Plasma Insulin Predicts Early Amyloid-ß Pathology Changes in Alzheimer's Disease.

Background: Evidence suggests that type 2 diabetes (T2D) is an independent risk factor for Alzheimer's disease (AD), sharing similar pathophysiological traits like impaired insulin signaling. Objective: To test the association between plasma insulin and cerebrospinal fluid (CSF) AD pathology. Methods: A total of 304 participants were included in the Alzheimer's Disease Neuroimaging Initiative, assessing plasma insulin and CSF AD pathology. We explored the cross-sectional and longitudinal associations between plasma insulin and AD pathology and compared their associations across different AD clinical and pathological stages. Results: In the non-demented group, amyloid-ß (Aß)+ participants (e.g., as reflected by CSF Aß42) exhibited significantly lower plasma insulin levels compared to non-demented Aß-participants (p < 0.001). This reduction in plasma insulin was more evident in the A+T+ group (as shown by CSF Aß42 and pTau181 levels) when compared to the A-T- group within the non-dementia group (p = 0.002). Additionally, higher plasma insulin levels were consistently associated with more normal CSF Aß42 levels (p < 0.001) across all participants. This association was particularly significant in the Aß-group (p = 0.002) and among non-demented individuals (p < 0.001). Notably, baseline plasma insulin was significantly correlated with longitudinal changes in CSF Aß42 (p = 0.006), whereas baseline CSF Aß42 did not show a similar correlation with changes in plasma insulin over time. Conclusions: These findings suggest an association between plasma insulin and early Aß pathology in the early stages of AD, indicating that plasma insulin may be a potential predictor of changes in early Aß pathology.

A Chinese Multi-Specialty Delphi Consensus to Optimize RAASi Usage and Hyperkalaemia Management in Patients with Chronic Kidney Disease and Heart Failure.

Objective: Variations are present in common clinical practices regarding best practice in managing hyperkalaemia (HK), there is therefore a need to establish a multi-specialty approach to optimal renin-angiotensin-aldosterone system inhibitors (RAASi) usage and HK management in patients with chronic kidney disease (CKD) & heart failure (HF).This study aimed to establish a multi-speciality approach to the optimal use of RAASi and how to manage HK in patients with CKD and HF.Methods: A steering expert group of cardiology and nephrology experts from across China convened to discuss challenges to HK management through a nominal group technique (NGT). The group then created a list of 41 statements for a consensus questionnaire, which was distributed for a further survey of in extended panel group of cardiologists and nephrologists across China. Consensus was assessed using a modified Delphi technique, with agreement defined as "strong" (≥75% and <90%) and "very strong" (≥90%). The steering group, data collection, and analysis were aided by an independent facilitator. Results: A total of 150 responses from 21 provinces across China were recruited in the survey. Respondents were comprised of an even split (n=75, 50%) between cardiologists and nephrologists. All 41 statements achieved the 75% consensus agreement threshold, of which 27 statements attained very strong consensus (≥90% agreement) and 14 attained strong consensus (agreement between 75% and 90%). Conclusions: Based on the agreement levels from respondents, the steering group agreed a set of recommendations intended to improve patient outcomes in the use of RAASi therapy and HK management in China.

Sex differences in the relationship between depression and Alzheimer's disease-mechanisms, genetics, and therapeutic opportunities.

Depression and Alzheimer's disease (AD) are prevalent neuropsychiatric disorders with intriguing epidemiological overlaps. Their interrelation has recently garnered widespread attention. Empirical evidence indicates that depressive disorders significantly contribute to AD risk, and approximately a quarter of AD patients have comorbid major depressive disorder, which underscores the bidirectional link between AD and depression. A growing body of evidence substantiates pervasive sex differences in both AD and depression: both conditions exhibit a higher incidence among women than among men. However, the available literature on this topic is somewhat fragmented, with no comprehensive review that delineates sex disparities in the depression-AD correlation. In this review, we bridge these gaps by summarizing recent progress in understanding sex-based differences in mechanisms, genetics, and therapeutic prospects for depression and AD. Additionally, we outline key challenges in the field, holding potential for improving treatment precision and efficacy tailored to male and female patients' distinct needs.

