Prevalence and symptoms of Long Covid-19 in the workplace.

Background: The symptoms of Long coronavirus disease 2019 (Covid-19) are heterogeneous, creating uncertainty for employers regarding the diagnosis. The prevalence of Long Covid-19 in the workforce is also unknown. Furthermore, workers affected by Long Covid-19 encounter considerable difficulties in ensuring work safety and returning to their jobs due to this condition.

Prevalence and risk factors of long covid and its associated adverse work outcomes among workers in the manufacturing sector in Malaysia - a mixed-methods study protocol.

Background: The manufacturing sector in Malaysia has been severely impacted by the COVID-19 pandemic. This is further exacerbated by Long COVID-19 symptoms among the manufacturing workers, which are proven to influence work performance and quality of life. Of note, there is currently a lack of knowledge regarding the burden of Long COVID-19 in the Malaysian manufacturing sector.

Elucidating the Interplay between Symmetry Distortions in Passivated MAPbI and the Rashba Splitting Effect.

Hybrid organic-inorganic perovskites play a critical role in modern optoelectronic applications, particularly as single photon sources due to their unusual bright ground state. However, the presence of trap states resulting from surface dangling bonds hinders their widespread commercial application. This work uses density functional theory (DFT) to study the effects of various passivating ligands and their binding sites on Rashba splitting, a phenomenon directly linked to the bright ground state.

Guide to extraluminal fish bone retrieval with serial computed tomography scans: a case series.

Background: Fish bone ingestion is commonly encountered in emergency department. It poses a diagnostic and therapeutic challenge particularly when it migrates extraluminally, necessitating a comprehensive and multidisciplinary approach for successful management.

Case Presentation: Here we reported four cases of extraluminal fish bone.

Ocular image-based deep learning for predicting refractive error: A systematic review.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done.