Respiratory syncytial virus (RSV) is an important viral pathogen in children, older adults and adults with certain high-risk conditions. In our prospective community-based cohort study of adults 50 years and older before the COVID-19 pandemic, the incidence of RSV acute respiratory infection (ARI) was 48.6 cases/1000 person-years (PY), which decreased to near zero during the COVID-19 pandemic. Our objective was to determine the incidence of RSV-ARI during the 2 years following the initial phase of the pandemic (2021–2022 and 2022–2023).

This is a community-based prospective cohort study.

Adults living in southeast Minnesota from October 2021 to September 2022 (year 3) and October 2022 to September 2023 (year 4).

Adults≥50 years (n=2500).

We calculated incidence and attack rates for RSV-ARI as primary outcome and reported hospitalisations, pneumonia and death following ARI as secondary outcome.

There were 2500 participants in the study with a mean age of 68.2 years (SD 9.3) at the start of year 3. Participants were predominantly female (60%), non-Hispanic white (96%) and residing in urban areas (76%). The incidence rate of RSV-ARI was 6.12/1000 PY (95% CI 3.42 to 10.09) in 2021–2022 and 16.40/1000 PY (95% CI 11.66 to 22.42) in 2022–2023. We noted higher attack rates of RSV-ARI during the winter months. There were no hospitalisations, pneumonia or deaths within 30 days of RSV-ARI.

Compared with the period before the COVID-19 pandemic, RSV-ARI incidence was lower in both 2021–2022 and 2022–2023 periods. In 2022–2023, the incidence increased 2.7-fold compared with the 2021–2022 period. The observed incidence rates, particularly the significant increase in the most recent season, underscore the continued public health relevance of RSV-ARI and support the rationale for ongoing RSV vaccination efforts to mitigate its overall burden in the population.

Semen samples are commonly processed by a density gradient centrifugation method to isolate the most motile spermatozoa for fertilisation during in vitro fertilisation (IVF). Centrifugation can cause considerable damage to spermatozoa by the reactive oxygen species produced during the process, which has a negative effect on the IVF success. A microfluidic chip is an alternative sperm preparation technique using a miniaturised device containing channels and chambers in the microscale range for nanoparticle preparation. This technology enables the selection of mobile spermatozoa from semen samples without the need of centrifugation, leading to reduced DNA fragmentation. This study aims to compare the cumulative live birth rate of IVF following sperm preparation by a microfluidic chip method versus a density gradient centrifugation method.

This is a randomised double-blind study. Infertile patients attending the Centre of Assisted Reproduction and Embryology, Queen Mary Hospital and Kwong Wah Hospital for IVF will be recruited. With an anticipated 10% increase in live birth rate following the use of a microfluidic chip method, the calculated sample size is 516 women in each group to give a power of 0.8 and type 1 error of 0.05. Assuming a 10% drop-out rate, the total sample size is set to be 1136 or 568 women in each group. They will be randomly assigned on the day of oocyte retrieval by a laboratory staff into one of the following two groups: (1) the microfluidic chip group and (2) the density gradient group for sperm preparation and subsequent use in fertilisation. Other IVF procedures will be the same as our standard practice. Both patients and clinicians were blinded from the group allocation. The primary outcome is the cumulative live birth rate defined as the number of pregnancies leading to live birth within 6 months of randomisation. The cumulative live birth rates between the two groups will be analysed by ² test.

Ethical approval was granted from the Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (Approval Number: UW 23–293) and the Institutional Review Board of Kwong Wah Hospital (Approval Number: KC/KE-23-0108/FR-4). A written informed consent will be obtained from each woman before any study procedure according to good clinical practice. The results of this randomised trial will be disseminated in a peer-reviewed journal.

NCT06005311.

Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a high multimorbidity burden. However, existing risk assessment instruments prioritise physiological measures while overlooking systemic comorbidities. We aim to develop and validate an electronic health record (EHR)-embedded artificial intelligence (AI) model—AiRES (AI in patients with RESpiratory disease)—to predict the 30-day, 90-day and 180-day risks of all-cause and index-disease hospitalisations. This model represents a first step towards a clinical decision support tool for personalised multimorbidity management in patients with CRD.

Patients aged ≥18 years with a validated case definition of asthma and COPD will be identified from Singapore health administrative data (2012–2020). Candidate predictors will include age, sex, ethnicity, housing type, and comorbidities, measured across multiple care settings as visit frequency, grouped at quarterly intervals in Year 1 and annually for Years 2 and 3 over a 3-year lookback window. We will predict 30-day, 90-day, and 180-day risks of (1) all-cause and (2) asthma/COPD-specific hospital admissions using up to five randomly selected index dates per individual. Three machine learning algorithms—logistic regression (LR) with Lasso regularisation, eXtreme Gradient Boosting, and Categorical Boosting—will be trained using 10-fold cross-validation (CV) with an ensemble feature selection strategy. The optimal model, selected based on performance and feature importance, will be benchmarked against two reference models: a full LR and a Zero-Inflated Negative Binomial regression with hospitalisation history as the sole predictor. Discrimination and calibration will be assessed using internal-external cluster-based and temporal CV. Clinical utility will be evaluated using decision curve analysis.

This study obtained ethics approval from the National University of Singapore (NUS-IRB-2024-849). Results will be published in international peer-reviewed journals.