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. METHODS: This study involved 961 patients, with prospective analysis of data from 244 patients with major trauma at our hospital and retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine learning algorithms used to train models included logistic regression (LR), decision tree (DT), extreme gradient boosting machine (eXGBM), neural network (NN), random forest (RF), and light gradient boosting machine (LightGBM). RESULTS: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917-0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883-0.930) and 0.912 (95% CI: 0.880-0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866-0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860-0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751-0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM was emerged as the optimal model, and it was deployed online as an AI application. CONCLUSIONS: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.

A retrospective study on intracranial mixed infection with tuberculous meningitis in Shenzhen, China.

Tuberculous meningitis (TBM) is a prevalent global intracranial infection and the most lethal and disabling form of tuberculosis. TBM with mixed intracranial infections is clinically rare but has a higher mortality rate. To investigate the clinical characteristics of TBM with mixed intracranial infections, demographic and clinical data of TBM and pulmonary tuberculosis (PTB) patients admitted to Shenzhen Third People's Hospital between January 2015 and October 2022 were collected anonymously. A total of 207 cases of TBM were diagnosed, of which 16 cases (7.73%) were TBM with mixed intracranial infections. The overall mortality rate of TBM cases was 16.4%, while the mortality rate of TBM cases with mixed intracranial infections was as high as 35.7%. Compared to simple TBM cases, TBM cases with mixed intracranial infections had severer clinical symptoms. The percentage of human immune deficiency virus (HIV)-positive TBM cases with mixed intracranial infections reached up to 68.8%. HIV co-infection, CD4+/CD8+ T-cell counts less than 1, cranial nerve impairment, paralysis, cerebral infarction, PRO less than 450 mg/L, WBC less than 10 × 106 /L, and CL more than 120 mmol/L were risk factors for TBM cases with mixed intracranial infections. Compared to PTB, HIV co-infection, CD4+ T cell less than 550 /uL, and age less than 45 years were risk factors for TBM, and TBM was associated with higher mortality rates. Our study provides additional data to better understand single TBM and TBM with mixed intracranial infections. More than two-thirds of TBM cases with mixed intracranial infections were HIV-positive. Clinicians should consider the possibility of multiple infections in people with TBM/HIV co-infection. IMPORTANCE: TBM can cause severe neurological damage and death, and TBM with mixed intracranial infections can exacerbate the damage and poor prognosis of the disease. TBM with mixed intracranial infections is a rare disease, which has led to an incomplete understanding of its clinical features. This study investigated the clinical features of TBM and its associated factors by comparing the characteristics of TBM with mixed intracranial infections, single TBM and pulmonary tuberculosis. This information will help to improve the understanding of TBM, diagnostic accuracy and treatment outcomes.

Design, docking optimization, and evaluation of biotin-PEG4-1,8-naphthalimide as a potent and safe antitumor agent with dual targeting of ferroptosis and DNA.

A set of biotin-polyethylene glycol (PEG)-naphthalimide derivatives 4a-4h with dual targeting of ferroptosis and DNA were designed and optimized using docking simulation as antitumor agents. Docking simulation optimization results indicated that biotin-PEG4-piperazine-1,8-naphthalimide 4d should be the best candidate among these designed compounds 4a-4h, and therefore, we synthesized and evaluated it as a novel antitumor agent. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and MGC-803 and U251 xenograft models identified 4d as a good candidate antitumor agent with potent efficacy and safety profiles, compared with amonafide and temozolomide. The findings of the docking simulations, fluorescence intercalator displacement (FID), western blot, comet, 5-ethynyl-2'-deoxyuridine (EdU), flow cytometry, transmission electron microscopy, and BODIPY-581/591-C11, FerroOrange, and dihydroethidium (DHE) fluorescent probe assays revealed that 4d could induce DNA damage, affect DNA synthesis, and cause cell cycle arrest in the S phase in MGC-803 cells. Also, it could induce lipid peroxidation and thus lead to ferroptosis in MGC-803 cells, indicating that it mainly exerted antitumor effects through dual targeting of ferroptosis and DNA. These results suggested that it was feasible to design, optimize using docking simulation, and evaluate the potency and safety of biotin-PEG-1,8-naphthalimide as a antitumor agent with dual targeting of ferroptosis and DNA, based on a multi-target drug strategy.

Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients.

BACKGROUND: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients. METHODS: We retrospectively analyzed data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH) in China. We utilized the machine learning model Extreme Gradient Boosting (XGBoost) and Shapley's additive interpretation method to predict the incidence of AAD in elderly ICU patients in an interpretable manner. RESULTS: A total of 848 adult ICU patients were eligible for this study. The XGBoost model predicted the incidence of AAD with an area under the receiver operating characteristic curve (ROC) of 0.917, sensitivity of 0.889, specificity of 0.806, accuracy of 0.870, and an F1 score of 0.780. The XGBoost model outperformed the other models, including logistic regression, support vector machine (AUC = 0.809), K-nearest neighbor algorithm (AUC = 0.872), and plain Bayes (AUC = 0.774). CONCLUSIONS: While the XGBoost model may not excel in absolute performance, it demonstrates superior predictive capabilities compared to other models in forecasting the incidence of AAD in elderly ICU patients categorized based on their characteristics.

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a National cohort study of 52,707 cases.

BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. METHODS: A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. FINDINGS: The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001). CONCLUSIONS: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

A novel ratiometric sensor for fluorimetric and visual dual-mode detection of Al3+ in environmental water based on the target-regulated formation of Eu MOFs.

Herein, a novel ratiometric sensor for fluorimetric and smartphone-assisted visual detection of Al3+ in environmental water was developed based on the target-regulated formation of Eu metal-organic frameworks (Eu MOFs). By employing 2-[4-(2-hydroxyethyl) piperazin-1-yl] ethanesulfonic acid (Hepes), Eu3+ and tetracycline (TC) as raw materials, Eu MOFs with red emission were facilely synthesized through the coordination of Eu3+ with Hepes and TC. However, upon the introduction of Al3+, a higher affinity of TC towards Al3+ resulted in the formation of a TC-Al3+ complex with green fluorescence and inhibited the generation of Eu MOFs. This led to an increase in green fluorescence and a decrease in red fluorescence accompanied by the fluorescence color of the solution changing from red to green under the illumination of the UV lamp. Thus, a ratiometric sensor for fluorimetric and the smartphone-assisted visual detection of Al3+ was established. The ratiometric sensor exhibited high sensitivity for Al3+ detection with a detection limit of 0.14 µM for fluorescence detection and 1.21 µM for visual detection. Additionally, the proposed strategy was successfully applied to detect Al3+ in the environmental water samples with satisfactory results, indicating great application prospects for environmental monitoring.

Editorial for the Special Issue "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare".

The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...].

Enantioselective Cyanofunctionalization of Aromatic Alkenes via Radical Anions.

Alkene radical ions constitute an integral and unique class of reactive intermediates for the synthesis of valuable compounds because they have both unpaired spins and charge. However, relatively few synthetic applications of alkene radical anions have emerged due to a dearth of generally applicable and mild radical anion generation approaches. Precise control over the chemo- and stereoselectivity in alkene radical anion-mediated processes represents another long-standing challenge due to their high reactivity. To overcome these issues, here, we develop a new redox-neutral strategy that seamlessly merges photoredox and copper catalysis to enable the controlled generation of alkene radical anions and their orthogonal enantioselective cyanofunctionalization via distonic-like species. This new strategy enables highly regio-, chemo-, and enantioselective hydrocyanation, deuterocyanation, and cyanocarboxylation of alkenes without stoichiometric reductants or oxidants under visible light irradiation. This protocol provides a new blueprint for the exploration of the transformation potential of alkene radical anions.

