The knotless barbed suture is an innovative type of suture that can accelerate the placement of sutures and eliminate knot tying. However, the outcomes of previous studies are still confounding. This study reviewed the application of different types of barbed sutures in different surgeries. We searched PubMed, EMBASE, CENTRAL and ClinicalTrials.gov to identify randomized controlled trials (RCTs) addressing the application of barbed sutures up to Feb. 2015. Two reviewers independently screened the literature and assessed the risk of bias of included studies. Then meta-analysis was performed using RevMan 5.3 software. Sensitivity analysis and subgroup analysis was performed. Seventeen RCTs (low to moderate risk of bias) involving 1992 patients were included. Compared with conventional sutures, the barbed suture could reduce suture time (SMD=−0.95, 95%CI −1.43 to −0.46, P = 0.0001) and the operative time (SMD=−0.28, 95%CI −0.46 to −0.10, P = 0.003), not significantly increase the estimated blood loss (SMD=−0.09, 95%CI −0.52 to 0.35, P = 0.70), but could lead to more postoperative complications (OR = 1.43, 95%CI 1.05 to 1.96, P = 0.03), These results varied in subgroups. Thus, barbed sutures are effective in reducing the suture and operative time, but the safety evidences are still not sufficient. It need be evaluated based on special surgeries and suture types before put into clinical practice.

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning. Graphical Abstract

The Coronavirus Disease 2019 (COVID-19) has swept the whole world with high mortality. Since droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure. However, the virus has spread quite quickly, causing severe mask shortage. Finding alternative materials for homemade masks while ensuring the significant performance indicators will help alleviate the shortage of masks. Referring to the national standard for the “Surgical Mask” of China, 17 materials to be selected for homemade masks were tested in four key indicators: pressure difference, particle filtration efficiency, bacterial filtration efficiency and resistance to surface wetting. Eleven single-layer materials met the standard of pressure difference (≤49 Pa), of which 3 met the standard of resistance to surface wetting (≥3), 1 met the standard of particle filtration efficiency (≥30%), but none met the standard of bacterial filtration efficiency (≥95%). Based on the testing results of single-layer materials, fifteen combinations of paired materials were tested. The results showed that three double-layer materials including double-layer medical non-woven fabric, medical non-woven fabric plus non-woven shopping bag, and medical non-woven fabric plus granular tea towel could meet all the standards of pressure difference, particle filtration efficiency, and resistance to surface wetting, and were close to the standard of the bacterial filtration efficiency. In conclusion, if resources are severely lacking and medical masks cannot be obtained, homemade masks using available materials, based on the results of this study, can minimize the chance of infection to the maximum extent.

We report a high-performance, fully automated digital microfluidic PCR system for point-of-care testing of multiple pathogens in just 80 min.