R and bioinformatics tools to process mRNA sequencing data, compare differential gene expression, and examine functional signaling pathways and survival analysis. After screening the database, 38 out of 74 cases with metastasis were selected for the analysis. The computational analysis identified a significant increase in cancer invasion transcription factors, such as GATA2, SP1 and MYC in the metastatic group, whereas CREB1 was more associated with the nonmetastatic group. Functional analysis suggested new prognostic biomarkers for unfavourable outcomes (TRIML2 and EXOSC4) and favourable outcomes (HS3ST4 and SFTPA1). New suggested markers for oral and tongue cancer such as TRIML2, EXOSC4 are known for their role in non-oral cancer including lung and liver cancer. Especially head and neck squamous cell carcinoma (HNSCC) survival analysis and protein atlas investigation confirmed the indirect biological roles of found markers. The study indicates that TRIML2 and EXOSC4 could serve as biological markers for predicting lymph node metastasis in tongue squamous cell carcinoma among male patients who consume alcohol. These findings suggest that gene expression profiles may play a role in the progression and metastasis of tongue cancer. Further research is needed to validate these results and to develop targeted therapies for patients with tongue cancer.

Objective: This study aimed to investigate the impact of hyperandrogenism (HA) on the outcomes of ovulation induction (OI) using gonadotropin and intrauterine insemination (IUI) in patients with polycystic ovary syndrome (PCOS). Methods: This was a retrospective cohort study including 415 patients undergoing OI using gonadotropin and IUI treatment between January 2018 and December 2020 at a single infertility center. Baseline characteristics, clinical and laboratory parameters, and pregnancy outcomes were investigated. Results: Among the study population, there were 105 hyperandrogenic (25.3%) and 310 non-hyperandrogenic patients (74.7%). The live birth rate was lower in the HA group than in the non-HA group, but this difference did not reach statistical significance due to the limited sample size (14.3% vs. 21.0%, relative risk=0.68; 95% CI, 0.41–1.14, p=0.153). No predictive factors for live birth were identified through logistic regression analysis. Conclusion HA did not negatively affect the outcomes of OI using gonadotropin and IUI cycles in Vietnamese women with PCOS. The result may not be applicable elsewhere due to the large variation in the characteristics of women with PCOS across races and populations.