Virus classification for viral genomic fragments using PhaGCN2.

Viruses are the most ubiquitous and diverse entities in the biome. Due to the rapid growth of newly identified viruses, there is an urgent need for accurate and comprehensive virus classification, particularly for novel viruses. Here, we present PhaGCN2, which can rapidly classify the taxonomy of viral sequences at the family level and supports the visualization of the associations of all families. We evaluate the performance of PhaGCN2 and compare it with the state-of-the-art virus classification tools, such as vConTACT2, CAT and VPF-Class, using the widely accepted metrics. The results show that PhaGCN2 largely improves the precision and recall of virus classification, increases the number of classifiable virus sequences in the Global Ocean Virome dataset (v2.0) by four times and classifies more than 90% of the Gut Phage Database. PhaGCN2 makes it possible to conduct high-throughput and automatic expansion of the database of the International Committee on Taxonomy of Viruses. The source code is freely available at https://github.com/KennthShang/PhaGCN2.0.

Defunctioning stoma and anastomotic stricture in rectal cancer surgery: a propensity score matching study.

BACKGROUND: In cases of rectal cancer surgery, patients at high risk of anastomotic leakage often receive a defunctioning stoma (DS). However, its role in postoperative anastomotic strictures (AS) remains unclear. This study aimed to investigate the correlation between DS and AS and outcomes of transanal endoscopic microsurgery (TEM) in treating rectal AS. METHODS: This retrospective study was conducted from January 2019 to September 2021 and included patients who underwent rectal cancer surgery. A 1:1 ratio was used for propensity score matching (PSM). Univariate analyses were performed to identify statistically significant variables, and multivariate analyses were conducted to determine the factors affecting AS. RESULTS: This study included 383 patients. The results of the univariate analysis suggested that surgery time (HR 4.597, 95% CI 1.563-13.525, P=0.006), postoperative anastomotic leakage (HR 11.830, 95% CI 3.773-37.094, P<0.001), and DS (HR 15.475, 95% CI 6.042-39.641, P<0.001) were significantly associated with AS. In the multivariate analysis, postoperative anastomotic leakage (HR 7.596, 95% CI 1.987-29.044, P= 0.003) and DS (HR 11.252, 95% CI 4.113-30.779, P<0.001) were identified as significant risk factors for AS. After matching, the univariate analysis revealed that postoperative anastomotic leakage (HR 8.333, 95% CI 1.541-45.052, P= 0.014) and DS (HR 9.965, 95% CI 2.200-45.142, P= 0.003) were associated with AS. The multivariate analysis indicated that postoperative anastomotic leakage (HR 14.549, 95% CI 1.765-119.913, P= 0.013) and DS (HR 12.450, 95% CI 2.418-64.108, P= 0.003) were significant risk factors for AS. CONCLUSIONS: This study provides evidence that DS is independently associated with AS, and postoperative anastomotic leakage increases the risk of AS. Furthermore, this study suggests that TEM could be a valuable treatment option for AS.

Clinical significance of FBXW7 tumor suppressor gene mutations and expression in human colorectal cancer: a systemic review and meta-analysis.

BACKGROUND: Various studies investigating the clinical significance of FBXW7 mutation and/or expression have yielded inconclusive results in colorectal cancer (CRC) patients. Therefore, the present meta-analysis summarizes previous evidence and evaluates the clinical significance, including the prognostic role, of FBXW7 status in CRCs. METHODS: The meta-analysis was conducted by searching the databases of PubMed, China National Knowledge Infrastructure (CNKI), WANFANG data, Web of Science, Embase, and Web of Science. Pooled odds ratios (ORs) and hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated to assess the relationships between FBXW7 status and clinicopathological features and survival in CRC, respectively. RESULTS: Ten studies involving 4199 patients met the inclusion criteria and included in our meta-analysis. FBXW7 mutation/low expression was obviously correlated with advanced T stage (OR = 0.44, 95% CI: 0.27-0.74, P <  0.01) and lymph node metastasis (OR = 1.88, 95% CI: 1.40-2.53, P <  0.01), but was not associated with other parameters. Further investigation found that FBXW7 mutation/low expression predicted poor OS (HR = 1.25, 95% CI: 1.06-1.47, P <  0.01), but not DFS in CRC (HR = 1.04, 95% CI: 0.60-1.82, P = 0.88). Subgroup analysis found that FBXW7 status was obviously correlated with OS in cohorts recruited after 2009 (HR = 1.32, 95% CI: 1.17-1.50, P <  0.01), from eastern Asia (HR = 1.27, 95% CI: 1.04-1.55, P = 0.02), detected by immunohistochemistry/qRT-PCR (HR = 1.39, 95% CI: 1.22-1.59, P <  0.01), and analysed with multivariate method (HR = 1.47, 95% CI: 1.25-1.74, P <  0.01). CONCLUSIONS: This study indicates that FBXW7 status, expression level especially, is associated with OS but not DFS in CRC. FBXW7 expression level may function as a prognostic biomarker in CRC.

