个人防护用品相关性头痛发病机制及影响因素研究进展

 随着疫情防控常态化,个人防护用品（PPE）的使用可能会越来越普遍,而伴随的PPE相关性头痛（HAPPE）的高发生率,也逐渐成为众多研究探讨的热点之一。本文结合有关研究,对HAPPE发病机制,相关影响因素和干预措施进行综述,旨在为制定针对HAPPE医务人员的干预方案提供参考。     

新型冠状病毒传播途径及个人防护措施研究进展

新型冠状病毒肺炎（COVID-19）大流行对世界造成了重大影响。了解新型冠状病毒（SARS-CoV-2）传播规律有助于公共卫生人员在实际工作中明确防控重点。本文通过对现有文献（截至2022年5月2日）进行总结,基于典型案例、实验室研究等结果分析梳理目前已知的SARS-CoV-2主要传播方式并分类,提出相应的个人防护策略,为新冠病毒传播防控提供参考。     

Taqman MGB探针荧光定量PCR检测HBV DNA含量及YMDD变异的方法学评价

目的评价Taqman MGB探针荧光定量PCR技术检测血清乙型肝炎病毒DNA含量及YMDD变异的方法学特性。方法照美国临床和实验室标准协会(clinical and laboratory standards institute,CLSI)制定的评价方案,对该方法HBV DNA定量的线性范围、精密度、准确性作评价。以序列直接测定法作为诊断YMDD变异的金标准,对该方法YMDD变异检测的灵敏度、特异性和诊断符合率作评价。结果该方法HBV DNA定量在103~108copy/ml范围内呈线性,精密度好(<10%),准确度高(平均回收率为97.5%)。CT(FAM)≤35,Ct设定临界值为7时,当HBV YMDD变异>25%时能检测到变异存在;Ct设定临界值为UNDETECT时,当HBV YMDD变异>50%时能检测到变异存在。以直接测序方法为金标准,该方法YMDD变异检测的灵敏度97%,特异性100%,诊断符合率99.5%。结论该方法检测HBV DNA载量和YMDD变异可以在同一管中完成,特异性高,灵敏度相对较低,但具有快速、准确、经济实用等特点。     

Effectiveness of prevention of SARS-CoV-2 transmission among unvaccinated Italian healthcare workers

Background:We aimed to investigate the association between personal protective equipment (PPE) use and SARS-CoV-2 infection among healthcare workers (HCWs).Methods:We analyzed occupational surveillance contact forms followed by a PCR test notified between March and September 2020 by Italian HCWs. The odds ratios (ORs) and 95% Confidence Intervals (CIs) for positive PCR based on HCWs and contacts characteristics were calculated through multivariable logistic regression models. When multiple contacts were potentially effective for a PCR test, they were weighted by the inverse of their number.Results:Overall, 4,883 contacts reported by 2,952 HCWs were analyzed, and 224 contacts among 144 HCWs had positive PCR. No difference was found according to sex, age, employment, or job title, except for an OR of 0.30 (95%CI 0.11-0.78) for resident physicians, compared to administrative staff. The ORs for use of surgical mask were 0.59 (95%CI=0.40-0.86) for use only by HCW, 0.49 (95%CI=0.22-1.07) only by the infected person, and 0.40 (95%CI=0.27-0.60) by both, compared to use by neither. Use of other PPEs was not associated with infection, while the OR for hand sanitation was 0.61 (95%CI=0.40-0.93). HCWs reporting fever, cough, and asthenia had a higher risk of infection.Conclusions:Use of surgical masks was associated with a 40-60% lower risk of infection, especially when both HCWs and infected individuals used them. Our results quantify the role played by mask use and hand sanitation in preventing SARS-CoV-2 transmission in high-risk circumstances.     

Wireless Connectivity for People with Hearing Loss: Argumentum ad Ignorantiam: Smartphone-Connected Listening Devices

In this article, we review the current literature assessing the application and benefits of connected hearing technologies, as well as their potential to improve accessibility to and affordability of hearing healthcare. Over the past decade, there has been a proliferation of hearing devices that connect wirelessly to smartphone technologies via Bluetooth. These devices include (1) smartphone-connected hearing aids that must be obtained from a licensed audiologist or hearing aid dispenser; (2) direct-to-consumer devices, such as personal sound amplification products; and (3) smartphone-based hearing aid applications (or apps). Common to all these connected devices is that they permit the user to self-adjust and customize their device programs via an accompanying smartphone app. There has been a growing body of literature assessing connected hearing devices in adults living with hearing loss. Overall, the evidence to date supports the notion that all connected hearing devices can improve accessibility to and affordability of amplification. It is unclear, however, whether connected technologies are a clinically effective alternative to traditional hearing aids. Even so, the impact of connectivity is especially pertinent given the sudden disruption caused by the recent global COVID-19 pandemic, whereby connected technologies enable patients to receive treatment through mobile-based, tele-audiology platforms.     

Realizing Present and Future Promise of DIY Biology and Medicine through a Trust Architecture

The speed and scale of the COVID-19 pandemic has highlighted the limits of current health systems and the potential promise of non-establishment research such as “DIY” research. We consider one example of how DIY research is responding to the pandemic, discuss the challenges faced by DIY research more generally, and suggest that a “trust architecture” should be developed now to contribute to successful future DIY efforts.     

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.