Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta).

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.

Does caring for patients with advanced non-small cell lung cancer affect health-related quality of life of caregivers? A multicenter, cross-sectional study.

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) mostly receive essential routine care and support from informal caregivers, who usually experience poorer health-related quality of life (HRQoL). The study aimed to evaluate the HRQoL and its predictors among informal caregivers of patients with advanced NSCLC in China. METHODS: We interviewed the adult caregiver population of patients with advanced NSCLC (stage IIIB~IV) in nine tertiary hospitals from multiple provinces in China between November 2020 and June 2021. The EQ-5D-5L instrument measured the HRQoL of caregivers, as analyzed by employing descriptive analysis, univariate analysis, Tobit regression, and multivariate logistic regression, and investigated the important influencing factors further. RESULTS: A valid sample of 553 caregivers was analyzed. The mean EQ-5D-5L utility score of caregivers was 0.92 (SD = 0.14). Caregivers reported the greatest problems in mental health, with 45.39% reporting slight, moderate, severe, or extreme anxiety/depression. The potential influencing factors of HRQoL in caregivers included patients' age and cancer histology, relationship with the patients, and daily caregiving hours. Compared to other caregivers, patients' spouses had the lowest HRQoL. In addition, over six hours of caregiving per day was associated with lower HRQoL in caregivers of patients with advanced NSCLC. CONCLUSIONS: The HRQoL of caregivers for patients with advanced NSCLC was investigated for the first time in China. The informal caregivers experience decreased HRQoL, with anxiety /depression problems being reported the most. The findings of this study would provide extensive information on the HRQoL of advanced NSCLC patients' caregivers for future health-promoting self-care.

Evolution of GCGR family ligand-receptor extensive cross-interaction systems suggests a therapeutic direction for hyperglycemia in mammals.

Glucose is essential to the physiological processes of vertebrates. Mammalian physiological stability requires a relatively stable blood glucose level (~5 mM), whereas other vertebrates have greater flexibility in regulating blood glucose (0.5-25 mM). GCGR family receptors play an important role in vertebrate glucose regulation. Here, we examine the evolution of the GCGR family ligand-receptor systems in different species. Comparatively, we discover that the conserved sequences among GCG family ligands lead to the non-specific activation of ligands across species. In particular, we observe that glucagon-like peptide 1 receptor (GLP1R), glucagon-like peptide 2 receptor (GLP2R), and glucagon-like receptor (GCGLR, also called GCRPR) are arbitrarily activated by other members of the ligand family in birds. Moreover, we reveal that Gallus gallus GLP2 (gGLP2) effectively activates mammalian GLP1R and improves glucose tolerance in diabetic mice. Our study has important implications for understanding blood glucose stabilization in vertebrates and demonstrates that gGLP2 may be a potential drug for treating type 2 diabetes.

Metabolic dysfunction-associated steatotic liver disease: ferroptosis related mechanisms and potential drugs.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is considered a "multisystem" disease that simultaneously suffers from metabolic diseases and hepatic steatosis. Some may develop into liver fibrosis, cirrhosis, and even hepatocellular carcinoma. Given the close connection between metabolic diseases and fatty liver, it is urgent to identify drugs that can control metabolic diseases and fatty liver as a whole and delay disease progression. Ferroptosis, characterized by iron overload and lipid peroxidation resulting from abnormal iron metabolism, is a programmed cell death mechanism. It is an important pathogenic mechanism in metabolic diseases or fatty liver, and may become a key direction for improving MASLD. In this article, we have summarized the physiological and pathological mechanisms of iron metabolism and ferroptosis, as well as the connections established between metabolic diseases and fatty liver through ferroptosis. We have also summarized MASLD therapeutic drugs and potential active substances targeting ferroptosis, in order to provide readers with new insights. At the same time, in future clinical trials involving subjects with MASLD (especially with the intervention of the therapeutic drugs), the detection of serum iron metabolism levels and ferroptosis markers in patients should be increased to further explore the efficacy of potential drugs on ferroptosis.

Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.

Impacts of the zero mark-up policy on hospitalization expenses of T2DM and cholecystolithiasis inpatients in SC province, western China: an interrupted time series analysis.

Background: Since 2009, a series of ambitious health system reforms have been launched in China, including the zero mark-up drug policy (ZMDP); the policy was intended to reduce substantial medicine expenses for patients by abolishing the 15% mark-up on drugs. This study aims to evaluate the impacts of ZMDP on medical expenditures from the perspective of disease burden disparities in western China. Method: Two typical diseases including Type 2 diabetes mellitus (T2DM) in internal medicine and cholecystolithiasis (CS) in surgery were selected from medical records in a large tertiary level-A hospital in SC Province. The monthly average medical expenses of patients from May 2015 to August 2018 were extracted to construct an interrupted time series (ITS) model to evaluate the impact of policy implementation on the economic burden. Results: A total of 5,764 cases were enrolled in our study. The medicine expenses for T2DM patients maintained a negative trend both before and after the intervention of ZMDP. It had declined by 74.3 CNY (P < 0.001) per month on average in the pre-policy period and subsequently dropped to 704.4 CNY (P = 0.028) immediately after the policy. The level change of hospitalization expenses was insignificant (P = 0.197), with a reduction of 677.7 CNY after the policy, while the post-policy long-term trend was significantly increased by 97.7 CNY (P = 0.035) per month contrasted with the pre-policy period. In addition, the anesthesia expenses of T2DM patients had a significant increase in the level under the impact of the policy. In comparison, the medicine expenses of CS patients significantly decreased by 1,014.2 CNY (P < 0.001) after the policy, while the total hospitalization expenses had no significant change in level and slope under the influence of ZMDP. Furthermore, the expenses of surgery and anesthesia for CS patients significantly increased by 320.9 CNY and 331.4 CNY immediately after the policy intervention. Conclusion: Our study indicated that the ZMDP has been an effective intervention to reduce the excessive medicine expenses for both researched medical and surgical diseases, but failed to show any long-term advantage. Moreover, the policy has no significant impact on relieving the overall hospitalization burden for either condition.

Measuring direct non-medical burden among patients with advanced non-small cell lung cancer in China: is there a difference in health status?

Objective: This study was conducted to estimate the direct non-medical cost of advanced non-small cell lung cancer (NSCLC) patients and explore whether its associated factors vary by health status. Methods: Data were obtained from 13 centers in five provinces for patients with advanced NSCLC in China. The direct non-medical cost of patients since the patients were diagnosed with NSCLC included the cost of transportation, accommodation, meal, hired caregiving, and nutrition. We measured patients' health status by EQ-5D-5L instrument and divided them into good (≥0.75) and poor (<0.75) groups based on the utility score. A generalized linear model (GLM) was used to assess independent associations between statistically significant factors and non-medical financial burden in health status subgroups. Results: Data from 607 patients were analyzed. The direct non-medical cost associated with advanced NSCLC since diagnosis was $2,951 per case ($4,060 in the poor health group and $2,505 in the other), with nutrition costing the most. GLM results showed that residence(Urban area vs. Rural area: -1.038, [-2.056, -0.02]), caregivers' occupation type (Farmer vs. Employee: -1.303, [-2.514, -0.093]), hospitalization frequency (0.077, [0.033, 0.12]), average length of hospital stay (0.101, [0.032, 0.17]), and pathological type (Squamous carcinoma vs. Non-squamous carcinoma: -0.852, [-1.607, -0.097]) were independent factors influencing direct non-medical cost in the poor health group. Among participants with good health status, residence (Urban area vs. Rural area: -0.621, [-1.005, -0.236]), marital status (Others vs. Married: 0.762, [0.035, 1.488]), patients' employment status, current caregiving time per day (more than 9 hours per day vs. less than 3 hours per day: 0.471, [0.134, 0.807]), duration of disease (0.015, [0.007, 0.024]), and hospitalization frequency (0.091, [0.068, 0.113]) were statistically associated factors. Conclusion: The direct non-medical economic burden of advanced NSCLC patients in China is considerable and differs by health status. Strengthening accessibility for more effective therapies and early nutritional intervention to improve prognosis, and further promoting accessible care forms within relevant healthcare insurance coverage may be potentially feasible approaches to alleviate the direct non-medical economic burden for patients and their families.

