Distinct oral-associated gastric microbiota and Helicobacter pylori communities for spatial microbial heterogeneity in gastric cancer.

The gastric microbial community plays a fundamental role in gastric cancer (GC), and the two main anatomical subtypes of GC, non-cardia and cardia GC, are associated with different risk factors (Helicobacter pylori for non-cardia GC). To decipher the different microbial spatial communities of GC, we performed a multicenter retrospective analysis to characterize the gastric microbiota in 223 GC patients, including H. pylori-positive or -negative patients, with tumors and paired adjacent normal tissues, using third-generation sequencing. In the independent validation cohort, both dental plaque and GC tumoral tissue samples were collected and sequenced. The prevalence of H. pylori and oral-associated bacteria was verified using fluorescence in situ hybridization (FISH) assays in GC tumoral tissues and matched nontumoral tissues. We found that the vertical distribution of the gastric microbiota, at the upper, middle, and lower third sites of GC, was likely an important factor causing microbial diversity in GC tumor tissues. The oral-associated microbiota cluster, which included Veillonella parvula, Streptococcus oralis, and Prevotella intermedia, was more abundant in the upper third of the GC. However, H. pylori was more abundant in the lower third of the GC and exhibited a significantly high degree of microbial correlation. The oral-associated microbiota module was co-exclusive with H. pylori in the lower third site of the GC tumoral tissue. Importantly, H. pylori-negative GC patients with oral-associated gastric microbiota showed worse overall survival, while the increase in microbial abundance in H. pylori-positive GC patients showed no difference in overall survival. The prevalence of V. parvula in both the dental plaque and GC tissue samples was concordant in the independent validation phase. We showed that the oral-associated species V. parvula and S. oralis were correlated with overall survival. Our study highlights the roles of the oral-associated microbiota in the upper third of the GC. In addition, oral-associated species may serve as noninvasive screening tools for the management of GC and an independent prognostic factor for H. pylori-negative GCs. IMPORTANCE: Our study highlights the roles of the oral-associated microbiota in the upper third of gastric cancer (GC).We showed that the oral-associated species Veillonella parvula and Streptococcus oralis were correlated with overall survival. In addition, oral-associated species may serve as noninvasive screening tools for the management of GC and an independent prognostic factor for Helicobacter pylori-negative GCs.

Dissecting positive selection events and immunological drives during the evolution of adeno-associated virus lineages.

Adeno-associated virus (AAV) serotypes from primates are being developed and clinically used as vectors for human gene therapy. However, the evolutionary mechanism of AAV variants is far from being understood, except that genetic recombination plays an important role. Furthermore, little is known about the interaction between AAV and its natural hosts, human and nonhuman primates. In this study, natural AAV capsid genes were subjected to systemic evolutionary analysis with a focus on selection drives during the diversification of AAV lineages. A number of positively selected sites were identified from these AAV lineages with functional relevance implied by their localization on the AAV structures. The selection drives of the two AAV2 capsid sites were further investigated in a series of biological experiments. These observations did not support the evolution of the site 410 of the AAV2 capsid driven by selection pressure from the human CD4+ T-cell response. However, positive selection on site 548 of the AAV2 capsid was directly related to host humoral immunity because of the profound effects of mutations at this site on the immune evasion of AAV variants from human neutralizing antibodies at both the individual and population levels. Overall, this work provides a novel interpretation of the genetic diversity and evolution of AAV lineages in their natural hosts, which may contribute to their further engineering and application in human gene therapy.

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Phenomics and transcriptomic profiling of fruit development in distinct apple varieties.

Apple is one of the most economically important and popular temperate fruit trees. The domestication of apple has resulted in substantial phenotypic differences, particularly between wild and cultivated varieties. However, the relationship between gene expression and phenotypic variations in apple remains poorly understood. Here, we present a comprehensive dataset featuring five distinct apple varieties, including two wild varieties and three representative cultivated varieties. The dataset comprises of both phenomics data, encompassing twelve fruit quality-related traits continuously measured over two years, and transcriptomic data obtained at different developmental stages with three biological replicates. We performed basic quality control process, gene expression normalization and differential gene expression analysis to demonstrate the utility and reliability of the dataset. Our findings indicate that gene expression strongly related with phenotypic variations in apple. This dataset serves as a valuable resource, encompassing phenomics and transcriptomic data in multiple formats, thereby facilitating further exploration of the relationships between gene expression and phenotypic traits in apple.

Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.

MicroRNA (miRNA) serves as a pivotal regulator of numerous cellular processes, and the identification of miRNA-disease associations (MDAs) is crucial for comprehending complex diseases. Recently, graph neural networks (GNN) have made significant advancements in MDA prediction. However, these methods tend to learn one type of node representation from a single heterogeneous network, ignoring the importance of multiple network topologies and node attributes. Here, we propose SMDAP (Sequence hierarchical modeling-based Mirna-Disease Association Prediction framework), a novel GNN-based framework that incorporates multiple network topologies and various node attributes including miRNA seed and full-length sequences to predict potential MDAs. Specifically, SMDAP consists of two types of MDA representation: following a heterogeneous pattern, we construct a transfer learning-like synchronous mutual learning network to learn the first MDA representation in conjunction with the miRNA seed sequence. Meanwhile, following a homogeneous pattern, we design a subgraph-inspired asynchronous multi-scale embedding network to obtain the second MDA representation based on the miRNA full-length sequence. Subsequently, an adaptive fusion approach is designed to combine the two branches such that we can score the MDAs by the downstream classifier and infer novel MDAs. Comprehensive experiments demonstrate that SMDAP integrates the advantages of multiple network topologies and node attributes into two branch representations. Moreover, the area under the receiver operating characteristic curve is 0.9622 on DB1, which is a 5.06% increase from the baselines. The area under the precision-recall curve is 0.9777, which is a 7.33% increase from the baselines. In addition, case studies on three human cancers validated the predictive performance of SMDAP. Overall, SMDAP represents a powerful tool for MDA prediction.

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

BACKGROUND: Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. RESULTS: In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. CONCLUSION: Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Dynamics of The Γδtcr Repertoires During The Dedifferentiation Process and Pilot Implications for Immunotherapy of Thyroid Cancer.

γδ T cells are evolutionarily conserved T lymphocytes that manifest unique antitumor efficacy independent of tumor mutation burden (TMB) and conventional human leukocyte antigen (HLA) recognition. However, the dynamic changes in their T cell receptor (TCR) repertoire during cancer progression and treatment courses remain unclear. Here, a comprehensive characterization of γδTCR repertoires are performed in thyroid cancers with divergent differentiation states through cross-sectional studies. The findings revealed a significant correlation between the differentiation states and TCR repertoire diversity. Notably, highly expanded clones are prominently enriched in γδ T cell compartment of dedifferentiated patients. Moreover, by longitudinal investigations of the γδ T cell response to various antitumor therapies, it is found that the emergence and expansion of the Vδ2neg subset may be potentially associated with favorable clinical outcomes after post-radiotherapeutic immunotherapy. These findings are further validated at single-cell resolution in both advanced thyroid cancer patients and a murine model, underlining the importance of further investigations into the role of γδTCR in cancer immunity and therapeutic strategies.

Optimal timing of surgery for gastric cancer after neoadjuvant chemotherapy: a systematic review and meta-analysis.

BACKGROUND: Following neoadjuvant chemotherapy, surgical resection is one of the most preferred treatment options for locally advanced gastric cancer patients. However, the optimal time interval between chemotherapy and surgery is unclear. This review aimed to identify the optimal time interval between neoadjuvant chemotherapy and surgery for advanced gastric cancer. METHODS: Beginning on November 12, 2022, we searched the PubMed, Cochrane Library, Web of Science databases, and Embase.com databases for relevant English-language research. Two authors independently screened the studies, assessed their quality, extracted the data, and analyzed the results. The primary goal was to investigate the relationship between the time interval to surgery (TTS) and long-term survival outcomes for patients. This study has been registered with PROSPERO (CRD42022365196). RESULTS: After an initial search of 4880 articles, the meta-analysis review ultimately included only five retrospective studies. Ultimately, this meta-analysis included 1171 patients, of which 411 patients had TTS of < 4 weeks, 507 patients had TTS of 4-6 weeks, and 253 patients had TTS of > 6 weeks. In survival analysis, patients with TTS of > 6 weeks had poorer overall survival outcomes than patients with TTS of 4-6 weeks (HR = 1.34, 95% CI: 1.03-1.75, P = 0.03). No significant differences were found in terms of disease-free survival the groups. CONCLUSION: Based on the current clinical evidence, patients with locally advanced gastric cancer may benefit better with a TTS of 4-6 weeks; however, this option still needs additional study.

