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AbstractThe combination of an anticoagulant and a hypolipidemic agent is commonly used in patients at risk for stroke because it controls thrombosis and lowers cholesterol levels. However, the use of anticoagulants carries a high risk of life-threatening bleeding. To address the need for personalized clinical monitoring and risk management, we developed a rapid and sensitive HPLC method with a streamlined sample preparation process. This method enables the simultaneous determination of concentrations for two commonly used anticoagulant drugs (apixaban and rivaroxaban) and three hypolipidemic drugs (rosuvastatin, atorvastatin calcium, and simvastatin). The chromatographic conditions and sample preparation methods were optimized. The drugs were well separated by the gradient elution of phosphoric acid (0.01%, v/v) and acetonitrile at a flow rate of 1.0 mL/min within 40 min on an Athena C18-WP column (4.6 × 150 mm, 5-μm particle size). The proposed method was linear (r2 ≥ 0.994) within the concentration ranges of 0.22–20 μg·mL−1 for the five analytes, including the expected range of therapeutic concentrations. This method enables simultaneous monitoring of the levels of the mentioned drugs in clinical practice. Furthermore, the chromatographic conditions were simple and feasible, making them highly suitable for emergency clinical medication monitoring in basic medical institutions. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to investigate and compare the efficacy and safety of intraoperative 125I implantation and postoperative irradiation after surgical decompression and stabilization in the treatment of patients with metastatic epidural spinal cord compression (MESCC). Methods: The study retrospectively enrolled 122 MESCC patients treated with surgical decompression and pedicle stabilization combined with 125I brachytherapy (the brachytherapy group) or postoperative radiotherapy (the irradiation group). Operation time, intraoperative blood loss, pain relief, postoperative ambulatory status, postoperative survival outcome, complications, and length of hospitalization were collected and compared between the two groups. Ten potential risk factors were analyzed for postoperative survival outcome. Results: No significant difference was found in baseline characteristics between the two groups (P> 0.05). Postoperative VAS score was significantly decreased, as compared with preoperative scores in both groups (P˂0.001). The VAS in the brachytherapy group was significantly lower than that in the irradiation group at postoperative 1 month, 3 months, and 6 months (P˂0.05). The postoperative ambulatory rates were 90.0% (54/60) in the brachytherapy group and 83.9% (52/62) in the irradiation group (P=0.32). The median overall survival time was similar between the two groups (7.43 months vs 7.27 months, P=0.37). Of all patients in the brachytherapy group, 25.0% (15/60) of patients suffered from complications, while 46.8% (29/62) of patients had complications in the irradiation group (P=0.0086). According to the multiple Cox regression, primary sites (P=0.038), ECOG performance status (P=0.014), and visceral metastases (P=0.0016) showed significance for postoperative survival outcome. Conclusion: Surgical decompression and spine stabilization combined with 125I brachytherapy is a relatively safe and useful method in MESCC patients. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to develop a scoring system for prediction of survival prognosis after surgery in patients with symptomatic metastatic spinal cord compression (MSCC) from non-small cell lung cancer (NSCLC).Methods: We retrospectively analyzed nine preoperative characteristics for survival in a series of 64 patients with NSCLC who were operated with posterior decompression and spine stabilization for MSCC. Characteristics significantly associated with survival on multivariate analysis were included in the scoring system. The scoring point for each significant characteristic was derived from the hazard ratios on Cox proportional hazards model. The total score for each patient was obtained by adding the scoring points of all significant characteristics.Results: Eastern Cooperative Oncology Group (ECOG) performance status, number of involved vertebrae, visceral metastases, and time developing motor deficits had significant impact on survival on multivariate analysis and were included in the scoring system. According to the prognostic scores, which ranged from 4 to 10 points, three prognostic groups were designed: 4-5 points (n = 22), 6-7 points (n = 23), and 8-10 points (n = 19). The corresponding 6-month survival rates were 95, 47 and 11%, respectively (P < 0.0001). In addition, the functional outcome was worse in the group of patients with 8-10 points compared with other two prognostic groups.Conclusions: The new scoring system will enable physicians to identify patient with MSCC from NSCLC who may be a candidate for decompression and spine stabilization, more radical surgery, or supportive care alone. Patients with scores of 4-5, who have the most favorable survival prognosis and functional outcome, can be treated with more radical surgery in order to realize better local control of disease and prevent the occurrence of local disease. Patients with scores of 6-7 points should be surgical candidates, because survival prognosis and functional outcome are acceptable after surgery, while patients with scores of 8-10 points, who have the shortest survival time and poorest functional outcome after surgery, appear to be best treated with radiotherapy or best supportive care. [ABSTRACT FROM AUTHOR]

