Evaluation of Depression and Its Correlates in Terms of Demographics, Eating Habits, and Exercises Among University Students: A Multicenter Cross-Sectional Analysis

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.

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.

Crosstalk between endothelial cells and dermal papilla entails hair regeneration and angiogenesis during aging

INTRODUCTION

Tissues maintain their function through interaction with microenvironment. During aging, both hair follicles and blood vessels (BV) in skin undergo degenerative changes. However, it is elusive whether the changes are due to intrinsic aging changes in hair follicles or blood vessels respectively, or their interactions.

OBJECTIVE

To explore how hair follicles and blood vessels interact to regulate angiogenesis and hair regeneration during aging.

METHODS

Single-cell RNA-sequencing (scRNA-seq) analyses were used to identify the declined ability of dermal papilla (DP) and endothelial cells (ECs) during aging. CellChat and CellCall were performed to investigate interaction between DP and ECs. Single-cell metabolism (scMetabolism) analysis and iPATH were applied to analyze downstream metabolites in DP and ECs. Hair-plucking model and mouse cell organoid model were used for functional studies.

RESULTS

During aging, distance and interaction between DP and ECs are decreased. DP interacts with ECs, with decreased EDN1-EDNRA signaling from ECs to DP and CTF1-IL6ST signaling from DP to ECs during aging. ECs-secreted EDN1 binds to DP-expressed EDNRA which enhances Taurine (TA) metabolism to promote hair regeneration. DP-emitted CTF1 binds to ECs-expressed IL6ST which activates alpha-linolenic acid (ALA) metabolism to promote angiogenesis. Activated EDN1-EDNRA-TA signaling promotes hair regeneration in aged mouse skin and in organoid cultures, and increased CTF1-IL6ST-ALA signaling also promotes angiogenesis in aged mouse skin and organoid cultures.

CONCLUSIONS

Our finding reveals reciprocal interactions between ECs and DP. ECs releases EDN1 sensed by DP to activate TA metabolism which induces hair regeneration, while DP emits CTF1 signal received by ECs to enhance ALA metabolism which promotes angiogenesis. Our study provides new insights into mutualistic cellular crosstalk between hair follicles and blood vessels, and identifies novel signaling contributing to the interactions of hair follicles and blood vessels in normal and aged skin.

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis

BACKGROUND

Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models.

METHODS

This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease.

RESULTS

Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts.

CONCLUSIONS

The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

Promoting postoperative recovery in patients with metastatic epidural spinal cord compression based on the concept of ERAS: a multicenter analysis of 304 patients

BACKGROUND CONTEXT

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.

PURPOSE

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.

STUDY DESIGN/SETTING

An observational cohort study.

PATIENT SAMPLE

A total of 304 patients with MESCC who underwent surgery were collected between January 2016 and January 2023 at two large tertiary hospitals.

OUTCOME MEASURES

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma

BACKGROUND

Given the intricate and grave nature of trauma-related injuries in ICU settings, it is imperative to develop and deploy reliable predictive tools that can aid in the early identification of high-risk patients who are at risk of early death. The objective of this study is to create and validate an artificial intelligence (AI) model that can accurately predict early mortality among critical fracture patients.

METHODS

A total of 2662 critically ill patients with orthopaedic trauma were included from the MIMIC III database. Early mortality was defined as death within 30 days in this study. The patients were randomly divided into a model training cohort and a model validation cohort. Various algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), support vector machine (SVM), random forest (RF), and neural network (NN), were employed. Evaluation metrics, including discrimination and calibration, were used to develop a comprehensive scoring system ranging from 0 to 60, with higher scores indicating better prediction performance. Furthermore, external validation was carried out using 131 patients. The optimal model was deployed as an internet-based AI tool.

RESULTS

Among all models, the eXGBM demonstrated the highest area under the curve (AUC) value (0.974, 95%CI: 0.959-0.983), followed by the RF model (0.951, 95%CI: 0.935-0.967) and the NN model (0.922, 95%CI: 0.905-0.941). Additionally, the eXGBM model outperformed other models in terms of accuracy (0.915), precision (0.906), recall (0.926), F1 score (0.916), Brier score (0.062), log loss (0.210), and discrimination slope (0.767). Based on the scoring system, the eXGBM model achieved the highest score (53), followed by RF (42) and NN (39). The LR, DT, and SVM models obtained scores of 28, 18, and 32, respectively. Decision curve analysis further confirmed the superior clinical net benefits of the eXGBM model. External validation of the model achieved an AUC value of 0.913 (95%CI: 0.878-0.948). Consequently, the model was deployed on the Internet at https://30-daymortalityincriticallyillpatients-fnfsynbpbp6rgineaspuim.streamlit.app/, allowing users to input patient features and obtain predicted risks of early mortality among critical fracture patients. Furthermore, the AI model successfully stratified patients into low or high risk of early mortality based on a predefined threshold and provided recommendations for appropriate therapeutic interventions.

CONCLUSION

This study successfully develops and validates an AI model, with the eXGBM algorithm demonstrating the highest predictive performance for early mortality in critical fracture patients. By deploying the model as a web-based AI application, healthcare professionals can easily access the tool, enabling them to predict 30-day mortality and aiding in the identification and management of high-risk patients among those critically ill with orthopedic trauma.

An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study

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.

A New Treatment Strategy for Spinal Metastasis: The "Systemic Conditions, Effectiveness of Systemic Treatment, Neurology, and Oncology" Decision Framework System

BACKGROUND AND OBJECTIVES

Treating metastatic spinal tumors poses a significant challenge because there are currently no universally applied guidelines for managing spinal metastases. This study aims to propose a new decision framework for the 12-point epidural spinal cord compression grading system to treat patients with metastatic spinal tumors and investigate its clinical effectiveness in a multicenter analysis.

METHODS

This study analyzed 940 patients with metastatic spinal tumors between December 2017 and March 2023. The study provided the clinical evidence for the systemic conditions, effectiveness of systemic treatment, neurology, and oncology (SENO) decision framework among spine metastases. The SENO decision framework was launched in January 2021 in our hospitals, classifying patients into 2 groups: The non-SENO group (n = 489) consisted of patients treated between December 2017 and January 2021, while the SENO group (n = 451) comprised patients treated from January 2021 to March 2023.

