Development and validation of a new prognostic model for patients with acute-on-chronic liver failure in intensive care unit.

BACKGROUND: Cirrhotic patients with acute-on-chronic liver failure (ACLF) in the intensive care unit (ICU) have a poor but variable prognoses. Accurate prognosis evaluation can guide the rational management of patients with ACLF. However, existing prognostic scores for ACLF in the ICU environment lack sufficient accuracy. AIM: To develop a new prognostic model for patients with ACLF in ICU. METHODS: Data from 938 ACLF patients in the Medical Information Mart for Intensive Care (MIMIC) database were used to develop a new prognostic model (MIMIC ACLF) for ACLF. Discrimination, calibration and clinical utility of MIMIC ACLF were assessed by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA), respectively. MIMIC ACLF was then externally validated in a multiple-center cohort, the Electronic Intensive Care Collaborative Research Database and a single-center cohort from the Second Hospital of Hebei Medical University in China. RESULTS: The MIMIC ACLF score was determined using nine variables: ln (age) × 2.2 + ln (white blood cell count) × 0.22 - ln (mean arterial pressure) × 2.7 + respiratory failure × 0.6 + renal failure × 0.51 + cerebral failure × 0.31 + ln (total bilirubin) × 0.44 + ln (internationalized normal ratio) × 0.59 + ln (serum potassium) × 0.59. In MIMIC cohort, the AUROC (0.81/0.79) for MIMIC ACLF for 28/90-day ACLF mortality were significantly greater than those of Chronic Liver Failure Consortium ACLF (0.76/0.74), Model for End-stage Liver Disease (MELD; 0.73/0.71) and MELD-Na (0.72/0.70) (all P < 0.001). The consistency between actual and predicted 28/90-day survival rates of patients according to MIMIC ACLF score was excellent and superior to that of existing scores. The net benefit of MIMIC ACLF was greater than that achieved using existing scores within the 50% threshold probability. The superior predictive accuracy and clinical utility of MIMIC ACLF were validated in the external cohorts. CONCLUSION: We developed and validated a new prognostic model with satisfactory accuracy for cirrhotic patients with ACLF hospitalized in the ICU. The model-based risk stratification and online calculator might facilitate the rational management of patients with ACLF.

Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy.

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Lauinoids A-X: Labdane-type diterpenoids with anti-inflammatory activity from Croton laui.

Croton laui (Euphorbiaceae) is a traditional medicinal plant used by the Li ethnic group in China to treat headaches, stomachaches, and diphtheria. To understand the pharmacological basis of its medicinal use, an extensive investigation of the ethanolic extract of the bark of C. laui was performed. After repeated chromatography, twenty-four undescribed labdane-type diterpenoids, lauinoids A-X (1-24), and five known analogs (25-29) were isolated. Their structures and absolute configurations were established using a combination of spectroscopic analyses, electronic circular dichroism, nuclear magnetic resonance calculations, and single-crystal X-ray diffraction. Among them, compounds 1-3 exhibited an 11(12 â†’ 13)-abeo-16-nor-labdane skeleton, which originated putatively from 9 through a plausible pathway that involves a semipinacol rearrangement process. Compounds 11 and 12 belong to the rare class of 14,15-dinor-labdane diterpenoids. Compounds 18 and 28 exhibited substantial inhibitory effects by suppressing lipopolysaccharide-induced NO production in RAW 264.7 macrophages, with IC50 values of 3.37 ± 0.23 and 5.82 ± 0.28 µM, respectively. This study has greatly expanded the chemical diversity of labdane diterpenoids from C. laui and will guide future research on this ethnomedicinal plant.

Drymariamides A-J, Antiadipogenic Cyclopeptides Incorporating Noncanonical Amino Acids from Drymaria cordata.

Herein, we report an extensive phytochemical study on the whole plant of Drymaria cordata, which led to the isolation of ten new orbitides, named drymariamides A-J (1-10). Compounds 2, 3, and 5 incorporate rare residues of noncanonical amino acids of kynurenine (Kyn) or 3a-hydroxypyrroloindoline (HPI). Their structures with absolute configurations were elucidated by a combination of spectroscopic analysis, advanced Marfey's method, X-ray diffraction, and electronic circular dichroism analysis. Compounds 1-10 exhibited antiadipogenic effects in 3T3-L1 adipocytes, and the most potent compound 7 showed an EC50 value of 1.17 ± 0.19 µM.