Hair follicles have characteristic sizes corresponding to their cycle-specific stage. However, how the anagen hair follicle specifies its size remains elusive. Here, we showed that in response to prolonged ectopic Wnt10b-mediated β-catenin activation, regenerating anagen hair follicles grew larger in size. In particular, the hair bulb, dermal papilla and hair shaft became enlarged, while the formation of different hair types ( Guard, Awl, Auchene and Zigzag) was unaffected. Interestingly, we found that the effect of exogenous WNT10b was mainly on Zigzag and less on the other kinds of hairs. We observed dramatically enhanced proliferation within the matrix, DP and hair shaft of the enlarged Ad Wnt10b-treated hair follicles compared with those of normal hair follicles at P98. Furthermore, expression of CD34, a specific hair stem cell marker, was increased in its number to the bulge region after Ad Wnt10b treatment. Ectopic expression of CD34 throughout the ORS region was also observed. Many CD34-positive hair stem cells were actively proliferating in Ad Wnt10b-induced hair follicles. Importantly, subsequent co-treatment with the Wnt inhibitor, DKK1, reduced hair follicle enlargement and decreased proliferation and ectopic localization of hair stem cells. Moreover, injection of DKK1 during early anagen significantly reduced the width of prospective hairs. Together, these findings strongly suggest that Wnt10b/ DKK1 can modulate hair follicle size during hair regeneration. [ABSTRACT FROM AUTHOR]

Background: Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students. Methods: A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application. Results: Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911–0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905–0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918. Conclusions: The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures. [ABSTRACT FROM AUTHOR]

• Cement leak is common for percutaneous vertebroplasty in metastatic spinal disease. • A proposed and validated algorithm can screen patients with high cement leak risk. • The algorithm included four significant characteristics. • The validation group AUROC was 0.69, and the goodness-of-fit test P-value was 0.50. To investigate the epidemiology of cement leaks and further develop an algorithm to detect the high risk of cement leaks among advanced cancer patients with metastatic spinal disease treated with percutaneous vertebroplasty. This study retrospectively analyzed 309 patients with metastatic spinal disease treated with percutaneous vertebroplasty. Patients were randomly divided into a training group and a validation group. In the training group, 13 potential characteristics were analyzed for their abilities to predict cement leaks. Discal cement leakage and paravertebral cement leakage were excluded from the analysis. Those characteristics identified as having significant predictive value were used to develop a predictive algorithm. Internal validation of the algorithm was performed based on discrimination and calibration qualities. Overall, cement leaks occurred in 61.17% (189/309) patients. Among the 13 characteristics analyzed, younger age (P = 0.03), extravertebral bone metastases (P = 0.02), increased number of treated vertebrae levels (P < 0.01), and cortical osteolytic destruction in the posterior wall (P = 0.01) were included in the algorithm. This algorithm generates a score between 0 and 16 points, with higher scores indicating a higher risk of cement leakage. The area under the receiver operating characteristic curve (AUROC) value for the algorithm was 0.75 in the training group and 0.69 in the validation group. The mean correct classification rates for the training and validation groups were 73.5% and 64.9%, respectively, and the corresponding P-values of the goodness-of-fit test were 0.70 and 0.50. Cement leaks are common in patients with metastatic spinal disease treated with percutaneous vertebroplasty. The present study proposed and internally validated an algorithm that can be used to screen patients at high risk of cement leakage. [ABSTRACT FROM AUTHOR]