RESULTS

Patients in the SENO group were more likely to receive minimally invasive surgery (67.85% vs 58.69%) and less chance of receiving spinal cord circular decompression surgery (14.41% vs 24.74%) than patients in the non-SENO group ( P < .001). Furthermore, patients in the SENO group experienced fewer perioperative complications (9.09% vs 15.34%, P = .004), incurred lower hospitalization costs ( P < .001), had shorter length of hospitalization ( P < .001), and received systematic treatments for tumors earlier ( P < .001). As a result, patients in the SENO group (329.00 [95% CI: 292.06-365.94] days) demonstrated significantly improved survival outcomes compared with those in the non-SENO group (279.00 [95% CI: 256.91-301.09], days) ( P < .001). At 3 months postdischarge, patients in the SENO group reported greater improvements in their quality of life, encompassing physical, social, emotional, and functional well-being, when compared with patients in the non-SENO group.

CONCLUSION

The SENO decision framework is a promising approach for treating patients with metastatic spinal tumors.

CHRONIC CRITICAL ILLNESS-INDUCED MUSCLE ATROPHY: INSIGHTS FROM A TRAUMA MOUSE MODEL AND POTENTIAL MECHANISM MEDIATED VIA SERUM AMYLOID A

ABSTRACT

Background: Chronic critical illness (CCI), which was characterized by persistent inflammation, immunosuppression, and catabolism syndrome (PICS), often leads to muscle atrophy. Serum amyloid A (SAA), a protein upregulated in critical illness myopathy, may play a crucial role in these processes. However, the effects of SAA on muscle atrophy in PICS require further investigation. This study aims to develop a mouse model of PICS combined with bone trauma to investigate the mechanisms underlying muscle weakness, with a focus on SAA. Methods: Mice were used to examine the effects of PICS after bone trauma on immune response, muscle atrophy, and bone healing. The mice were divided into two groups: a bone trauma group and a bone trauma with cecal ligation and puncture group. Tibia fracture surgery was performed on all mice, and PICS was induced through cecal ligation and puncture surgery in the PICS group. Various assessments were conducted, including weight change analysis, cytokine analysis, hematological analysis, grip strength analysis, histochemical staining, and immunofluorescence staining for SAA. In vitro experiments using C2C12 cells (myoblasts) were also conducted to investigate the role of SAA in muscle atrophy. The effects of inhibiting receptor for advanced glycation endproducts (RAGE) or JAK2 on SAA-induced muscle atrophy were examined. Bioinformatic analysis was conducted using a dataset from the GEO database to identify differentially expressed genes and construct a coexpression network. Results: Bioinformatic analysis confirmed that SAA was significantly upregulated in muscle tissue of patients with intensive care unit-induced muscle atrophy. The PICS animal models exhibited significant weight loss, spleen enlargement, elevated levels of proinflammatory cytokines, and altered hematological profiles. Evaluation of muscle atrophy in the animal models demonstrated decreased muscle mass, grip strength loss, decreased diameter of muscle fibers, and significantly increased expression of SAA. In vitro experiment demonstrated that SAA decreased myotube formation, reduced myotube diameter, and increased the expression of muscle atrophy-related genes. Furthermore, SAA expression was associated with activation of the FOXO signaling pathway, and inhibition of RAGE or JAK2/STAT3-FOXO signaling partially reversed SAA-induced muscle atrophy. Conclusions: This study successfully develops a mouse model that mimics PICS in CCI patients with bone trauma. Serum amyloid A plays a crucial role in muscle atrophy through the JAK2/STAT3-FOXO signaling pathway, and targeting RAGE or JAK2 may hold therapeutic potential in mitigating SAA-induced muscle atrophy.

Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques

BACKGROUND CONTEXT

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.

PURPOSE

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.

STUDY DESIGN/SETTING

An observational cohort study.

PATIENT SAMPLE

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.

OUTCOME MEASURES

The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more.

METHODS

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.

RESULTS

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/.

CONCLUSIONS

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.

Development of a novel 12-point grading system for evaluating epidural spinal cord compression and its clinical implications

BACKGROUND CONTEXT

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.

PURPOSE

The study aims to develop a reliable grading system for evaluating ESCC and to investigate its association with the neurological status of patients.

STUDY DESIGN/SETTING

A prospective cohort study.

PATIENT SAMPLE

A total of 330 patients with metastatic spinal disease were included in the study.

OUTCOME MEASURES

The main outcome was the neurological status evaluated using the American Spinal Injury Association (ASIA) scale.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Effect of different application duration of a venous foot pump on prevention of venous thromboembolism after hip and knee arthroplasty: a multicenter prospective clinical trial

OBJECTIVE

To investigate the optimal duration of applying a venous foot pump (VFP) in the prevention of venous thromboembolism (VTE) following hip and knee arthroplasty.

METHODS

A total of 230 patients undergoing hip and knee arthroplasty between March 2021 and March 2022 in orthopaedic departments of four major teaching hospitals were prospectively enrolled. Patients were randomly divided into five groups based on the duration of the VFP application. Postoperative deep vein thromboses (DVT), including proximal, distal, and intermuscular DVT, were recorded for analysis. Postoperative blood coagulation examinations, such as D-dimer and active partial thromboplastin time (APTT), pain outcome, and degree of comfort were also collected.

RESULTS

Two of the 230 patients withdrew due to early discharge from the hospital, and 228 patients were included in the final analysis. The mean age was 60.38 ± 13.33 years. The baseline characteristics were comparable among the five groups. Compared with the other groups, patients treated with 6-hour VFP had the lowest incidence of DVT (8.7%, 4/46), followed by those treated with 1-hour VFP (15.2%, 7/46), 12-hour VFP (15.6%, 7/45), 18-hour VFP(17.8%, 8/45) and 20-hour VFP(21.7%, 10/46), but with no significant difference (P = 0.539). Regarding postoperative blood coagulation examinations, patients treated with 6-hour VFP had the lowest D-dimer (P = 0.658) and the highest APTT (P = 0.262) compared with the other four groups. 6-hour VFP also had the lowest pain score (P = 0.206) and the highest comfort score (P = 0.288) compared with the other four groups.

