Laparoscopic Management of Benign Colorectal Diseases

Background: Laparoscopic colectomy is safe and effective, and in some cases, superior to open surgery, for a range of benign illnesses. The short-term advantages include less gastrointestinal discomfort, decreased wound infection and surgical morbidity, quicker bowel function restoration, and a shorter duration of hospital stay.Aim of the work & Methodology: evaluate our practice in laparoscopic colectomy by studied 20 patients with benign colorectal disorders admitted to our university hospital between Dec. 2015 and Dec. 2020. Inclusion criteria: 1. Age ranging from 15 to 70 years. 2. Patients with benign colorectal diseases, e.g., diverticular diseases, inflammatory bowel diseases, colonic polyps, rectal prolapse, etc. Exclusion criteria; malignant colorectal tumors and relative contraindication for laparoscopy. We were using classical laparoscopic techniques. Patients were discharged from the hospital when they could tolerate a regular diet. They were followed up at least six months, starting on the 30th postoperative day on a regular visit every two weeks—the data recorded including the intraoperative events and the difficulties and postoperative follow-up.Results: Technical difficulties are more remarkable for benign conditions than for cancer, especially for patients with inflammatory bowels, such as diverticular disease or inflammatory bowel disease, which frequently involve adjacent structures, peri-colic fibrosis, and lost planes. Surgeons should choose their patients before beginning laparoscopic colorectal surgeries.Conclusions: Laparoscopic surgery provides numerous advantages over open surgery, including minor discomfort, a shorter hospital stay, reduced morbidity, and a faster postoperative recovery. Our study cannot be utilized in a comparison study since it only evaluates our practice, and the findings may not be generalizable.

Exploring Gut Microbiota in Patients with Colorectal Disease Based on 16S rRNA Gene Amplicon and Shallow Metagenomic Sequencing

The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies.

AI based colorectal disease detection using real-time screening colonoscopy

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117,055 images from 16,004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.

Transanal irrigation (TAI) in the paediatric population: Literature review and consensus of an Italian multicentre working group

Constipation and fecal incontinence in pediatric patients are conditions due to either functional or organic bowel dysfunction and may represent a challenging situation both for parents, pediatricians, and pediatric surgeons. Different treatments have been proposed throughout the past decades with partial and alternant results and, among all proposed techniques, in the adult population the Transanal Irrigation (TAI) has become popular. However, little is known about its efficacy in children. Therefore, a group of Italian pediatric surgeons from different centers, all experts in bowel management, performed a literature review and discussed the best-practice for the use of TAI in the pediatric population. This article suggests some tips, such as the careful patients’ selection, a structured training with expert in pediatric colorectal diseases, and a continuous follow-up, that are considered crucial for the full success of treatment.

DNA Methylation Change Profiling of Colorectal Disease: Screening towards Clinical Use

Colon cancer remains one of the leading causes of cancer-related deaths worldwide. Transformation of colon epithelial cells into invasive adenocarcinomas has been well known to be due to the accumulation of multiple genetic and epigenetic changes. In the past decade, the etiology of inflammatory bowel disease (IBD) which is characterized by chronic inflammation of the intestinal mucosa, was only partially explained by genetic studies providing susceptibility loci, but recently epigenetic studies have provided critical evidences affecting IBD pathogenesis. Over the past decade, A deep understanding of epigenetics along with technological advances have led to identifying numerous genes that are regulated by promoter DNA hypermethylation in colorectal diseases. Recent advances in our understanding of the role of DNA methylation in colorectal diseases could improve a multitude of powerful DNA methylation-based biomarkers, particularly for use as diagnosis, prognosis, and prediction for therapeutic approaches. This review focuses on the emerging potential for translational research of epigenetic alterations into clinical utility as molecular biomarkers. Moreover, this review discusses recent progress regarding the identification of unknown hypermethylated genes in colon cancers and IBD, as well as their possible role in clinical practice, which will have important clinical significance, particularly in the era of the personalized medicine.

A Structured Modular Approach: The Answer to Training in Laparoscopic Colorectal Surgery

Bakground: Laparoscopic techniques are now an integral part of the operative management of colorectal diseases. However, the specialist training that is required for this is not uniformly available. There is, therefore, a need for a structured competency-based training method so that trainees can navigate the learning curve safely. Aim. To develop a modular structured training programme for laparoscopic colorectal surgery (LCS) with the capability of ensuring competency-based progression from a novice level to independent operator. Methodology. Over the past decade, we have developed a structured approach, starting with junior surgical trainees and progressing through to consultant level, with 7 clearly defined levels of progression attending courses to achieving a trainer status. This approach allows trainees to maintain objective records of their progression and trainers to provide targeted learning opportunities. It also allows for several trainees of varying experience to be trained during the same procedure. Conclusion. Our structured training module for junior surgeons has successfully produced several competent laparoscopic colorectal surgeons in the United Kingdom and around the world. This approach may also be adaptable to training in other laparoscopic procedures as the levels of progression are generic and not procedure-specific.

Results Of Reconstructive Surgery In Patients With Colostomies

This report presents an analysis of the results of reconstructive and reconstructive surgery in 106 patients previously operated on for colorectal diseases from 2015 to 2020 at the Department of FUV Surgery of the Andijan State Medical Institute. During the study it was found that 12 (27.9%) patients in the control group developed various postoperative complications. The problem of isolation and preparation of the rectal stump also remains an important problem.

Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network

Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classification and segmentation. Because of label unbalanced and difficult colorectal data, the classification based on self-paced transfer VGG network (STVGG) is proposed. ImageNet pretraining network parameters are transferred to VGG network with training colorectal data to acquire good initial network performance. And self-paced learning is used to optimize the network so that the classification performance of label unbalanced and difficult samples is improved. In order to assist the colonoscopist to accurately determine whether the polyp needs surgical resection, feature of trained STVGG model is shared to Unet segmentation network as the encoder part and to avoid repeat learning of polyp segmentation model. The experimental results on 3061 colorectal images illustrated that the proposed method obtained higher classification accuracy (96%) and segmentation performance compared with a few other methods. The polyp can be segmented accurately from around tissues by the proposed method. The segmentation results underpin the potential of deep learning methods for assisting colonoscopist in identifying polyps and enabling timely resection of these polyps at an early stage.