Association of low muscle mass with cognitive function and mortality in USA seniors: results from NHANES 1999-2002.

BACKGROUND: Sarcopenia and cognitive impairment have been linked in prior research, and both are linked to an increased risk of mortality in the general population. Muscle mass is a key factor in the diagnosis of sarcopenia. The relationship between low muscle mass and cognitive function in the aged population, and their combined impact on the risk of death in older adults, is currently unknown. This study aimed to explore the correlation between low muscle mass and cognitive function in the older population, and the relationship between the two and mortality in older people. METHODS: Data were from the National Health and Nutrition Examination Survey 1999-2002. A total of 2540 older adults aged 60 and older with body composition measures were included. Specifically, 17-21 years of follow-up were conducted on every participant. Low muscle mass was defined using the Foundation for the National Institute of Health and the Asian Working Group for Sarcopenia definitions: appendicular lean mass (ALM) (< 19.75 kg for males; <15.02 kg for females); or ALM divided by body mass index (BMI) (ALM: BMI, < 0.789 for males; <0.512 for females); or appendicular skeletal muscle mass index (ASMI) (< 7.0 kg/m2 for males; <5.4 kg/m2 for females). Cognitive functioning was assessed by the Digit Symbol Substitution Test (DSST). The follow-up period was calculated from the NHANES interview date to the date of death or censoring (December 31, 2019). RESULTS: We identified 2540 subjects. The mean age was 70.43 years (43.3% male). Age-related declines in DSST scores were observed. People with low muscle mass showed lower DSST scores than people with normal muscle mass across all age groups, especially in the group with low muscle mass characterized by ALM: BMI (60-69 years: p < 0.001; 70-79 years: p < 0.001; 80 + years: p = 0.009). Low muscle mass was significantly associated with lower DSST scores after adjusting for covariates (ALM: 43.56 ± 18.36 vs. 47.56 ± 17.44, p < 0.001; ALM: BMI: 39.88 ± 17.51 vs. 47.70 ± 17.51, p < 0.001; ASMI: 41.07 ± 17.89 vs. 47.42 ± 17.55, p < 0.001). At a mean long-term follow-up of 157.8 months, those with low muscle mass were associated with higher all-cause mortality (ALM: OR 1.460, 95% CI 1.456-1.463; ALM: BMI: OR 1.452, 95% CI 1.448-1.457); ASMI: OR 3.075, 95% CI 3.063-3.088). In the ALM: BMI and ASMI-defined low muscle mass groups, participants with low muscle mass and lower DSST scores were more likely to incur all-cause mortality ( ALM: BMI: OR 0.972, 95% CI 0.972-0.972; ASMI: OR 0.957, 95% CI 0.956-0.957). CONCLUSIONS: Low muscle mass and cognitive function impairment are significantly correlated in the older population. Additionally, low muscle mass and low DSST score, alone or in combination, could be risk factors for mortality in older adults.

Association between oxidative balance score and sarcopenia in US adults: NHANES 2011-2018.

Background: Sarcopenia, a condition characterized by diminished skeletal muscle mass, strength, and function, accompanied by inflammation and oxidative stress, remains an area of limited exploration concerning its correlation with the Oxidative Balance Score (OBS). Methods: Leveraging data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES), we meticulously examined 16 dietary and four lifestyle factors to derive the OBS. Adjusting appendicular skeletal muscle mass (ASM) by body mass index (BMI) served as the designated marker for sarcopenia. To scrutinize the association between OBS and sarcopenia, we conducted weighted logistic regression and engaged in sensitivity analysis. Furthermore, we implemented subgroup analysis and interaction tests to gain comprehensive insights into the relationship across diverse populations. Results: In a sample comprising 6,677 individuals aged 20-59, logistic regression illuminated a negative association between OBS and sarcopenia [OR = 0.942 (0.920, 0.964), p < 0.001]. Robust associations were also discerned between diseases and both dietary and lifestyle OBS. Subgroup analysis unveiled a more pronounced negative association in older, married/living with partner or more educated individuals. Moreover, this association persisted in populations grappling with comorbidities such as hypertension, diabetes, cancer, and arthritis. Conclusion: Our study posits a perceptible link between OBS and the prevalence of sarcopenia among American adults.

