Anterior lens capsule and epithelium thickness measurements using spectral-domain optical coherence tomography.

BACKGROUND: To investigate the anterior lens capsule and epithelium thickness (defined as anterior lens capsular complex: ALCC) in normal Chinese subjects using spectral-domian optical coherence tomography (SD-OCT) and examine the factors that may influence the ALCC, such as age, gender, pupil diameter (PD) and signal strength index (SSI). METHODS: A prospective observational case series. One-hundred-thirty-four normal subjects (134 eyes) were included. The ALCCs were determined manually via SD-OCT. Using the pupil center as a reference position, the central ALCC (CALCC), nasal 1-mm ALCC (NALCC), temporal 1-mm ALCC (TALCC) and PD were measured manually. RESULTS: The mean CALCC, NALCC and TALCC were 33 ± 6 µm, 36 ± 7 µm and 34 ± 6 µm, respectively. The NALCC was significantly thicker than the CALCC (P < .001) and TALCC (P < .001). Moreover, CALCC was significantly thinner than TALCC (P = 0.013). Age was positively correlated with the CALCC (r = 0.292, P < .001), NALCC (r = 0.400, P < .001) and TALCC (r = 0.521, P < .001). PD, gender and SSI were not significantly correlated with the three ALCC parameters. CONCLUSIONS: The SD-OCT can be used to demonstrate the ALCC thickness, and age is positively correlated with the ALCC in the central, nasal and temporal sides.

Efficacy of an Omaha system-based remote ergonomic intervention program on self-reported work-related musculoskeletal disorders (WMSDs) - A randomized controlled study.

Purpose: Heavy biomechanical loadings at workplaces may lead to high risks of work-related musculoskeletal disorders. This study aimed to explore the efficacy of an Omaha System-based remote ergonomic intervention program on self-reported work-related musculoskeletal disorders among frontline nurses. Materials and methods: From July to October 2020, 94 nurses with self-reported pain in one of the three body parts, i.e., neck, shoulder, and low back, were selected and were randomly divided into two groups. The intervention group received a newly developed remote program, where the control group received general information and guidance on health and life. Program outcome was evaluated by a quick exposure check approach. Results: After 6 weeks, the intervention group exhibited significantly less stress in the low back, neck, and shoulder/forearms, compared to the control group (p < 0.05). In addition, the occurrence of awkward postures, such as extreme trunk flexion or twisting, was also significantly reduced (p < 0.05). Conclusions: The newly developed Omaha System-based remote intervention program may be a valid alternative to traditional programs for frontline nurses during the COVID-19 pandemic, reducing biomechanical loadings and awkward postures during daily nursing operations.

Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks.

BACKGROUND AND OBJECTIVES: Spectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images. METHODS: This study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy. RESULTS: The proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net. CONCLUSIONS: The proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.

Prevalence of Work-Related Musculoskeletal Disorders among Nurses: A Meta-Analysis.

Background: Work-related musculoskeletal diseases (WMSDs) have a greater negative impact on nurses' physical and mental health. However, the epidemiologic characteristics of nurse WMSDs are unclear, and the reported prevalence of WMSDs varies widely. The aim of this meta-analysis was to provide a quantitative synthesis of WMSDs' prevalence in nurses and estimate the pooled prevalence of its. Methods: The PubMed, ScienceDirect, Web of Science, Cochrane Library, EMBASE, CINAHL, Ovid, WANFANG, VIP, China Knowledge Integrated, and CBM databases were searched for relevant studies. The retrieval period extended from database initiation to Mar 2022. After data extraction and quality assessment, a meta-analysis was performed using the Stata 16.0 software package. Results: Overall, 42 articles were included, yielding a total sample size of 36,934. The annual prevalence of WMSDs among nurses was found to be 77.2% (95% confidence interval: 0.725-0.819). The three anatomical areas with the highest prevalence of WMSDs among nurses were the lower back (at 59.5%), neck (at 53.0%) and shoulder (at 46.8%). Nurses in developed countries have a higher prevalence of WMSDs than those in developing countries. Conclusion: There was currently moderate evidence to suggest a high prevalence of WMSDs in nurses. National policies should aim to reduce their prevalence in this population.