The association between high-oxygen saturation and prognosis for intracerebral hemorrhage.

BACKGROUND: Concerns about the adverse effects of excessive oxygen have grown over the years. This study investigated the relationship between high oxygen saturation and short-term prognosis of patients with spontaneous intracerebral hemorrhage (sICH) after liberal use of oxygen. METHODS: This retrospective cohort study collected data from the Medical Information Mart for Intensive Care III (MIMIC-III) database (ICU cohort) and a tertiary stroke center (general ward cohort). The data on pulse oximetry-derived oxygen saturation (SpO2) during the first 24 h in ICU and general wards were respectively extracted. RESULTS: Overall, 1117 and 372 patients were included in the ICU and general ward cohort, respectively. Among the patients from the ICU cohort, a spoon-shaped association was observed between minimum SpO2 and the risk of in-hospital mortality (non-linear P<0.0001). In comparison with minimum SpO2 of 93-97%, the minimum SpO2>97% was associated with a significantly higher risk of in-hospital mortality after adjustment for confounders. Sensitivity analysis conducted using propensity score matching did not change this significance. The same spoon-shaped association between minimum SpO2 and the risk of in-hospital mortality was also detected for the general ward cohort. In comparison with the group with 95-97% SpO2, the group with SpO2>97% showed a stronger association with, but non-significant risk for, in-hospital mortality after adjustment for confounders. The time-weighted average SpO2>97% was associated significantly with in-hospital mortality in both cohorts. CONCLUSION: Higher SpO2 (especially a minimum SpO2>97%) was unrewarding after liberal use of oxygen among patients with sICH and might even be potentially detrimental.

Cinnamaldehyde Alleviates Bone Loss by Targeting Oxidative Stress and Mitochondrial Damage via the Nrf2/HO-1 Pathway in BMSCs and Ovariectomized Mice.

Osteoporosis (OP) is typically brought on by disruption of bone homeostasis. Excessive oxidative stress and mitochondrial dysfunction are believed to be the primary mechanisms underlying this disorder. Therefore, in order to restore bone homeostasis effectively, targeted treatment of oxidative stress and mitochondrial dysfunction is necessary. Cinnamaldehyde (CIN), a small molecule that acts as an agonist for the nuclear factor erythroid 2-related factor (Nrf2), has been found to possess antiapoptotic, anti-inflammatory, and antioxidant properties. We found that CIN, while rescuing apoptosis, can also reduce the accumulation of reactive oxygen species (ROS) to improve mitochondrial dysfunction and thus restore the osteogenic differentiation potential of BMSCs disrupted by hydrogen peroxide (H2O2) exposure. The role of CIN was preliminarily considered to be a consequence of Nrf2/HO-1 axis activation. The ovariectomized mice model further demonstrated that CIN treatment ameliorated oxidative stress in vivo, partially reversing OVX-induced bone loss. This improvement was seen in the trabecular microarchitecture and bone biochemical indices. However, when ML385 was concurrently injected with CIN, the positive effects of CIN were largely blocked. In conclusion, this study sheds light on the intrinsic mechanisms by which CIN regulates BMSCs and highlights the potential therapeutic applications of these findings in the treatment of osteoporosis.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Plant-on-chip: Core morphogenesis processes in the tiny plant Wolffia australiana.

A plant can be thought of as a colony comprising numerous growth buds, each developing to its own rhythm. Such lack of synchrony impedes efforts to describe core principles of plant morphogenesis, dissect the underlying mechanisms, and identify regulators. Here, we use the minimalist known angiosperm to overcome this challenge and provide a model system for plant morphogenesis. We present a detailed morphological description of the monocot Wolffia australiana, as well as high-quality genome information. Further, we developed the plant-on-chip culture system and demonstrate the application of advanced technologies such as single-nucleus RNA-sequencing, protein structure prediction, and gene editing. We provide proof-of-concept examples that illustrate how W. australiana can decipher the core regulatory mechanisms of plant morphogenesis.

Liver histopathological lesions is severe in patients with normal alanine transaminase and low to moderate hepatitis B virus DNA replication.