Observation of an isothermal glass transition in metallic glasses.

Glass transition, commonly manifested upon cooling a liquid, is continuous and cooling rate dependent. For decades, the thermodynamic basis in liquid-glass transition has been at the center of debate. Here, long-time isothermal annealing was conducted via molecular dynamics simulations for metallic glasses to explore the connection of physical aging in supercooled liquid and glassy states. An anomalous two-step aging is observed in various metallic glasses, exhibiting features of supercooled liquid dynamics in the first step and glassy dynamics in the second step, respectively. Furthermore, the transition potential energy is independent of initial states, proving that it is intrinsic for a metallic glass at a given temperature. We propose that the observed dynamic transition from supercooled liquid dynamics to glassy dynamics could be glass transition manifested isothermally. On this basis, glass transition is no longer cooling rate dependent, but is shown as a clear phase boundary in the temperature-energy phase diagram. Hence, a modified out-of-equilibrium phase diagram is proposed, providing new insights into the nature of glass transition.

Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement.

Objective: This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding. Study design: Cone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference. In addition, a local level-set model with adaptive edge feature enhancement was proposed to segment individual teeth precisely. This model adaptively enhances edge features based on the structure of the root boundary and accurately distinguishes between the close-contact root and alveolar bone resulting from tooth rotation or displacement. Results: The experimental results showed that the average Dice similarity coefficient values for incisors, canines, premolars, and molars were 93.30%, 93.47%, 93.24%, and 93.89%, respectively, and the average tooth centroid distances were 0.66, 0.61, 0.87, and 0.80 mm, respectively. Conclusion: The proposed method can effectively segment teeth without relying on highly precise annotated datasets, yielding satisfactory results even under complex structural interference between adjacent teeth or tooth rotation and displacement caused by dental crowding. It is more robust than the other methods and provides valuable data for further research and clinical practice.

Third ventricular width by transcranial sonography is associated with cognitive impairment in Parkinson's disease.

BACKGROUND: One-fourth of Parkinson's disease (PD) patients suffer from cognitive impairment. However, few neuroimaging markers have been identified regarding cognitive impairment in PD. OBJECTIVE: This study aimed to explore the association between third ventricular width by transcranial sonography (TCS) and cognitive decline in PD. METHOD: Participants with PD were recruited from one medical center in China. Third ventricular width was assessed by TCS, and cognitive function was analyzed by the Mini-Mental State Examination (MMSE). Receiver operating characteristic (ROC) analysis and Cox model analysis were utilized to determine the diagnostic and predictive accuracy of third ventricular width by TCS for cognitive decline in PD patients. RESULT: A total of 174 PD patients were recruited. Third ventricular width was negatively correlated with MMSE scores. ROC analysis suggested that the optimal cutoff point for third ventricular width in screening for cognitive impairment in PD was 4.75 mm (sensitivity 62.7%; specificity 75.6%). After 21.5 (18.0, 26.0) months of follow-up in PD patients without cognitive impairment, it was found that those with a third ventricular width greater than 4.75 mm exhibited a 7.975 times higher risk of developing cognitive impairment [hazard ratio = 7.975, 95% CI 1.609, 39.532, p = 0.011] compared with patients with a third ventricular width less than 4.75 mm. CONCLUSION: Third ventricular width based on TCS emerged as an independent predictor of developing cognitive impairment in PD patients.

You don't know why you (don't) exercise: The relationship between automatic processes and physical activity (or sedentary behavior): A meta-analysis.