Advances and key focus areas in gastric cancer immunotherapy: A comprehensive scientometric and clinical trial review (1999-2023).

BACKGROUND: Gastric cancer (GC) is the sixth most common cancer and third leading cause of cancer-related deaths worldwide. Current treatments mainly rely on surgery- and chemotherapy-based systemic; however, the prognosis remains poor for advanced disease. Recent studies have suggested that immunotherapy has significant potential in cancer therapy; thus, GC immunotherapy may improve quality of life and survival for patients with this disease. AIM: To provide a comprehensive overview of the knowledge structure and research hotspots of GC immunotherapy. METHODS: We conducted a bibliometric analysis of publications on immunotherapy related to GC in the Web of Science Core Collection database. We analyzed 2013 pub-lications from 1999 to February 1, 2023, using the VOSviewer and CiteSpace software. We assessed publication and citation distributions using the WoS platform and explored research countries, institutions, journals, authors, references, and keywords (co-occurrence, timeline view, and burst analysis). In addition, we examined 228 trials on immunotherapy, 137 on adoptive cell therapy, 274 on immune checkpoint inhibitors (ICIs), and 23 on vaccines from ClinicalTrials.gov and the International Clinical Trials Registry Platform. The Impact Index Per Article for the top ten high-cited papers collected from Reference Citation Analysis (RCA) are presented. RESULTS: Our bibliometric analysis revealed that the study of immunotherapy in GC has developed rapidly in recent years. China accounted for almost half the publications, followed by the United States. The number of publications in recent years has been growing continuously, and most institutions and authors with the most publications are from China. The main keywords or clusters identified were "tumor microenvironment", "adoptive immunotherapy", "dendritic therapy", and "microsatellite instability". CONCLUSION: Our analysis of 2013 publications indicated that immunotherapy for GC has led to several new developments in recent years. Considerable progress has been made in vaccinations, immune checkpoint therapy, and adoptive cellular therapy. In particular, ICIs and chimeric antigen receptor T-cells are novel options for the treatment of GC. We suggest that the combination of ICIs, chemotherapy, targeted therapy, and other immunotherapies should be the primary research direction in the future.

How does financial decentralization synergies carbon reduction and pollution control in China?

This study looks at how carbon reduction, pollution management, and monetary decentralization in China all work together for a win-win situation. Since China is the most significant contributor to global warming, the country must implement policies to cut carbon emissions and curb pollution. One possible answer is financial decentralization, delegating federal financial responsibilities, and decisions to state and regional governments. The study used the weighted matrix analysis technique, LM matrix analysis technique, and ARDL short-run and long-run analysis estimates. However, the degree to which it will help China reduce carbon emissions and regulate pollution is unclear. This study takes a multifaceted approach to the investigation of this problem. Determining the efficacy of financial decentralization in addressing environmental concerns and drawing policy implications for China's environmental governance framework requires investigating the drivers of this trend and the mechanisms through which it operates. We perform a comprehensive empirical analysis to examine the results of using Chinese data from 1999 to 2019. This study's results provide new information to the literature by showcasing the power of fiscal decentralization in propelling environmentally sound policies in China. Central policy takeaways from the report include decentralizing financial authority to local governments, encouraging cooperation across multiple tiers of government, and setting up effective systems for monitoring and enforcing compliance. These policy suggestions can help China decrease carbon emissions and regulate pollution more efficiently, paving the way to better environmental results and a more sustainable future.

Design of online formative assessment of nursing humanities curriculum during the COVID-19 pandemic: A teaching practice research.