BACKGROUND: Chronic hepatitis B virus (HBV) infection remains a major global public health problem. Chronic hepatitis B (CHB) patients can be divided into treatment indication and non-treatment indication individuals according to alanine transaminase (ALT), HBV DNA, serum hepatitis B e antigen status, disease status [liver cirrhosis, hepatocellular carcinoma (HCC), or liver failure], liver necroinflammation or fibrosis, patients' age, and family history of HCC or cirrhosis. For example, normal ALT patients in 'immune-tolerant' phase with HBV DNA higher than 107 or 2 × 107 IU/mL, and those in 'inactive-carrier' phase with HBV DNA lower than 2 × 103 IU/mL do not require antiviral therapy. However, is it reasonable to set the defined values of HBV DNA as the fundamental basis to estimate the disease state and to determine whether to start treatment? In fact, we should pay more attention to those who do not match the treatment indications (gray-zone patients both in the indeterminate phase and in the 'inactive-carrier' phase). AIM: To analyze the correlation of HBV DNA level and liver histopathological severity, and to explore the significance of HBV DNA for CHB with normal ALT. METHODS: From January 2017 to December 2021, a retrospective cross-sectional set of 1299 patients with chronic HBV infection (HBV DNA > 30 IU/mL) who underwent liver biopsy from four hospitals, including 634 with ALT less than 40 U/L. None of the patients had received anti-HBV treatment. The degrees of liver necroinflammatory activity and liver fibrosis were evaluated according to the Metavir system. On the basis of the HBV DNA level, patients were divided into two groups: Low/moderate replication group, HBV DNA ≤ 107 IU/mL [7.00 Log IU/mL, the European Association for the Study of the Liver (EASL) guidelines] or ≤ 2 × 107 IU/mL [7.30 Log IU/mL, the Chinese Medical Association (CMA) guidelines]; high replication group, HBV DNA > 107 IU/mL or > 2 × 107 IU/mL. Relevant factors (demographic characteristics, laboratory parameters and noninvasive models) for liver histopathological severity were analyzed by univariate analysis, logistics analysis and propensity score-matched analysis. RESULTS: At entry, there were 21.45%, 24.29%, and 30.28% of the patients had liver histopathological severities with ≥ A2, ≥ F2, and ≥ A2 or/and ≥ F2, respectively. HBV DNA level (negative correlation) and noninvasive model liver fibrosis 5 value (positive correlation) were independent risk factors for liver histopathological severities (liver necroinflammation, liver fibrosis, and treatment indication). The AUROCs of the prediction probabilities (PRE_) of the models mentioned above (< A2 vs ≥ A2, < F2 vs ≥ F2, < A2 and < F2 vs ≥ A2 or/and ≥ F2) were 0.814 (95%CI: 0.770-0.859), 0.824 (95%CI: 0.785-0.863), and 0.799 (95%CI: 0.760-0.838), respectively. HBV DNA level (negative correlation) was still an independent risk factor when diagnostic models were excluded, the P values (< A2 vs ≥ A2, < F2 vs ≥ F2, < A2 and < F2 vs ≥ A2 or/and ≥ F2) were 0.011, 0.000, and 0.000, respectively. For the propensity score-matched pairs, whether based on EASL guidelines or CMA guidelines, the group with significant liver histology damage (≥ A2 or/and ≥ F2) showed much lower HBV DNA level than the group with non- significant liver histology damage (< A2 and < F2). Patients in the moderate replication group (with indeterminate phase) had the most serious liver disease pathologically and hematologically, followed by patients in the low replication group (with 'inactive-carrier' phase) and then the high replication group (with 'immune-tolerant' phase). CONCLUSION: HBV DNA level is a negative risk factor for liver disease progression. The phase definition of CHB may be revised by whether the level of HBV DNA exceeds the detection low limit value. Patients who are in the indeterminate phase or 'inactive carriers' should receive antiviral therapy.

Assessment of iris volume in glaucoma patients with type 2 diabetes mellitus by AS-OCT.

AIM: To examine the change of iris volume measured by CASIA2 anterior segment optical coherence tomography (AS-OCT) in glaucoma patients with or without type 2 diabetes mellitus (T2DM) and explore if there is a correlation between hemoglobin A1c (HbA1c) level and iris volume. METHODS: In a cross-sectional study, 72 patients (115 eyes) were divided into two groups: primary open angle glaucoma (POAG) group (55 eyes) and primary angle-closure glaucoma (PACG) group (60 eyes). Patients in each group were separately classified into patients with or without T2DM. Iris volume and glycosylated HbA1c level were measured and analyzed. RESULTS: In the PACG group, diabetic patients' iris volume was significantly lower than those of non-diabetics (P=0.02), and there was a significant correlation between iris volume and HbA1c level in the PACG group (r=-0.26, P=0.04). However, diabetic POAG patients' iris volume was noticeably higher than those of non-diabetics (P=0.01), and there was a significant correlation between HbA1c level and iris volume (r=0.32, P=0.02). CONCLUSION: Diabetes mellitus impact iris volume size, as seen by increased iris volume in the POAG group and decreased iris volume in the PACG group. In addition, iris volume is significantly correlated with HbA1c level in glaucoma patients. These findings imply that T2DM may compromise iris ultrastructure in glaucoma patients.

