Sessile serrated lesion(SSL)is a major precancerous lesion type of colorectal cancer and one of the main causes of colorectal cancer.SSL has unique tissue structures and morphological features,with low detection rate under endoscopy and high variability in histopathological diagnosis,making it one of the most easily missed lesions in colorectal cancer screening.In recent years,the application of artificial intelligence(AI)in the diagnosis of colorectal cancer has been rapidly increasing.Previous studies have found that AI-assisted endoscopic technology can improve the detection rate and diagnostic accuracy of colorectal SSL,and AI-assisted pathological diagnosis can improve diagnostic efficiency and consistency.This article aims to provide a review of current research on AI technology in colorectal SSL.

Colorectal cancer(CRC)is one of the most common malignant tumors worldwide,and its incidence has been increasing in recent years,especially among young adults.Non-genetic factors,such as dietary habits,lifestyle and intestinal flora,play an important role in the development of CRC.Dietary factors have a close relationship with CRC development.Insufficient fiber intake and excessive consumption of red and processed meat are generally considered major high-risk factors for CRC,while vegetables and fruits are considered as beneficial factors.The roles of calcium supplements,vitamin D,dairy products,and different dietary patterns in the development of CRC are still controversial.Furthermore,obesity,smoking,alcohol consumption,and lack of physical exercise are also associated with the risk of CRC.The imbalance of intestinal flora is also believed to be associated with the development of CRC.Optimizing dietary habits and maintaining a healthy lifestyle can significantly reduce the risk of CRC.Non-steroidal anti-inflammatory drugs and estrogen supplementation may have beneficial effects in reducing the risk of CRC.For certain individuals at high risk,pharmacological intervention may serve as an effective preventive measure.

Biological clock is a pattern of rhythms in which organisms exhibit cycles approaching 24 h in order to adapt to periodic changes in the external environment.Gut microbiota is the sum of extremely large number of microorganisms parasitized in humans and animals.Disorders of the biological clock increase the risk of metabolic diseases,and the occurrence of these disorders is closely related to the interaction with the dysbiosis of the gut microbiota.With the deepening of the understanding of the brain-gut axis,targeting gut microbiota to ameliorate biological clock disorders and associated diseases has achieved major theoretical breakthroughs and was partially applied in clinical practice,including dietary interventions such as time restricted feeding,development of microbial-related products(probiotics and postbiotics),and fecal microbiota transplantation.This article reviews the research progress of intestinal flora and rhythmicity,as well as targeting intestinal flora for the treatment of biological clock disorders,so as to provide new ideas for the prevention and treatment of rhythm disorders and concurrent diseases.

A fixation device for experimental rat in moxibustion was developed. Using this device, the points for moxibustion can be fully exposed, and the temperature of moxibustion be relatively constant. This fixation device consists of three parts: fixation seat, fixation clip and moxa-stick holder; characterized as the flexible fixation and easy operation. The device is advantaged at simultaneous operation of moxibustion at many points and supports the point combinations. It makes up for the shortcomings of the existing fixation devices, reduces the workload of moxibustion in experimental rat and improves the work efficiency and experimental safety of experimenters.
Animals ; Rats ; Moxibustion/methods* ; Equipment Design ; Acupuncture Points ; Humans ; Male ; Rats, Sprague-Dawley

Objective:To investigate the current application of colonoscopy at hospitals in China.Methods:From November 2019 to January 2020, an online questionnaire survey was conducted among gastroenterologists and colonoscopists in hospitals of different levels. The contents of questionnaire survey included basic information of colonoscopy at the respondent′s hospital, protocols and patient education of bowel preparation, implementation of colonoscopy quality control, and colonoscopists′ understanding of polypectomy techniques and post-polypectomy follow-up.Results:A total of 236 valid questionnaires were collected, involving 187 hospitals, and 143 (76.5%) had an annual operation capacity of more than 5 000 cases. In terms of bowel preparation, split-dosed polyethylene glycol electrolyte powder (PEG) was the most commonly used (60.4%, 113/187) and the most common volume of PEG was 3 L (67.4%, 126/187). Verbal (90.9%, 170/187) and written (79.7%, 149/187) instructions were given more often than other methods for patient education of bowel preparation. Antifoaming agent was routinely used in 124 (66.3%) hospitals. In terms of quality control, only 11.5% (20/174) hospitals implemented all four measures. In terms of polypectomy techniques, 98.1% (203/207) colonoscopists chose hot snare polypectomy or endoscopic mucosal resection for lesions of diameter>1 cm, while options varied for lesions of diameter<1 cm. The interval of follow-up after polypectomy recommended by colonoscopists was shorter than that by guidelines.Conclusion:Several problems are found in the survey in the application of colonoscopy in China, i. e., patient education of bowel preparation is not diversified; quality control of colonoscopy still needs to be strengthened; polypectomy techniques and follow-up after polypectomy need to be further standardized.

Objective:To explore the feasibility of estimating adenoma detection rate (ADR) based on polyp detection rate (PDR) in colonscopy.Methods:In the present single-center retrospective study, the conversion coefficient was calculated based on the total colonoscopy cases in 2017. ADR of each colonoscopists was estimated based on PDR and conversion coefficient, which was then verified compared with the actual ADR for consistency.Results:A total of 25 112 colonoscopies with 20 experienced colonoscopists were included. The overall conversion coefficient was 0.483. The intraclass correlation coefficient of the actual ADR and the estimated ADR was 0.818 (95% CI: 0.596-0.924, P<0.01). Conclusion:It is feasible to estimate ADR based on PDR, but this method is only an expediency. More attention should be paid to the establishment of a standardized electronic database.