Neurotoxicity induced by taxane-derived drugs: analysis of the FAERS database 2017-2021.

OBJECTIVES: Taxane-related neurotoxicity is a frequent clinical problem but lacks postmarketing data regarding neurological disorders. This study aimed to evaluate the potential association between neurological adverse events and several taxanederived drugs via the Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: Disproportionality analysis was applied to data mining of the suspected cases of neurological disorders after using different taxanes based on the FAERS data from January 2017 and December 2021. We also investigated the times to onset, fatality, and hospitalization proportions of taxanerelated neurotoxicity. RESULTS: In total, 3,940 cases were screened out, which were more prevalent in elderly patients and females. Peripheral neuropathy was a common adverse event among all taxanes with relatively strong association. Generally, the median time to neurological adverse effect onset was 27 days (interquartile range, 11.0 ~ 78.0 days) following taxane regimens, and the majority of cases were detected within the first 30 days. Among cases of neurological adverse events treated with taxane, the fatality and hospitalization proportions were 6.13% and 28.63%, respectively. CONCLUSION: By analyzing the FAERS data, we provided a detailed profile of neurotoxicity and different taxanes in detail in terms of clinical characteristics, time to onset, and patient outcomes.

Measuring the indirect cost associated with advanced non-small cell lung cancer: a nationwide cross-sectional study in China.

PURPOSE: This study was conducted to estimate the indirect cost of locally advanced and metastatic non-small cell lung cancer (NSCLC) without sensitizing EGFR and ALK alterations in China and explore the predictors from both patient and caregiver perspectives. METHODS: Data were obtained from a nationwide cross-sectional study for the patients with advanced NSCLC (stage IIIB-IV) and their caregivers. Indirect medical cost was estimated as health productivity loss based on self-reported income and loss of work time. The generalized linear model was used to assess the independent associations between statistically significant variables and indirect economic burden. RESULTS: 611 pairs of patients and patient caregivers from 13 medical centers in five provinces in China participated in this investigation. The indirect medical cost associated with advanced NSCLC since the patient diagnosed was $1413 per capita in China. General linear regression results showed that the indirect medical cost was significantly influenced by duration of disease since diagnosis, treatment options, caregivers' occupation and age (P < 0.05). CONCLUSION: The indirect economic burden linked to advanced NSCLC in China is considerable on patients, and their caregivers. To minimize the severe challenges of indirect economic burden related to advanced NSCLC, expanding the coverage of the medical insurance and assistance system to reimburse part of the indirect costs related to cancer, as well as strengthening the accessibility for more effective therapies to improve the prognosis of advanced NSCLC, and further promote the patients and their caregivers to return to work or normal life may be the potentially feasible approaches.

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

External validation of vancomycin population pharmacokinetic models in ten cohorts of infected Chinese patients.

OBJECTIVES: Appropriate dosage of vancomycin is critical for safety and efficacy in treating infectious diseases. Various population pharmacokinetic (PPK) models have been established. To lay a foundation for the clinical application, it is important to perform external validation of the published model. The aim of this study was to find the most suitable vancomycin PPK model for treatment of infection in China amongst all studied models developed for adults. METHODS: A systematic literature search of published population vancomycin pharmacokinetic analyses was performed using the PubMed database. The identified models were evaluated in ten independent cohorts from China. Nonlinear mixed-effects modeling (NONMEM) was used for data analysis. The median prediction error (MPE), the distribution of prediction error (PE), and normalized prediction distribution errors (NPDE) were calculated and described. Predictive performance was evaluated by bias and accuracy. RESULTS: A total of 449 vancomycin concentrations from 397 infected participants were used for external validation. The MPE of the three models from the Chinese population was less than 10%. Half of the models had an acceptable MPE. For patients with different site infections and different renal functions, each model differed in its predictive ability to some extent. The NPDE results showed that all models had an obvious bias. CONCLUSIONS: None of the models had good performance in both prediction- and simulation-based diagnostics. Carrying out sufficient external validation of the PPK model before clinical application is of utmost importance. In clinical practice, the model's population closest to the application population should be used.

