Aim Native Tibetans have adapted to chronic hypoxia, yet remain susceptible to Chronic Mountain Sickness (CMS), including high-altitude polycythemia (HAPC) and high-altitude pulmonary hypertension (HAPH). CMS prevalence rises with altitude, but mechanisms remain unclear. We aim to investigate biomarker profiles in native Tibetans with and without CMS across altitudes, including extreme altitude >5,000m. Methods We enrolled 91 native Tibetans across 4 altitudes (500 m/3,680 m/4,750 m/5,373 m), categorized into healthy, HAPC and HAPH groups. Seventeen biomarkers were quantified via Luminex. Biomarker associations with altitude and disease were analyzed using linear regression with Type-III-F-tests, followed by post-hoc comparison and effect-size analysis. Results Among healthy Tibetans, multiple biomarkers exhibited non-linear altitude-dependent changes. C-X-C motif chemokine ligand 8 (CXCL8), interferon-γ (IFN-γ), fibroblast growth factor (FGF), vascular endothelial growth factor (VEGF), C-X-C motif chemokine ligand 4 (CXCL4) and thrombopoietin (TPO) peaked at 4,750 m and declined at 5,373 m, whereas matrix metalloproteinase-3 (MMP-3) increased at 5,373 m. Among plateau-dwelling Tibetans (including HAPC and HAPH), no independent main disease-status effects were detected across biomarkers. However, an altitude-disease interaction was observed for angiogenin, with reduction at 5,373 m in HAPH, but not in HAPC. Conclusion Rising altitude drives distinct biomarker profiles in healthy Tibetans, with CXCL8/IFN-γ/FGF/VEGF/CXCL4/TPO exhibiting an inverted U-shaped response peaking at 4,750 m, and isolated MMP-3 elevation at 5,373 m, indicating a transition from adaptive angiogenesis to pathological tissue remodeling at extreme-high altitude. The altitude-dependent angiogenin reduction observed exclusively in HAPH at extreme altitude, indicating a dysregulated vascular reparative signaling in Tibetans with HAPH. These findings provide new insights into CMS pathogenesis across hypoxic gradient.
Background Artificial intelligence is increasingly used for infectious disease surveillance, traveller risk assessment, outbreak forecasting and decision support. Travel creates a distinctive transportability problem because people move between epidemiological, geographic, climatic and healthcare contexts in which the conditions supporting an inference may change rapidly. Methods This conceptual/theoretical methodological-development study was informed by a targeted narrative review of travel medicine, infectious disease surveillance, human mobility, border health, mass gatherings, antimicrobial resistance and health-AI validation literature. Results We develop a mobility-conditioned admissibility framework that treats travel as movement across origin, transit, destination and return regimes. Interpretation is conditioned on mobility state, epidemiological regime, exposure history, time-dependent constraints and clinical or biological constraints. Five proposed operational states are retained: supported inference, conditionally transportable inference, active monitoring, restricted interpretation and withholding. The framework also distinguishes low probability from infeasibility and requires preservation of rare but epidemiologically feasible events. Conclusions Model availability and conventional predictive validity do not establish inferential transportability across changing travel contexts. Mobility-conditioned admissibility provides a complementary use-boundary layer for travel health AI by making cross-regime support explicit before outputs are interpreted or released. Its incremental value requires prospective evaluation against calibrated uncertainty, external validation and dataset-shift monitoring.
Background Bhutan's malaria elimination is constrained by imported malaria. Hence, this study aimed to quantify the risk factors of imported malaria in Bhutan. Methods Individual malaria data for the period 2013 - 2024 were obtained from the Bhutan National Malaria Control Programme. Seasonal trend decomposition and hotspot analysis were conducted to explore trends and high-risk areas of imported malaria. Multivariable Bayesian logistic regression was used to quantify risk factors of imported malaria. Results Imported malaria made up 81% (563/693) of total cases and 61.8% (348) originated from India. Imported malaria did not show significant trend; however, hotspots were observed in major points of entry, in the capital city, and at hydropower projects sites. Malaria cases recorded after 2018 were eight times (adjusted odds ratio [AOR] = 8.0 (95% credible interval [CrI] 4.9 – 13.0) more at odds of being imported than those reported before 2018. Districts without borders with India had higher odds of reporting imported malaria (AOR 4.4, 95% CrI 1.2 – 24.7) compared to districts sharing a border with India. Labourers (AOR = 38.1; 95% CrI 10.5 – 187.1) and military personnel (AOR = 5.0; 95% CrI 1.5 – 20.3) were 38 and five times more likely of being imported malaria compared to public servants. Conclusion The imported malaria poses significant threat to national elimination plan. The interior non-border districts, migrant labourers and security force personnels were significantly associated with increased odds of imported malaria. Therefore, surveillance and response strategies should be targeted to these hotspots and hotpops to reduce importation risks and sustain malaria-free status.