Background: As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aimed to assess the risk factors and establish a model for predicting worsening diabetic retinopathy (DR-worsening) within five years after PRP. Methods: Patients who were diagnosed with severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and treated with PRP were included, and those patients were randomly assigned to either a training or validation cohort. The multivariate logistic regression analysis was used to screen potential risk factors for DR-worsening in the training cohort. Then the model was established after including significant independent risk factors and further validated using discrimination and calibration. Results: A total of 271 patients were included, and 56.46% of patients had an outcome of DR-worsening. In the training cohort (n = 135), age (odds ratio [OR] = 0.94, 95% confidence interval [CI] 0.90–0.98), baseline best corrected visual acuity (logMAR) (OR = 10.74, 95% CI 1.84–62.52), diabetic nephropathy (OR = 9.32, 95% CI 1.49–58.46), and hyperlipidemia (OR = 3.34, 95% CI 1.05–10.66) were screened out as the independent risk factors, which were incorporated into the predictive model. The area under the receiver operating characteristic curve and calibration slope in the training and validation cohort were 0.79, 0.96 (95% CI 0.60–1.31), and 0.79, 1.00 (95% CI 0.66–1.34), respectively. Two risk groups were developed depending on the best cut-off value of the predicted probability, and the actual probability was 34.90% and 82.79% in the low-risk and high-risk groups, respectively (P < 0.001). Conclusions: This study developed and internally validated a new model to predict the probability of DR-worsening after PRP treatment within five years. The model can be used as a rapid risk assessment system for clinical prediction of DR-worsening and identify individuals at a high risk of DR-worsening at an early stage and prescribe additional treatment. [ABSTRACT FROM AUTHOR]

Background: University students are a vulnerable population prone to mental health challenges. This study aimed to investigate depression and its associated factors among university students in terms of demographics, eating habits, and exercises. Methods: A total of 2891 university students from three universities participated in this study between January 2024 and February 2024. An online survey questionnaire was distributed using a snow-ball strategy. The survey collected demographic, lifestyle, and psychological data, including depression and anxiety scores using the PHQ-9 and GAD-7 screening tools. Subgroup analysis was conducted according to sport frequency and sport type using Chi-square test for qualitative data and t-test for quantitative data. Multiple linear regression analysis was performed to identify risk factors for depression. Results: A total of 44.2% and 39.5% of the participants reported symptoms of depression and anxiety, respectively. Significant differences were observed in various characteristics across different sport frequency groups, with participants with higher sport frequency tending to have less depression (P< 0.001) and anxiety (P< 0.001) symptoms. As the frequency of weekly exercise increased, anxiety and depression scores gradually decreased. The mean PHQ-9 and GAD-7 scores were highest in the group with no sports and lowest in the group with a sport frequency of 3– 4 times per week (P< 0.001). Additionally, once exercise frequency reached 5 times per week or more, anxiety and depression scores no longer decreased. Subgroup analysis based on sport type revealed that participants engaging in specific sports, such as basketball, tennis, dance, and running, had lower depression (P< 0.001) and anxiety (P< 0.001) scores compared to the overall average. Based on multiple linear regression analysis, married status (P=0.036), enjoying barbecue food (P< 0.001), prolonged sedentary time (P=0.001), experiencing stress events (P< 0.001), and electronic device usage time (P< 0.001) were positively associated with depression scores, while loving eating vegetables (P=0.007), a relatively longer sport time (P=0.005), a higher exercise frequency (P=0.064), and no chronic disease (P< 0.001) were negatively associated with depression scores. Conclusion: This study highlights the importance of a healthy lifestyle, including regular exercise, limited exposure to electronic screens, and a balanced diet, in preventing and mitigating depression among university students. This study also suggests that exercising 3– 4 times a week is associated with the lowest levels of anxiety and depression. Activities such as basketball, tennis, dance, and running are effective in alleviating these mental health issues through regular exercise. [ABSTRACT FROM AUTHOR]

