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This study aimed to determine the associations of childhood exposure to bisphenol A (BPA) and parabens (PBs) with oxidative stress and the activity of outer hair cells (OHCs) in the cochlea of children with hearing loss (HL). A total of 641 children were enrolled in this cross-sectional study. Urinary concentrations of BPA and four PBs including methyl paraben (MP), ethyl paraben (EP), propyl paraben (PP), and butyl paraben (BP) were quantified by using liquid chromatography–tandem mass spectrometry (LC/MSMS). Four urinary biomarkers of oxidative stress, 8-hydroxy-2′-deoxyguanosine (8-OHdG), 8-nitroguanine (8-NO2Gua), 4-hydroxynonenal mercapturic acid (HNE-MA), and 8-isoprostaglandin F2α (8-iso-PGF2α), were measured using high-performance liquid chromatography-electrospray ionization mass spectrometry. Hearing tests were conducted by an audiologist in the audiometric test room, and the results were confirmed by an otolaryngologist. The activity of OHCs in the cochlea was measured by distortion product otoacoustic emissions (DPOAEs). The associations of BPA/PB exposure and oxidative stress with the activity of OHCs at different frequencies were evaluated in the multivariable linear regression models. There were 91 children with HL, for an incidence of approximately 14.2 %. There was a significant negative association between the presence of EP (1.5 K Hz, 3 K Hz) or PP (2 K Hz) or 8-OHdG (1 K Hz, 1.5 K Hz, 2 K Hz) and the activity of OHCs in the left ear. Significant results were also observed for BPA (2 K Hz), MP (1 K Hz, 1.5 K Hz, 2 K Hz), EP (3 K Hz), and 8-OHdG (2 K Hz) in the right ear. This study revealed that exposure to BPA/PBs reduces the activity of OHCs, especially at middle frequencies, in children.

ChatGPT, a large language model, has gained significance in medical writing, particularly in case reports that document the course of an illness. This article explores the integration of ChatGPT and how ChatGPT shapes the process, product, and politics of medical writing in the real world. We conducted a bibliometric analysis on case reports utilizing ChatGPT and indexed in PubMed, encompassing publication information. Furthermore, an in-depth analysis was conducted to categorize the applications and limitations of ChatGPT and the publication trend of application categories. A total of 66 case reports utilizing ChatGPT were identified, with a predominant preference for the online version and English input by the authors. The prevalent application categories were information retrieval and content generation. Notably, this trend remained consistent across different months. Within the subset of 32 articles addressing ChatGPT limitations in case report writing, concerns related to inaccuracies and a lack of clinical context were prominently emphasized. This pointed out the important role of clinical thinking and professional expertise, representing the foundational tenets of medical education, while also accentuating the distinction between physicians and generative artificial intelligence.

Abstract We explored the effect of 3 mg/kg of caffeine supplementation on the cognitive ability and shooting performance of elite e-sports players. Nine e-sports players who had received professional training in e-sports and had won at least eighth place in national-level e-sports shooting competitions. After performing three to five familiarization tests, we employed a single blind, randomized crossover design to divide participants into caffeine trial (CAF) and placebo trial (PL). The CAF trial took capsules with 3 mg/kg of caffeine, whereas the PL trial took a placebo capsule. After a one-hour rest, the Stroop task, the visual search ability test, and the shooting ability test were conducted. The CAF trial’s performance in the Stroop task in terms of congruent condition (P = 0.023) and visual search reaction time with 20 items (P = 0.004) was significantly superior to those of the PL trial. In the shooting test, the CAF trial’s kill ratio (P = 0.020) and hit accuracy (P = 0.008) were significantly higher, and the average time to target (P = 0.001) was significantly shorter than those of the PL trial. Caffeine supplementation significantly improves e-sports players’ reaction times and shooting performance.

Abstract Previous neuroscientific research has expounded on the fundamental role played by emotion during moral decision-making. Negative emotionality has been observed to exert a general inhibitory effect towards harmful behaviors against others. Nevertheless, the downregulation of negative affects at different levels of moral processing (e.g. impersonal versus personal moral dilemmas) alongside its possible interactions with other factors (e.g. perspective taking) hasn’t been directly assessed; both of which can assist in predicting future moral decision-making. In the present research, we empirically test (Study 1, N = 41) whether downregulating negative emotionality through pharmacological interventions using lorazepam (a GABA receptor agonist), modulate the permissibility of harm to others –i.e. if participants find it more morally permissible to harm others when harm is unavoidable (inevitable harm moral dilemmas), than when it may be avoided (evitable harm moral dilemmas). Furthermore, using another sample (Study 2, N = 31), we assess whether lorazepam’s effect is modulated by different perspective-taking conditions during a moral dilemma task –e.g. “is it morally permissible for you to […]?” (1st person perspective), relative to “is it morally permissible for [x individual] to […]?” (3rd person perspective)–, where the outcome of the different scenarios is controlled. The results of both studies converge, revealing an emotion-dependent, rather than an outcome-dependent, pharmacological modulation. Lorazepam only influenced interpersonal moral judgments when not modulated by the evitable/inevitable condition. Furthermore, there was a significant interaction between perspective-taking and drug administration, as lorazepam exerted a larger effect in modulating moral choices rather than moral judgements.

