Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Identifying the best common factor model via exploratory eactor analysis

Currently,there is no solid criterion for judging the quality of the estimators in factor analysis.This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction.The proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model.Examples are presented to demonstrate how the method is implemented and to verify its effectiveness.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis

BACKGROUND Breast cancer(BC)has become the most common malignancy in women.The incidence and detection rates of BC brain metastasis(BCBM)have increased with the progress of imaging,multidisciplinary treatment techniques and the extension of survival time of BC patients.BM seriously affects the quality of life and survival prognosis of BC patients.Therefore,clinical research on the clinicopathological features and prognostic factors of BCBM is valuable.By analyzing the clinicopathological parameters of BCBM patients,and assessing the risk factors and prognostic indicators,we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM,and achieve clinical benefits of early diagnosis and treatment.AIM To explore the clinicopathological features and prognostic factors of BCBM,and provide references for diagnosis,treatment and management of BCBM.METHODS The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center,Chinese People’s Liberation Army(formerly Air Force General Hospital)from 2000 to 2022 were collected.Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis.Categorical data were subjected to χ^(2) test or Fisher’s exact probability test,and the variables with P<0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM,with a hazard ratio(HR)>1 suggesting poor prognostic factors.The survival time of patients was estimated by the Kaplan-Meier method,and overall survival was compared between groups by log-rank test.RESULTS Multivariate Cox regression analysis showed that patients with stage Ⅲ/Ⅳ tumor at initial diagnosis[HR:5.58,95% confidence interval(CI):1.99–15.68],lung metastasis(HR:24.18,95%CI:6.40-91.43),human epidermal growth factor receptor 2(HER2)-overexpressing BC and triple-negative BC were more prone to BM.As can be seen from the prognostic data,52 of the 68 BCBM patients had died by the end of follow-up,and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo,respectively.It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms(HR:1.923,95%CI:1.005-3.680),with bone metastasis(HR:2.011,95%CI:1.056-3.831),and BM of HER2-overexpressing and triple-negative BC had shorter survival time.CONCLUSION HER2-overexpressing,triple-negative BC,late tumor stage and lung metastasis are risk factors of BM.The presence of neurological symptoms,bone metastasis,and molecular type are influencing prognosis factors of BCBM.

A comprehensive approach to a variability analysis between earthquake activity and hydro-environmental factors on the Korean Peninsula

Following the Pohang and Gyeongju earthquakes and their aftershocks,there is no longer any zone that is safe from earthquake-related disasters in the Korean Peninsula.In order to monitor and predict earthquakes,correlation analysis of earthquakes and hydro-environmental factors are insufficient,and the development and application of hydro-environmental factor measurement equipment is still in the early stages.This study developes and verifies a more precise radon measurement device.Four specific earthquake cases(2019–2020)were selected,and the correlation of the analyses of the earthquakes and hydro-environmental factors(radon,electric conductivity(EC),water-level(WL),and water-temperature(WT))was conducted at the three specific groundwater stations.Accordingly,was confirmed that four factors are affected by earthquakes or seismic movement.Furthermore,the variability of the EC showed an identical tendency for a certain period before an earthquake occurred,and,in particular,the variability trends for radon,WL,and EC coincided at the time of the earthquake′s occurrence.

Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

Quality of life of hospitalized patients after lung cancer operation and analysis of influencing factors

