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Abstract Background Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for patient care and resource allocation. Predicting prolonged dependence on mechanical ventilation is crucial for improving patient outcomes, preventing ventilator-associated complications, and guiding targeted clinical interventions. However, specific tools for predicting prolonged mechanical ventilation among ICU patients, particularly those with critical orthopaedic trauma, are currently lacking. The purpose of the study was to establish and validate an artificial intelligence (AI) platform to assess the prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma. Methods This study analyzed 1400 patients with critical orthopaedic trauma who received mechanical ventilation, and the prolonged dependence on mechanical ventilation was defined as not weaning from mechanical ventilation for ≧ 7 days. Patients were randomly classified into a training cohort and a validation cohort based on the ratio of 8:2. Patients in the training cohort were used to establish models using machine learning techniques, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), random forest (RF), support vector machine (SVM), and light gradient boosting machine (LightGBM), whereas patients in the validation cohort were used to validate these models. The prediction performance of these models was evaluated using discrimination and calibration. A scoring system was used to comprehensively assess and compare the prediction performance of the models, based on ten evaluation metrics. External validation of the model was performed in 122 patients with critical orthopaedic trauma from a university teaching hospital. Furthermore, the optimal model was deployed as an AI calculator, which was accessible online, to assess the risk of prolonged dependence on mechanical ventilation. Results Among the developed models, the eXGBM model had the highest score of 50, followed by the LightGBM model (48) and the RF model (37). In detail, the eXGBM model outperformed other models in terms of recall (0.892), Brier score (0.088), log loss (0.291), and calibration slope (0.999), and the model was the second best in terms of area under the curve value (0.949, 95%: 0.933–0.961), accuracy (0.871), F1 score (0.873), and discrimination slope (0.647). The SHAP revealed that the most important five features were respiratory rate, lower limb fracture, glucose, PaO2, and PaCO2. External validation of the eXGBM model also demonstrated favorable prediction performance, with an AUC value of 0.893 (95%CI: 0.819–0.967). The eXGBM model was successfully deployed as an AI platform, which was at https://prolongedmechanicalventilation-lqsfm6ecky6dpd4ybkvohu.streamlit.app/ . By simply clicking the link and inputting features, users were able to obtain the risk of experiencing prolonged dependence on mechanical ventilation for individuals. Based on the risk of prolonged dependence on mechanical ventilation, patients were stratified into the high-risk or the low-risk groups, and corresponding therapeutic interventions were recommended, accordingly. Conclusions The AI model shows potential as a valuable tool for stratifying patients with a high risk of prolonged dependence on mechanical ventilation. The AI model may offer a promising approach for optimizing patient care and resource allocation in critical care settings. Clinical trial number Not applicable.

Abstract 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.

BackgroundAdolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.MethodsThis study analyzed 1966 university students. All participants were classified into a training set and a validation set at the ratio of 8:2 at random. Participants in the training set were utilized to establish models, and the logistic regression (LR) and five machine learning algorithms, including the eXtreme Gradient Boosting Machine (XGBM), Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), CatBoosting Machine (CatBM), were utilized to develop models. Whereas, those in the validation set were used to validate the developed models.ResultsThe incidence of severe sleep disturbance was 5.28% (104/1969). Among all developed models, the XGBM model performed best in AUC (0.872 [95%CI: 0.848-0.896]), followed by the CatBM model (0.853 [95% CI: 0.821-0.878]) and DT model (0.843 [95% CI: 0.801-0.870]), whereas the AUC of the logistic regression model was only 0.822 (95% CI: 0.777-0.856). Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).ConclusionsThe XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.

Abstract Objective This study was designed to develop and validate a robust predictive model for one-year mortality in elderly coronary heart disease (CHD) patients with anemia using machine learning methods. Methods Demographics, tests, comorbidities, and drugs were collected for a cohort of 974 elderly patients with CHD. A prospective analysis was performed to evaluate predictive performances of the developed models. External validation of models was performed in a series of 112 elderly CHD patients with anemia. Results The overall one-year mortality was 43.6%. Risk factors included heart rate, chronic heart failure, tachycardia and β receptor blockers. Protective factors included hemoglobin, albumin, high density lipoprotein cholesterol, estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), aspirin, clopidogrel, calcium channel blockers, angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs), and statins. Compared with other algorithms, an ensemble machine learning model performed the best with area under the curve (95% confidence interval) being 0.828 (0.805–0.870) and Brier score being 0.170. Calibration and density curves further confirmed favorable predicted probability and discriminative ability of an ensemble machine learning model. External validation of Ensemble Model also exhibited good performance with area under the curve (95% confidence interval) being 0.825 (0.734–0.916) and Brier score being 0.185. Patients in the high-risk group had more than six-fold probability of one-year mortality compared with those in the low-risk group (P

We at Cell Reports speak with Mingxing Lei about his journey in science and his recent paper in which he and his fellow authors identified a mechanical-chemical signaling cascade that mediates the interaction between dendritic cells and fibroblasts and regulates basal epidermal cell proliferation.

