Following the publication of the above paper, it was drawn to the Editor's attention by a concerned reader that certain of the western blot data shown in Fig. 3B on p. 3428 had already appeared previously in an article written by different authors at different research institutes that was published in the journal BMC Cancer, but which has subsequently been retracted. Upon performing an independent analysis of the data in this paper, it also came to light that other western blot data in Figs. 4B and 5B had appeared in articles in other journals written by different authors at different research institutes, that the Control data panels in Fig. 2A and B were also overlapping within the figure itself, and other data in Fig. 2 had appeared subsequently in an unrelated paper in Oncology Reports. Owing to the fact that the contentious data in the above article had already been published prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. fter having been in contact with the authors, they did not accept the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 13: 3423‑3432, 2016; DOI: 10.3892/mmr.2016.4947].

Diabetes mellitus (DM) is a group of metabolic disorders in which high blood sugar levels occur over a prolonged period. Approximately 4% of the global population is affected by DM. Western medical treatment methods for diabetes including injection or oral hypoglycemic drugs have some toxic or side effects, economic pressures, and so on. Many researchers turn to discover new drugs from natural products or Traditional Chinese Medicine (TCM). Flavonoids are widely distributed in plants, and many studies have shown that flavonoids possess antidiabetic properties, exhibiting not only well-recognized antidiabetic and hypoglycemic activities but also activity in the treatment of diabetic complications. In this review, we systematically summarized anti-diabetic flavonoid compounds based on structure classification by examining the PubMed, Springer Link, Web of Science, and CNKI databases. There are 13 flavonoid compounds listed which have been studied extensively and have antidiabetic features respectively. Apigenin, baicalein, and catechin mainly reduces blood glucose via anti-oxidation; hesperidin is good for diabetic neuropathy; glycyrrhiza flavonoids have a significant effect on gestational DM; quercetin takes advantage of crossing the blood–brain barrier and improving renal function. Some compounds have protective and preventive effects on diabetic complications, such as kaempferol and puerarin which are beneficial to cardiomyopathy; myricetin has therapeutic potential in the treatment of DN; dihydromyricetin might improve CI. It is a pity or might be a pointcut that most studies remain in the animal experimental stage, and further investigation should be carried out.

Aims Sodium tanshinone IIA sulfonate (STS) injection has been widely used as adjunctive therapy for pulmonary heart disease (PHD) in China. Nevertheless, the efficacy of STS injection has not been systematically evaluated so far. Hence, the efficacy of STS injection as adjunctive therapy for PHD was explored in this study. Methods Randomized controlled trials (RCTs) were screened from China Science and Technology Journal Database, China National Knowledge Infrastructure, Wanfang Database, PubMed, Sino-Med, Google Scholar, Medline, Chinese Biomedical Literature Database, Cochrane Library, Embase and Chinese Science Citation Database until 20 January 2024. Literature searching, data collection and quality assessment were independently performed by two investigators. The extracted data was analyzed with RevMan 5.4 and STATA 14.0. Basing on the methodological quality, dosage of STS injection, control group measures and intervention time, sensitivity analysis and subgroup analysis were performed. Results 19 RCTs with 1739 patients were included in this study. Results showed that as adjunctive therapy, STS injection combined with Western medicine showed better therapeutic efficacy than Western medicine alone for PHD by increasing the clinical effective rate (RR = 1.22; 95% CI, 1.17 to 1.27; p < 0.001), partial pressure of oxygen (MD = 10.16; 95% CI, 5.07 to 15.24; p < 0.001), left ventricular ejection fraction (MD = 8.66; 95% CI, 6.14 to 11.18; p < 0.001) and stroke volume (MD = 13.10; 95% CI, 11.83 to 14.38; p < 0.001), meanwhile decreasing the low shear blood viscosity (MD = -1.16; 95% CI, -1.57 to -0.74; p < 0.001), high shear blood viscosity (MD = -0.64; 95% CI, -0.86 to -0.42; p < 0.001), plasma viscosity (MD = -0.23; 95% CI, -0.30 to -0.17; p < 0.001), hematokrit (MD = -8.52; 95% CI, -11.06 to -5.98; p < 0.001), fibrinogen (MD = -0.62; 95% CI, -0.87 to -0.37; p < 0.001) and partial pressure of carbon dioxide (MD = -8.56; 95% CI, -12.09 to -5.02; p < 0.001). Conclusion STS injection as adjunctive therapy seemed to be more effective than Western medicine alone for PHD. However, due to low quality of the included RCTs, more well-designed RCTs were necessary to verify the efficacy of STS injection.

Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. Methods: The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. Results: A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. Conclusions: An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients.

Echinococcosis and tuberculosis are two common zoonotic diseases that can cause severe pulmonary infections. Early screening and treatment monitoring are of great significance, especially in areas with limited medical resources. Herein, we designed an operation-friendly and rapid magnetic enrichment-silver acetylene chromogenic immunoassay (Me-Sacia) to monitor the antibody. The main components included secondary antibody-modified magnetic nanoparticles (MNP-Ab2) as capture nanoparticles, specific peptide (EG95 or CFP10)-modified silver nanoparticles (AgNP-PTs) as detection nanoparticles, and alkyne-modified gold nanoflowers as chromogenic nanoparticles. Based on the magnetic separation and plasma luminescence techniques, Me-Sacia could completely replace the colorimetric assay of biological enzymes. It reduced the detection time to approximately one hour and simplified the labor-intensive and equipment-intensive processes associated with conventional ELISA. Meanwhile, the Me-Sacia showed universality for various blood samples and intuitive observation with the naked eye. Compared to conventional ELISA, Me-Sacia lowered the detection limit by approximately 96.8%, increased the overall speed by approximately 15 times, and improved sensitivity by approximately 7.2%, with a 100% specificity and a coefficient of variation (CV) of less than 15%.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes.Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People’s Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution.Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model.Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

Background:Invasive fungal disease (IFD) has become a serious threat to human health in China and around the world, with high mortality and morbidity. Currently, the misdiagnosis rate of IFD is extremely high, compounded with the low quality of prescription antifungals and the high incidence of adverse events associated with IFD treatment, resulting in lengthy hospitalization, low clinical response, and high disease burden, which have become serious challenges in clinical practice. Antifungal stewardship (AFS) can not only significantly increase the early diagnosis rate of IFD, reduce inappropriate utilization of antifungal drugs, improve patient prognosis, but can also improve therapeutic safety and reduce healthcare expenses. Thus, it is urgent to identify key AFS metrics suitable for China's current situation. Methods:Based on metrics recommended by international AFS consensuses, combined with the current situation of China and the clinical experience of authoritative experts in various fields, several metrics were selected, and experts in the fields of respiratory diseases, hematology, intensive care units (ICUs), dermatology, infectious diseases, microbiology laboratory and pharmacy were invited to assess AFS metrics by the Delphi method. Consensus was considered to be reached with an agreement level of ≥80% for the metric. Results:Consensus was reached for 24 metrics, including right patient metrics (n=4), right time metrics (n=3), and right use metrics (n=17). Right use metrics were further subdivided into drug choice (n=8), drug dosage (n=4), drug de-escalation (n=1), drug duration (n=2), and drug consumption (n=2) metrics. Forty-six authoritative experts assessed and reviewed the above metrics, and a consensus was reached with a final agreement level of ≥80% for 22 metrics. Conclusions:This consensus is the first to propose a set of AFS metrics suitable for China, which helps to establish AFS standards in China and is also the first AFS consensus in Asia, and may improve the standard of clinical diagnosis and treatment of IFD, and guide hospitals to implement AFS, ultimately promoting the rational use of antifungal drugs and improving patient prognosis.

Immunosenescence increases the risk and severity of diseases in elderly individuals and leads to impaired vaccine-induced immunity. With aging of the global population and the emerging risk of epidemics, developing adjuvants and vaccines for elderly individuals to improve their immune protection is pivotal for healthy aging worldwide. Deepening our understanding of the role of immunosenescence in vaccine efficacy could accelerate research focused on optimizing vaccine delivery for elderly individuals. In this review, we analyzed the characteristics of immunosenescence at the cellular and molecular levels. Strategies to improve vaccination potency in elderly individuals are summarized, including increasing the antigen dose, preparing multivalent antigen vaccines, adding appropriate adjuvants, inhibiting chronic inflammation, and inhibiting immunosenescence. We hope that this review can provide a review of new findings with regards to the impacts of immunosenescence on vaccine-mediated protection and inspire the development of individualized vaccines for elderly individuals.

