Comparison of sevoflurane and propofol in combination with remifentanil on the quality of postoperative recovery in patients undergoing laparoscopic bariatric surgery.

Objective: This study aimed to evaluate and compare the effects of sevoflurane + remifentanil (Sev + Rem) and propofol + remifentanil (Pro + Rem) on the postoperative recovery quality of patients undergoing laparoscopic bariatric surgery to determine which anesthesia regimen provides a better overall recovery experience. Methods: Sixty patients were divided into two groups based on the treatments they underwent: Sev + Rem (n = 30) and Pro + Rem (n = 30). The Sev + Rem group received sevoflurane inhalation (0.5%, increasing to 0.5-4%) and remifentanil via target-controlled infusion. The Pro + Rem group received propofol [4-8 mg/(kg·h)] and remifentanil via target-controlled infusion. Anesthesia depth was maintained at a bispectral index of 40-60 in both groups. Perioperative data, hemodynamic parameters, and postoperative recovery quality were assessed. Results: Compared to the Pro + Rem group, the dose of remifentanil in the Sev + Rem group was significantly lower (1693.67 ± 331.75 vs. 2,959 ± 359.77, p < 0.001), the proportion of patients used norepinephrine was markedly higher [16 (53.33) vs. 8 (26.67), p = 0.035], and the time of extubation was earlier (356.33 ± 63.17 vs. 400.3 ± 50.11, p = 0.004). The Hemodynamic results showed the HR in the Sev + Rem group was faster than that in the Pro + Rem group at the beginning of surgery and 1 h post-surgery (67.37 ± 4.40 vs. 64.33 ± 4.44, p = 0.010, 69.07 ± 4.23 vs. 66.40 ± 5.03, p = 0.030). In regard to the assessment of postoperative recovery quality, the emotional state scores in the Sev + Rem group were significantly lower than the Pro + Rem group (36.83 ± 2.79 vs. 39.50 ± 4.64, p = 0.009). Conclusion: The two anesthesia modalities (Sev + Rem and Pro + Rem) have their advantages and disadvantages for patients undergoing laparoscopic bariatric surgery and have comparable effects on postoperative recovery quality.

Invasive metastatic tumor-camouflaged ROS responsive nanosystem for targeting therapeutic brain injury after cardiac arrest.

Drug transmission through the blood-brain barrier (BBB) is considered an arduous challenge for brain injury treatment following the return of spontaneous circulation after cardiac arrest (CA-ROSC). Inspired by the propensity of melanoma metastasis to the brain, B16F10 cell membranes are camouflaged on 2-methoxyestradiol (2ME2)-loaded reactive oxygen species (ROS)-triggered "Padlock" nanoparticles that are constructed by phenylboronic acid pinacol esters conjugated D-a-tocopheryl polyethylene glycol succinate (TPGS-PBAP). The biomimetic nanoparticles (BM@TP/2ME2) can be internalized, mainly mediated by the mutual recognition and interaction between CD44v6 expressed on B16F10 cell membranes and hyaluronic acid on cerebral vascular endothelial cells, and they responsively release 2ME2 by the oxidative stress microenvironment. Notably, BM@TP/2ME2 can scavenge excessive ROS to reestablish redox balance, reverse neuroinflammation, and restore autophagic flux in damaged neurons, eventually exerting a remarkable neuroprotective effect after CA-ROSC in vitro and in vivo. This biomimetic drug delivery system is a novel and promising strategy for the treatment of cerebral ischemia-reperfusion injury after CA-ROSC.

SGCL: Spatial guided contrastive learning on whole-slide pathological images.

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL.

DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.

Fiber-optic radio frequency transfer based on active phase noise compensation using a carrier suppressed double-sideband signal.

In this Letter, we propose a fiber-optic radio frequency (RF) transfer based on active phase noise compensation adopting a carrier suppressed double-sideband (CSDSB) signal. The forward CSDSB signal is generated based on the transmitted RF signal at the local site to discriminate from the backward RF signal. The forward and backward signals are transmitted over the same fiber with the same wavelength to guarantee the bidirectional propagation symmetry. The impact of backscattering is efficiently suppressed by electrical filtering at the sites. A 1 GHz signal transfer over a 40 km optical link is performed in a laboratory. The results show that the proposed scheme can improve the short-term stability from 1.9 e-13/s to 3.9 e-14/s in contrast to the scheme with backscattering while reaching a long-term stability of 2.0 e-16/10000 s.