Background: The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. Objective: To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. Methods: Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. Results: There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium, norank_f__Oscillospiraceae, Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas, Erysipelotrichaceae_UCG-003, Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. Conclusion: The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Manganese (Mn) is a crucial trace element that actively participates in a diverse array of physiological processes. Mn is maintained at appropriate levels in the body by absorption and excretion by the body. Dysregulation of Mn homeostasis can lead to a variety of diseases, especially the accumulation of Mn in the brain, resulting in toxic side effects. We reviewed the metabolism and distribution of Mn at multiple levels, including organ, cellular and sub-cell levels. Mitochondria are the main sites of Mn metabolism and energy conversion in cells. Enhanced Mn superoxide dismutase activity reduces mitochondrial oxidative stress and inhibits cancer development. In addition, Mn enhances anti-cancer immune responses through the cGAS–STING pathway. We introduced various delivery vectors for Mn delivery to cancer sites for Mn supplementation and anti-cancer immunity. This review aims to provide new research perspectives for the application of Mn in the prevention and treatment of human diseases, especially by enhancing anti-cancer immune responses to inhibit cancer progression.

Background: The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer.Objective: To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice.Methods: Gastric juice samples were collected from 61 healthy people and 78 patients with gastric cancer (48 cases of early gastric cancer and 30 cases of advanced gastric cancer). The bacterial 16 S rRNA V1-V4 region of gastric juice samples was sequenced. The Shannon index, Simpson index, Ace index and Chao index were used to analyze the diversity of gastric juice samples. The RDP classifier Bayesian algorithm was used to analyze the community structure of 97% OTU representative sequences with similar levels. Linear discriminant analysis and ST-test were used to analyze the differences. Six machine learning algorithms, including the logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Catboost algorithm and gradient lifting tree algorithm, were used to construct risk prediction models for gastric cancer and advanced gastric cancer.Results: The microbiota diversity and the abundance of bacteria was different in the healthy group, early gastric cancer and advanced gastric cancer (P < 0.05). The top five abundant bacteria among the three groups wereStreptococcus, Rhodococcus, Prevotella, PseudomonasandHelicobacter.Bacterial flora such asStreptococcus, RhodococcusandOchrobactrumwere significantly different between the healthy group and the gastric cancer group. The accuracy of the random forest prediction model is the highest (82.73% correct). The bacteria with the highest predictive value includedStreptococcus, LactobacillusandOchrobactrum. The abundance of bacteria such asFusobacterium, Capnocytophaga, Atopobium, Corynebacteriumwas high in the advanced gastric cancer group.Conclusion: Gastric juice bacteria can be used as potential biomarkers to predict the occurrence and development of gastric cancer.

Tongue diagnosis can assess the physiological functions and pathological conditions of colorectal cancer (CRC). To investigate the characteristics of tongue images and the bacteria composition of tongue coating in patients with CRC. A total of 59 CRC patients were enrolled and underwent tongue image analysis using a diagnostic instrument. Third-generation sequencing technique was used to determine tongue coating bacteria. The relationships among tongue images, tongue coating bacteria and clinical information (age, gender, and location) were further analyzed. Statistical analyses were performed to investigate the potential associations between tongue coating microbiota and two key variables (tongue images and age). CRC patients typically present with four distinct tongue images, including yellow greasy coating, petechiae, white greasy coating, and red tongue with little coating. Notably, the microbiota composition of tongue coatings demonstrates a statistically significant correlation with both tongue diagnostic features and patient age compared to other clinical parameters. Alloprevotella rava may be primary contributors to the formation of yellow greasy coating, while Prevotella intermedia appears to be associated with the development of white greasy coating. Furthermore, Streptococcus parasanguinis might emerged as a potential biomarker for CRC patients under 62 years of age, whereas Prevotella sp. 000163055 appears to be a predominant species in the tongue coating microbiota of CRC patients over 62 years. Overall, this study emphasizes the variations in the community structure and diversity of tongue coating bacteria in CRC patients through different tongue images. The findings provide novel insights that could enhance the predictive capacity of tongue diagnosis for clinical progression monitoring and contribute to the development of early intervention strategies in CRC management. Colorectal cancer (CRC) is a prevalent malignant tumor of the digestive system, characterized by high morbidity and mortality rates. In recent years, increasing attention has been directed towards the investigation of tongue imaging in CRC patients. As one of the four fundamental diagnostic methods in Traditional Chinese Medicine (TCM), tongue diagnosis has emerged as a significant auxiliary tool for identifying various diseases. To enhance the sensitivity and specificity of tongue imaging in the early screening and widespread application of CRC, this study aims to establish a connection between tongue images and CRC at a microscopic level. By doing so, it seeks to provide a novel perspective for the early intervention strategies in CRC management and to facilitate the integration of tongue diagnosis into modern clinical practice. Patients with CRC might exhibit four distinct tongue images, including yellow greasy coating, petechiae, white greasy coating, and red tongue with little coating. Alloprevotella rava may serve as primary contributors to the formation of yellow greasy coating, whereas Prevotella intermedia was potentially associated with the development of white greasy coating. Streptococcus parasanguinis emerged as a potential biomarker distinguishing CRC patients aged 62 years, whereas Prevotella sp. 000163055 appeared to dominate the tongue coating microbiota in CRC patients aged 62 years and above.

The new method of fecal microbiota transplantation, based on automatic facilities and washing processes, was coined as washed microbiota transplantation (WMT). The first recommendations on WMT were released as Nanjing Consensus report by the fecal microbiota transplantation (FMT)‐standardization Study Group in 2019. This is the second consensus report on WMT by the expert group of the CHINAGUT Conference. Delphi and grading of recommendations assessment, development, and evaluation (GRADE) systems were used to develop the consensus and appraise the quality of evidence and grading of recommendations. The present consensus named as Nanjing Consensus II with 28 statements was developed according to the preferred reporting items for microbiotherapy guidelines in 2024 (PRIM 2024) for improving the clinical use of WMT. The expert panel ranked the top 10 indications of WMT rather than voting on Clostridioides difficile infection. This consensus will promote the multidisciplinary application of WMT and advance the development of microbiota medicine.