Latest news with #HKUMed
Business Mayor
27-04-2025
- Health
- Business Mayor
Single-dose baloxavir reduces household influenza transmission
A landmark study published in The New England Journal of Medicine reveals that a single oral dose of baloxavir marboxil (baloxavir) significantly reduces the transmission of influenza within households, marking a major advancement in influenza management. Conducted by an international team of researchers including the LKS Faculty of Medicine, the University of Hong Kong (HKUMed), the CENTERSTONE trial provides the first robust evidence that an antiviral treatment can curb the spread of influenza to close contacts. The phase 3b, double-blind, randomised, placebo-controlled trial enrolled 1,457 influenza-positive index patients and 2,681 household contacts across 15 countries from 2019 to 2024. The index patients, aged 5 to 64, were assigned to receive either baloxavir or a placebo within 48 hours of symptom onset. The primary endpoint was laboratory-confirmed influenza transmission to household contacts by day 5. Key Findings: Treatment with baloxavir reduced the odds of untreated household members contracting the virus by 32%. Transmission resulting in symptomatic influenza was lower with baloxavir (5.8% vs. 7.6%), though the difference was not statistically significant (P=0.16). Baloxavir led to a faster reduction in viral titers, with a mean reduction of 2.22 log₁₀ TCID₅₀/mL by day 3 compared to 1.85 log₁₀ TCID₅₀/mL for placebo. Drug-resistant viruses emerged in 7.2% of baloxavir-treated index patients but were not detected in household contacts, suggesting limited transmission risk. No new safety concerns were identified, with adverse events reported in 4.6% of baloxavir-treated patients compared to 7.0% in the placebo group. 'These results highlight baloxavir's potential not only to treat influenza but also to reduce its spread within communities,' said Professor Benjamin Cowling, co-author of the study and Helen and Francis Zimmern Professor in Population Health, Chair Professor of Epidemiology, and Head of the Division of Epidemiology and Biostatistics, School of Public Health, HKUMed. 'This dual effect could transform how we manage seasonal influenza and prepare for future pandemics.' The study underscores the complementary role of antiviral drugs alongside vaccination, particularly in unvaccinated populations or during pandemics when vaccines may not be immediately available.
HKFP
23-04-2025
- Health
- HKFP
University of Hong Kong medical school develops world's first AI model for thyroid cancer diagnosis
The University of Hong Kong's medical school (HKUMed) has developed the world's first AI model for diagnosing thyroid cancer, showing over 90 per cent accuracy and slashing clinicians' pre-consultation time by 50 per cent. The new AI model, trained to analyse clinical documents, can classify the stage and risk category of thyroid cancer, HKUMed announced on Wednesday. The medical school said its model is more efficient than the traditional manual integration of clinical information conducted through the systems of the American Joint Committee on Cancer (AJCC) and the American Thyroid Association (ATA). Researchers trained the AI model with pathology reports of 50 thyroid cancer patients from The Cancer Genome Atlas Programme (TCGA) using four offline open-source large language models, including Google's Gemma and Meta's Llama. The team then checked the results against pathology reports from 289 TCGA patients, as well as 35 pseudo-cases created by endocrine surgeons. The accuracy exceeded 90 per cent in classifying cancer stages and risk categories, HKUMed said. The AI assistant provides high accuracy in extracting and analysing information from complicated pathology reports, operation records, and clinical notes, said Matrix Fung, chief of endocrine surgery at HKUMed and one of the project's two leading researchers. The model can also be 'readily integrated' into the public and private healthcare sectors, as well as local and overseas research institutes, Fung said. '[O]ur AI model also dramatically reduces doctors' preparation time by almost half compared to human interpretation,' he said, adding that 'doctors will have more time to counsel with their patients.' Professor Joseph Wu, another HKUMed academic leading the research, pointed out the model's offline capability as a major advantage, allowing doctors to use it without having to share or upload patients' information online, thus protecting patient privacy. HKUMed said the next step would be reviewing the performance of the AI assistant with a large amount of real-world patient data. The model can be deployed in real clinical settings and hospitals once the results are validated, it said.