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

OBJECTIVE: This study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice. DESIGN: A nested case-control study. SETTING: National Center for ADR Monitoring and the Electronic Medical Record (EMR) system. PARTICIPANTS: All patients were from five medical institutions in Sichuan Province from January 2010 to December 2018. MAIN OUTCOMES/MEASURES: Data of patients with ADR who used Chinese herbal injections containing Panax notoginseng saponin were collected from the National Center for ADR Monitoring. A nested case-control study was used to randomly match patients without ADR from the EMR system by the ratio of 1:4. Eighteen machine learning algorithms were applied for the development of ADR prediction models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. An ADR prediction system was established by the best model selected from the 1080 models. RESULTS: A total of 530 patients from five medical institutions were included, and 1080 ADR prediction models were developed. Among these models, the AUC of the best capable one was 0.9141 and the accuracy was 0.8947. According to the best model, a prediction system, which can provide early identification of patients at risk for the ADR of Panax notoginseng saponin, has been established. CONCLUSION: The prediction system developed based on the machine learning model in this study had good predictive performance and potential clinical application.

Assessment of Health-Related Quality of Life Using EuroQoL-5 Dimension in Populations With Prediabetes, Diabetes, and Normal Glycemic Levels in Southwest China.

Objectives: This study aimed to describe and compare health-related quality of life (HRQoL) among populations with normal glycemic levels, prediabetes, and diabetes in southwest China and to offer baseline data that can be easily compared to other regions in China or across countries. Methods: A quality of life survey based on the EuroQoL-5 Dimension-5 level (EQ-5D-5L) scale was conducted through face-to-face or telephone interviews. A total of 403 respondents with diabetes, 404 with prediabetes, and 398 with normal blood glucose were enrolled in the survey. Propensity score matching (PSM) was used to decrease the bias of three groups, conditioned on age and gender, body mass index (BMI), and household income. For the three groups, we matched two groups first and then matched the result with the third group. Differences among groups were compared by chi-square test one-way ANOVA after adjusting by PSM. Results: In general, the blood glucose of people with diabetes was generally well-controlled in southwest China, but they were often accompanied by the circulatory system and nutritional metabolic diseases. Ninety-nine individuals from each group were matched. The EuroQoL-5 Dimension index of the population with normal glycemic levels, prediabetes, and patients with diabetes was 0.901, 0.948, and 0.897. The EuroQol-visual analog scales (EQ-VAS) scores of each group above were 73.76, 77.45, and 68.34. HRQoL in males was higher than that of females in the three study groups. The results after PSM were consistent with that before matching. Conclusion: There was a general trend that patients were associated with a decline of HRQoL from the prediabetic population, population with normal glycemic levels to diabetic population. Pain/discomfort and anxiety/depression might not be specific for the population with or without diabetes.

Cost-Effectiveness Analysis of Triple Combination Preparations in the Treatment of Moderate-to-Severe Chronic Obstructive Pulmonary Disease.