OBJECTIVES: This meta-analysis aimed to examine the relationship between automatic processes and behavior as well as their moderators. METHODS: There were 126 effect sizes (Fisher's Z) extracted from 55 independent research studies involving 10,432 participants. Meta-analyses were conducted using Comprehensive Meta-Analysis Version 3.3. Univariate meta-regression models were used to analyze underlying moderators. RESULTS: Meta-analysis after correction found that automatic processes could significantly and positively predict behavior, but the effect size was small (Fisher's Z = 0.057, p < 0.001, 95% CI [0.043, 0.070]). CONCLUSIONS: Different components of automatic processes, task reliability, and study design moderated the relationship between automatic processes and behavior. Future research could explore automatic measures at a neurophysiological level or use population-matched stimuli for specific populations to improve measure validity. In addition, future research should accumulate evidence on how to reduce sedentary behavior.

Artificial intelligence and cognitive diagnosis based teaching resource recommendation algorithm.

In the realm of advanced technology, deep learning capabilities are harnessed to analyze and predict novel data, once it has absorbed existing information. When applied to the sphere of education, this transformative technology becomes a catalyst for innovation and reform, leading to advancements in teaching modes, methodologies, and curricula. In light of these possibilities, the application of deep learning technology to teaching resource recommendations is explored in this article. Within the context of the study, a bespoke recommendation algorithm for teaching resources is devised, drawing upon the integration of deep learning and cognitive diagnosis (ADCF). This intricately constructed model consists of two core elements: the Multi-layer Perceptron (MLP) and the Generalized Matrix Factorization (GMF), operating cohesively through stages of linear representation and nonlinear learning of the interaction function. The empirical analysis reveals that the ADCF model achieves 0.626 and 0.339 in the hits ratio (HR) and the Normalized Discounted Cumulative Gain (NDCG) respectively due to the traditional model, signifying its potential to add significant value to the domain of teaching resource recommendations.

Time course efficiency of MICE and HIIE on inhibitory control and HRV in adolescents with obesity and different cardiorespiratory fitness.

Background: Adolescent obesity is associated with impaired inhibitory control. Acute exercise can improve executive function. However, due to the influence of exercise intensity, cognitive test timing, and cardiorespiratory fitness (CF) level, the most effective exercise program remains controversial. Methods: The current study investigated the time-course effects of moderate-intensity continuous exercise (MICE) and high-intensity interval exercise (HIIE) on inhibitory control (Stroop) and task-related heart rate variability (HRV) in adolescents with different CF. A mixed experimental design of 2 CF levels (high CF, HCF; low CF, LCF) × 3 exercise methods (MICE, HIIE, CON) × 3 test timing (pre, post-0, post-20) was adopted. Heart rate variability (HRV) and Stroop task tests were conducted before exercise (pre), immediately after exercise (post-0), and 20 min after exercise (post-20). Results: Individuals with HCF exhibited a positive decrease in Stroop response time immediately and 20 min after MICE and HIIE, compared to pretest response times (RT). Conversely, individuals with LCF showed a slight increase in Stroop task (RT) only immediately after HIIE. All individuals had a slight increase in ACC after MICE and HIIE compared to before exercise. In addition, compared with the control group, the time-domain index (the square root of the mean squared differences of successive NN intervals, RMSSD) of HRV was significantly decreased, the frequency-domain index (the absolute power of the Low-Frequency band/the absolute power of the High-Frequency band ratio, LF/HF) was significantly increased after MICE and HIIE, and the effect of HIIE on RMSSD and LF/HF was significantly greater than that of MICE. Conclusion: The current study found that the acute effects of MICE and HIIE on inhibitory control in obese adolescents were influenced by the interaction of cognitive test timing and cardiorespiratory fitness. Individuals with high cardiorespiratory fitness performed better on the Stroop task than individuals with low cardiorespiratory fitness. The inhibitory control of HIIE in high-cardiorespiratory obese adolescents produced positive effects similar to those in MICE but more lasting, suggesting that HIIE is more beneficial for high-cardiorespiratory obese adolescents. MICE promoted inhibitory control in obese adolescents with low cardiorespiratory fitness, but HIIE impaired inhibitory control in obese adolescents with low cardiorespiratory fitness immediately after exercise, suggesting that low cardiopulmonary fitness obese adolescents may be suitable for MICE rather than HIIE exercise intervention. The shift from balanced HRV to sympathetic dominance after acute exercise reflects increased arousal levels and may be one of the underlying mechanisms by which acute exercise brings benefits to executive function.