CONCLUSIONS

Six hours may be the optimal duration of applying VFP for the prevention of VTE in patients undergoing hip and knee arthroplasty in terms of VTE incidence, postoperative blood coagulation examinations, pain outcomes, and comfort scores.

Epidermal-dermal coupled spheroids are important for tissue pattern regeneration in reconstituted skin explant cultures

Tissue patterning is critical for the development and regeneration of organs. To advance the use of engineered reconstituted skin organs, we study cardinal features important for tissue patterning and hair regeneration. We find they spontaneously form spheroid configurations, with polarized epidermal cells coupled with dermal cells through a newly formed basement membrane. Functionally, the spheroid becomes competent morphogenetic units (CMU) that promote regeneration of tissue patterns. The emergence of new cell types and molecular interactions during CMU formation was analyzed using scRNA-sequencing. Surprisingly, in newborn skin explants, IFNr signaling can induce apical-basal polarity in epidermal cell aggregates. Dermal-Tgfb induces basement membrane formation. Meanwhile, VEGF signaling mediates dermal cell attachment to the epidermal cyst shell, thus forming a CMU. Adult mouse and human fetal scalp cells fail to form a CMU but can be restored by adding IFNr or VEGF to achieve hair regeneration. We find different multi-cellular configurations and molecular pathways are used to achieve morphogenetic competence in developing skin, wound-induced hair neogenesis, and reconstituted explant cultures. Thus, multiple paths can be used to achieve tissue patterning. These insights encourage more studies of "in vitro morphogenesis" which may provide novel strategies to enhance regeneration.

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study

BACKGROUND

Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions.

OBJECTIVE

This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis.

METHODS

This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model.

RESULTS

In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator.

CONCLUSIONS

This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

The mechano-chemical circuit drives skin organoid self-organization

Stem cells in organoids self-organize into tissue patterns with unknown mechanisms. Here, we use skin organoids to analyze this process. Cell behavior videos show that the morphological transformation from multiple spheroidal units with morphogenesis competence (CMU) to planar skin is characterized by two abrupt cell motility-increasing events before calming down. The self-organizing processes are controlled by a morphogenetic module composed of molecular sensors, modulators, and executers. Increasing dermal stiffness provides the initial driving force (driver) which activates Yap1 (sensor) in epidermal cysts. Notch signaling (modulator 1) in epidermal cyst tunes the threshold of Yap1 activation. Activated Yap1 induces Wnts and MMPs (epidermal executers) in basal cells to facilitate cellular flows, allowing epidermal cells to protrude out from the CMU. Dermal cell-expressed Rock (dermal executer) generates a stiff force bridge between two CMU and accelerates tissue mixing via activating Laminin and β1-integrin. Thus, this self-organizing coalescence process is controlled by a mechano-chemical circuit. Beyond skin, self-organization in organoids may use similar mechano-chemical circuit structures.

COX2-ATP Synthase Regulates Spine Follicle Size in Hedgehogs

Skin evolves essential appendages with adaptive patterns that synergistically insulate the body from environmental insults. How similar appendages in different animals generate diversely-sized appendages remain elusive. Here we used hedgehog spine follicles and mouse hair follicles as models to investigate how similar follicles form in different sizes postnatally. Histology and immunostaining show that the spine follicles have a significantly greater size than the hair follicles. By RNA-sequencing analysis, we found that ATP synthases are highly expressed in hedgehog skin compared to mouse skin. Inhibition of ATP synthase resulted in smaller spine follicle formation during regeneration. We also identified that the mitochondrial gene COX2 functions upstream of ATP synthase that influences energy metabolism and cell proliferation to control the size of the spine follicles. Our study identified molecules that function differently in forming diversely-sized skin appendages across different animals, allowing them to adapt to the living environment and benefit from self-protection.

Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease

BACKGROUND CONTEXT

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.

PURPOSE

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.

STUDY DESIGN/SETTING

A prospective cohort study.

PATIENT SAMPLE

A total of 1043 cancer patients with spinal metastatic disease were included.

OUTCOME MEASURES

The main outcome was severe psychological distress.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

Background

Health-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC.

Methods

A total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram's performance with and without the consideration of comorbidities.

Results

Four variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074-0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004-0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities.

Conclusions

This study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.

Treatment of patients with metastatic epidural spinal cord compression using an enhanced recovery after surgery program

Purpose: The aims of this study were to introduce a new medical, pathway based on the concept of "enhanced recovery after surgery" (ERAS) for patients with metastatic epidural spinal cord compression (MESCC), and to test whether the ERAS program could improve clinical metrics among such patients. Methods: Data from patients with MESCC (n = 98), collected between December 2016 and December 2019 (Non-ERAS cohort), and from 86 patients with metastatic epidural spinal cord compression collected between January 2020 and December 2022 (ERAS cohort), were retrospectively analyzed. Patients were treated by decompressive surgery combined with transpedicular screw implantation and internal fixation. Patient baseline clinical characteristics were collected and compared between the two cohorts. Surgical outcomes analyzed included operation time; intraoperative blood loss; postoperative length of hospital stay; time to ambulation, regular diet, urinary catheter removal, and radiation therapy; perioperative complications; anxiety; depression; and satisfaction with treatment. Results: No significant differences in clinical characteristics were found between the non-ERAS and enhanced recovery after surgery cohorts (all p > 0.050), indicating that the two cohorts were comparable. Regarding surgical outcomes, the enhanced recovery after surgery cohort had significantly less intraoperative blood loss (p < 0.001); shorter length of postoperative hospital stay (p < 0.001); shorter time to ambulation (p < 0.001), regular diet (p < 0.001), urinary catheter removal (p < 0.001), radiation administration (p < 0.001), and systemic internal therapy (p < 0.001); lower perioperative complication rate (p = 0.024); less postoperative anxiety (p = 0.041); and higher score for satisfaction with treatment (p < 0.001); whereas operation time (p = 0.524) and postoperative depression (p = 0.415) were similar between the two cohorts. Compliance analysis demonstrated that ERAS interventions were successfully conducted in the vast majority of patients. Conclusion: The enhanced recovery after surgery intervention is beneficial to patients with metastatic epidural spinal cord compression, according to data on intraoperative blood loss; length of hospital stay; time to ambulation, regular diet, urinary catheter removal, radiation exposure, and systemic internal therapy; perioperative complication; alleviation of anxiety; and improvement of satisfaction. However, clinical trials to investigate the effect of enhanced recovery after surgery are needed in the future.

