Trace elements in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five-year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. REVIEW: This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. CONCLUSION: Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

Advances in bacteria-based drug delivery systems for anti-tumor therapy.

In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti-tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria-based drug delivery systems for anti-tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria-based drug systems.

Advances in reprogramming of energy metabolism in tumor T cells.

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Identification of enterotype and its predictive value for patients with colorectal cancer.

BACKGROUND: Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined. OBJECTIVE: To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype. METHODS: The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated. RESULTS: Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters. CONCLUSION: This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Correlation of gut microbiota with leukopenia after chemotherapy in patients with colorectal cancer.

BACKGROUND: The most common toxic side effect after chemotherapy, one of the main treatments for colorectal cancer (CRC), is myelosuppression. OBJECTIVE: To analyze the correlation between gut microbiota and leukopenia after chemotherapy in CRC patients. METHODS: Stool samples were collected from 56 healthy individuals and 55 CRC patients. According to the leukocytes levels in peripheral blood, the CRC patients were divided into hypoleukocytes group (n = 13) and normal leukocytes group (n = 42). Shannon index, Simpson index, Ace index, Chao index and Coverage index were used to analyze the diversity of gut microbiota. LDA and Student's t-test(St test) were used for analysis of differences. Six machine learning algorithms, including logistic regression (LR) algorithm, random forest (RF) algorithm, neural network (NN) algorithm, support vector machine (SVM) algorithm, catboost algorithm and gradient boosting tree algorithm, were used to construct the prediction model of gut microbiota with leukopenia after chemotherapy for CRC. RESULTS: Compared with healthy group, the microbiota alpha diversity of CRC patients was significantly decreased (p < 0.05). After analyzing the gut microbiota differences of the two groups, 15 differential bacteria, such as Bacteroides, Faecalibacterium and Streptococcus, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Peptostreptococcus, Faecalibacterium, and norank_f__Ruminococcaceae, respectively. Compared with normal leukocytes group, the microbiota alpha diversity of hypoleukocytes group was significantly decreased (p < 0.05). The proportion of Escherichia-Shigella was significantly decreased in the hypoleukocytes group. After analyzing the gut microbiota differences of the two groups, 9 differential bacteria, such as Escherichia-Shigella, Fusicatenibacter and Cetobacterium, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Fusicatenibacte, Cetobacterium, and Paraeggerthella. CONCLUSION: Gut microbiota is related to leukopenia after chemotherapy. The gut microbiota may provide a novel method for predicting myelosuppression after chemotherapy in CRC patients.

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria

Background Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis. Objective To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model. Methods First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis. Results The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG − 004 was the most important variable in the SVM model. Conclusion CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis (AU)

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria.

BACKGROUND: Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis. OBJECTIVE: To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model. METHODS: First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis. RESULTS: The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG - 004 was the most important variable in the SVM model. CONCLUSION: CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis.

Medicine Procurement and Inventory Control Strategies Based on Inventory Model Approach / 中国药房

OBJECTIVE:To work out the optimal inventory levels in zero inventory management mode through model predic-tion and control strategy,by using the inventory upper & lower limits settings generally available in the information management system of health care institutions. METHODS:Multi-varieties joint ordering modelwas constructed by referring to operations management,time series analysis and quantitative approach to decision-making,that is,to make a prediction of upper&lower lim-its on medicine inventory based on historical data and applicable mathematical models(fixed order interval model and re-order mod-el,i.e. FOI and ROP),and compared with real results;based on above,specific medicine procurement and inventory control strat-egies would be developed and an evaluation of the application effects would be made. RESULTS:The error test and reproducibility test exhibited that the out-of-stock ratio remained under 3.36%,of which 71.24% could be effectively alarmed;under computer simulation and practical operation,the instant replenishment rate reduced by 9.33% and 13.03%,OOS ratio down by 11.11% and 27.45%,and average daily inventory turnover rate up by 30.19% and 15.85% respectively,all showing remarkable improvements compared to before the implementation of the mode. CONCLUSIONS:This model is of favorable accuracy and operability,there-fore it can lay foundation for rational and well-founded decisions in medicine procurement and inventory control in zero inventory management mode.