Summary: 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.

Abstract 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.

Abstract 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.

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.

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.

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.

Abstract 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

Abstract 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

BackgroundPatients with bone metastasis often experience a significantly limited survival time, and a life expectancy of

BackgroundHealth-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.MethodsA 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.ResultsFour 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.ConclusionsThis 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.

Abstract 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

Abstract 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

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.

PurposeUsing 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

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

BackgroundIndividualized 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.MethodsThis 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.ResultsThe 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

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.

BackgroundPersistent 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.MethodsThis 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.Results16.0% (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/.ConclusionsPatients 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.

PurposeBone 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.MethodsThis 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.ResultsAmong 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).ConclusionsUsing 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.

Abstract Background Cement leakage into venous blood posed significant challenge to surgeons. The aim of the study was to create a Peking University First Hospital Score (PUFHS) which could evaluate the probability of vascular cement leakage among spine metastases patients following percutaneous vertebroplasty. Methods The study retrospectively enrolled 272 spine metastases patients treated with percutaneous vertebroplasty. We randomly extracted all enrolled patients as the training or validation group and baseline characteristic comparison was assessed between the two groups. Creation of the PUFHS was performed in the training group and validation of the PUFHS was performed in the validation group. Results Of all the 272 patients, the total number of included vertebrae was 632 and the median treated levels were 2 per patient. Vascular cement leakage occurred in 26.47% (72/272) of patients. The baseline characteristics were comparable between the two groups (P > 0.05). Three risk predictors (primary cancer types, number of treated vertebrae levels, and vertebrae collapse) were included in the PUFHS. The area under the receiver operating characteristic curve (AUROC) of the PUFHS was 0.71 in the training group and 0.69 in the validation group. The corresponding correct classification rates were 73.0 and 70.1%, respectively. The calibration slope was 0.78 (95% confidence interval[CI]: 0.45–1.10) in the training group and 1.10 (95% CI: 0.73–1.46) in the validation group. The corresponding intercepts were 0.06 (95% CI: − 0.04–0.17) and − 0.0079 (95% CI: − 0.11–0.092), respectively. Conclusions Vascular cement leakage is common among spine metastases after percutaneous vertebroplasty. The PUFHS can calculate the probability of vascular cement leakage, which can be a useful tool to inform surgeons about vascular cement leakage risk in advance.

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

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.

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.

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.

Abstract Background How high-salt intake leads to the occurrence of many cardiovascular diseases such as atherosclerosis is a fundamental question in pathology. Here we postulated that high-salt-induced NFAT5 controls the inflammasome activation by directly regulating NLRP3, which mediates the expression of inflammatory- and adhesion-related genes in vascular endothelium, resulting in the formation of atherosclerosis. Methods Atherosclerosis-prone apolipoprotein E-deficient (ApoE−/−) mice which accumulate cholesterol ester-enriched particles in the blood due to poor lipoprotein clearance capacity were used as the atherosclerosis model in vivo. Cultured endothelial cells (ECs) and monocytes under high-salt condition were used to explore the atheroprone role of the activation of NFAT5-NLRP3 inflammasome in vascular endothelium in vitro. Bioinformatic analysis and chromatin immunoprecipitation assay were used to identify the DNA binding sites of NFAT5 on promoters of NLRP3 and IL-1β. Results We first observe that high-salt intake promotes atherosclerosis formation in the aortas of ApoE−/− mice, through inducing the expression of NFAT5, NLRP3, and IL-1β in endothelium. Overexpression of NFAT5 activates NLRP3-inflammasome and increases the secretion of IL-1β in ECs partly via ROS. Chromatin immunoprecipitation assay demonstrates that NFAT5 directly binds to the promoter regions of NLRP3 and IL-1β in endothelial cells subjected to the high-salt environment. Conclusions Our study identifies NFAT5 as a new and essential transcription factor that is required for the early activation of NLRP3-inflammasome-mediated endothelium innate immunity, contributing to the formation of atherosclerosis under hypertonic stress induction.