Objective:To explore the current status and influencing factors of quality of life in patients with lung cancer after surgery in a tertiary hospital in Hainan province.Methods:To investigate the influencing factors of quality of life of lung cancer patients after surgery in a tertiary hospital in Hainan province by cross‑sectional survey method.Results:The scores of insomnia,appetite loss,constipation and pain in 186 lung cancer patients after surgery in a tertiary hospital in Hainan Province were significantly higher than the reference value.Multiple linear regression analysis showed that older patients(>60 years)had lower scores in physical function domain(β=-0.193),and female patients had more appetite loss symptoms(β=0.245).Compared with other minority ethnic groups,Han ethnic group had lower scores in role function domain(β=0.179),more severe fatigue symptoms(β=-0.162),and higher general health level(β=0.166).Patients with employee medical insurance had lower scores of emotional function(β=0.194),cognitive function(β=0.281),the lowest score in social function(β=0.188),and severe pain in other parts(β=-0.227).Smokers had less cough symptoms(β=0.175)and more arm and shoulder pain symptoms(β=-0.21)than non‑smokers.Patients with secondhand smoke exposure had lower cognitive function scores(β=-0.158)and more obvious symptoms of oral ulcer(β=0.185).Patients who drank alcohol frequently(drinking frequency>1 time/day)had more severe cough symptoms(β=0.27).Patients with small number of children(0‑1)had milder cough symptoms(β=0.178).Patients who did not understand the disease had obvious symptoms of arm and shoulder pain(β=0.151).Patients with early pathological stage(stageⅠ‑Ⅱ)had more severe shortness of breath(β=-0.159)and pain(β=-0.181).The symptoms of appetite loss were more obvious in patients living in cities(β=0.192).The symptoms of peripheral neuropathy were more obvious(β=0.174).Patients who often consumed pickulated food had severe pain symptoms(β=-0.219),and pain in other parts was obvious(β=-0.149).Male patients had obvious alopecia symptoms(β=-0.306).Conclusion:Age,ethnicity,residence,type of medical insurance,number of children,pathological stage of lung cancer,smoking,second‑hand smoke exposure,alcohol consumption,and frequent consumption of pickled food were related to the quality of life of lung cancer patients in hospital after surgery.Medical staff and family members should pay attention to the emotional communication of patients during the treatment of lung cancer patients in hospital after surgery.Patients should avoid exposure to smoking,alcohol and second‑hand smoke,and reduce consumption of pickled food.

Analysis of mental health status and related factors in patients with acute cerebral infarction

BACKGROUND Acute cerebral infarction(ACI)is characterized by a high incidence of morbidity,disability,recurrence,death and heavy economic burden,and has become a disease of concern in global researchers.As ACI has serious effects on patients’physical status,life and economy,often causing anxiety,depression and other psychological problems,these problems can lead to the aggravation of physical symptoms;thus,it is very important to understand the factors affecting the mental health of these patients.AIM To understand the elements that affect the mental health of patients who have suffered an ACI.METHODS A questionnaire survey was conducted among patients with ACI admitted to three tertiary hospitals(Quanzhou First Hospital,Fuqing City Hospital Affiliated to Fujian Medical University,and the 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China)in Fujian Province from January 2022 to December 2022 using the convenience sampling method.ACI inpatients who met the inclusion criteria were selected.Informed consent was obtained from the patients before the investigation,and a face-to-face questionnaire survey was conducted using a unified scale.The questionnaire included a general situation questionnaire,Zung’s self-rating depression scale and Zung’s self-rating anxiety scale.All questionnaires were checked by two researchers and then the data were input and sorted using Excel software.The general situation of patients with ACI was analyzed by descriptive statistics,the influence of variables on mental health by the independent sample t test and variance analysis,and the influencing factors on psychological distress were analyzed by multiple stepwise regression.RESULTS The average age of the 220 patients with ACI was 68.64±10.74 years,including 142 males and 78 females.Most of the patients were between 60 and 74 years old,the majority had high school or technical secondary school education,most lived with their spouse,and most lived in cities.The majority of patients had a personal income of 3001 to 5000 RMB yuan per month.The new rural cooperative medical insurance system had the largest number of participants.Most stroke patients were cared for by their spouses and of these patients,52.3%had previously smoked.Univariate analysis showed that gender,age,residence,course of disease,number of previous chronic diseases and smoking history were the main factors affecting the anxiety scores of patients with ACI.Age,living conditions,monthly income,course of disease and knowledge of disease were the primary variables influencing the depression score in patients with ACI.The findings of multivariate analysis revealed that the course of disease and gender were the most important factors influencing patients’anxiety scores,and the course of disease was also the most important factor influencing patients’depression scores.CONCLUSION Long disease course and female patients with ACI were more likely to have psychological problems such as a high incidence of emotional disorders.These groups require more attention and counseling.

