Comparing single versus multiple virus detection in pediatric acute gastroenteritis postimplementation of routine multiplex RT-PCR diagnostic testing.

Utilizing multiplex real time polymerase chain reaction (RT-PCR) for rapid diagnosis of gastroenteritis, enables simultaneous detection of multiple pathogens. A comparative analysis of disease characteristics was conducted between cases with single and multiple viruses. Rotavirus vaccine was introduced in 2010, reaching a 70% coverage in 2 years. All rectal swabs collected from diarrheic children (<5 years) between December 2017 and March 2022 were included. Detection of the same viruses within 2 months was considered a single episode. Episodes with positive stool bacterial PCR were excluded. A total of 5879 samples were collected, revealing 86.9% (1509) with single virus detection and 13.1% (227) with multiple viruses. The most frequent combination was rotavirus and norovirus (27.8%), these infections followed a winter-spring seasonality akin to rotavirus. Children with multivirus infections exhibited higher immunodeficiency (OR 2.06) rates, but lower food allergy (OR 0.45) and prematurity rates (OR 0.55) compared to single infections. Greater disease severity, evaluated by the Vesikari score, was observed in multivirus episodes (p < 0.001, OR 1.12). Multivirus infections accounted for 13.1% of symptomatic cases in hospitalized young children. Despite vaccination efforts, rotavirus remained prominent, frequently in co-infections with norovirus. Overall, multivirus infections were linked to more severe diseases than single virus cases.

Continuing professional development for primary care physicians: a pre-post study on lung point-of-care ultrasound curriculum.

BACKGROUND: Point-of-care ultrasound is rapidly gaining traction in clinical practice, including primary care. Yet, logistical challenges and geographical isolation hinder skill acquisition. Concurrently, an evidentiary gap exists concerning such guidance's effectiveness and optimal implementation in these settings. METHODS: We developed a lung point-of-care ultrasound (POCUS) curriculum for primary care physicians in a rural, medically underserved region of the south of Israel. The course included recorded lectures, pre-course assessments, hands-on training, post-workshop lectures, and individual practice. To evaluate our course, we measured learning outcomes and physicians' proficiency in different lung POCUS domains using hands-on technique assessment and gathered feedback on the course with a multi-modal perception approach: an original written pre- and post-perception and usage questionnaire. RESULTS: Fifty primary care physicians (PCPs) showed significant improvement in hands-on skills, increasing from 6 to 76% proficiency (p < 0.001), and in identifying normal versus abnormal views, improving from 54 to 74% accuracy (p < 0.001). Ten weeks after training, primary care physicians reported greater comfort using lung ultrasound, rising from 10 to 54% (p < 0.001), and improved grasp of its potential and limits, increasing from 27.5% to 84% (p < 0.001). Weekly usage increased from none to 50%, and the number of primary care physicians not using at all decreased from 72 to 26% (p < 0.001). CONCLUSIONS: A two-day focused in-person and remote self-learning lung-POCUS training significantly improved primary care physicians' lung ultrasound skills, comfort, and implementation.

Chest area segmentation in 3D images of sleeping patients.

Although the field of sleep study has greatly developed over recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory. This examination measures several vital signals by polysomnograph during a full night's sleep using multiple sensors connected to the patient's body. Nevertheless, despite being the gold standard, the sensors and the unfamiliar environment's connection inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of accurate and affordable 3D sensing devices, new approaches for non-contact sleep study have emerged. These methods utilize different techniques to extract the same breathing parameters but with contactless methods. However, to enable reliable remote extraction, these methods require accurate identification of the basic region of interest (ROI), i.e., the patient's chest area. The lack of automated ROI segmenting of 3D time series is currently holding back the development process. We propose an automatic chest area segmentation algorithm that given a time series of 3D frames containing a sleeping patient as input outputs a segmentation image with the pixels that correspond to the chest area. Beyond significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can enable a more precise feature extraction. In addition, further tests of the algorithm on existing data demonstrate its ability to improve the sensitivity of a prior solution that uses manual ROI selection. The approach is on average 46.9% more sensitive with a maximal improvement of 220% when compared to manual ROI. All mentioned can pave the way for placing non-contact algorithms as leading candidates to replace existing traditional methods used today.