Objectives: To investigate the epidemiology of cement leaks and further develop an algorithm to detect the high risk of cement leaks among advanced cancer patients with metastatic spinal disease treated with percutaneous vertebroplasty. Methods: This study retrospectively analyzed 309 patients with metastatic spinal disease treated with percutaneous vertebroplasty. Patients were randomly divided into a training group and a validation group. In the training group, 13 potential characteristics were analyzed for their abilities to predict cement leaks. Discal cement leakage and paravertebral cement leakage were excluded from the analysis. Those characteristics identified as having significant predictive value were used to develop a predictive algorithm. Internal validation of the algorithm was performed based on discrimination and calibration qualities. Results: Overall, cement leaks occurred in 61.17% (189/309) patients. Among the 13 characteristics analyzed, younger age (P = 0.03), extravertebral bone metastases (P = 0.02), increased number of treated vertebrae levels (P

During aging, the skin undergoes changes in architecture and composition. Skin aging phenotypes occur due to accumulated changes in the genome/epigenome, cytokine/cell adhesion, cell distribution/extracellular matrix (ECM), etc. Here we review data suggesting that tissue mechanics also plays a role in skin aging. While mouse and human skin share some similarities, their skin architectures differ in some respects. However, we use recent research in haired murine skin because of the available experimental data. Skin suffers from changes in both its appendages and inter-appendage regions. The elderly exhibit wrinkles and loose dermis and are more likely to suffer from wounds and superficial abrasions with poor healing. They also have a reduction in the number of skin appendages. While telogen is prolonged in aging murine skin, hair follicle stem cells can be rejuvenated to enter anagen if transplanted to a young skin environment. We highlight recent single-cell analyses performed on epidermis and aging human skin which identified new basal cell subpopulations that shift in response to wounding. This may be due to alterations of basement membrane stiffness which would change tissue mechanics in aging skin, leading to altered homeostatic dynamics. We propose that the extracellular matrix (ECM) may play a key role as a chemo-mechanical integrator of the multi-layered senescence-associated signaling pathways, dictating the tissue mechanical landscape of niche microenvironments in aging phenotypes. We show examples where failed chemo-mechanical signaling leads to deteriorating homeostasis during skin aging and suggest potential therapeutic strategies to guide future research to delay the aging processes.

Light has attracted special attention as a stimulus for triggered drug delivery systems (DDS) due to its intrinsic features of being spatially and temporally tunable. Ultraviolet A (UVA) radiation has recently been used as a source of external light stimuli to control the release of drugs using a “switch on- switch off” procedure. This review discusses the promising potential of UVA radiation as the light source of choice for photo-controlled drug release from a range of photo-responsive and photolabile nanostructures via photo-isomerization, photo-cleavage, photo-crosslinking, and photo-induced rearrangement. In addition to its clinical use, we will also provide here an overview of the recent UVA-responsive drug release approaches that are developed for phototherapy and skin photoprotection.

A cyst is a closed sac-like structure in which cyst walls wrap certain contents typically including air, fluid, lipid, mucous, or keratin. Cyst cells can retain multipotency to regenerate complex tissue architectures, or to differentiate. Cysts can form in and outside the skin due to genetic problems, errors in embryonic development, cellular defects, chronic inflammation, infections, blockages of ducts, parasites, and injuries. Multiple types of skin cysts have been identified with different cellular origins, with a common structure including the outside cyst wall engulfs differentiated suprabasal layers and keratins. The skin cyst is usually used as a sign in pathological diagnosis. Large or surfaced skin cysts affect patients’ appearance and may cause the dysfunction or accompanying diseases of adjacent tissues. Skin cysts form as a result of the degradation of skin epithelium and appendages, retaining certain characteristics of multipotency. Surprisingly, recent organoid cultures show the formation of cyst configuration as a transient state toward more morphogenetic possibility. These results suggest, if we can learn more about the molecular circuits controlling upstream and downstream cellular events in cyst formation, we may be able to engineer stem cell cultures toward the phenotypes we wish to achieve. For pathological conditions in patients, we speculate it may also be possible to guide the cyst to differentiate or de-differentiate to generate structures more akin to normal architecture and compatible with skin homeostasis.