BACKGROUND: Under the digital transformation trend nursing education, online formative assessment (OFA) provides a new opportunity. However, the OFA of nursing humanities course lacks design and practice, and faces the challenge of enhancing effective communication between teachers and students, student participation and autonomous learning. OBJECTIVES: To enhance the reliability of OFA in nursing humanities courses and provide practical experience for online teaching in the nursing profession. DESIGN: A quantitative research approach was used. SETTING: This study was conducted in a comprehensive university in China. PARTICIPANTS: We conducted teaching practice on 185 nursing undergraduates, with 89 students in the experimental group, and 96 students in the control group. METHODS: In the 2020-2021 multicultural nursing course, student learning outcomes and questionnaires were analyzed through the online learning tool Superstar Learning, student feedback and satisfaction questionnaires, and descriptive analysis and independent sample t-tests were conducted using SPSS 25.0 software. RESULTS: The OFA of students using Superstar Learning differed in learning performance and time to receive feedback from teachers between the experimental and control groups, and both groups had higher satisfaction levels. The experimental group's instructional design contained a synchronous classroom discussion module with better participation. CONCLUSIONS: During the COVID-19 pandemic, the use of online learning tools can support the implementation of OFA, build an environment where teachers and students participate together, have a positive impact on the continuous updating of teachers' teaching programs and students' learning outcomes. Simultaneous classroom discussions are expected to be an effective way to improve the reliability of OFA. Our instructional design, provides best practice suggestions for future online teaching and learning.

Reflection on the teaching of student-centred formative assessment in medical curricula: an investigation from the perspective of medical students.

BACKGROUND: Formative assessment (FA) is becoming increasingly common in higher education, although the teaching practice of student-centred FA in medical curricula is still very limited. In addition, there is a lack of theoretical and pedagogical practice studies observing FA from medical students' perspectives. The aim of this study is to explore and understand ways to improve student-centred FA, and to provide a practical framework for the future construction of an FA index system in medical curricula. METHODS: This study used questionnaire data from undergraduate students in clinical medicine, preventive medicine, radiology, and nursing at a comprehensive university in China. The feelings of medical students upon receiving student-centred FA, assessment of faculty feedback, and satisfaction were analysed descriptively. RESULTS: Of the 924 medical students surveyed, 37.1% had a general understanding of FA, 94.2% believed that the subject of teaching assessment was the teacher, 59% believed that teacher feedback on learning tasks was effective, and 36.3% received teacher feedback on learning tasks within one week. In addition, student satisfaction results show that students' satisfaction with teacher feedback was 1.71 ± 0.747 points, and their satisfaction with learning tasks was 1.83 ± 0.826 points. CONCLUSION: Students as participants and collaborators in FA provide valid feedback for improving student-centred FA in terms of student cognition, empowered participation, and humanism. In addition, we suggest that medical educators avoid taking student satisfaction as a single indicator for measuring student-centred FA and to try to build an assessment index system of FA, to highlight the advantages of FA in medical curricula.

Immunotherapy in cervical cancer: From the view of scientometric analysis and clinical trials.

Background: Cervical cancer is the fourth most cancer and the fourth leading cause of cancer-related deaths in women worldwide. Current treatment for patients with advanced cervical cancer is limited. And in the urgent demand for novel effective therapies both as the first and the second line treatment for these patients, immunotherapy is developing fast and has made some achievements. Methods: This study incorporated 1,255 topic-related articles and reviews from 1999 to 2022 in the Web of Science Core Collection (WoSCC). The WoS platform, Citespace, and VOS viewer provided the annual distribution of publications and citations, the analysis of researching countries and institutions, references, keywords (co-occurrence analysis, burst analysis, and timeline view analysis), and researching authors, respectively. For clinical trials, 720 trials and 114 trials from ClinicalTrials.gov and ICTRP were retrieved, respectively. And 296 trials were finally incorporated into the analysis. Results: The scientometric analysis showed that the study of immunotherapies in cervical cancer developed fast in recent years. Most publications were from the United States, followed by China. Seven of the top 10 co-cited references belong to clinical trials, and five of them were published in recent five years. There are lots of clinical trials us specific treatment patterns, some of which have represented excellent effects. Conclusions: Both the scientometric analysis of the 1,255 publications and the analysis of clinical trials showed that the field of immunotherapies in cervical cancer developed so fast in recent years. It was found that a lot of clinical trials using various immunotherapies (mainly vaccine therapy, adoptive cell therapy, immune checkpoint blockade, and antibody-drug conjugate) for advanced cervical cancer are currently ongoing or have represented considerable effect. Centered in immunotherapies, immune checkpoint blockades have represented great efficacy and huge potential, especially combined with other therapies such as chemotherapy, targeted therapy, and other immunotherapies.

