Association Between Risk Factors and Prevalence of Musculoskeletal Disorders Among Laboratory Professionals-an European Perspective.

Background: Musculoskeletal disorders (MSDs) are a large group of diseases that severely affect work productivity and quality of life. Objective: To examine the differences in the prevalence of MSDs among laboratory professionals (LP) and to assess their association with individual, psychosocial, and occupational risk factors. Methods: In a multicenter, cross-sectional study, a questionnaire was distributed online through professional association networks. Participants were divided into two groups based on their territorial affiliation-European Union (EU) and non-EU member states. Descriptive and inferential statistics were included in the analysis, and statistical significance was set at ≤0.05. Results: A total of 640 LPs from 20 European countries participated in the study, predominantly females (78.4%) with university degree (59.4%) and mean age of 41.2 ± 10.1 years. Statistically significant differences between groups were confirmed for several variables studied: neck flexion > 15o (p = 0.008), hands at chest level (p = 0.000), longer screen time, and sitting (p = 0.000). One-third of participants reported wrist (35.6%), shoulder (32.7%), and elbow (31.6%) pain, while low back pain was more common (48.9%). A statistically significant association was confirmed between the incidence of MSDs and stress at work, repetitive movements, and prolonged standing (p = 0.000). Several variables showed significant correlations with MSDs in different body parts (p <0.05). Conclusion: Our results show a higher prevalence of MSDs in LPs and recommend the development of targeted prevention programs and additional measures to modify the work environment and organizational activities.

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach.

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.

With Great Hopes Come Great Expectations: Access and Adoption Issues Associated With COVID-19 Vaccines.

Although COVID-19 vaccines are becoming increasingly available, their ability to effectively control and contain the spread of the COVID-19 pandemic is highly contingent on an array of factors. This paper discusses how limitations to vaccine accessibility, issues associated with vaccine side effects, concerns regarding vaccine efficacy, along with the persistent prevalence of vaccine hesitancy among the public, including health care professionals, might impact the potential of COVID-19 vaccines to curb the pandemic. We draw insights from the literature to identify practical solutions that could boost people's adoption of COVID-19 vaccines and their accessibility. We conclude with a discussion on health experts' and government officials' moral and ethical responsibilities to the public, even in light of the urgency to adopt and endorse "the greatest amount of good for the greatest number" utilitarian philosophy in controlling and managing the spread of COVID-19.

A race for a better understanding of COVID-19 vaccine non-adopters.

In this paper, we aim to underscore the need for a more nuanced understanding of vaccine non-adopters. As the availability of vaccines does not translate into their de facto adoption-a phenomenon that may be more pronounced amid "Operation Warp Speed"-it is important for public health professionals to thoroughly understand their "customers" (i.e., end users of COVID-19 vaccines) to ensure satisfactory vaccination rates and to safeguard society at large.