Weight Analysis of the Influencing Factors of Homestay Competitiveness in Rural Guangzhou,China

At present,Guangzhou homestay industry is facing a bottleneck.Therefore,it is particularly important to analyze the factors that influence the competitiveness of rural homestays in Guangzhou,determine the evaluation system of competitiveness,and determine the weight of each factor.Based on Porter’s diamond theory,this paper analyzes and summarizes the influencing factors of homestay competitiveness,and divides the influencing factors into 5 primary factors and 34 secondary factors.The analytic hierarchy process(AHP)was used to determine the judgment matrix to form the weight results of each factor,and the results show that product characteristics account for the largest proportion among first level factors.Secondary factors such as theme creativity,personalized brand and the overall score account for a large proportion.The research results can act as a reference for the construction of competitiveness evaluation mechanism and model of local rural quality homestays.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Prognostic factors for lacrimal gland adenoid cystic carcinoma:a retrospective study in Chinese patients

AIM:To explore the prognostic factors for lacrimal gland adenoid cystic carcinoma(LGACC)in Chinese patients.METHODS:Clinical and histopathological data were reviewed in patients with pathologically confirmed LGACC.Local recurrence,metastasis,and disease-specific death were the main outcome measures.Univariate and multivariate analyses were performed by the Kaplan-Meier method and a Cox proportional hazard model.RESULTS:This retrospective cohort study included 45 patients with pathologically confirmed LGACC between January 2008 and June 2022.Tumor(T)classification(P=0.005),nodal metastasis(N)classification(P=0.018)and positive margin(P=0.008)were independent risk factors of recurrence;T(P=0.013)and N(P=0.003)classification and the basaloid tumor type(P=0.032)were independent risk factors for metastasis;T classification(P<0.001)was an independent factor of death of disease.In the further analysis,the durations from first surgery to radiotherapy is correlated with metastatic risk in LGACC patients with basaloid component(P=0.022).CONCLUSION:Histological subtype should be emphasized when evaluating prognosis and guiding treatment.Timely radiotherapy may reduce the risk of metastasis in patients with basaloid component.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Variation Characteristics of Root Traits of Different Alfalfa Cultivars under Saline-Alkaline Stress and their Relationship with Soil Environmental Factors

Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield.It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments.In this study,the root biomass,root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment group.At the same time,the correlation analysis of soil salinity indexes,soil nutrient indexes and the activities of key enzymes involved in soil carbon,nitrogen and phosphorus cycles was carried out.The results showed that compared with the control group,the pH,EC,and urease(URE)of the soil surrounding the roots of two alfalfa cultivars were significantly increased,while soil total nitrogen(TN),total phosphorus(TP),organic carbon(SOC),andα-glucosidase activity(AGC)were significantly decreased under saline-alkaline stress.There was no significant difference in root biomass and root morphological parameters of saline-alkaline tolerant cultivar GN under saline-alkaline stress.The number of root tips(RT),root surface area(RS)and root volume(RV)of AG were reduced by 61.16%,44.54%,and 45.31%,respectively,compared with control group.The ratios of K^(+)/Na^(+),Ca^(2+)/Na^(+)and Mg^(2+)/Na^(+)of GN were significantly higher than those of AG(p<0.05).The root fresh weight(RFW)and dry weight(RDW),root length(RL),RV and RT of alfalfa were positively regulated by soil SOC and TN,but negatively regulated by soil pH,EC,and URE(p<0.01).Root Ca^(2+)/Na+ratio was significantly positively correlated with soil TN,TP and SOC(p<0.01).The absorption of Mg and Ca ions in roots is significantly negatively regulated by soilβ-glucosidase activity(BGC)and acid phosphatase activity(APC)(p<0.05).This study improved knowledge of the relationship between root traits and soil environmental factors and offered a theoretical framework for elucidating how plant roots adapt to saline-alkaline stressed soil environments.

Association of education with cholelithiasis and mediating effects of cardiometabolic factors:A Mendelian randomization study