Effectiveness of inactivated COVID-19 vaccines against mild disease, pneumonia, and severe disease among persons infected with SARS-CoV-2 Omicron variant: real-world study in Jilin Province, China.

It is critical to determine the real-world performance of vaccines against coronavirus disease 2019 (COVID-19) so that appropriate treatments and policies can be implemented. There was a rapid wave of infections by the Omicron variant in Jilin Province (China) during spring 2022. We examined the effectiveness of inactivated vaccines against Omicron using real-world data from this epidemic. This retrospective case-case study of vaccine effectiveness (VE) examined infected patients who were quarantined and treated from April 16 to June 8, 2022 and responded to an electronic questionnaire. Data were analyzed by univariable and multivariable analyses. A total of 2968 cases with SARS-CoV-2 infections (asymptomatic: 1061, mild disease: 1763, pneumonia: 126, severe disease: 18) were enrolled in the study. Multivariable regression indicated that the risk for pneumonia or severe disease was greater in those who were older or had underlying diseases, but was less in those who received COVID-19 vaccines. Relative to no vaccination, VE against the composite of pneumonia and severe disease was significant for those who received 2 doses (60.1%, 95%CI: 40.0%, 73.5%) or 3 doses (68.1%, 95%CI: 44.6%, 81.7%), and VE was similar in the subgroups of males and females. However, VE against the composite of all three classes of symptomatic diseases was not significant overall, nor after stratification by sex. There was no statistical difference in the VE of vaccines from different manufacturers. The inactivated COVID-19 vaccines protected patients against pneumonia and severe disease from Omicron infection, and booster vaccination enhanced this effect.

Analysis of Environmental and Social Significant Factors Affecting the Flow of Maternal Patients in Jilin, China.

Background: With the implementation of China's Two-child policy, the number of pregnant women has been increasing year by year in recent years. However, the pregnancy success rate of pregnant women is declining year by year, and it is almost necessary for all the elderly mothers to do pregnancy protection. Objective: The purpose of this study is to analyze the social and environmental factors that affect the patient flow of pregnant women in Jilin area of China, and further utilize the favorable factors to avoid the negative effects of adverse factors, so as to improve the pregnancy success rate and eugenics level. Methods: Monthly patient flow data from 2018 to 2020 were collected in the obstetrics department of the First Hospital of Jilin University. The decompose function in R software was used to decompose the time series data, and the seasonal and trend change rules of the data were obtained; the significant factors influencing patient flow were analyzed by using Poisson regression model, and the prediction model was verified by using assumptions, such as the normal distribution of residuals and the constant difference of residuals. Results: Temperature in environmental factors (P = 4.00E-08) had a significant impact on the flow of obstetric patient. The flow of patients was also significantly affected by the busy farming (P = 0.0013), entrance (P = 3.51E-10) and festivals (P = 0.00299). The patient flow was accompanied by random flow, but also showed trend change and seasonal change. The trend of change has been increasing year by year. The seasonal variation rule is that the flow of patients presents a trough in February every year, and reaches the peak in July. Conclusion: In this article, Poisson regression model is used to obtain the social and environmental significant factors of obstetric patient flow. According to the significant factors, we should give full play to significant factors to further improve the level of eugenics. By using time series decomposition model, we can obtain the rising trend and seasonal trend of patient flow, and then provide the management with decision support, which is conducive to providing pregnant women with higher level of medical services and more comfortable medical experience.

The prognostic significance of bromodomain protein 4 expression in solid tumor patients: A meta-analysis.