Clinical significance of mucinous component in colorectal adenocarcinoma: a propensity score-matched study.

BACKGROUND: This study aims to investigate the clinical significance and prognostic value of mucinous component (MC) in colorectal adenocarcinoma (AC). METHODS: Patients with colorectal AC and AC with MC (ACMC) (1-100%) underwent surgical resection between January 2007 and February 2018 were retrospectively reviewed. Propensity score matching (PSM) was performed according to a 1:1 ratio. Receiver-operating characteristic (ROC) curve was used to identify the optimal cut-off value of MC ratio for prognostic prediction. The clinicopathological features and 3-year overall survival (OS) of AC patients, mucinous adenocarcinoma (MAC) (MC > 50%) patients, and ACMC (1-50%) patients were compared before and after matching. Multivariable analysis was used for analyzing independent risk factors related to prognosis. RESULTS: A total of 532 patients were enrolled in this study. Patients with AC, MAC, and ACMC (1-50%) exhibited different clinicopathological features. However, their 3-year OS rates were similar (82.00% vs. 74.11% vs. 81.48%, P = 0.38). After matching, ROC curve determined 70% as the optimal cut-off value. And patients with ACMC > 70% had a much poorer 3-year OS compared with ACMC (1-70%) patients and AC patients (47.37% vs. 86.15% vs. 79.76%, P < 0.001). In addition, ACMC > 70% was revealed as a risk factor for poor survival in univariate analysis (HR = 1.643, 95%CI = 1.025-2.635, P = 0.039), though not an independent risk factor in multivariable analysis (HR = 1.550, 95%CI = 0.958-2.507, P = 0.074). CONCLUSIONS: MAC is usually diagnosed at an advanced stage. MAC has a similar survival with AC and ACMC (1-50%) patients before and after matching. Patients with ACMC > 70% exhibited a much poorer OS, and should be given more clinical attention.

Semi-supervised multi-modal medical image segmentation with unified translation.

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervised segmentation models can mitigate the problem of insufficient annotations by utilizing a small amount of labeled data. However, most of these models are limited to single-modal data and cannot exploit the complementary information from multi-modal medical images. A few semi-supervised multi-modal models have been proposed recently, but they have rigid structures and require additional training steps for each modality. In this work, we propose a novel flexible method, semi-supervised multi-modal medical image segmentation with unified translation (SMSUT), and a unique semi-supervised procedure that can leverage multi-modal information to improve the semi-supervised segmentation performance. Our architecture capitalizes on unified translation to extract complementary information from multi-modal data which compels the network to focus on the disparities and salient features among each modality. Furthermore, we impose constraints on the model at both pixel and feature levels, to cope with the lack of annotation information and the diverse representations within semi-supervised multi-modal data. We introduce a novel training procedure tailored for semi-supervised multi-modal medical image analysis, by integrating the concept of conditional translation. Our method has a remarkable ability for seamless adaptation to varying numbers of distinct modalities in the training data. Experiments show that our model exceeds the semi-supervised segmentation counterparts in the public datasets which proves our network's high-performance capabilities and the transferability of our proposed method. The code of our method will be openly available at https://github.com/Sue1347/SMSUT-MedicalImgSegmentation.

Development and validation of a nomogram for predicting operating time in laparoscopic anterior resection of rectal cancer.

Aims: The goal of this study is to create and verify a nomogram estimate operating time in rectal cancer (RC) patients based on clinicopathological factors and MRI/CT measurements before surgery. Materials and Methods: The nomogram was developed in a cohort of patients who underwent laparoscopic anterior resection (L-AR) for RC. The clinicopathological and pelvis parameters were collected. Risk factors for a long operating time were determined by univariate and multivariate logistic regression analyses, and a nomogram was established with independent risk factors. The performance of the nomogram was evaluated. An independent cohort of consecutive patients served as the validation dataset. Results: The development group recruited 159 RC patients, while 54 patients were enrolled in the validation group. Independent risk factors identified in multivariate analysis were a distance from the anal verge <5 cm (P = 0.024), the transverse diameter of the pelvic inlet (P < 0.001), mesorectal fat area (P = 0.017), and visceral fat area (P < 0.001). Then, a nomogram was built based on these four independent risk factors. The C-indexes of the nomogram in the development and validation group were 0.886 and 0.855, respectively. And values of AUC were the same with C-indexes in both groups. Besides, the calibration plots showed satisfactory consistency between actual observation and nomogram-predicted probabilities of long operating time. Conclusions: A nomogram for predicting the risk of long operating duration in L-AR of RC was developed. And the nomogram displayed a good prediction effect and can be utilized as a tool for evaluating operating time preoperatively.

A remarkably diverse and well-organized virus community in a filter-feeding oyster.