Mechanical force drives the initial mesenchymal-epithelial interaction during skin organoid development

Rationale: Stem cells self-organize to form organoids that generate mini-organs that resemble the physiologically-developed ones. The mechanism by which the stem cells acquire the initial potential for generating mini-organs remains elusive. Here we used skin organoids as an example to study how mechanical force drives initial epidermal-dermal interaction which potentiates skin organoids to regenerate hair follicles. Methods: Live imaging analysis, single-cell RNA-sequencing analysis, and immunofluorescence were used to analyze the contractile force of dermal cells in skin organoids. Bulk RNA-sequencing analysis, calcium probe detection, and functional perturbations were used to verify that calcium signaling pathways respond to the contractile force of dermal cells. In vitro mechanical loading experiment was used to prove that the stretching force triggers the epidermal Piezo1 expression which negatively regulates dermal cell attachment. Transplantation assay was used to test the regenerative ability of skin organoids. Results: We found that dermal cell-derived contraction force drives the movement of dermal cells surrounding the epidermal aggregates to trigger initial mesenchymal-epithelial interaction (MEI). In response to dermal cell contraction force, the arrangement of the dermal cytoskeleton was negatively regulated by the calcium signaling pathway which further influences dermal-epidermal attachment. The native contraction force generated from the dermal cell movement exerts a stretching force on the adjacent epidermal cells, activating the stretching force sensor Piezo1 in the epidermal basal cells during organoid culture. Epidermal Piezo1 in turn drives strong MEI to negatively regulate dermal cell attachment. Proper initial MEI by mechanical-chemical coupling during organoid culture is required for hair regeneration upon transplantation of the skin organoids into the back of the nude mice. Conclusion: Our study demonstrated that mechanical-chemical cascade drives the initial event of MEI during skin organoid development, which is fundamental to the organoid, developmental, and regenerative biology fields.

Dynamic nomogram for long-term survival in patients with locally advanced oropharyngeal cancer after (chemo)radiotherapy

PURPOSE

This study aimed to establish a nomogram for predicting overall survival (OS) in oropharyngeal cancer patients treated with curative (chemo)radiotherapy.

MATERIALS AND METHODS

The dynamic nomogram was constructed on 273 patients with oropharyngeal squamous cell carcinoma treated in a Tertiary Head and Neck Cancer Unit. The clinical features that were previously reported to be associated with OS were analyzed. The performance of the nomogram was assessed using concordance index (C-index) and calibration curves.

RESULTS

The nomogram incorporated three explanatory variables derived from a decision tree approach including HPV status, N classification according to 8th edition TNM and early response to (chemo)radiotherapy. The nomogram was capable to predict OS with a validation C-index of 0.768. The proposed stratification in risk groups allowed significant distinction between Kaplan-Meier curves for OS outcome (p < 0.0001).

CONCLUSIONS

The nomogram provided an accurate evaluation of OS for oropharyngeal cancer patients treated with curative (chemo)radiotherapy.

Mechanical stimuli-induced CCL2 restores adult mouse cells to regenerate hair follicles

Aged cells have declined regenerative ability when subjected to environmental insult. Here we elucidate the mechanism by which mechanical stimulus induces hair regeneration at the microenvironmental regulation level using the hair plucking and organoid culture models. We observed that the skin cells harvested from post-plucking day 3 (PPD3) have the best self-organizing ability during skin organoid culture and have the highest hair regeneration upon transplantation. By bulk RNA sequencing (RNA-seq) and single-cell RNA-seq analysis and in situ hybridization, we identified that the chemokine signaling pathway genes including CCL2 are significantly increased in the skin at PPD3 and in skin organoid cultures. Immunostaining shows that the PPD3 skin epithelial cells have increased multipotency, which is verified by the ability to self-organize to form epidermal aggregates during organoid culture. By adding CCL2 recombinant protein to the organoid culture using an environmental reprogramming protocol, we observed the PPD0 adult skin cells, which lose their regenerative ability can self-organize in organoid culture and regenerate hair follicles robustly upon transplantation. Our study demonstrates that CCL2 functions in immune regulation of hair regeneration under mechanical stimulus, and enhances cell multipotency during organoid culture. This provides a therapeutic potential for future clinical application.

A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study

INTRODUCTION

Few studies have investigated the in-hospital mortality among critically ill patients with hip fracture. This study aimed to develop and validate a model to estimate the risk of in-hospital mortality among critically ill patients with hip fracture.

METHODS

For this study, data from the Medical Information Mart for Intensive Care III (MIMIC-III) Database and electronic Intensive Care Unit (eICU) Collaborative Research Database were evaluated. Enrolled patients (n=391) in the MIMIC-III database were divided into a training (2/3, n=260) and a validation (1/3, n=131) group at random. Using machine learning algorithms such as random forest, gradient boosting machine, decision tree, and eXGBoosting machine approach, the training group was utilized to train and optimize models. The validation group was used to internally validate models and the optimal model could be obtained in terms of discrimination (area under the receiver operating characteristic curve, AUROC) and calibration (calibration curve). External validation was done in the eICU Collaborative Research Database (n=165). To encourage practical use of the model, a web-based calculator was developed according to the eXGBoosting machine approach.