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.

BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. METHODS: This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. RESULTS: A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. CONCLUSIONS: This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.

A Novel Anti-Osteoporosis Mechanism of VK2: Interfering with Ferroptosis via AMPK/SIRT1 Pathway in Type 2 Diabetic Osteoporosis.

Type 2 diabetic osteoporosis (T2DOP) is a chronic bone metabolic disease. Compared with traditional menopausal osteoporosis, the long-term high glucose (HG) microenvironment increases patients' risk of fracture and osteonecrosis. We were accumulating evidence that implicated ferroptosis as a pivotal mechanism of glucolipotoxicity-mediated death of osteocytes and osteoblast, a novel form of programmed cell death resulting from uncontrolled lipid peroxidation depending on iron. Vitamin K2 (VK2), a fat-soluble vitamin, is clinically applied to prevent osteoporosis and improve coagulation. This study aimed to clarify the role and mechanism of VK2 in HG-mediated ferroptosis. We established the mouse T2DOP model by intraperitoneal injection of streptozotocin solution and a high-fat and high-sugar diet. We also cultured bone marrow mesenchymal stem cells (BMSCs) in HG to simulate the diabetic environment in vitro. Based on our data, VK2 inhibited HG-mediated bone loss and ferroptosis, the latter manifested by decreased levels of mitochondrial reactive oxygen species, lipid peroxidation, and malondialdehyde and increased glutathione in vitro. In addition, VK2 treatment was capable of restoring bone mass and strengthening the expression of SIRT1, GPX4, and osteogenic markers in the distal femurs. As for further mechanism exploration, we found that VK2 could activate AMPK/SIRT1 signaling, and knockdown of SIRT1 by siRNA prevented the VK2-mediated positive effect in HG-cultured BMSCs. Summarily, VK2 could ameliorate T2DOP through the activation of the AMPK/SIRT1 signaling pathway to inhibit ferroptosis.

Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma.

BACKGROUND: Osteosarcoma is well-established as the most common bone cancer in children and adolescents. Patients with localized disease have different prognoses and management than those with metastasis at the time of diagnosis. The purpose of this study was to explore potential risk factors for metastatic disease. METHODS: The Surveillance, Epidemiology, and End Results (SEER) Program database was used to identify patients diagnosed with osteosarcoma between 2004 and 2015. We developed prediction models for distant metastasis using six machine learning (ML) techniques, including logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GaussianNB), Extreme Gradient Boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (kNN). The adaptive synthetic (ADASYN) technique was used to deal with imbalanced data. The Shapley Additive Explanation (SHAP) analysis generated visualized explanations for each patient. Finally, the average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) were conducted to evaluate the models' effectiveness. RESULTS: The six machine learning algorithms achieved AP of 0.661-0.781 for predicting distant metastasis. The RF model yielded the best performance with an accuracy of 71.8 percent and an AP of 0.781 and was highly dependent on tumor size, primary surgery, and age. SHAP analysis provided model-independent interpretation, highlighting significant clinical factors associated with the risk of metastasis in osteosarcoma patients. CONCLUSIONS: An accurate machine learning-based prediction model was established for metastasis in osteosarcoma patients to help clinicians during clinical decision-making.

Use of a tissue clearing technique combined with retrograde trans-synaptic viral tracing to evaluate changes in mouse retinorecipient brain regions following optic nerve crush.

