Background: Robotic-assisted gastrectomy has been used for treating gastric cancer since 2002. This meta-analysis was conducted to systematically evaluate the efficacy of Da Vinci robotic distal subtotal gastrectomy (RDG) or laparoscopic distal subtotal gastrectomy (LDG) in patients with gastric cancer. Methods: We conducted searches in domestic and foreign databases, and collected literature in Chinese and English on the efficacy of RDG and LDG for gastric cancer that have been published since the inception of the database. RevMan 5.4.1 was used for meta-analysis and drawing and Stata14.0 was used for publication bias analysis. Results: A total of 3293 patients in 15 studies were included, including 1193 patients in the RDG group and 2100 patients in the LDG groups respectively. The meta-analysis showed that intraoperative blood loss was significantly lower and the number of resected lymph nodes was higher in the RDG group compared to that in the LDG group. In addition, the times to first postoperative food intake and postoperative hospital stay were shortened, and there was a longer length of distal resection margin and prolonged duration of operation. No significant differences were found between the 2 groups with respect to the first postoperative anal exhaust time, length of proximal resection margin, total postoperative complication rate, postoperative anastomotic leakage rate, incidence of postoperative gastric emptying disorder, pancreatic fistula rate, recurrence rate, and mortality rate. Conclusion: RDG is a safe and feasible treatment option for gastric cancer, and it is non-inferior or even superior to LDG with respect to therapeutic efficacy and radical treatment.

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For any battery system involving lithium containing cathode, the anode-free lithium metal battery (AFLMB) exhibits the highest potential energy density. However, the lack of excess lithium amplifies the problems of...

Mammalian cells are extremely vulnerable to external assaults compared with plant and microbial cells because of the weakness of cell membranes compared with cell walls. Construction of ultrathin and robust artificial shells on mammalian cells with biocompatible materials is a promising strategy for protecting single cells against harsh environmental conditions. Herein, layer-by-layer assembly combined with a transglutaminase-catalyzed cross-linking reaction was employed to prepare cross-linked and biocompatible gelatin nanoshells on individual human cervical carcinoma cell line (HeLa) cells and mouse insulinoma cell line 6 (MIN6) cells. The encapsulated HeLa and MIN6 cells showed high viability and a prolonged encapsulation period. Moreover, the nanoshells can protect encapsulated cells from cytotoxic enzymes (such as trypsin) and polycation (polyethylenimine) attacks and help cells resist high physical stress. We also investigated how nanoshells would affect the cell viability, proliferation, and cell cycle distribution of encapsulated and released cells. The approach presented here may provide a new and versatile method for nanoencapsulation of individual mammalian cells, which would help cells endure various environmental stresses and thereby expand the application field of isolated mammalian cells.

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In this study, deep learning algorithm-based energy/spectral computed tomography (CT) for the spinal metastasis from lung cancer was used. A dilated convolutional U-Net model (DC-U-Net model) was first proposed, which was used to segment the energy/spectral CT image of patients with the spinal metastasis from lung cancer. Subsequently, energy/spectral CT images under different energy levels were collected for the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) comparison. It was found the learning rate of the model decreased exponentially as the number of training increased, with the lung contour segmented out of the image. Under 40–65 keV, the CT value of bone metastasis from lung cancer decreased with increasing energy, as with the average rank sum test result. The SNR and CNR values were the highest under 60 keV. The detection rate of the deep learning algorithm below 60 keV was 81.41%, and that of professional doctors was 77.56%. The detection rate of the deep learning algorithm below 140 keV was 66.03%, and that of professional doctors was 64.74%. In conclusion, the DC-U-Net model demonstrates better segmentation effects versus the convolutional neutral networ k (CNN), with the lung contour segmented. Further, a higher energy level leads to worse segmentation effects on the energy/spectral CT image.

Grapevine is extensively grown for fresh table grapes, wine, and other processed products worldwide. DNA methylation levels are regulated by DNA methylation maintenance and DNA methylation removal involved in the grapevine growth. We comprehensively analyzed the transcriptome and metabolome of the ‘Kyoho’ fruit with or without demethylation and screened for a large number of differential genes and metabolites. Color, hardness, and aroma are the most obvious traits reflecting the ripening of grapes. We used gas chromatography–mass spectrometry and high-performance liquid chromatography to understand the changes in metabolites during ripening. We cloned many key genes selected by transcriptome analysis and found that intron retention was observed in VvCHS, VvDFR, and VvGST. The imbalance of methylation levels affects the alternative splicing of pre-mRNA, which makes the translation process abnormal and affects gene expression. In addition, analyzing promoters of some genes, such as proVvGST4 and proVvUFGT, found that the promoters of these genes after demethylating were more difficult to methylate. Taken together, this study will provide new insights into comprehension of the molecular mechanism of methylation during ripening of grape berries. In addition, the study provides some genetic information to help guide our improvement, cultivation, and management of grapes in the future.

Lithium metal batteries (LMBs) are a promising candidate for next-generation energy storage devices. However, the high irreversibility and dead Li accumulation of the lithium metal anode caused by its fragile original solid electrolyte interface (SEI) seriously hinder the practical application of LMBs. Herein, a facile slurry-coating and one-step thermal fluorination reaction method is proposed to construct the 3D structural LiF-protected Li/G composite anode. The existence of a 3D LiF protection layer is convincingly confirmed and the function of the Li/G skeleton is discussed in detail. The 3D structural LiF protection layer results in superior electrochemical performance by improving the utilization of Li and suppressing the accumulation of dead Li in symmetric and full coin cells. Moreover, a 0.85 Ah pouch cell strictly following the parameters of the practical battery industry can work stably for 140 cycles with a gradual internal resistance increase. This novel Li/G composite anode indicates a promising strategy in lithium/carbon composite anodes for LMBs, and the facile thermal fluorination reaction method presented in this paper offers a new method for the construction of a 3D structural protection layer for lithium metal anodes.