Enhanced recovery after surgery (ERAS) has proven beneficial for patients undergoing orthopedic surgery. However, the application of ERAS in the context of metastatic epidural spinal cord compression (MESCC) remains undefined. This study aims to establish a medical pathway rooted in the ERAS concept, with the ultimate goal of scrutinizing its efficacy in enhancing postoperative outcomes among patients suffering from MESCC. An observational cohort study. A total of 304 patients with MESCC who underwent surgery were collected between January 2016 and January 2023 at two large tertiary hospitals. Surgery-related variables, patient quality of life, and pain outcomes. Surgery-related variables in the study included surgery time, surgery site, intraoperative blood loss, and complication. From January 2020 onwards, ERAS therapies were implemented for MESCC patients in both institutions. Thus, the ERAS cohort included 138 patients with MESCC who underwent surgery from January 2020 to January 2023, whereas the traditional cohort consisted of 166 patients with MESCC who underwent surgery from January 2016 to December 2019. Clinical baseline characteristics, surgery-related features, and surgical outcomes were collected. Patient quality of life was evaluated using the Functional Assessment of Cancer Therapy-General Scale (FACT-G), and pain outcomes were assessed using the Visual Analogue Scale (VAS). Comparison of baseline characteristics revealed that the two cohorts were similar (all p>.050), indicating comparable distribution of clinical characteristics. In terms of surgical outcomes, patients in the ERAS cohort exhibited lower intraoperative blood loss (p<.001), shorter postoperative hospital stays (p<.001), lower perioperative complication rates (p=.020), as well as significantly shorter time to ambulation (P<0.001), resumption of regular diet (p<.001), removal of urinary catheter (p<.001), initiation of radiation therapy (p<.001), and initiation of systemic internal therapy (p<.001) compared with patients in the traditional cohort. Regarding pain outcomes and quality of life, patients undergoing the ERAS program demonstrated significantly lower VAS scores (p<.010) and higher scores for physical (p<.001), social (p<.001), emotional (p<.001), and functional (p<.001) well-being compared with patients in the traditional cohort. The ERAS program, renowned for its ability to expedite postoperative recuperation, emerges as a promising approach to ameliorate the recovery process in MESCC patients. Not only does it exhibit potential in enhancing pain management outcomes, but it also holds the promise of elevating the overall quality of life for these individuals. Future investigations should delve deeper into the intricate components of the ERAS program, aiming to unravel the precise mechanisms that underlie its remarkable impact on patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students. Methods: A total of 2243 university students were included, with 1882 students from five universities comprising the model derivation group and 361 students from two additional universities forming the external validation group. Six machine learning techniques, including extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), neural network (NN), and support vector machine (SVM), were employed to train models using the same set of features. The models' prediction performance was assessed based on discrimination and calibration, and feature importance was determined using Shapley Additive exPlanations (SHAP) analysis. Results: The prevalence of sleep disturbance was 44.69% in the model derivation group and 49.58% in the external validation group. Among the developed models, eXGBM exhibited superior performance, surpassing other models in metrics such as area under the curve (0.779, 95% CI: 0.728– 0.830), accuracy (0.710), precision (0.737), F1 score (0.692), Brier score (0.193), and log loss (0.569). Calibration and decision curve analyses demonstrated favorable calibration ability and clinical net benefits, respectively. SHAP analysis identified five key features: stress score, severity of depression, vegetable consumption, age, and sedentary time. The AI platform was made available online at https://sleepdisturbancestudents-xakgzwectsw85cagdgkax9.streamlit.app/ , enabling users to calculate individualized risk of sleep disturbance. Conclusion: Sleep disturbance is prevalent among university students. This study presents an AI model capable of identifying students at high risk for sleep disturbance. The AI platform offers a valuable resource to guide interventions and improve sleep outcomes for university students. [ABSTRACT FROM AUTHOR]

Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies. The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease. An observational cohort study. Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort. The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more. Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance. The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/. The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery. [ABSTRACT FROM AUTHOR]

The assessment of epidural spinal cord compression (ESCC) plays a crucial role in clinical decision-making, yet the current grading system lacks reliability and requires improvements. The study aims to develop a reliable grading system for evaluating ESCC and to investigate its association with the neurological status of patients. A prospective cohort study. A total of 330 patients with metastatic spinal disease were included in the study. The main outcome was the neurological status evaluated using the American Spinal Injury Association (ASIA) scale. We proposed a novel grading system, called the 12-point ESCC grading system, to evaluate ESCC based on findings from spinal magnetic resonance imaging (MRI). This new grading system consists of 12 grades, ranging from Grade 0 to 3, with higher grades indicating more severe ESCC. The detailed information about the sagittal image of the spine and the severity of spinal cord swelling was considered in this new grading system. The Spearman correlation analysis and logistic regression analysis were employed to investigate the correlation between the previous 6-point grading system and ASIA, as well as between the new 12-point ESCC grading system and ASIA. The prediction effectiveness was evaluated using the area under curve (AUC) analysis. Patients with higher grades in the 12-point ESCC grading system exhibited a higher likelihood of experiencing a worse neurological condition. Specifically, patients with grades 2a to 2d and 3a to 3d according to the new 12-point ESCC grading system were significantly associated with more complete paralysis (p<.001) compared with patients with grade 0. The Spearman correlation coefficient was 0.729 between the previous 6-point ESCC grading system and ASIS and 0.750 between the new 12-point ESCC grading system and ASIS. When categorizing ASIS into complete paralysis and other neurological statuses, the 6-point ESCC score yielded an AUC of 0.820, which increased to 0.860 with the new 12-point ESCC grading system. Furthermore, when ASIS was divided into normal and abnormal neurological statuses, the AUC increased from 0.889 to 0.906. Additionally, spinal cord swelling was significantly associated with more complete paralysis (p<.001) and abnormal neurological status (p<.001) based on the new 12-point ESCC grading system. The new 12-point ESCC grading system provides more detailed information and further improves the prediction effectiveness for evaluating neurological status compared with the previous 6-point ESCC grading system. In the new 12-point ESCC grading system, higher grades or the presence of spinal cord swelling are indicative of a worse neurological condition. [ABSTRACT FROM AUTHOR]

Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited. The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease. A prospective cohort study. A total of 1043 cancer patients with spinal metastatic disease were included. The main outcome was severe psychological distress. The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set. Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788–0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768–0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770–0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756–0.916; Accuracy: 0.783). Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress. [ABSTRACT FROM AUTHOR]

Background: Cell therapy is a key technology to prevent sacrificing normal skin. Although some studies have shown the promise of human epidermal stem cells (EpiSCs), the efficacy, biosafety and quality control of EpiSC therapy have not been systematically reported. Methods: The biosafety, stemness maintenance and wound repair of EpiSC were systematically verified by in vitro and in vivo experiments. EpiSC were prepared from the foreskin using a collagen type IV rapid adherence method. The EpiSCs were identified by flow cytometry, immunofluorescence staining and cell morphology. The well-growing passage 1 (P1) EpiSCs were used to determine the proliferation curve (counting method). EpiSC clone formation assay was performed by Giemsa staining. Nude mice were used to prepare a full-thickness skin defect wound model to detect the repair effect of EpiSCs. The biosafety of EpiSCs was double tested in vitro and in vivo. Results: The results showed that the expression of specific markers and clone formation efficiency was stable when passage 1 (P1) to P8 cells were cultured, and the stemness rate of P8 cells was close to 85.1%. EpiSCs were expanded in vitro for 25 days, the number of cells reached 2.5 × 108, and the transplantable area was approximately 75% of the total body surface area (TBSA). At 45 days, the total number of cells was approximately 30 billion, and the transplantable area was approximately the size of a volleyball court. A nude mouse wound model indicated that EpiSCs could rapidly close a wound. On postinjury day 7, the wound epithelialization rate in the cell transplantation group was significantly higher than that in the NaCl group (P < 0.05). In vitro, cell senescence increased, and telomerase activity decreased in P1 to P8 EpiSCs. In vivo, there were no solid tumors or metastatic tumors after EpiSC (P8) transplantation. In addition, the quality control of cultured cells met the clinical application criteria for cell therapy. Conclusion: This preclinical study showed the stability and biosafety of human EpiSC therapy for wound repair. [ABSTRACT FROM AUTHOR]