BACKGROUND: Cancer is a leading cause of death worldwide. At present, several inhibitors of bromodomain protein 4 have shown promising anti-tumor responses in clinical trials. Numerous studies have reported the value of bromodomain protein 4 expression in predicting the prognosis of patients with cancers, but their conclusions remain controversial. Therefore, we conducted a meta-analysis to explore the association between bromodomain protein 4 and patient prognosis with the aim to provide new directions for the development of strategies for targeted cancer therapy. METHODS: The meta-analysis was registered in the International Prospective Register of Systematic Reviews (https://www.crd.york.ac.uk/prospero/; Registration No. CRD42020184948) and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. PubMed Central, PubMed, Cochrane Library and Embase were thoroughly searched to identify eligible studies published through March 31, 2021. Odds ratios with 95% confidence intervals were calculated to demonstrate the relationship between bromodomain protein 4 expression and clinicopathological features. We computed pooled estimated hazard ratios with 95% confidence intervals using Stata 12.0 software to clarify the relationship between bromodomain protein 4 expression and overall survival of various cancers. A quality assessment of the eligible articles was performed based on the Newcastle-Ottawa scale. RESULTS: A total of 974 patients from 10 studies were enrolled in the meta-analysis. Our results revealed that compared to low bromodomain protein 4 expression, high bromodomain protein 4 expression in cancer tissues was significantly associated with lymph node metastasis (Odds ratio = 3.59, 95% confidence interval: 2.62-4.91), distant metastasis (Odds ratio = 4.22, 95% confidence interval: 2.40-7.45), advanced TNM stage (III+IV vs. I+II: Odds ratio = 3.23, 95% confidence interval: 1.29-8.08), and poorly differentiated tumors (Odds ratio = 1.87, 95% confidence interval: 1.33-2.63). In addition, an elevated expression of bromodomain protein 4 tended to shorten survival time (Hazard ratio = 2.23, 95% confidence interval: 1.62-3.07). The subgroup analysis results showed that bromodomain protein 4 upregulation was related to poor prognosis in patients with digestive system cancers (Hazard ratio = 2.54, 95% confidence interval: 1.85-3.50). CONCLUSION: This meta-analysis indicated that bromodomain protein 4 may serve as a promising prognostic biomarker for cancers and a direct effective cancer treatment target.

Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.

BACKGROUND: With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care. OBJECTIVE: The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care. METHODS: Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes. RESULTS: Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases. CONCLUSIONS: Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.

A study of factors impacting disease based on the Charlson Comorbidity Index in UK Biobank.

Objective: With advances in medical diagnosis, more people are diagnosed with more than one disease. The damage caused by different diseases varies, so relying solely on the number of diseases to represent multimorbidity is limited. The Charlson comorbidity index (CCI) is widely used to measure multimorbidity and has been validated in various studies. However, CCI's demographic and behavioral risk factors still need more exploration. Methods: We conduct multivariate logistic regression analysis and restricted cubic splines to examine the influence factors of CCI and the relationship between covariates and risk of CCI, respectively. Our research employs the Multivariate Imputation by Chained Equations method to interpolate missing values. In addition, the CCI score for each participant is calculated based on the inpatient's condition using the International Classification of Diseases, edition 10 (ICD10). Considering the differences in the disease burden between males and females, the research was finally subgroup analyzed by sex. Results: This study includes 5,02,411 participants (2,29,086 female) with CCI scores ranging from 0 to 98. All covariates differed between CCI groups. High waist-hip ratio (WHR) increases the risk of CCI in both males [OR = 19.439, 95% CI = (16.261, 23.241)] and females [OR = 12.575, 95% CI = (11.005, 14.370)], and the effect of WHR on CCI is more significant in males. Associations between age, Body Mass Index (BMI) and WHR, and CCI risk are J-shaped for all participants, males, and females. Concerning the association between Townsend deprivation index (TDI) and CCI risk, the U-shape was found in all participants and males and varied to a greater extent in males, but it is a J-shape in females. Conclusions: Increased WHR, BMI, and TDI are significant predictors of poor health, and WHR showed a greater role. The impact of deprivation indices on health showed differences by sex. Socio-economic factors, such as income and TDI, are associated with CCI. The association of social status differences caused by these socioeconomic factors with health conditions should be considered. Factors might interact with each other; therefore, a comprehensive, rational, and robust intervention will be necessary for health.

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis.

Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management. Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year. Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy. Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

Research on obstetric ward planning combining lean thinking and mixed-integer programming.