Successful establishment of reconnection between retinal ganglion cells and retinorecipient regions in the brain is critical to optic nerve regeneration. However, morphological assessments of retinorecipient regions are limited by the opacity of brain tissue. In this study, we used an innovative tissue cleaning technique combined with retrograde trans-synaptic viral tracing to observe changes in retinorecipient regions connected to retinal ganglion cells in mice after optic nerve injury. Specifically, we performed light-sheet imaging of whole brain tissue after a clearing process. We found that pseudorabies virus 724 (PRV724) mostly infected retinal ganglion cells, and that we could use it to retrogradely trace the retinorecipient regions in whole tissue-cleared brains. Unexpectedly, PRV724-traced neurons were more widely distributed compared with data from previous studies. We found that optic nerve injury could selectively modify projections from retinal ganglion cells in the hypothalamic paraventricular nucleus, intergeniculate leaflet, ventral lateral geniculate nucleus, central amygdala, basolateral amygdala, Edinger-Westphal nucleus, and oculomotor nucleus, but not the superior vestibular nucleus, red nucleus, locus coeruleus, gigantocellular reticular nucleus, or facial nerve nucleus. Our findings demonstrate that the tissue clearing technique, combined with retrograde trans-synaptic viral tracing, can be used to objectively and comprehensively evaluate changes in mouse retinorecipient regions that receive projections from retinal ganglion cells after optic nerve injury. Thus, our approach may be useful for future estimations of optic nerve injury and regeneration.

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery.

BACKGROUND: Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture. METHODS: Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. RESULTS: The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance. CONCLUSION: In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.

A Tumor Accelerator Based on Multicomponent Bone Scaffolds and Cancer Cell Homing.

Bone tissue attracts cancer cell homing biologically, mechanically, or chemically. It is difficult and time consuming to identify their complex cross-talk using existed methods. In this study, a multi-component bone matrix was fabricated using gelatin, hydroxyapatite (HAp), and epidermal growth factor (EGF) as raw materials to investigate how "acellular" bone matrix affects cancer cell homing in bone. Then, EGF-responsive cancer cells were cultured with the scaffold in a dynamical bioreactor. For different culture periods, the effects of HAp, gelatin, and EGF on the cell adhesion, proliferation, 3D growth, and migration of cancer were evaluated. The results indicated that a small amount of calcium ion released from the scaffolds accelerated cancer MDA-MB-231 adhesion on the surface of inner pores. Moreover, degradable gelatin key caused cancer cell growth on the scaffold surface to turn into a 3D aggregation. Despite this, the formation of cancer spheroids was slow, and required 14 days of dynamic culture. Thankfully, EGF promoted cancer cell adhesion, proliferation, and migration, and cancer spheroids were observed only after 3-day culture. We concluded that the combination of the multiple components in this scaffold allows cancer cells to meet multiple requirements of cancer dynamic progression.

Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture.

BACKGROUND: Compared with open comminuted calcaneal fractures, less emphasis is placed on postoperative surgical site infection (SSI) of closed comminuted calcaneal fractures. This study aimed to identify the risk factors associated with SSI and build a nomogram model to visualize the risk factors for postoperative SSI. METHODS: We retrospectively collected patients with closed comminuted calcaneal fractures from the Second Affiliated Hospital of Wenzhou Medical University database from 2017 to 2020. Risk factors were identified by logistics regression analysis, and the predictive value of risk factors was evaluated by ROC (receiver operating characteristic curve). Besides, the final risk factors were incorporated into R4.1.2 software to establish a visual nomogram prediction model. RESULTS: The high-fall injury, operative time, prealbumin, aspartate aminotransferase (AST), and cystatin-C were independent predictors of SSI in calcaneal fracture patients, with OR values of 5.565 (95%CI 2.220-13.951), 1.044 (95%CI 1.023-1.064), 0.988 (95%CI 0.980-0.995), 1.035 (95%CI 1.004-1.067) and 0.010 (95%CI 0.001-0.185) (Ps < 0.05). Furthermore, ROC curve analysis showed that the AUC values of high-fall injury, operation time, prealbumin, AST, cystatin-C, and their composite indicator for predicting SSI were 0.680 (95%CI 0.593-0.766), 0.756 (95%CI 0.672-939), 0.331 (95%CI 0.243-0.419), 0.605 (95%CI 0.512-0.698), 0.319 (95%CI 0.226-0.413) and 0.860 (95%CI 0.794-0.926), respectively (Ps < 0.05). Moreover, the accuracy of the nomogram to predict SSI risk was 0.860. CONCLUSIONS: Our study findings suggest that clinicians should pay more attention to the preoperative prealbumin, AST, cystatin C, high-fall injury, and operative time for patients with closed comminuting calcaneal fractures to avoid the occurrence of postoperative SSI. Furthermore, our established nomogram to assess the risk of SSI in calcaneal fracture patients yielded good accuracy and can assist clinicians in taking appropriate measures to prevent SSI.