Nowadays, electricity has become an integral part of human lives. Most of our daily appliances, tools, and personal belongings are inseparable from electricity. To ensure a proper electricity distribution with an efficient transfer capability, Extra-High Voltage (EHV) transmission towers are needed. To design such a structure, it is of utmost importance to account for the cost of said tower. However, the process to estimate the cost of EHV transmission towers is both time-consuming and strenuous on human labor since a lot of consideration have to be taken. To overcome this, an imperative requirement exists for a prompt, precise, and automated tool to replace the existing manual cost estimation method. This research endeavor aims to craft a tool using support vector regression (SVR) with the capacity to prognosticate construction expenses for projects involving EHV transmission towers. The exploration of pertinent literature has enabled us to amass historical data and delineate the attributes essential for estimating costs linked to EHV transmission tower construction. The investigation delves into a comprehensive dataset spanning the past decade in Taiwan. Within this timeframe, 317 EHV transmission towers were erected between 2009 and 2019. However, 79 of these instances are excluded due to incomplete information, thereby yielding 238 viable datasets (comprising 75 % of the overall total) to underpin the development of the SVR model. By configuring the parameters to C = 0.2 and γ = 0.1, followed by 5-fold cross-validation, the resultant SVR model attains a remarkable prediction accuracy of 97.91 %, on average. As a result, the proposed SVR-based model can effectively and accurately predict the cost of constructing an EHV transmission tower project and reduce the time spent on estimation, thus contributing to the enhancement of the resilience and robustness of the transmission network system.

Background: The time from submission to publication can significantly impact the speed of knowledge dissemination and is influenced by multiple factors. This research aims to investigate the time from submission to publication of journals of primary health care and to explore the factors that influence this duration. Methods: We selected journals of primary health care and extracted their impact factors, annual publication frequencies, and open access status. The time from submission to acceptance (SA lag), acceptance to publication (AP lag), and submission to publication (SP lag) were calculated. Additionally, we conducted statistical analyses to determine whether impact factors, annual publication frequencies, and journal open access status had an influence on publication time. Results: This study revealed the average SP lag was 243.4 days (interquartile range, IQR 159–306), the average SA lag was 177.8 days (IQR 99–229.3), and the average AP lag was 65.6 days (IQR 14–101). Variations were observed in SP lag, SA lag, and AP lag among different journals. SP lag generally decreased with higher impact factors. Journals with open access had longer SA lag but shorter AP lag. There was a general trend of decreasing SP lag and SA lag with an increasing number of annual publications, but no clear trend was observed for AP lag. Conclusions: Improvements are needed in reducing the duration from submission to publication for primary health care journals. Significant variation exists among journals. Additionally, factors such as the impact factor, open access status, and the number of annual publications may influence publication speed.

Increasing insomnia signals a public health problem, alongside rising zolpidem use. This study investigates the factors behind the disproportionate rise in zolpidem prescriptions in Taiwan. It aims to identify the determinants of high-dose zolpidem users in Taiwan’s Yilan County and employ an innovative approach to outline their medication-seeking patterns, using Taiwan’s healthcare database. The associations between sociodemographic and clinical factors and low-dose and high-dose users were analyzed using multiple logistic regression. Social network analysis was employed to explore medication-seeking behavior among these user groups across different healthcare institutions. Of our 5290 participants, 22.82% are high-dose users. This study found that males face a 1.33-fold higher risk and that having chronic diseases is a major risk factor, contributing to a more than four-times higher risk (adjusted OR = 4.27, 95% CI 1.55–11.70) of being a high-dose user of zolpidem. A social network analysis showed a higher density (0.52) for high-dose users, revealing their frequent visits, for zolpidem, to different healthcare institutions. Psychiatrists have a central role in both low-dose and high-dose user networks, with a greater influence on low-dose users (64.4) than high-dose users (32.2). In sum, patients seeking high doses of zolpidem are driven by personal factors. Future efforts should include regulated dispensing, public health education, and specialized training for healthcare professionals on drug addiction.

This study evaluates the incidence and characteristics of adverse events (AEs) following the second COVID-19 booster dose, leveraging Taiwan’s distinctive approach of extending booster vaccinations to all citizens, unlike the targeted high-risk group strategies in other countries. Utilizing data from Taipei Veterans General Hospital’s Vaccine Adverse Event Reporting System (VAERS) from 27 October 2022 to 19 January 2023, this research examines AEs in 441 out of 1711 booster recipients, considering factors like age, vaccine brands, and booster combinations. The findings revealed incidence rates (IRs) of 25.6% (95% CI: 21.1–30.8) after the first booster and 24.9% (95% CI: 20.5–30.0) after the second, mostly non-serious, with those having AEs post-first booster being five times more likely to report them again (incidence rate ratio, 5.02, p < 0.001). Significantly, switching from the mRNA1273 vaccine to another brand reduced AE risk by 18%. This study underscores that AEs are more repetitive than cumulative with additional booster doses, advocating for personalized vaccination strategies based on individual medical histories and previous vaccine reactions. These insights are valuable for healthcare providers in discussing potential AEs with patients, thereby improving vaccine compliance and public trust, and for policymakers in planning future booster vaccination strategies.