AbstractAimsThere is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay (‘L’), acute (emergent) admission (‘A’), Charlson comorbidity index (‘C’) and visits to the emergency department during the previous 6 months (‘E’)] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.MethodsElderly patients with arrhythmia who were hospitalized at Sichuan Provincial People's Hospital between 1 June 2018 and 31 May 2020 were enrolled. The LACE index was calculated for each patient, and the area under the receiver operating characteristic curve (AUROC) was calculated. Six machine learning algorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop machine learning models. AUROC and area under the precision–recall curve (AUPRC) were used to assess discrimination. Shapley additive explanations (SHAP) analysis was used to explain the contributions of the features.ResultsA total of 523 patients were enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning model had the best predictive performance, with an AUROC of 0.7571 and an AUPRC of 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotransferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level.ConclusionsCompared with the LACE index, the machine learning model can accurately identify the risk of 1 year readmission for heart failure in elderly patients with arrhythmia.

Shenfu injection (SFI), composed of ginseng and aconite, is a Chinese patent developed from the classic traditional prescription Shenfu Decoction created more than 700 years ago. SFI has been widely used in China for over 30 years for treating cardiovascular diseases. The main components in it include ginsenosides and aconitum alkaloids. In recent years, the role of SFI in the treatment of cardiovascular diseases has attracted much attention. The pharmacological effects and therapeutic applications of SFI in cardiovascular diseases are summarized here, highlighting pharmacological features and potential mechanisms developments, confirming that SFI can play a role in multiple ways and is a promising drug for treating cardiovascular diseases.

Glioblastoma (GBM) is the most common malignant tumor in the brain with temozolomide (TMZ) as the only approved chemotherapy agent. GBM is characterized by susceptibility to radiation and chemotherapy resistance and recurrence as well as low immunological response. There is an urgent need for new therapy to improve the outcome of GBM patients. We previously reported that 3-O-acetyl-11-keto-β-boswellic acid (AKBA) inhibited the growth of GBM. In this study we characterized the anti-GBM effect of S670, a synthesized amide derivative of AKBA, and investigated the underlying mechanisms. We showed that S670 dose-dependently inhibited the proliferation of human GBM cell lines U87 and U251 with IC 50 values of around 6 μM. Furthermore, we found that S670 (6 μM) markedly stimulated mitochondrial ROS generation and induced ferroptosis in the GBM cells. Moreover, S670 treatment induced ROS-mediated Nrf2 activation and TFEB nuclear translocation, promoting protective autophagosome and lysosome biogenesis in the GBM cells. On the other hand, S670 treatment significantly inhibited the expression of SXT17, thus impairing autophagosome-lysosome fusion and blocking autophagy flux, which exacerbated ROS accumulation and enhanced ferroptosis in the GBM cells. Administration of S670 (50 mg·kg −1 ·d −1 , i.g.) for 12 days in a U87 mouse xenograft model significantly inhibited tumor growth with reduced Ki67 expression and increased LC3 and LAMP2 expression in the tumor tissues. Taken together, S670 induces ferroptosis by generating ROS and inhibiting STX17-mediated fusion of autophagosome and lysosome in GBM cells. S670 could serve as a drug candidate for the treatment of GBM.

Bletilla striata has been used for thousands of years and shows the functions of stopping bleeding, reducing swelling, and promoting healing in traditional applications. For Bletilla striata, Bletilla striata polysaccharides (BSP) is the main active ingredient, exhibiting biological functions of anti-inflammatory, anti-oxidant, anti-fibrotic, immune modulation, anti-glycation, and so on. In addition, BSP has exhibited the characteristics of excipient such as bio-adhesion, bio-degradability, and bio-safety and has been prepared into a series of preparations such as nanoparticles, microspheres, microneedles, hydrogels, etc. BSP, as both a drug and an excipient, has already aroused more and more attention. In this review, publications in recent years related to the extraction and identification, biological activities, and excipient application of BSP are reviewed. Specifically, we focused on the advances in the application of BSP as a formulation excipient. We hold opinion that BSP not only needed more researches in the mechanisms, but also the development into hydrogels, nano-formulations, tissue engineering, and so on. And we believe that this paper provides a beneficial reference for further BSP innovation and in-depth research and promotes the use of these natural products in pharmaceutical applications.