CORM-3 Attenuates Oxidative Stress-Induced Bone Loss via the Nrf2/HO-1 Pathway.

Bone metabolism occurs in the entire life of an individual and is required for maintaining skeletal homeostasis. The imbalance between osteogenesis and osteoclastogenesis eventually leads to osteoporosis. Oxidative stress is considered a major cause of bone homeostasis disorder, and relieving excessive oxidative stress in bone mesenchymal stem cells (BMSCs) is a potential treatment strategy for osteoporosis. Carbon monoxide releasing molecule-3 (CORM-3), the classical donor of carbon monoxide (CO), possesses antioxidation, antiapoptosis, and anti-inflammatory properties. In our study, we found that CORM-3 could reduce reactive oxygen species (ROS) accumulation and prevent mitochondrial dysfunction thereby restoring the osteogenic potential of the BMSCs disrupted by hydrogen peroxide (H2O2) exposure. The action of CORM-3 was preliminarily considered the consequence of Nrf2/HO-1 axis activation. In addition, CORM-3 inhibited osteoclast formation in mouse primary bone marrow monocytes (BMMs) by inhibiting H2O2-induced polarization of M1 macrophages and endowing macrophages with M2 polarizating ability. Rat models further demonstrated that CORM-3 treatment could restore bone mass and enhance the expression of Nrf2 and osteogenic markers in the distal femurs. In summary, CORM-3 is a potential therapeutic agent for the treatment of osteoporosis.

Screening and evaluating honokiol from Magnolia officinalis against Nocardia seriolae infection in largemouth bass (Micropterus Salmoides).

Nocardiosis caused by Nocardia seriolae is a major threat to the aquaculture industry. Given that prolonged therapy administration can lead to a growth of antibiotic resistant strains, new antibacterial agents and alternative strategies are urgently needed. In this study, 80 medicinal plants were selected for antibacterial screening to obtain potent bioactive compounds against N. seriolae infection. The methanolic extracts of Magnolia officinalis exhibited the strongest antibacterial activity against N. seriolae with the minimal inhibitory concentration (MIC) of 12.5 µg/ml. Honokiol and magnolol as the main bioactive components of M. officinalis showed higher activity with the MIC value of 3.12 and 6.25 µg/ml, respectively. Sequentially, the evaluation of antibacterial activity of honokiol in vivo showed that honokiol had good biosafety, and could significantly reduce the bacterial load of nocardia-infected largemouth bass (p < .001). Furthermore, the survival rate of nocardia-infected fish fed with 100 mg/kg honokiol was obviously improved (p < .05). Collectively, these results suggest that medicinal plants represent a promising reservoir for discovering active components against Nocardia, and honokiol has great potential to be developed as therapeutic agents to control nocardiosis in aquaculture.

Risk Factors for Pulmonary Embolism in ICU Patients: A Retrospective Cohort Study from the MIMIC-III Database.

Pulmonary embolism (PE) is a common and potentially lethal form of venous thromboembolic disease in ICU patients. A limited number of risk factors have been associated with PE in ICU patients. In this study, we aimed to screen the independent risk factors of PE in ICU patients that can be used to evaluate the patient's condition and provide targeted treatment. We performed a retrospective cohort study using a freely accessible critical care database Medical Information Mart for Intensive Care (MIMIC)-III. The ICU patients were divided into two groups based on the incidence of PE. Finally, 9871 ICU patients were included, among which 204 patients (2.1%) had pulmonary embolism. During the multivariate logistic regression analysis, sepsis, hospital_LOS (the length of stay in hospital), type of admission, tumor, APTT (activated partial thromboplastin time) and platelet were independent risk factors for patients for PE in ICU, with OR values of 1.471 (95%CI 1.001-2.162), 1.001 (95%CI 1.001-1.001), 3.745 (95%CI 2.187-6.414), 1.709 (95%CI 1.247-2.341), 1.014 (95%CI 1.010-1.017) and 1.002 (95%CI 1.001-1.003) (Ps < 0.05). ROC curve analysis showed that the composite indicator had a higher predictive value for ICU patients with PE, with a ROC area under the curve (AUC) of 0.743 (95%CI 0.710 -0.776, p < 0.001). Finally, sepsis, tumor, platelet count, length of stay in the hospital, emergency admission and APTT were independent predictors of PE in ICU patients.