BACKGROUND Education,cognition,and intelligence are associated with cholelithiasis occurrence,yet which one has a prominent effect on cholelithiasis and which cardiometabolic risk factors mediate the causal relationship remain unelucidated.AIM To explore the causal associations between education,cognition,and intelligence and cholelithiasis,and the cardiometabolic risk factors that mediate the associations.METHODS Applying genome-wide association study summary statistics of primarily European individuals,we utilized two-sample multivariable Mendelian randomization to estimate the independent effects of education,intelligence,and cognition on cholelithiasis and cholecystitis(FinnGen study,37041 and 11632 patients,respectively;n=486484 participants)and performed two-step Mendelian randomization to evaluate 21 potential mediators and their mediating effects on the relationships between each exposure and cholelithiasis.RESULTS Inverse variance weighted Mendelian randomization results from the FinnGen consortium showed that genetically higher education,cognition,or intelligence were not independently associated with cholelithiasis and cholecystitis;when adjusted for cholelithiasis,higher education still presented an inverse effect on cholecystitis[odds ratio:0.292(95%CI:0.171-0.501)],which could not be induced by cognition or intelligence.Five out of 21 cardiometabolic risk factors were perceived as mediators of the association between education and cholelithiasis,including body mass index(20.84%),body fat percentage(40.3%),waist circumference(44.4%),waist-to-hip ratio(32.9%),and time spent watching television(41.6%),while time spent watching television was also a mediator from cognition(20.4%)and intelligence to cholelithiasis(28.4%).All results were robust to sensitivity analyses.CONCLUSION Education,cognition,and intelligence all play crucial roles in the development of cholelithiasis,and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.

Status quo and factors of depression and anxiety in patients with non-muscle invasive bladder cancer after plasma electrocision

BACKGROUND Bladder cancer is a type of cancer with a high incidence in men.Plasma electrosurgery(PES)is often used in the treatment of bladder cancer.Postoperative complications often cause depression and anxiety in patients after surgery.AIM To investigate the current state of depression and anxiety after PES in patients with non-muscle-invasive bladder cancer and analyze the factors affecting them.METHODS A retrospective study was conducted to compare the baseline data of patients by collecting their medical history and grouping them according to their mental status into negative and normal groups.Logistic regression analysis was used to explore the risk factors affecting the occurrence of anxiety and depression after surgery in patients with bladder cancer.RESULTS Comparative analyses of baseline differences showed that the patients in the negative and normal groups differed in terms of their first surgery,economic status,educational level,and marital status.A logistic regression analysis showed that it affected the occurrence of anxiety in patients with bladder cancer,and the results showed that whether the risk factors were whether or not it was the first surgery,monthly income between 3000 and 3000-6000,secondary or junior high school education level,single,divorced,and widowed statuses.CONCLUSION The risk factors affecting the onset of anxiety and depression in bladder cancer patients after PES are the number of surgeries,economic status,level of education,and marital status.This study provides a reference for the clinical treatment and prognosis of bladder cancer patients in the future.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Examining the Hypothesis of Common Factors Shared by Different Addictive Behaviors and Gender Effects on Propensity to Addiction Type

Background: From the two facts reported by previous research: 1) frequent co-occurrence of more than one addictive behavior, 2) childhood adversities identified as origins of different types of addictive behaviors, it is assumed that all types of addictive behaviors, regardless of substance, behavioral, or relationship, share common factors which have not yet been proven by epidemiological research. The Shorter PROMIS Questionnaire (SPQ) was previously developed to assess 16 types of addictive behaviors. Its factor structure, however, has not been fully investigated. Confirming the factor structure will enable us to hypothesize the common factor(s) shared by all, or if not all, most types of addictive behaviors. Aims: This study aimed at 1) examining the factor structure of the SPQ, 2) confirming the reliability of the questionnaire, and 3) examining the impacts of gender and age on each addictive behavior. Methods: Data obtained from 232 Japanese adults who completed all items of the SPQ were used for the analyses. After confirming the one-factor structure model for each of the 16 subscales, the validity of the one-factor structure of the SPQ was evaluated using Confirmatory Factor Analysis (CFA), by adapting 16 subscale scores as observed variables. If its validity was not confirmed, another model which showed better compatibility to the data was explored. The reliability of the SPQ as well as that of all 16 subscales was evaluated. Also, the impacts of gender and age on each subscale score were examined. Results: The one-factor structure for each of the 16 subscales was confirmed. The compatibility of the SPQ one-factor model was not acceptable. The best fit model was a bi-factor model in which one main factor was shared by all 16 subscales, and three factors were shared by some specific addictive behaviors. Male respondents were more likely than female respondents to show high scores in Alcohol, Tobacco, Gambling, Sex, and Recreational Drugs, and low scores only in Shopping. Respondents’ age did not impact any of the 16 subscale scores. Conclusion: It was demonstrated that there are common factors shared by all different types, as well as selected types of addictive behaviors, by conducting CFAs of the SPQ. Reliability was proven for the SPQ and for all 16 subscales. Male respondents were more likely to show physically hedonic addictive behaviors.