How is occupational identity affected by the family environment of publicly funded students in local normal colleges? A moderated mediation model.

The occupational identity of local publicly funded students is associated with the stability of rural teachers. To discuss the influence mechanisms of family environment, psychological capital, and professional commitment on occupational identity, this study examined 395 local publicly funded students with the occupational identity scale, psychological capital scale, professional commitment scale compiled, and family environment scale in China. We found no significant difference in terms of sex, source of birth, only children or not, and from teachers' families or not. Junior students' occupational identity was significantly higher than that of freshmen. In addition, we found that psychological capital plays a total mediation role between family environment and occupational identity. Psychological capital explains the influence of family environment on occupational identity. While the moderating role of professional commitment on the family environment and psychological capital is not supported in this study, it positively moderates the mediation role of psychological capital and occupational identity. Overall, this study will be significant in improving the training quality of local publicly funded students.

Ginsenosides in endometrium-related diseases: Emerging roles and mechanisms.

Ginsenosides, agents extracted from an important herb (ginseng), are expected to provide new therapies for endometrium-related diseases. Based on the molecular types of ginsenosides, we reviewed the main pharmacological effects of ginsenosides against endometrium-related diseases (e.g., endometrial cancers, endometriosis, and endometritis). The mechanism of action of ginsenosides involves inducing apoptosis of endometrium-related cells, promoting autophagy of endometrium-related cells, regulating epithelial-mesenchymal transition (EMT) in endometrium-related cells, and activating the immune system to kill cells associated with endometrial diseases. We hope to provide a theoretical foundation for the treatment of endometrium-related diseases by ginsenosides.

Association between serum uric acid, hyperuricemia and periodontitis: a cross-sectional study using NHANES data.

OBJECTIVES: Diabetes and other metabolic diseases have been linked to the development of periodontitis, but little research has been done to determine whether serum uric acid (SUA) levels and hyperuricemia play a role. This study aimed to investigate the relationship between SUA, hyperuricemia, and periodontitis. METHODS: Using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, we created a nationally representative data set. We used multivariable logistic regression models to assess the relationship between SUA, hyperuricemia, and periodontitis and presented odds ratios (OR) in women and men, respectively. RESULTS: In women, adjusted multivariable regression models showed that SUA (4.1-4.3mg/dl) was associated with higher odds of periodontitis (OR = 1.43; 95% confidence interval (CI):1.0 ~ 2.03, p = 0.047) with SUA (≤ 3.3mg/dl) as reference. The risk of periodontitis tended to increase slightly but insignificantly with increasing SUA levels, and the adverse effects occurred only when SUA increased to a certain level, and then reached a plateau. In men, the adjusted OR values for SUA (4.9-5.2mg/dl), SUA (5.3-5.5mg/dl), SUA (5.9-6.2mg/dl), and SUA (6.3-6.5mg/dl) were 0.66 (95% CI: 0.45 ~ 0.96, p = 0.029), 0.58 (95% CI: 0.40 ~ 0.85, p = 0.006), 0.67(95% CI: 0.47 ~ 0.97, p = 0.035), and 0.67 (95% CI: 0.45 ~ 0.99, p = 0.043), respectively, with SUA (≤ 4.3mg/dl) as reference. The elevated SUA levels are protective against periodontitis, but there is a range within which the risk of periodontitis decreases, followed by a non-significant tendency to increase. CONCLUSIONS: The levels of SUA that are linked to the risk of periodontitis. Future prospective longitudinal studies and strategies are required to further confirm whether controlled SUA treatment is an effective adjunct to systematic periodontal therapy and whether SUA can be used as a diagnostic biomarker to assess the risk or progression of periodontitis.