Objectives: This study analyzed the long-term cost-effectiveness of fluticasone/umeclidinium/vilanterol triple combination (FF/UMEC/VI) vs. budesonide/formoterol double combination (BUD/FOR) in the treatment of moderate-to-severe chronic obstructive pulmonary disease (COPD) and provides evidence for COPD treatment decisions. Methods: From the perspective of the healthcare system, a Markov model was established that consists of four states-stable period, non-severely deteriorating period, severely deteriorating period, and death-according to real-world COPD progression. The model period comprises 6 months, with a cycle length of 14 years. The initial state, transition probabilities, costs, and utility data were collected from the FULFIL trial, published literature, hospital record surveys, and China Health Statistics Yearbook. The discount rate was 5%, and the threshold was set as the Chinese per capita GDP in 2020 (¥72,447). The cost, utility, transition probabilities, and discount rate were calculated through TreeagePro11 software. The results were analyzed via one-way factor analysis and probability sensitivity analysis. Results: The baseline study shows that the 14-year treatment for FF/UMEC/VI and BUD/FOR groups are ¥199,765.55 and ¥173,030.05 with effectiveness at 8.54 quality-adjusted life years (QALYs) and 7.73 QALYs, respectively. The incremental cost-effectiveness ratio is ¥33,006.80/QALY, which is below the threshold. A tornado diagram of a one-way sensitivity analysis shows that the top three factors that affected the results are the non-severe deterioration rates of FF/UMEC/VI, the cost of FF/UMEC/VI and the non-severe deterioration rates of BUD/FOR. Probabilistic sensitivity analysis shows that FF/UMEC/VI (compared to BUD/FOR) can be made cost-effective under the willingness-to-pay (WTP) threshold (¥38,000). Furthermore, the likelihood of cost-effectiveness increases with a higher WTP. Conclusions: Compared with the double combination (BUD/FOR), the triple combination (FF/UMEC/VI) is more cost-effective under the Chinese per capita GDP threshold.

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes.

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People's Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set. Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively. Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study.

OBJECTIVE: The prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat. METHODS: We identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system. RESULTS: Grade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes. CONCLUSION: We have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms.

OBJECTIVE: Medication adherence plays a key role in type 2 diabetes (T2D) care. Identifying patients with high risks of non-compliance helps individualized management, especially for China, where medical resources are relatively insufficient. However, models with good predictive capabilities have not been studied. This study aims to assess multiple machine learning algorithms and screen out a model that can be used to predict patients' non-adherence risks. METHODS: A real-world registration study was conducted at Sichuan Provincial People's Hospital from 1 April 2018 to 30 March 2019. Data of patients with T2D on demographics, disease and treatment, diet and exercise, mental status, and treatment adherence were obtained by face-to-face questionnaires. The medication possession ratio was used to evaluate patients' medication adherence status. Fourteen machine learning algorithms were applied for modeling, including Bayesian network, Neural Net, support vector machine, and so on, and balanced sampling, data imputation, binning, and methods of feature selection were evaluated by the area under the receiver operating characteristic curve (AUC). We use two-way cross-validation to ensure the accuracy of model evaluation, and we performed a posteriori test on the sample size based on the trend of AUC as the sample size increase. RESULTS: A total of 401 patients out of 630 candidates were investigated, of which 85 were evaluated as poor adherence (21.20%). A total of 16 variables were selected as potential variables for modeling, and 300 models were built based on 30 machine learning algorithms. Among these algorithms, the AUC of the best capable one was 0.866±0.082. Imputing, oversampling and larger sample size will help improve predictive ability. CONCLUSIONS: An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling, balanced sampling, and so on, which may provide a technical tool for individualized diabetes care.

Upregulated OCT3 has the potential to improve the survival of colorectal cancer patients treated with (m)FOLFOX6 adjuvant chemotherapy.