Traditional Chinese medicine for smoking cessation: An umbrella review of systematic reviews and meta-analysis of randomized controlled trials.

INTRODUCTION: Traditional Chinese medicine (TCM) may have special advantages in facilitating smoking cessation, but consensus on effectiveness is lacking. We aim to comprehensively review, update, and refine current evidence on TCM effectiveness and safety. METHODS: Nine databases were searched from their inception up to 28 February 2023. Systematic reviews (SRs) and meta-analysis of TCM for smoking cessation were identified and retrieved. Additional databases and hand searches of RCTs from included SRs were performed for data pooling. Cochrane ROB tools and AMSTAR-2 were used to evaluate the methodological quality of RCTs and SRs, respectively. RCT data are presented as relative risks (RR) or mean differences (MD) with 95% confidence intervals (CI) using RevMan 5.4. RESULTS: Thirteen SRs involving 265 studies with 33081 participants were included. Among these 265 studies, 157 were duplicates (58.36%) and 52 were non-RCTs (19.62%). Combined with the remaining 56 RCTs identified through hand searches, 88 RCTs involving 12434 participants were finally included for data synthesis. All the SRs focused on acupoint stimulation, and the majority were of low or very low quality. The methodological quality of RCTs was either unclear or high risk. For continuous abstinence rate, TCM external interventions were better than placebo in 6 months to 1 year (RR=1.60; 95% CI: 1.14-2.25; I2=27%; n=5533 participants). Compared with placebo, TCM external application was effective in reducing nicotine withdrawal symptoms, and the effect was gradually stable and obvious in the fourth week (MD= -4.46; 95% CI: -5.43 - -3.49; n=165 participants). Twelve RCTs reported adverse events as outcome indicators for safety evaluation, and no serious adverse events occurred. CONCLUSIONS: Despite the methodological limitations of the original studies, our review suggests that TCM intervention shows potential effectiveness on the continuous abstinence rate. Extending the intervention time can enhance the effect of TCM on nicotine withdrawal symptoms. Referred to adverse events, more data for safety evaluation are required.

Clinical significance of tumor deposits in gastric cancer after radical gastrectomy: a propensity score matching study.

OBJECTIVE: The value of tumor deposits (TDs) in the prognosis and staging of gastric cancer (GC) is still under debate. This study aims to evaluate the prognostic value of TDs and the best ways to incorporate TDs in the TNM classification of GC. METHODS: Patients (n = 3460) undergoing curative gastrectomy for GC in the West China Hospital from 2005 to 2017 were retrospectively reviewed and divided into two groups according to the TD status (positive vs. negative). Later, clinicopathological features and overall survival (OS) between the two groups were compared. Thereafter, the associations between the presence of TD and other clinicopathological factors were evaluated through logistic regression. In addition, univariate and multivariate Cox regression were conducted for determining prognostic factors. The possibility of selection bias was reduced through conducting the 1:1 propensity score matching (PSM) analysis. The modified classification systems proposed previously that incorporated TDs into the TNM staging system were assessed. RESULTS: There were 10.5% of patients (362/3460) diagnosed with TDs. TDs were significantly related to unfavorable factors such as advanced T stage and N stage and independently associated with poor prognosis. The 5-year OS of patients with TDs was significantly lower than that of patients without TDs (31.0% vs. 60.9%, P < 0.001), whereas higher than that of patients with peritoneal metastasis (31.0% vs. 5.0%, P < 0.001). In patients receiving chemotherapy, the 5-year OS of patients with TDs was also significantly lower than that of patients without TDs (42.0% vs. 50.9%, P = 0.026). Moreover, the system incorporating TDs in the TNM classification as metastatic lymph nodes outperformed others. CONCLUSIONS: TDs are related to the aggressive characteristics and are an independent prognostic factor for GC. Incorporating TDs in the TNM classification as the metastatic lymph nodes increases the accuracy in predicting prognosis.

Targeting drug-tolerant cells: A promising strategy for overcoming acquired drug resistance in cancer cells.