RESULTS

The in-hospital death rate was 13.81% (54/391) in the MIMIC-III database and 10.91% (18/165) in the eICU Collaborative Research Database. Age, gender, anemia, mechanical ventilation, cardiac arrest, and chronic airway obstruction were the six model parameters which were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with 10-fold cross-validation. The model established using the eXGBoosting machine approach showed the highest area under curve (AUC) value (0.797, 95% CI: 0.696-0.898) and the best calibrating ability, with a calibration slope of 0.999 and intercept of -0.019. External validation also revealed favorable discrimination (AUC: 0.715, 95% CI: 0.566-0.864; accuracy: 0.788) and calibration (calibration slope: 0.805) in the eICU Collaborative Research Database. The web-based calculator could be available at https://doctorwangsj-webcalculator-main-yw69yd.streamlitapp.com/.

CONCLUSION

The model has the potential to be a pragmatic risk prediction tool that is able to identify hip fracture patients who are at a high risk of in-hospital mortality in ICU settings, guide patient risk counseling, and simplify prognosis bench-marking by controlling for baseline risk.

The Impact of Interactive MRI-Based Radiologist Review on Radiotherapy Target Volume Delineation in Head and Neck Cancer

BACKGROUND AND PURPOSE

Peer review of head and neck cancer radiation therapy target volumes by radiologists was introduced in our center to optimize target volume delineation. Our aim was to assess the impact of MR imaging-based radiologist peer review of head and neck radiation therapy gross tumor and nodal volumes, through qualitative and quantitative analysis.

MATERIALS AND METHODS

Cases undergoing radical radiation therapy with a coregistered MR imaging, between April 2019 and March 2020, were reviewed. The frequency and nature of volume changes were documented, with major changes classified as per the guidance of The Royal College of Radiologists. Volumetric alignment was assessed using the Dice similarity coefficient, Jaccard index, and Hausdorff distance.

RESULTS

Fifty cases were reviewed between April 2019 and March 2020. The median age was 59 years (range, 29-83 years), and 72% were men. Seventy-six percent of gross tumor volumes and 41.5% of gross nodal volumes were altered, with 54.8% of gross tumor volume and 66.6% of gross nodal volume alterations classified as "major." Undercontouring of soft-tissue involvement and unidentified lymph nodes were predominant reasons for change. Radiologist review significantly altered the size of both the gross tumor volume (P  = .034) and clinical target tumor volume (P  = .003), but not gross nodal volume or clinical target nodal volume. The median conformity and surface distance metrics were the following: gross tumor volume Dice similarity coefficient = 0.93 (range, 0.82-0.96), Jaccard index = 0.87 (range, 0.7-0.94), Hausdorff distance = 7.45 mm (range, 5.6-11.7 mm); and gross nodular tumor volume Dice similarity coefficient = 0.95 (0.91-0.97), Jaccard index = 0.91 (0.83-0.95), and Hausdorff distance = 20.7 mm (range, 12.6-41.6). Conformity improved on gross tumor volume-to-clinical target tumor volume expansion (Dice similarity coefficient = 0.93 versus 0.95, P  = .003).

CONCLUSIONS

MR imaging-based radiologist review resulted in major changes to most radiotherapy target volumes and significant changes in volume size of both gross tumor volume and clinical target tumor volume, suggesting that this is a fundamental step in the radiotherapy workflow of patients with head and neck cancer.

Stability and biosafety of human epidermal stem cell for wound repair: preclinical evaluation

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.

Hair Zinc and Chromium Levels Were Associated with a Reduced Likelihood of Age Related Cognitive Decline in Centenarians and Oldest-Old Adults

BACKGROUND

Cognitive function has inevitable decline with advancing age in nature, and age-related cognitive decline (ARCD) is of increasing concern to aging population. Scarce study has involved the associations between hair trace elements and ARCD in older adults, especially in centenarians and oldest-old adults. This study was to investigate the associations between hair trace elements and ARCD in centenarians and oldest-old adults.

METHODS

Based on the household registration information of centenarians and oldest-old adults provided by the Civil Affairs Department of Hainan Province, China, the investigators conducted a one-to-one household survey among centenarians (≥100 years old) and oldest-old adults (80-99 years old). All 50 centenarians had a median age of 103 years and females accounted for 68.0%. All 73 oldest-old adults aged 80-99 years had a median age of 90 years and females accounted for 82.2%. Basic information were obtained with questionnaire interview, physical examination, biological test and hair collection by pre-trained local doctors and nurses. An inductively coupled plasma mass spectrometer was used to measure hair trace elements. All data in this study comes from China. Age, sex, body mass index, systolic blood pressure, diastolic blood pressure, smoking, drinking, hemoglobin, albumin, fasting blood pressure, zinc, chromium, copper, selenium, iron, manganese, strontium, lead, magnesium, potassium, and barium were simultaneously included in multivariate Logistic regression analysis. One adjusted model was done with all hair trace elements together.

RESULTS

Zinc and chromium levels were significantly lower in participants with ARCD than those without ARCD (P < 0.05 for all). Multivariate Logistic regression analysis indicated that zinc [odds ratio (OR): 0.988, 95%confidence interval (95%CI): 0.977-0.999] and chromium (OR: 0.051, 95%CI: 0.004-0.705) were associated with a reduced likelihood of ARCD (P < 0.05 for all).

CONCLUSIONS

Hair zinc and chromium levels were associated with a reduced likelihood of ARCD in centenarians and oldest-old adults. Further studies are necessary to verify if zinc and chromium supplementation has the potential to improve cognitive function and prevent ARCD development.

Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma

Purpose

Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study's objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.

Methods

We extracted a cohort of 124,770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of 3 months or less were considered to have had early death. To compare patients with and without early mortality, subgroup analysis was used. Patients were randomly divided into two groups: a training cohort (n = 1509, 80%) and an internal testing cohort (n = 388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models for predicting early mortality, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting, and it was able to combine the results from the multiply machine learning algorithms. The study employed both internal and external validations, and the key performance indicators included the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n = 98). Feature importance and reclassification were both operated in the study.

