Prescribing antibiotics for children with dengue infection in Taiwan: who are at risk and who are high prescribers?

Inappropriate antibiotic use contributes to antimicrobial resistance, a global public health threat. The non-specific manifestations of dengue, itself a growing public health threat, lead to avoidable empiric antibiotic prescription, particularly in children. In this national pooled population-based cross-sectional study, we evaluated child and physician characteristics associated with antibiotics prescription in confirmed dengue cases in Taiwan. Linking national health care insurance claims and reports of confirmed dengue cases from 2008 to 2015, there were 7086 children with confirmed dengue with 21 744 outpatient visits and 2520 inpatient admissions. We assessed the presence of antibiotic prescription in outpatient and inpatient settings separately a week before or after the confirmation date. Logistic regression models with generalized estimating equations were applied to identify patient, practitioner, and other factors associated with antibiotic prescription. A total of 29.4% of children <18 years old with dengue who did not have a concomitant bacterial infection were prescribed antibiotics during the 14-day assessment period. Antibiotics prescription was reduced from 13.5% to 6.3% and from 43.2% to 19.3% in outpatient and inpatient settings, respectively, after dengue was confirmed. Young children were more likely to receive antibiotics. Significant variations in antibiotic prescribing across physicians were observed only in outpatient settings: physicians ≥60 years old and physicians practicing at clinics and in non-urban facilities were more likely to prescribe antibiotics. Antibiotics were less likely to be prescribed during an exceptional 2-year epidemic than in other years. Antibiotic prescribing for dengue, an arboviral infection affecting half of the global population, was shown to occur in 29% of paediatric cases in Taiwan. That potentially avoidable antibiotic consumption could be reduced by improving antibiotic stewardship, informed by understanding the conditions under which antibiotics are prescribed and the availability of prevention strategies for viral diseases, including dengue. We identified a number of such factors in this national population-based study.

Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images.

Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson's correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.

Safety, Tolerability, and Immunogenicity of Booster Dose with MVC-COV1901 or MVC-COV1901-Beta SARS-CoV-2 Vaccine in Adults: A Phase I, Prospective, Randomized, Open-Labeled Study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines based on variant strains have been in use as booster doses to update immunity against circulating variants. Here we present the results of a phase one prospective, randomized, and open-labeled trial to study the safety and immunogenicity of a booster dose consisting of a subunit vaccine based on the stabilized prefusion SARS-CoV-2 spike protein, MVC-COV1901, or its Beta version, MVC-COV1901-Beta. Participants aged ≥18 and <55 years who received two or three prior doses of MVC-COV1901 vaccines were enrolled and were to receive a booster dose of either 15 mcg of MVC-COV1901, 15 mcg, or 25 mcg of MVC-COV1901-Beta in a 1:1:1 ratio. Adverse reactions after either MVC-COV1901 or MVC-COV1901-Beta booster doses after two or three doses of MVC-COV1901 were comparable and mostly mild and transient. At four weeks after the booster dose, participants with two prior doses of MVC-COV1901 had higher levels of neutralizing antibodies against ancestral SARS-CoV-2, Beta, and Omicron variants than participants with three prior doses of MVC-COV1901, regardless of the type of booster used. MVC-COV1901 and MVC-COV1901-Beta can both be effectively used as booster doses against SARS-CoV-2, including the BA.4/BA.5 Omicron variants.