A mucosal recovery software tool for endoscopic submucosal dissection in early gastric cancer.

Background: Due to the limited diagnostic ability, the low detection rate of early gastric cancer (EGC) is a serious health threat. The establishment of the mapping between endoscopic images and pathological images can rapidly improve the diagnostic ability to detect EGC. To expedite the learning process of EGC diagnosis, a mucosal recovery map for the mapping between ESD mucosa specimen and pathological images should be performed in collaboration with endoscopists and pathologists, which is a time-consuming and laborious work. Methods: 20 patients at the Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College from March 2020 to July 2020 were enrolled in this study. We proposed the improved U-Net to obtain WSI-level segmentation results, and the WSI-level results can be mapped to the macroscopic image of the specimen. For the convenient use, a software pipeline named as "Pathology Helper" for integration the workflow of the construction of mucosal recovery maps was developed. Results: The MIoU and Dice of our model can achieve 0.955 ± 0.0936 and 0.961 ± 0.0874 for WSI-level segmentation, respectively. With the help of "Pathology Helper", we can construct the high-quality mucosal recovery maps to reduce the workload of endoscopists and pathologists. Conclusion: "Pathology Helper" will accelerate the learning of endoscopists and pathologists, and rapidly improve their abilities to detect EGC. Our work can also improve the detection rate of early gastric cancer, so that more patients with gastric cancer will be treated in a timely manner.

Featured services and performance of BDS-3.

BeiDou Global Navigation Satellite System (BDS-3) not only performs the normal positioning, navigation and timing (PNT) functions, but also provides featured services, which are divided into geostationary orbit (GEO) and medium earth orbit (MEO) satellite-based featured services in this paper. The former refers to regional services consisting of the regional short message communication service (RSMCS), the radio determination satellite service (RDSS), the BDS satellite-based augmented service (BDSBAS) and the satellite-based precise point positioning service via B2b signal (B2b-PPP). The latter refers to global services consisting of the global short message communication service (GSMCS) and the MEO satellite-based search and rescue (MEOSAR) service. The focus of this paper is to describe these featured services and evaluate their performances. The results show that the inter-satellite link (ISL) contributes a lot to the accuracy improvement of orbit determination and time synchronization for the whole constellation. Compared with some other final products, the root mean squares (RMS) of the BDS-3 precise orbits and broadcast clock are 25.1 cm and 2.01 ns, respectively. The positioning accuracy of single frequency is better than 6 m, and that of the generalized RDSS is usually better than 12 m. For featured services, the success rates of RSMCS and GSMCS are better than 99.9% and 95.6%, respectively; the positioning accuracies of single and dual frequency BDSBAS are better than 3 and 2 m, respectively; the positioning accuracy of B2b-PPP is better than 0.6 m, and the convergence time is usually smaller than 30 min; the single station test shows that the success rate of MEOSAR is better than 99%. Due to the ISL realization in the BDS-3 constellation, the performance and capacities of the global featured services are improved significantly.

Fast retinal layer segmentation of spectral domain optical coherence tomography images.

An approach to segment macular layer thicknesses from spectral domain optical coherence tomography has been proposed. The main contribution is to decrease computational costs while maintaining high accuracy via exploring Kalman filtering, customized active contour, and curve smoothing. Validation on 21 normal volumes shows that 8 layer boundaries could be segmented within 5.8 s with an average layer boundary error <2.35 µm. It has been compared with state-of-the-art methods for both normal and age-related macular degeneration cases to yield similar or significantly better accuracy and is 37 times faster. The proposed method could be a potential tool to clinically quantify the retinal layer boundaries.