Results

The early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (p = 0.019), marital status (p = 0.004), tumor stage (p = 0.025), node stage (p = 0.001), fibrosis score (p = 0.040), AFP level (p = 0.032), tumor size (p = 0.001), lung metastases (p < 0.001), cancer-directed surgery (p < 0.001), radiation (p < 0.001), and chemotherapy (p < 0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confidence interval [CI]: 0.727-0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed similar results; with an AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Feature importance demonstrated that the top three most crucial features were chemotherapy, radiation, and lung metastases based on the ensemble model. Reclassification of patients revealed a substantial difference in the two risk groups' actual probabilities of early mortality (74.38% vs. 31.35%, p < 0.001). Patients in the high-risk group had significantly shorter survival time than patients in the low-risk group (p < 0.001), according to the Kaplan-Meier survival curve.

Conclusions

The ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases. With the aid of routinely accessible clinical characteristics, this model can be a trustworthy prognostic tool to predict the early death of those patients and facilitate clinical decision-making.

A machine learning-Based model to predict early death among bone metastatic breast cancer patients: A large cohort of 16,189 patients

Purpose: This study aims to develop a prediction model to categorize the risk of early death among breast cancer patients with bone metastases using machine learning models. Methods: This study examined 16,189 bone metastatic breast cancer patients between 2010 and 2019 from a large oncological database in the United States. The patients were divided into two groups at random in a 90:10 ratio. The majority of patients (n = 14,582, 90%) were served as the training group to train and optimize prediction models, whereas patients in the validation group (n = 1,607, 10%) were utilized to validate the prediction models. Four models were introduced in the study: the logistic regression model, gradient boosting tree model, decision tree model, and random forest model. Results: Early death accounted for 17.4% of all included patients. Multivariate analysis demonstrated that older age; a separated, divorced, or widowed marital status; nonmetropolitan counties; brain metastasis; liver metastasis; lung metastasis; and histologic type of unspecified neoplasms were significantly associated with more early death, whereas a lower grade, a positive estrogen receptor (ER) status, cancer-directed surgery, radiation, and chemotherapy were significantly the protective factors. For the purpose of developing prediction models, the 12 variables were used. Among all the four models, the gradient boosting tree had the greatest AUC [0.829, 95% confident interval (CI): 0.802-0.856], and the random forest (0.828, 95% CI: 0.801-0.855) and logistic regression (0.819, 95% CI: 0.791-0.847) models came in second and third, respectively. The discrimination slopes for the three models were 0.258, 0.223, and 0.240, respectively, and the corresponding accuracy rates were 0.801, 0.770, and 0.762, respectively. The Brier score of gradient boosting tree was the lowest (0.109), followed by the random forest (0.111) and logistic regression (0.112) models. Risk stratification showed that patients in the high-risk group (46.31%) had a greater six-fold chance of early death than those in the low-risk group (7.50%). Conclusion: The gradient boosting tree model demonstrates promising performance with favorable discrimination and calibration in the study, and this model can stratify the risk probability of early death among bone metastatic breast cancer patients.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Background

Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques.

Methods

This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator.

Results

The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001).

Conclusions

The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.

A novel nomogram to stratify quality of life among advanced cancer patients with spinal metastatic disease after examining demographics, dietary habits, therapeutic interventions, and mental health status

Background

It would be very helpful to stratify patients and direct patient selection if risk factors for quality of life were identified in a particular population. Nonetheless, it is still challenging to forecast the health-related quality of life among individuals with spinal metastases. The goal of this study was to stratify patient’s populations for whom the assessment of quality of life should be encouraged by developing and validating a nomogram to predict the quality of life among advanced cancer patients with spine metastases.

Methods

This study prospectively analyzed 208 advanced cancer patients with spine metastases, and collected their general characteristics, food preferences, addictions, comorbidities, therapeutic strategies, and mental health status. The functional assessment of cancer therapy-general (FACT-G) and hospital anxiety and depression scale (HADS) were used to assess quality of life and mental health, respectively. The complete cohort of patients was randomly divided into two groups: a training set and a validation set. Patients from the training set were conducted to train and develop a nomogram, while patients in the validation set were performed to internally validate the nomogram. The nomogram contained significant variables discovered using the least absolute shrinkage and selection operator (LASSO) approach in conjunction with 10-fold cross-validation. The nomogram’s predictive ability was assessed utilizing discrimination, calibration, and clinical usefulness. Internal validation was also completed using the bootstrap method after applying 500 iterations of procedures. A web calculator was also developed to promote clinical practice.

Results

Advance cancer patients with spinal metastases had an extremely low quality of life, as indicated by the average FACT-G score of just 60.32 ± 20.41. According to the LASSO and 10-fold cross-validation, Eastern Cooperative Oncology Group (ECOG) score, having an uncompleted life goal, preference for eating vegetables, chemotherapy, anxiety status, and depression status were selected as nomogram predictors. In the training set, the area under the receiver operating characteristic curve (AUROC) was 0.90 (95% CI: 0.84–0.96), while in the validation set, it was 0.85 (95% CI: 0.78–0.93). They were 0.50 (95% CI: 0.41–0.58) and 0.44 (95% CI: 0.33–0.56), respectively, for the discrimination slopes. The nomogram had favorable capacity to calibrate and was clinically useful, according to the calibration curve and decision curve analysis. When compared to patients in the low-risk group, patients in the high-risk group were above four times more likely to experience a poor quality of life (82.18% vs. 21.50%, P < 0.001). In comparison to patients in the low-risk group, patients in the high-risk group also exhibited significant higher levels of anxiety and depression. The webpage for the web calculator was https://starshiny.shinyapps.io/DynNomapp-lys/.

Conclusions

This study suggests a nomogram that can be applied as a practical clinical tool to forecast and categorize the quality of life among patients with spine metastases. Additionally, patients with poor quality of life experience more severe anxiety and depression. Effective interventions should be carried out as soon as possible, especially for patients in the high-risk group, to improve their quality of life and mental health condition.

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques

BACKGROUND

Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, and to construct a reliable prediction model for patients who had suffered orthopedic trauma using machine learning techniques.