Objective: The aim was to systematically compare the drug compatibility with various closed intravenous (i.v.) infusion containers, to provide a reference for selecting a relatively superior infusion container and improve the medication safety for patients in clinical practice.Methods: The compatibility of four commonly used clinical injections (ceftazidime, pantoprazole sodium, ambroxol hydrochloride, edaravone) with three representative closed i. v. infusion containers (non-PVC infusion bags, upright polypropylene infusion bags, inner sealed polypropylene infusion bags) prefilled with infusion fluids (0.9% sodium chloride or 5% dextrose) in the Chinese market were investigated in this study. The particle counts of both infusion fluids and diluted chemical injections by infusion fluids in various infusion containers were determined by the light obscuration method. At 0, 2 and 6 h after four injections following dilution with infusion fluids in each container, the pH of the solutions was detected, and the physical properties were examined by visual inspection. Meanwhile, the drug concentrations were assessed by high performance liquid chromatography (HPLC).Results: As for either infusion fluids or diluted injections by infusion fluids, the particle counts in non-PVC infusion bags were significantly greater than those in the other two bags under some circumstances. The particle counts in diluted injections by infusion fluids increased dramatically compared with those in infusion fluids in all infusion containers, especially for the small-size particles. But pH, physical properties and drug concentrations of diluted infusion solutions in all infusion containers remained nearly unchanged over the test period.Conclusion: Closed i. v. infusion containers included in this study are all well-compatible with four injections. Moreover, the closed infusion containers produced by Chinese manufacturers have met the international quality standard. Particularly, the intravenous admixture preparation process needs to be optimized to reduce the overall particulate contaminants.

Background: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studied. Aims: The study aimed to develop 30-day and 1-year readmission prediction models for elderly IHD patients using combined machine learning features routinely collected at the time of hospital discharge, and to investigate their prognostic impact. Methods: Eight machine learning algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were used to assess discrimination. SHapley Additive exPlanations (SHAP) analysis was used to explain the contribution of features. Results: A total of 6687 patients were enrolled. For 30-day readmissions, the categorical boosting (CB) model had the best predictive performance with the highest AUROC (0.72), and the Brier score was 0.23. For 1-year readmissions, the CB model had the best predictive performance with the highest AUROC (0.66), and the Brier score was 0.14. The age-adjusted Charlson comorbidity index, brain natriuretic peptide, heart failure, cholesterol, free thyroxine, thymidine kinase 1, osmotic pressure, and red blood cell distribution width (standard deviation) were the common important features to predict 30-day and 1-year readmissions of elderly IHD patients. Conclusions: Elderly IHD patients with high risk of 30-day or 1-year readmission can be identified using machine learning and features collected at the time of discharge.

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.

An asymmetric dearomative tandem annulation between 3-nitroindoles and 7-oxo-5-heptenals was developed by using modularly designed organocatalysts self-assembled from cinchona-squaramide and d -proline.

Background:Elderly patients frequently experience a high incidence of adverse drug events (ADEs) due to the coexistence of multiple diseases, the combination of various medications, poor medication compliance, and other factors. Global Trigger Tool (GTT) is a new method for identifying ADEs, introducing the concept of a trigger, that is, clues including abnormal laboratory values, reversal drugs, and clinical symptoms that may suggest ADEs, and specifically locating information related to ADEs in the medical record to identify ADEs. The aim of this study was to establish a GTT-based trigger tool for adverse medication events in elderly patients and to investigate the risk variables associated with such events.Methods:The triggers were identified by reviewing the frequency of ADEs in elderly patients in Sichuan, China, retrieving relevant literature, and consulting experts. A retrospective analysis was carried out to identify adverse medication occurrences among 480 elderly inpatients in Sichuan People's Hospital.Results:A total of 56 ADEs were detected in 51 patients (10.62%), 13.04 per 1,000 patient days, and 11.67 per 100 admissions. The overall positive predictive value (PPV) of the triggers was 23.84, and 94.64% of ADEs caused temporary injury. Gastrointestinal system injury (27.87%) and metabolic and nutritional disorders (24.53%) were the primary organ-systems affected by ADEs. The majority of ADEs were caused by drugs used to treat cardiovascular diseases. 71.43% of ADE occurred within 2 days of administration and the risk factor analysis of ADE revealed that the number of medicines had a significant correlation.Conclusion:This study demonstrated GTT's value as a tool for ADEs detection in elderly inpatients in China. It enhances the level of medication management and comprehensively reflects the situation of ADE of the elderly.

Long-acting and specific targeting are two important properties of excellent drug delivery systems. Currently, the long-acting strategies based on polyethylene glycol (PEG) are controversial, and PEGylation is incapable of simultaneously possessing targeting ability. Thus, it is crucial to identify and develop approaches to produce long-acting and targeted drug delivery systems. Sialic acid (SA) is an endogenous, negatively charged, nine-carbon monosaccharide. SA not only mediates immune escape in the body but also binds to numerous disease related targets. This suggests a potential strategy, namely "sialylation," for preparing long-acting and targeted drug delivery systems. This review focuses on the application status of SA-based long-acting and targeted agents as a reference for subsequent research.