Background: This study aimed at investigating the characteristics of clinical trials related to hip fractures that were registered at ClinicalTrials.gov. It also aimed to identify potential risk factors associated with completion.Main Body: We obtained 733 clinical studies related to hip fractures from the ClinicalTrials.gov database and included 470 studies in the analysis. These clinical trials were divided into behavioral, drug/biological, device, procedure, and other categories based on intervention types. Clinical trials investigating drugs or biologics were categorized based on the specific agents administered in each trial. Multiple logistic and Cox regression models were used to test the ability of 24 potential risk factors in predicting recruitment status and completion time, respectively. Among the included clinical trials, 44.89% (211/470) had complete recruitment status. The overall median completion time was 931.00 days (95% confidence interval [CI]: 822.56-1039.44 days). The results of only 8.94% (42/470) of clinical trials were presented on the ClinicalTrials.gov website. Bupivacaine (a local anesthetic) was most commonly investigated (in 25 clinical trials); this was followed by ropivacaine (another local anesthetic, 23 clinical trials) and tranexamic acid (a hemostatic, 21 clinical trials). Multivariate analysis showed that trials including children (P = 0.03) and having National Institutes of Health funds (P < 0.01) had significantly higher rates of complete recruitment. Higher enrollment (P < 0.01), National Institutes of Health funding (P < 0.01), location in the United States (P = 0.04), and location in Europe (P = 0.03) predisposed to longer completion time, while studies involving drugs/biologics (P < 0.01) had shorter completion times.Conclusions: A considerable proportion of clinical trials related to hip fractures were completed, but the results of only a small fraction were presented on the ClinicalTrials.gov website. The commonly investigated drugs focused on anesthesia, pain relief, and hemostasis. Several independent risk factors that affect recruitment status and completion time were identified. These factors may guide the design of clinical trials relating to hip fractures. [ABSTRACT FROM AUTHOR]

Background: Poor mental health was reported among medical graduate students in some studies. Identification of risk factors for predicting the mental health is capable of reducing psychological distress among medical graduate students. Therefore, the aim of the study was to identify potential risk factors relating to mental health and further create a novel prediction model to calculate the risk of mental distress among medical graduate students. Methods: This study collected and analyzed 1079 medical graduate students via an online questionnaire. Included participants were randomly classified into a training group and a validation group. A model was developed in the training group and validation of the model was performed in the validation group. The predictive performance of the model was assessed using the discrimination and calibration. Results: One thousand and fifteen participants were enrolled and then randomly divided into the training group (n = 508) and the validation group (n = 507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The model was developed using the six variables, including the year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the model were 0.70 and 0.90 (95% CI: 0.65 ~ 1.15) in the training group, respectively, and 0.66 and 0.80 (95% CI, 0.51 ~ 1.09) in the validation group, respectively. Conclusions: The study identified six risk factors for predicting anxiety and depression and successfully created a prediction model. The model may be a useful tool that can identify the mental status among medical graduate students. Trial registration: No.ChiCTR2000039574, prospectively registered on 1 November 2020. [ABSTRACT FROM AUTHOR]