Much remains unknown about the regulatory networks which govern the dermal papilla’s (DP) ability to induce hair follicle neogenesis, a capacity which decreases greatly with age. To further define the core genes which characterize the DP cell and to identify pathways prominent in DP cells with greater hair inductive capacity, comparative transcriptome analyses of human fetal and adult dermal follicular cells were performed. 121 genes were significantly upregulated in fetal DP cells in comparison to both fetal dermal sheath cup (DSC) cells and interfollicular dermal (IFD) populations. Comparison of the set of enriched human fetal DP genes with human adult DP, newborn mouse DP, and embryonic mouse dermal condensation (DC) cells revealed differences in the expression of Wnt/β-catenin, Shh, FGF, BMP, and Notch signaling pathways. We chose R-spondin-1, a Wnt agonist, for functional verification and show that exogenous administration restores hair follicle neogenesis from adult mouse cells in skin reconstitution assays. To explore upstream regulators of fetal DP gene expression, we identified twenty-nine transcription factors which are upregulated in human fetal DP cells compared to adult DP cells. Of these, seven transcription factor binding motifs were significantly enriched in the candidate promoter regions of genes differentially expressed between fetal and adult DP cells, suggesting a potential role in the regulatory network which confers the fetal DP phenotype and a possible relationship to the induction of follicle neogenesis.

Abutment pressure distribution is different when a longwall panel is passing through the abandoned gate roads in a damaged coal seam. According to the geological condition of panel E13103 in Cuijiazhai Coal Mine in China, theoretical analysis and finite element numerical simulation were used to determine the front pressure distribution characteristics when the longwall face is 70, 50, 30, 20, 10, and 5 m from the abandoned roadways. The research results show that the influence range of abutment pressure is 40 to 45 m outby the face, and the peak value of front abutment pressure is related to the distance between the face and abandoned roadways. When the distance between the longwall face and abandoned roadways is reduced from 50 to 10 m, the front abutment pressure peak value kept increasing. When the distance is 10 m, it has reached the maximum. The peak value is located in 5 to 6 m outby the faceline. When the distance between the longwall face and abandoned roadways is reduced from10 to5 m, the front abutment pressure sharply decreases, the intact coal yields and is even in plastic state. The peak value transfers to the other side of the abandoned roadways. The research results provide a theoretical basis for determining the advance support distance of two roadways in the panel and the reinforcement for face stability when the longwall face is passing through the abandoned roadways. Keywords: Abandoned roadways, Abutment pressure, Theoretical calculation, Numerical simulation

Abnormal activation of the IGF1R signaling pathway accelerates melanoma development and metastases. RNAi systems with complex cloning procedures and unsatisfactory efficiency in suppressing gene expression have become the technical difficulties that hinder their utility when studying gene knockdown. Here we established a simplified adenovirus-mediated gene knockdown system by which a single adenoviral vector carries multiple siRNA fragments that can effectively suppress IGF1R expression in melanoma cells. We first generated the adenovirus that simultaneously expresses three human or mouse siRNAs targeting IGF1R (AdRIGF1R-OK). qRT-PCR and immunofluorescence staining revealed that IGF1R expression was significantly decreased in the melanoma cells that were infected with AdRIGF1R-OK. Bioluminescence imaging showed that the size of the tumor formed by the xenografts infected with AdRIGF1R-OK was significantly smaller than that of the controls. Annexin V-FITC flow cytometry assay, immunofluorescence staining for cleaved caspase-3, and Hoechst staining showed that more cells underwent apoptosis after infection with AdRIGF1R-OK. Luciferase reporter assay, crystal violet cell viability assay, and cell-cycle analysis showed that the proliferation of melanoma cells infected with AdRIGF1R-OK was significantly decreased compared to the controls. This study demonstrates that the OK system is effective in silencing gene expression, with promising potential to treat melanoma and other diseases. Keywords: RNAi technology, gene therapy, melanoma, IGF1R, one-step knockdown system, adenovirus

Wnt signaling plays crucial role in regulating melanocyte stem cells/melanocyte differentiation in the hair follicle. However, how the Wnt signaling is balanced to be overactivated to control follicular melanocytes behavior remains unknown. Here, by using immunofluorescence staining, we showed that secreted frizzled-related protein 4 (sFRP4) is preferentially expressed in the skin epidermal cells rather than in melanocytes. By overexpression of sFRP4 in skin cells in vivo and in vitro, we found that sFRP4 attenuates activation of Wnt signaling, resulting in decrease of melanocytes differentiation in the regenerating hair follicle. Our findings unveiled a new regulator that involves modulating melanocytes differentiation through a paracrine mechanism in hair follicle, supplying a hope for potential therapeutic application to treat skin pigmentation disorders.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]