METHODS

This study enrolled 1257 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Lymphocytes were tracked from ICU admission to more than 20 days following admission to examine the dynamic changes over time. Over 40 possible variables were gathered for investigation. Patients were split 80:20 at random into a training cohort (n=1035) and an internal validation cohort (n=222). Four machine learning algorithms, including random forest, gradient boosting machine, decision tree, and support vector machine, and a logistic regression technique were utilized to train and optimize models using data from the training cohort. Patients in the internal validation cohort were used to validate models, and the optimal one was chosen. Patients from two large teaching hospitals were used for external validation (n=113). The key metrics that used to assess the prediction performance of models mainly included discrimination, calibration, and clinical usefulness. To encourage clinical application based on the optimal machine learning-based model, a web-based calculator was developed.

RESULTS

16.0% (201/1257) of all patients had PerCI in the MIMIC-III database. The means of lymphocytes (%) were consistently below the normal reference range across the time among PerCI patients (around 10.0%), whereas in patients without PerCI, the number of lymphocytes continued to increase and began to be in normal range on day 10 following ICU admission. Subgroup analysis demonstrated that patients with PerCI were in a more serious health condition at admission since those patients had worse nutritional status, more electrolyte imbalance and infection-related comorbidities, and more severe illness scores. Eight variables, including albumin, serum calcium, red cell volume distributing width (RDW), blood pH, heart rate, respiratory failure, pneumonia, and the Sepsis-related Organ Failure Assessment (SOFA) score, were significantly associated with PerCI, according to the least absolute shrinkage and selection operator (LASSO) logistic regression model combined with the 10-fold cross-validation. These variables were all included in the modelling. In comparison to other algorithms, the random forest had the optimal prediction ability with the highest area under receiver operating characteristic (AUROC) (0.823, 95% CI: 0.757-0.889), highest Youden index (1.571), and lowest Brier score (0.107). The AUROC in the external validation cohort was also up to 0.800 (95% CI: 0.688-0.912). Based on the risk stratification system, patients in the high-risk group had a 10.0-time greater chance of developing PerCI than those in the low-risk group. A web-based calculator was available at https://starxueshu-perci-prediction-main-9k8eof.streamlitapp.com/.

CONCLUSIONS

Patients with PerCI typically remain in an immunosuppressive status, but those without PerCI gradually regain normal immunity. The dynamic changes of lymphocytes can be a reliable biomarker for PerCI. This work developed a reliable model that may be helpful in improving early diagnosis and targeted intervention of PerCI. Beneficial interventions, such as improving nutritional status and immunity, maintaining electrolyte and acid-base balance, curbing infection, and promoting respiratory recovery, are early warranted to prevent the onset of PerCI, especially among patients in the high-risk group and those with a continuously low level of lymphocytes.

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients

Purpose

Bone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data.

Methods

This study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H₂O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold.

Results

Among all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807-0.833), 0.820 (95% CI: 0.807-0.833), and 0.815 (95% CI: 0.801-0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H₂O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001).

Conclusions

Using machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

Effects of weight loss on bone turnover, inflammatory cytokines, and adipokines in Chinese overweight and obese adults

PURPOSE

Plenty of studies have examined the long term effect of weight loss on bone mineral density. This study aimed to explore the effects of 10% weight loss on early changes in bone metabolism as well as the possible influencing factors.

METHODS

Overweight and obese outpatients (BMI > 24.0 kg/m²) were recruited from the nutrition clinic and followed a calorie-restricted, high-protein, low-carbohydrate diet program. Dietary intake, body composition, serum procollagen type I N-propeptide (PINP), β-Crosslaps, PTH, 25(OH) VitD, a series of inflammatory cytokines and adipokines were measured for the participants before starting to lose weight and after 10% weight loss (NCT04207879).

RESULTS

A total of 75 participants were enrolled and 37 participants achieved a weight loss of at least 10%. It was found that PINP decreased (p = 0.000) and the β-Crosslaps increased (p = 0.035) in female participants. Decreases in PTH (p = 0.001), serum IL-2 (p = 0.013), leptin (p = 0.001) and increases in 25(OH) VitD (p = 0.001), serum ghrelin (p = 0.033) were found in 37 participants after 10% of their weight had been lost. Change in PINP was detected to be significantly associated with change in lean body mass (r = 0.418, p = 0.012) and change in serum ghrelin(r = - 0.374, p = 0.023).

CONCLUSIONS

Bone formation was suppressed and bone absorption was increased in female subjects after a 10% weight loss. Bone turnover was found to be associated with lean body mass and affected by the circulating ghrelin level.

A prediction model for worsening diabetic retinopathy after panretinal photocoagulation

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.

Characteristics of clinical trials related to hip fractures and factors associated with completion

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.

Quality of Life and Mental Health Status Among Cancer Patients With Metastatic Spinal Disease

This study aimed to investigate the quality of life and mental health status and further to identify relevant risk factors among advanced cancer patients with spine metastases. This study prospectively included and analyzed 103 advanced cancer patients with spine metastases. Patient's basic information, lifestyles, comorbidities, tumor characteristics, therapeutic strategies, economic conditions, quality of life, anxiety, and depression were collected. Patient's quality of life was assessed using the Functional Assessment of Cancer Therapy-General Scale (FACT-G), and anxiety and depression were evaluated using the Hospital Anxiety and Depression Scale (HADS). Subgroup analysis was performed based on different age groups, and a multivariate analysis was performed to test the ability of 20 potential risk factors to predict quality of life, anxiety, and depression. The mean total FACT-G score was only 61.38 ± 21.26. Of all included patients, 52.43% had skeptical or identified anxiety and 53.40% suffered from skeptical or identified depression. Patients had an age of 60 or more and <70 years had the lowest FACT-G score (54.91 ± 19.22), highest HADS anxiety score (10.25 ± 4.22), and highest HADS depression score (10.13 ± 4.94). After adjusting all other potential risk factors, age was still significantly associated with quality of life (OR = 0.57, 95%CI: 0.38–0.86, p < 0.01) and depression (OR = 1.55, 95%CI: 1.00–2.42, p = 0.05) and almost significantly associated with anxiety (OR = 1.52, 95%CI: 0.94–2.43, p = 0.08). Besides, preference to eating vegetables, time since knowing cancer diagnosis, surgical treatment at primary cancer, hormone endocrine therapy, and economic burden due to cancer treatments were found to be significantly associated with the quality of life. A number of comorbidities and economic burden due to cancer treatments were significantly associated with anxiety. Advanced cancer patients with spine metastases suffer from poor quality of life and severe anxiety and depression, especially among patients with an age of 60 or more and <70 years. Early mental health care and effective measures should be conducted to advanced cancer patients with spine metastases, and more attention should be paid to take care of patients with an age of 60 or more and <70 years in terms of their quality of life and mental health status.

