Mitophagy Promotes Hair Regeneration by Activating Glutathione Metabolism.

Mitophagy maintains tissue homeostasis by self-eliminating defective mitochondria through autophagy. How mitophagy regulates stem cell activity during hair regeneration remains unclear. Here, we found that mitophagy promotes the proliferation of hair germ (HG) cells by regulating glutathione (GSH) metabolism. First, single-cell RNA sequencing, mitochondrial probe, transmission electron microscopy, and immunofluorescence staining showed stronger mitochondrial activity and increased mitophagy-related gene especially Prohibitin 2 (Phb2) expression at early-anagen HG compared to the telogen HG. Mitochondrial inner membrane receptor protein PHB2 binds to LC3 to initiate mitophagy. Second, molecular docking and functional studies revealed that PHB2-LC3 activates mitophagy to eliminate the damaged mitochondria in HG. RNA-seq, single-cell metabolism, immunofluorescence staining, and functional validation discovered that LC3 promotes GSH metabolism to supply energy for promoting HG proliferation. Third, transcriptomics analysis and immunofluorescence staining indicated that mitophagy was down-regulated in the aged compared to young-mouse HG. Activating mitophagy and GSH pathways through small-molecule administration can reactivate HG cell proliferation followed by hair regeneration in aged hair follicles. Our findings open up a new avenue for exploring autophagy that promotes hair regeneration and emphasizes the role of the self-elimination effect of mitophagy in controlling the proliferation of HG cells by regulating GSH metabolism.

An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning.

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

The mechano-chemical circuit in fibroblasts and dendritic cells drives basal cell proliferation in psoriasis.

Psoriasis is an intractable immune-mediated disorder that disrupts the skin barrier. While studies have dissected the mechanism by which immune cells directly regulate epidermal cell proliferation, the involvement of dermal fibroblasts in the progression of psoriasis remains unclear. Here, we identified that signals from dendritic cells (DCs) that migrate to the dermal-epidermal junction region enhance dermal stiffness by increasing extracellular matrix (ECM) expression, which further promotes basal epidermal cell hyperproliferation. We analyzed cell-cell interactions and observed stronger interactions between DCs and fibroblasts than between DCs and epidermal cells. Using single-cell RNA (scRNA) sequencing, spatial transcriptomics, immunostaining, and stiffness measurement, we found that DC-secreted LGALS9 can be received by CD44+ dermal fibroblasts, leading to increased ECM expression that creates a stiffer dermal environment. By employing mouse psoriasis and skin organoid models, we discovered a mechano-chemical signaling pathway that originates from DCs, extends to dermal fibroblasts, and ultimately enhances basal cell proliferation in psoriatic skin.

Energy competition remodels the metabolic glucose landscape of psoriatic epidermal cells.

Rationale: Skin cells actively metabolize nutrients to ensure cell proliferation and differentiation. Psoriasis is an immune-disorder-related skin disease with hyperproliferation in epidermal keratinocytes and is increasingly recognized to be associated with metabolic disturbance. However, the metabolic adaptations and underlying mechanisms of epidermal hyperproliferation in psoriatic skin remain largely unknown. Here, we explored the role of metabolic competition in epidermal cell proliferation and differentiation in psoriatic skin. Methods: Bulk- and single-cell RNA-sequencing, spatial transcriptomics, and glucose uptake experiments were used to analyze the metabolic differences in epidermal cells in psoriasis. Functional validation in vivo and in vitro was done using imiquimod-like mouse models and inflammatory organoid models. Results: We observed the highly proliferative basal cells in psoriasis act as the winners of the metabolic competition to uptake glucose from suprabasal cells. Using single-cell metabolic analysis, we found that the "winner cells" promote OXPHOS pathway upregulation by COX7B and lead to increased ROS through glucose metabolism, thereby promoting the hyperproliferation of basal cells in psoriasis. Also, to prevent toxic damage from ROS, basal cells activate the glutathione metabolic pathway to increase their antioxidant capacity to assist in psoriasis progression. We further found that COX7B promotes psoriasis development by modulating the activity of the PPAR signaling pathway by bulk RNA-seq analysis. We also observed glucose starvation and high expression of SLC7A11 that causes suprabasal cell disulfide stress and affects the actin cytoskeleton, leading to immature differentiation of suprabasal cells in psoriatic skin. Conclusion: Our study demonstrates the essential role of cellular metabolic competition for skin tissue homeostasis.

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

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

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 (AI) 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 area under the curve (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.

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.

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 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.

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 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.

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.

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.

Acute lung injury caused by sepsis: how does it happen?

Sepsis is a systemic inflammatory disease caused by severe infections that involves multiple systemic organs, among which the lung is the most susceptible, leaving patients highly vulnerable to acute lung injury (ALI). Refractory hypoxemia and respiratory distress are classic clinical symptoms of ALI caused by sepsis, which has a mortality rate of 40%. Despite the extensive research on the mechanisms of ALI caused by sepsis, the exact pathological process is not fully understood. This article reviews the research advances in the pathogenesis of ALI caused by sepsis by focusing on the treatment regimens adopted in clinical practice for the corresponding molecular mechanisms. This review can not only contribute to theories on the pathogenesis of ALI caused by sepsis, but also recommend new treatment strategies for related injuries.

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.

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.

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.