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BackgroundEmerging surgical methods are utilized to treat endometrial cancer. The study aimed to assess the efficacy and safety of four common surgical methods of hysterectomy.MethodsWe systematically searched the PubMed, Cochrane Library databases, Medline, EMBASE and Web of Science from their inception until April 30, 2024. We used hazard ratios (HR) for overall survival (OS) and disease-free (DFS), odds ratios (OR) for categorical outcomes, and mean differences (MD) for continuous outcomes with 95% confidence intervals. These were pooled in Bayesian network meta-analysis models. The surface under the cumulative ranking curve (SUCRA) was used to illuminate the probability that each method would be the best for each outcome.ResultsThirty studies comprising 13446 patients were included. Robotic hysterectomy (RH) retrieved fewer pelvic lymph nodes than open hysterectomy (OH). OH showed a significantly higher postoperative complication rate than laparoscopic hysterectomy (LH) and RH. LH had a higher intraoperative complication rate than OH. According to SUCRA values, OH ranked the highest in the number of retrieved pelvic lymph nodes (0.89), intraoperative complications (0.73), and operative time (0.97). LH ranked the highest in DFS (0.81) and overall survival (OS) (0.87). RH ranked the highest in the postoperative complications (0.95). Laparoscopic-assisted vaginal hysterectomy (LAVH) ranked the highest in number of retrieved para-aortic lymph nodes (0.72).ConclusionsThere are no significant differences among the four surgical methods in DFS or OS. The use of uterine manipulators does not affect prognosis. OH is the best method for shortening operative time, dissecting the pelvic lymph nodes and controlling intraoperative complications. LH and LAVH have an advantage in para-aortic lymph node dissection. Besides, LH has the best advantage in DFS and OS. RH has advantages in controlling surgical complications.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024529974.

Abstract Studies of preadipocyte differentiation and fat deposition in sheep have mainly focused on functional genes, and with no emphasis placed on the role that long non-coding RNAs (lncRNAs) may have on the activity of those genes. Here, the expression profile of lncRNAs in ovine preadipocyte differentiation was investigated and the differentially expressed lncRNAs were screened on day 0 (D0), day 2(D2) and day 8(D8) of ovine preadipocyte differentiation, with their target genes being predicted. The competing endogenous RNA (ceRNA) regulatory network was constructed by GO and KEGG enrichment analysis for functional annotation, and some differentially expressed lncRNAs were randomly selected to verify the RNA-Seq results by RT-qPCR. In the study, a total of 2517 novel lncRNAs and 3943 known lncRNAs were identified from ovine preadipocytes at the three stages of differentiation, with the highest proportion being intergenic lncRNAs. A total of 3455 lncRNAs were expressed at all three stages of preadipocyte differentiation, while 214, 226 and 228 lncRNAs were uniquely expressed at day 0, day 2 and day 8, respectively. By comparing the expression of the lncRNAs between the three stages of differentiation stages, a total of 405, 272 and 359 differentially expressed lncRNAs were found in D0-vs-D2, D0-vs-D8, and D2-vs-D8, respectively. Functional analysis revealed that the differentially expressed lncRNAs were enriched in signaling pathways related to ovine preadipocyte differentiation, such as mitogen-activated protein kinase (MAPK) pathway, the phosphoinositide 3-kinase protein kinase B (PI3K-Akt) pathway, and the transforming growth factor beta (TGF-β) pathway. In summary, lncRNAs from preadipocytes at different stages of differentiation in sheep were identified and screened using RNA-Seq technology, and the regulatory mechanisms of lncRNAs in preadipocyte differentiation and lipid deposition were explored. This study provides a theoretical reference for revealing the roles of lncRNAs in ovine preadipocyte differentiation and also offers a theoretical basis for further understanding the regulatory mechanisms of ovine preadipocyte differentiation.

Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein–protein interaction signals. In the present study, we developed a novel tool phage–host interaction signal detector (PHISDetector) to predict phage–host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage–host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage–host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage–host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.

Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (>80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).

As an intracellular form of a bacteriophage in the bacterial host genome, a prophage usually integrates into bacterial DNA with high specificity and contributes to horizontal gene transfer (HGT). With the exponentially increasing number of microbial sequences uncovered in genomic or metagenomics studies, there is a massive demand for a tool that is capable of fast and accurate identification of prophages. Here, we introduce DBSCAN-SWA, a command line software tool developed to predict prophage regions in bacterial genomes. DBSCAN-SWA runs faster than any previous tools. Importantly, it has great detection power based on analysis using 184 manually curated prophages, with a recall of 85% compared with Phage_Finder (63%), VirSorter (74%), and PHASTER (82%) for (Multi-) FASTA sequences. Moreover, DBSCAN-SWA outperforms the existing standalone prophage prediction tools for high-throughput sequencing data based on the analysis of 19,989 contigs of 400 bacterial genomes collected from Human Microbiome Project (HMP) project. DBSCAN-SWA also provides user-friendly result visualizations including a circular prophage viewer and interactive DataTables. DBSCAN-SWA is implemented in Python3 and is available under an open source GPLv2 license from https://github.com/HIT-ImmunologyLab/DBSCAN-SWA/.