BACKGROUND: Viruses play critical roles in the marine environment because of their interactions with an extremely broad range of potential hosts. Many studies of viruses in seawater have been published, but viruses that inhabit marine animals have been largely neglected. Oysters are keystone species in coastal ecosystems, yet as filter-feeding bivalves with very large roosting numbers and species co-habitation, it is not clear what role they play in marine virus transmission and coastal microbiome regulation. RESULTS: Here, we report a Dataset of Oyster Virome (DOV) that contains 728,784 nonredundant viral operational taxonomic unit contigs (≥ 800 bp) and 3473 high-quality viral genomes, enabling the first comprehensive overview of both DNA and RNA viral communities in the oyster Crassostrea hongkongensis. We discovered tremendous diversity among novel viruses that inhabit this oyster using multiple approaches, including reads recruitment, viral operational taxonomic units, and high-quality virus genomes. Our results show that these viruses are very different from viruses in the oceans or other habitats. In particular, the high diversity of novel circoviruses that we found in the oysters indicates that oysters may be potential hotspots for circoviruses. Notably, the viruses that were enriched in oysters are not random but are well-organized communities that can respond to changes in the health state of the host and the external environment at both compositional and functional levels. CONCLUSIONS: In this study, we generated a first "knowledge landscape" of the oyster virome, which has increased the number of known oyster-related viruses by tens of thousands. Our results suggest that oysters provide a unique habitat that is different from that of seawater, and highlight the importance of filter-feeding bivalves for marine virus exploration as well as their essential but still invisible roles in regulating marine ecosystems. Video Abstract.

Factors contributing to the mortality of elderly patients with colorectal cancer within a year after surgery.

Aims: Patients with colorectal cancer (CRC) have a lower survival rate during the first year following resection surgery. We analyzed the factors influencing this early mortality. Methods and Material: The clinicopathological data of patients aged 70 years or older who underwent radical surgery for CRC between January 2012 and December 2018 were collected and analyzed retrospectively. A total of 242 patients (141 males and 101 females), including 93 with colon cancer and 139 with rectal cancer, were included in this study. Patients were divided into two groups according to whether they survived beyond the first year after surgery. The clinicopathological data of both groups were compared using Chi-square or Fisher's exact tests. The risk factors for mortality within 1-year after surgery were analyzed using the Cox regression model. Results: Forty-three patients experienced at least one complication, including 34 cases with Clavien-Dindo grade I-II complications and 12 with Clavien-Dindo grade III-IV complications. Eleven patients died in the year following surgery. Patients with postoperative complications had higher mortality rates within the first year. Univariate analysis revealed that carbohydrate antigen 19-9 (CA19-9) levels, American Society of Anesthesiologists (ASA) grades, and differentiation degree influenced the 1-year overall survival (OS) and disease-free survival (DFS). Multivariate analysis confirmed that CA19-9 levels and ASA grades were independent factors affecting OS and DFS during the first year after surgery. Conclusion: Postoperative complications were associated with the early death of elderly CRC patients. CA19-9 levels and ASA grades are independent factors influencing OS and DFS.

A discussion of RNA virus taxonomy based on the 2020 International Committee on Taxonomy of Viruses report.

RNA viruses have a higher mutation rate than DNA viruses; however, RNA viruses are insufficiently studied outside disease settings. The International Committee on Taxonomy of Viruses (ICTV) is an organization set up by virologists to standardize virus classification. To better understand ICTV taxonomy and the characteristics and rules of different RNA virus families, we analyzed the 3,529 RNA viruses included in the 2020 ICTV report using five widely used metrics: length, host, GC content, number of predicted ORFs, and sequence similarity. The results show that host type has a significant influence on viral genome length and GC content. The genome lengths of virus members within the same genus are quite similar: 98.28% of the genome length differences within any particular genus are less than 20%. The species within those genera containing segmented viruses also have a similar length and number of segments. The number of predicted ORFs in the RNA viral genomes also shows a strong, statistically significant correlation with genome length. We suggest that due to the high mutation rate of RNA virus genomes, current RNA virus classification should mainly rely on protein similarities rather than nucleic acid similarities.

Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images.

To fully define the target objects of interest in clinical diagnosis, many deep convolution neural networks (CNNs) use multimodal paired registered images as inputs for segmentation tasks. However, these paired images are difficult to obtain in some cases. Furthermore, the CNNs trained on one specific modality may fail on others for images acquired with different imaging protocols and scanners. Therefore, developing a unified model that can segment the target objects from unpaired multiple modalities is significant for many clinical applications. In this work, we propose a 3D unified generative adversarial network, which unifies the any-to-any modality translation and multimodal segmentation in a single network. Since the anatomical structure is preserved during modality translation, the auxiliary translation task is used to extract the modality-invariant features and generate the additional training data implicitly. To fully utilize the segmentation-related features, we add a cross-task skip connection with feature recalibration from the translation decoder to the segmentation decoder. Experiments on abdominal organ segmentation and brain tumor segmentation indicate that our method outperforms the existing unified methods.