Biomechanical Regulatory Factors and Therapeutic Targets in Keloid Fibrosis

Keloids are fibroproliferative skin disorder caused by abnormal healing of injured or irritated skin and are characterized by excessive extracellular matrix (ECM) synthesis and deposition, which results in excessive collagen disorders and calcinosis, increasing the remodeling and stiffness of keloid matrix. The pathogenesis of keloid is very complex, and may include changes in cell function, genetics, inflammation, and other factors. In this review, we aim to discuss the role of biomechanical factors in keloid formation. Mechanical stimulation can lead to excessive proliferation of wound fibroblasts, deposition of ECM, secretion of more pro-fibrosis factors, and continuous increase of keloid matrix stiffness. Matrix mechanics resulting from increased matrix stiffness further activates the fibrotic phenotype of keloid fibroblasts, thus forming a loop that continuously invades the surrounding normal tissue. In this process, mechanical force is one of the initial factors of keloid formation, and matrix mechanics leads to further keloid development. Next, we summarized the mechanotransduction pathways involved in the formation of keloids, such as TGF-β/Smad signaling pathway, integrin signaling pathway, YAP/TAZ signaling pathway, and calcium ion pathway. Finally, some potential biomechanics-based therapeutic concepts and strategies are described in detail. Taken together, these findings underscore the importance of biomechanical factors in the formation and progression of keloids and highlight their regulatory value. These findings may help facilitate the development of pharmacological interventions that can ultimately prevent and reduce keloid formation and progression.

The Effects of Research Activities on Biomedical Students' Mental Health: A National Cross-Sectional Study

Mental health disorders are prevalent among biomedical students, and scientific research is one of their main activities, yet less is known about the relationship between research activities and mental health among these students. The aim of this cross-sectional study was to assess the associations between research activities and mental health and to identify the potential risk factors for anxiety and depression among biomedical students in China. This study enrolled 1,079 participants between November 2020 and December 2020 from 29 Chinese provinces and collected participants' basic characteristics, work situations, scientific achievements, and potential stress sources via an online questionnaire. Anxiety and depression were evaluated by two widely used scales, the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9), respectively. The study also assessed the associations between scientific research duration and mental health. Univariate and multivariate analyses were performed to identify the predictors of anxiety and depression. Among the participants, 39.02% scored as having moderate to severe anxiety, and 37.54% scored as having moderate to severe depression. When the Youden index reached its maximum, the optimal cutoff was 7.17 h for the GAD-7 and 6.83 h for the PHQ-9. After adjustment for confounders, a longer research work duration was significantly associated with a higher anxiety [odds ratio (OR) = 1.32, 95% confidence interval (CI): 1.21-1.44, p < 0.01] and depression (OR = 1.27, 95% CI: 1.17-1.39, p < 0.01). Of all the participants working for 7 h a day, 37.04% had already published at least one paper and 25.93% had at least one Science Citation Index paper. Anxiety and depression are common among biomedical students. The research work duration of 7 h a day is the best cutoff for mental health, and it is associated with acceptable scientific research achievements. Not more than 7 h a day on research is recommended for biomedical students to maintain a balance between mental health and scientific research achievements.

An External-Validated Algorithm to Predict Postoperative Pneumonia Among Elderly Patients With Lung Cancer After Video-Assisted Thoracoscopic Surgery

The aim of the study was to develop an algorithm to predict postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery. We analyzed 3,009 patients from the Thoracic Perioperative Database for Geriatrics in our hospital and finally enrolled 1,585 elderly patients (age≧65 years) with lung cancer treated with video-assisted thoracoscopic surgery. The included patients were randomly divided into a training group (n = 793) and a validation group (n = 792). Patients in the training group were used to develop the algorithm after screening up to 30 potential risk factors, and patients in the validation group were used to internally validate the algorithm. External validation of the algorithm was achieved in the external validation dataset after enrolling 165 elderly patients with lung cancer treated with video-assisted thoracoscopic surgery from two hospitals in China. Of all included patients, 9.15% (145/1,585) of patients suffered from postoperative pneumonia in the Thoracic Perioperative Database for Geriatrics, and 10.30% (17/165) of patients had postoperative pneumonia in the external validation dataset. The algorithm consisted of seven variables, including sex, smoking, history of chronic obstructive pulmonary disease (COPD), surgery duration, leukocyte count, intraoperative injection of colloid, and intraoperative injection of hormone. The C-index from the receiver operating characteristic curve (AUROC) was 0.70 in the training group, 0.67 in the internal validation group, and 0.71 in the external validation dataset, and the corresponding calibration slopes were 0.88 (95% confident interval [CI]: 0.37–1.39), 0.90 (95% CI: 0.46–1.34), and 1.03 (95% CI: 0.24–1.83), respectively. The actual probabilities of postoperative pneumonia were 5.14% (53/1031) in the low-risk group, 15.07% (71/471) in the medium-risk group, and 25.30% (21/83) in the high-risk group (p < 0.001). The algorithm can be a useful prognostic tool to predict the risk of developing postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery.

The global regulatory logic of organ regeneration: circuitry lessons from skin and its appendages

In organ regeneration, the regulatory logic at a systems level remains largely unclear. For example, what defines the quantitative threshold to initiate regeneration, and when does the regeneration process come to an end? What leads to the qualitatively different responses of regeneration, which restore the original structure, or to repair which only heals a wound? Here we discuss three examples in skin regeneration: epidermal recovery after radiation damage, hair follicle fate choice after chemotherapy damage, and wound-induced feather regeneration. We propose that the molecular regulatory circuitry is of paramount significance in organ regeneration. It is conceivable that defects in these controlling pathways may lead to failed regeneration and/or organ renewal, and understanding the underlying logic could help to identify novel therapeutic strategies.