Drug resistance remains the greatest challenge in improving outcomes for cancer patients who receive chemotherapy and targeted therapy. Surmounting evidence suggests that a subpopulation of cancer cells could escape intense selective drug treatment by entering a drug-tolerant state without genetic variations. These drug-tolerant cells (DTCs) are characterized with a slow proliferation rate and a reversible phenotype. They reside in the tumor region and may serve as a reservoir for resistant phenotypes. The survival of DTCs is regulated by epigenetic modifications, transcriptional regulation, mRNA translation remodeling, metabolic changes, antiapoptosis, interactions with the tumor microenvironment, and activation of signaling pathways. Thus, targeting the regulators of DTCs opens a new avenue for the treatment of therapy-resistant tumors. In this review, we first provide an overview of common characteristics of DTCs and the regulating networks in DTCs development. We also discuss the potential therapeutic opportunities to target DTCs. Last, we discuss the current challenges and prospects of the DTC-targeting approach to overcome acquired drug resistance. Reviewing the latest developments in DTC research could be essential in discovering of methods to eliminate DTCs, which may represent a novel therapeutic strategy for preventing drug resistance in the future.

Genetic Influence on Gyral Peaks.

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion.

Identifying noncoding RNAs (ncRNAs)-drug resistance association computationally would have a marked effect on understanding ncRNA molecular function and drug target mechanisms and alleviating the screening cost of corresponding biological wet experiments. Although graph neural network-based methods have been developed and facilitated the detection of ncRNAs related to drug resistance, it remains a challenge to explore a highly trusty ncRNA-drug resistance association prediction framework, due to inevitable noise edges originating from the batch effect and experimental errors. Herein, we proposed a framework, referred to as RDRGSE (RDR association prediction by using graph skeleton extraction and attentional feature fusion), for detecting ncRNA-drug resistance association. Specifically, starting with the construction of the original ncRNA-drug resistance association as a bipartite graph, RDRGSE took advantage of a bi-view skeleton extraction strategy to obtain two types of skeleton views, followed by a graph neural network-based estimator for iteratively optimizing skeleton views aimed at learning high-quality ncRNA-drug resistance edge embedding and optimal graph skeleton structure, jointly. Then, RDRGSE adopted adaptive attentional feature fusion to obtain final edge embedding and identified potential RDRAs under an end-to-end pattern. Comprehensive experiments were conducted, and experimental results indicated the significant advantage of a skeleton structure for ncRNA-drug resistance association discovery. Compared with state-of-the-art approaches, RDRGSE improved the prediction performance by 6.7% in terms of AUC and 6.1% in terms of AUPR. Also, ablation-like analysis and independent case studies corroborated RDRGSE generalization ability and robustness. Overall, RDRGSE provides a powerful computational method for ncRNA-drug resistance association prediction, which can also serve as a screening tool for drug resistance biomarkers.

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

A comprehensive analysis of copy number variations in diverse apple populations.

BACKGROUND: As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives. RESULTS: In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ([Formula: see text]) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations. CONCLUSIONS: This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

Role of the gut microbiota in anticancer therapy: from molecular mechanisms to clinical applications.

In the past period, due to the rapid development of next-generation sequencing technology, accumulating evidence has clarified the complex role of the human microbiota in the development of cancer and the therapeutic response. More importantly, available evidence seems to indicate that modulating the composition of the gut microbiota to improve the efficacy of anti-cancer drugs may be feasible. However, intricate complexities exist, and a deep and comprehensive understanding of how the human microbiota interacts with cancer is critical to realize its full potential in cancer treatment. The purpose of this review is to summarize the initial clues on molecular mechanisms regarding the mutual effects between the gut microbiota and cancer development, and to highlight the relationship between gut microbes and the efficacy of immunotherapy, chemotherapy, radiation therapy and cancer surgery, which may provide insights into the formulation of individualized therapeutic strategies for cancer management. In addition, the current and emerging microbial interventions for cancer therapy as well as their clinical applications are summarized. Although many challenges remain for now, the great importance and full potential of the gut microbiota cannot be overstated for the development of individualized anti-cancer strategies, and it is necessary to explore a holistic approach that incorporates microbial modulation therapy in cancer.

Comparison of long-term quality of life between Billroth-I and Roux-en-Y anastomosis after distal gastrectomy for gastric cancer: a randomized controlled trial.