BACKGROUND: In recent years, there are many studies on scheduling methods of patient flow, nurse scheduling, bed allocation, operating room scheduling and other problems, but there is no report on the research methods of how to plan ward allocation from a more macroscopic perspective. OBJECTIVE: Refine and stratify the obstetric ward to provide more accurate medical service for pregnant women and improve the work efficiency of obstetricians and midwives. The problem of how to allocate the number of each type of ward is modeled as a mixed integer programming problem, which maximizes the patient flow of pregnant women in obstetric hospitals. METHODS: The obstetric wards are divided into observation ward, cesarean section ward and natural delivery ward according to lean thinking. CPLEX is used to solve the mixed-integer programming problem of ward allocation. In R software, multivariate Generalized Linear Models (GLM) regression model is used to analyze the influence of each factor on patient flow. RESULTS: The maximum patient flow of each case was obtained by CPLEX, which was 19-25% higher than that of patients without refinement, stratification and planning. GLM regression analysis was carried out on the abovementioned data, and the positive and negative correlation factors were obtained. CONCLUSION: According to lean thinking, obstetric wards are divided into three types of wards. Obstetricians and midwives work more efficiently and get more rest time. Pregnant women also enjoy more detailed medical services. By modeling the delivery ward allocation problem as a mixed-integer programming problem, we can improve the capacity of the service in obstetric hospitals from a macro perspective. Through GLM regression model analysis, it is conducive to improve the obstetric hospital capacity from the perspective of positive and negative correlation factors.

What makes clinical documents helpful and engaging? An empirical investigation of experience sharing in an online medical community.

BACKGROUND: Social media have emerged as a platform for experience and knowledge sharing in the medical community. The online medical community is garnering increasing research attention; however, there is a lack of understanding of what factors influence the helpfulness and engagement of experience sharing in the community. METHODS: Clinical documents manifest physicians' experience and knowledge. This study fills the knowledge gap by investigating what elements of clinical documents contribute to the helpfulness of sharing clinical documents online and what influence member engagement. Clinical documents follow certain architecture to specify their structure and semantics for exchange (e.g., HL7 C-CDA). Accordingly, the structural elements of clinical documents may influence document helpfulness for the online community. Member engagement is one of the indicators of community success. We collected 6514 clinical documents from a real-world online medical community, and normalized them with the structural elements of HL7 C-CDA. We performed regression analyses to identify the structural elements that have significant impacts on document helpfulness and member engagement. RESULTS: The results show that some structural elements of clinical documents such as assessment, chief complaints, medications, physical exams, procedures, results, and vital signs sections have positive effects whereas assessment and plan, general status, history and past illness of patients, instructions, problem and review of systems have negative effects on the helpfulness of clinical documents. The results also reveal that structural elements such as family history, history of past illness, medication, physical exam, review of systems, and vital signs positively; whereas assessment, assessment and plan, instruction, and result negatively; influence member engagement. CONCLUSIONS: The findings provide guide on how to improve the effectiveness of sharing clinical experience online. The new and in-depth insights may contribute to the success of online medical communities and the quality of medical decisions.

Silibinin inhibits migration and invasion of the rhabdoid tumor G401 cell line via inactivation of the PI3K/Akt signaling pathway.

Rhabdoid tumors, which tend to occur prior to the age of 2 years, are one of the most aggressive malignancies and have a poor prognosis due to the frequency of metastasis. Silibinin, a natural extract, has been approved as a potential tumor suppressor in various studies, however, whether or not it also exerts its antitumor capacity in rhabdoid tumors, particularly with regards to tumor migration and invasion, is unclear. The rhabdoid tumor G401 cell line was used in the present in vitro study. An MTT assay was used to assess the cytotoxicity of silibinin on G401 cells, cell migration was studied using a wound healing assay and a Transwell migration assay, and cell invasion was determined using a Transwell invasion assay. The underlying mechanism in silibinin inhibited cell migration and invasion was investigated by western blot analysis and further confirmed using a specific inhibitor. Experimental results demonstrated that high doses of silibinin suppressed cell viability, and that low doses of silibinin inhibited cell migration and invasion without affecting cell proliferation. The phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway was involved in the silibinin-induced inhibition of metastasis. Silibinin inactivated the PI3K/Akt pathway, and inhibited cell migration and invasion, an effect that was further enhanced when LY294002, a classic PI3K inhibitor, was used concurrently. In general, silibinin inhibits migration and invasion of the rhabdoid tumor G401 cell line via inactivation of the PI3K/Akt signaling pathway and may be a potential chemotherapeutic drug to combat rhabdoid tumors in the future.