PURPOSE: To investigate the influence of organic cation transporter 3 (OCT3) expression on the effect of the combination regimen of 5-fluorouracil, folinic acid and oxaliplatin ((m)FOLFOX6) in colorectal cancer (CRC) patients. METHODS: This is a retrospective study conducted at a single centre (Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, China). Patients with stage IIb-IV resectable CRC who were being postoperatively treated with (m)FOLFOX6 as a first-line adjuvant chemotherapy regimen for at least 5 cycles and had resected primary tumour samples available were eligible for the study. Patients who preoperatively received chemotherapy and/or radiotherapy or were treated with targeted drugs or other anticancer drugs were excluded from the study. Immunohistochemical staining and digital image analysis were used to assess OCT3 expression in tumour samples. According to OCT3 expression level, the receiver operating characteristic curve (ROC curve) was used to divide the patients into two groups. Cox proportional risk regression was performed with the forward LR (forward stepwise regression based on maximum likelihood estimation) method using SPSS17.0 software. The primary endpoint was the 2-year progression-free survival. RESULTS: In total, 57 patients were included between 2014 and 2016 according to the inclusion and exclusion criteria (22 had low OCT3 expression, and 35 had high OCT3 expression). The mean age was 55.7 (30-74) years, and 37 of the total patients were male. According to TNM stage, 5 patients had stage IV disease, 44 patients had stage III disease, and 8 patients had stage II disease. Through Cox regression analysis, we found that among patients receiving the (m)FOLFOX6 regimen, those with higher OCT3 expression had a higher two-year progression-free survival rate than those with lower OCT3 expression (P = 0.038). The hazard ratio of patients with high OCT3 expression compared with patients with low OCT3 expression was 0.247. Besides, it was found that the age of patients was negatively correlated with expression level of OCT3, which can explain why patients over 70 years do not benefit from oxaliplatin-containing chemotherapy. CONCLUSIONS: High OCT3 expression in CRC tissues may be a protective factor for CRC patients treated with (m)FOLFOX6.

Multidimensional Theranostics for Tumor Fluorescence Imaging, Photoacoustic Imaging and Photothermal Treatment Based on Manganese Doped Carbon Dots.

Multidimensional theranostics have received a lot of attention because of their improved diagnostic accuracy and therapeutic diversity. In this study, we developed a kind of manganese doped nigrosine originated carbon dots (Mn-NCDs), with the average particle size of approximate 3 nm and the long emission wavelength of 653 nm. In vitro experiment demonstrated that Mn-NCDs could be well internalized by 4T1 cells, and the temperature of Mn-NCDs solution could rapidly rise from 23.1 °C to more than 50 °C when exposed to NIR laser, thus it could generate enough hyperthermia to efficiently destroy 4T1 cells. Furthermore, the in vivo fluorescent and photoacoustic imaging studies highlighted the applicability of Mn-NCDs in dual-mode tumor diagnosis. The prepared Mn-NCDs with long emission wavelength and photothermal anti-cancer effect exhibited great potential for dual-mode imaging and treatment of triple negative breast cancer.

The Application of Natural Products in Cancer Therapy by Targeting Apoptosis Pathways.

BACKGROUND: Apoptosis is one of the pathways of programmed cell death (PCD), which leads to pathognomic cellular changes different from cellular necrosis. It includes the death receptor pathway and the mitochondrial pathway, both of which involve many key proteins abnormally expressed in apoptosis-related diseases, such as cancer. The majority of anti-cancer natural products promote apoptosis to induce cancer cell death. In this review, we aim to survey the current knowledge on key apoptotic proteins and some representative natural products that can induce cancer cell apoptosis by regulating these proteins. METHOD: We reviewed the recent relative literatures using a structured search of bibliographic databases, and the keywords, such as natural products, cancer therapy, and apoptosis pathway, were used for inclusion/exclusion criteria. RESULTS: One hundred and seventy-one papers are included in this review. Numerous natural products have been reported to induce cell apoptosis by regulating the expression of key apoptotic proteins, leading to a variety of antitumor effects. This review identifies the major apoptotic proteins and elaborates the interaction between the apoptotic proteins and various natural anti-cancer products. CONCLUSION: This review provides substantial evidences of apoptosis pathways in cancer cells and the apoptotic proteins modulated by active natural products, which may help to gain a comprehensive understanding for the development of natural drugs for better cancer treatment.