BACKGROUND: The results of studies comparing Billroth-I (B-I) with Roux-en-Y (R-Y) reconstruction on the quality of life (QoL) are still inconsistent. The aim of this trial was to compare the long-term QoL of B-I with R-Y anastomosis after curative distal gastrectomy for gastric cancer. METHODS: A total of 140 patients undergoing curative distal gastrectomy with D2 lymphadenectomy in West China Hospital, Sichuan University from May 2011 to May 2014 were randomly assigned to the B-I group ( N  = 70) and R-Y group ( N  = 70). The follow-up time points were 1, 3, 6, 9, 12, 24, 36, 48, and 60 months after the operation. The final follow-up time was May 2019. The clinicopathological features, operative safety, postoperative recovery, long-term survival as well as QoL were compared, among which QoL score was the primary outcome. An intention-to-treat analysis was applied. RESULTS: The baseline characteristics were comparable between the two groups. There were no statistically significant differences in terms of postoperative morbidity and mortality rates, and postoperative recovery between the two groups. Less estimated blood loss and shorter surgical duration were found in the B-I group. There were no statistically significant differences in 5-year overall survival (79% [55/70] of the B-I group vs. 80% [56/70] of the R-Y group, P  = 0.966) and recurrence-free survival rates (79% [55/70] of the B-I group vs. 78% [55/70] of the R-Y group, P  = 0.979) between the two groups. The scores of the global health status of the R-Y group were higher than those of the B-I group with statistically significant differences (postoperative 1 year: 85.4 ±â€Š13.1 vs . 88.8 ±â€Š16.1, P  = 0.033; postoperative 3 year: 87.3 ±â€Š15.2 vs . 92.8 ±â€Š11.3, P  = 0.028; postoperative 5 year: 90.9 ±â€Š13.7 vs . 96.4 ±â€Š5.6, P  = 0.010), and the reflux (postoperative 3 year: 8.8 ±â€Š12.9 vs . 2.8 ±â€Š5.3, P  = 0.001; postoperative 5 year: 5.1 ±â€Š9.8 vs . 1.8 ±â€Š4.7, P  = 0.033) and epigastric pain (postoperative 1 year: 11.8 ±â€Š12.7 vs. 6.1 ±â€Š8.8, P  = 0.008; postoperative 3 year: 9.4 ±â€Š10.6 vs. 4.6 ±â€Š7.9, P  = 0.006; postoperative 5 year: 6.0 ±â€Š8.9 vs . 2.7 ±â€Š4.6, P  = 0.022) were milder in the R-Y group than those of the B-I group at the postoperative 1, 3, and 5-year time points. CONCLUSIONS: Compared with B-I group, R-Y reconstruction was associated with better long-term QoL by reducing reflux and epigastric pain, without changing survival outcomes. TRIAL REGISTRATION: ChiCTR.org.cn, ChiCTR-TRC-10001434.

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers.

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis-Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant.

Is No. 12a Lymph Node Dissection Compliance Necessary in Patients Who Undergo D2 Gastrectomy for Gastric Adenocarcinomas? A Population-Based Retrospective Propensity Score Matching Study.

LN dissection is essential for accurately staging and improving GC patient prognosis. However, the compliance rate for No. 12a LND in practice is low, and its necessity is controversial. Data from GC patients who underwent total gastrectomy (TG)/distal gastrectomy (DG) plus D2 lymphadenectomy between January 2000 and December 2017 at West China Hospital, Sichuan University were reviewed. No. 12a LND noncompliance's effect on the long-term prognosis of patients with GC after D2 gastrectomy was explored. Of the 2788 patients included, No. 12a LND noncompliance occurred in 1753 patients (62.9%). Among 1035 patients with assessable LNs from station 12a, 98 (9.5%) had positive LNs detected at station 12a. No. 12a LN metastasis patients (stage IV not included) had significantly better overall survival (OS) than TNM stage IV patients (p = 0.006). Patients with No. 12a LND compliance had a significantly higher OS than those without, both before (p < 0.001) and after (p < 0.001) PSM. Cox multivariate analysis confirmed that No. 12a LND noncompliance was an independent prognostic factor before (HR 1.323, 95% CI 1.171-1.496, p < 0.001) and after (HR 1.353, 95% CI 1.173-1.560, p < 0.001) PSM. In conclusion, noncompliance with No. 12a LND compromised the long-term survival of patients who underwent D2 gastrectomy for GC.