Objective:To establish an artificial intelligence (AI)-assisted colorectal polyps classification system (AI polyps system) by using clinical big data, and to conduct the clinical verification.Methods:From June 2018 to June 2019, the colonoscopy images of polyps from 16 participating research centers were prospectively collected. The basic information of the polyps (location, size, shape and pathological biopsy results) in colonoscopy images of colorectal polyps were marked by senior colonoscopist, and the outline of the polyp was circled for the development of the AI polyps system. Taking pathological biopsy results of polyps as the gold standard, the sensitivity, specificity, and positivity predictive value (PPV), negative predictive value (NPV) and accuracy of white light model, narrow band imaging (NBI) model, the combination of white light and NBI model and colonoscopists′ identification of polyps were calculated respectively. Paired McNemar test and Kappa test were used for statistical analysis. Results:A total of 15 441 qualified colonoscopy images were collected, including 9 109 images in white light model and 6 332 images in NBI model. At laboratory level, the sensitivity, specificity, PPV, NPV and accuracy of white light model and NBI model in the identification of the polyps were 90.3%, 98.3%, 89.8%, 98.4%, 97.2%, and 90.5%, 92.5%, 92.3%, 90.6%, 91.5%, respectively. In clinical verification phase, a total of 78 polyps of 56 patients with colorectal polyps were enrolled. The sensitivity, specificity, PPV, NPV and accuracy of the white light model and NBI model in the identification of polyps were 70.3%, 82.1%, 78.8%, 74.4%, 76.3%, and 78.4%, 87.2%, 85.3%, 81.0%, 82.9%, respectively. There were no statistically significant differences between the diagnostic results of colonoscopists, the white light model, the NBI model and the results of pathological results (all McNemar test, all P>0.05), but the consistency were general and the Kappa values were 0.632, 0.525 and 0.657, respectively (all P<0.01). The Kappa value of combination of the white light and NBI model and the pathological results was 0.575, however the consistency was general, but the difterence was statistically significant (McNemar test, P=0.004). Conclusions:The established AI polyps system has a certain role in assisting diagnosis, but the accuracy still needs to be improved.

Objective To investigate the application value of Multi-Latex polygranular technique joint detection of kidney injury-related urinary microproteins in noninvasive diagnosis after renal transplantation. Methods Clinical data of 72 recipients undergoing renal transplantation were retrospectively analyzed. According to the level of serum creatinine (Scr), the recipients were divided into normal renal function group (group A, n=14), mild kidney injury (group B, n=37), and severe kidney injury group (group C, n=21). 20 healthy volunteers were selected as the healthy control group (HC group). The contents of urinary retinol binding protein (RBP), microalbumin (mAlb), IgG, transferrin (TRF), α₁-microglobulin (MG), and β₂-MG of subjects in each group were detected using the Multi-Latex polygranular technique. The correlation between urinary microproteins and Scr, blood urea nitrogen (BUN) was analyzed. The differences of urinary microproteins in each group were compared. And the diagnostic value of single and joint detection of urinary microproteins was evaluated. Results Six kinds of urinary microproteins in HC group and group A were significantly lower than those in group B and group C, and six kinds of urinary microproteins in group B were significantly lower than those in group C (all P < 0.01). Six kinds of urinary microproteins in renal transplant recipients were positively correlated with BUN. RBP, mAlb, α₁-MG, and β₂-MG were positively correlated with Scr. The correlations were statistically significant (P < 0.001-0.05). The diagnostic value of joint detection of urinary microproteins is better than the detection of single index, among which TRF+mAlb+RBP+α₁-MG quadruple detection had the highest diagnostic value. Conclusions Six kinds of urinary microproteins can be used as specific indicators to reflect graft renal function. The polygranular technique can simultaneously detect its contents and achieve noninvasive diagnosis. The diagnosis based on TRF+mAlb+RBP+α₁-MG quadruple detection is expected to further improve the noninvasive diagnosis system after renal transplantation.

The objective was to investigate the effect of kinsenoside (Kin) treatments on macrophage polarity and evaluate the resulting protection of chondrocytes to attenuate osteoarthritis (OA) progression. RAW264.7 macrophages were polarized to M1/M2 subtypes then administered with different concentrations of Kin. The polarization transitions were evaluated with quantitative real-time polymerase chain reaction (qRT-PCR), confocal observation and flow cytometry analysis. The mechanism of Kin repolarizing M1 macrophages was evaluated by Western blot. Further, macrophage conditioned medium (CM) and IL-1 were administered to chondrocytes. Micro-CT scanning and histological observations were conducted on anterior cruciate ligament transection (ACLT) mice with or without Kin treatment. We found that Kin repolarized M1 macrophages to the M2 phenotype. Mechanistically, Kin inhibited the phosphorylation of IB, which further reduced the downstream phosphorylation of P65 in nuclear factor-B (NF-B) signaling. Moreover, Kin inhibited mitogen-activated protein kinases (MAPK) signaling molecules p-JNK, p-ERK and p-P38. Additionally, Kin attenuated macrophage CM and IL-1-induced chondrocyte damage. , Kin reduced the infiltration of M1 macrophages, promoted M2 macrophages in the synovium, inhibited subchondral bone destruction and reduced articular cartilage damage induced by ACLT. All the results indicated that Kin is an effective therapeutic candidate for OA treatment.

With the development of computer science and technology, artificial intelligence and big data have made key breakthroughs in various areas. The medical and health field has also been greatly affected. Although the diagnosis, treatment, monitoring and management of pancreatic diseases is relatively difficult, artificial intelligence and medical big data are being applied in these aspects and showing obvious advantages and potential for the application.