Targeted activation of Nrf2/HO-1 pathway by Corynoline alleviates osteoporosis development.

Oxidative stress is preferentially treated as a risk factor for the development and progression of osteoporosis. Corynoline as a component of Corydalis bungeana Turcz presents antioxidative and anti-inflammatory properties. In the present study, the effects of Corynoline on osteoblasts following hydrogen peroxide (H2O2)-induced injury were evaluated accompanied by the investigation of the molecular mechanisms involved. It was found that Corynoline downregulated the intracellular reactive oxygen species (ROS) generation and restored the osteogenic potential of the disrupted osteoblasts by H2O2 exposure. Furthermore, Corynoline was revealed to activate the Nrf2/HO-1 signaling pathway, while ML385 (an Nrf2 inhibitor) would prevent the Corynoline-mediated positive effects on the disrupted osteoblasts. In terms of the animal experiments, Corynoline treatment contributed to a significantly alleviated bone loss. These findings indicate that Corynoline may significantly attenuate the H2O2-induced oxidative damage of osteoblasts via the Nrf2/HO-1 signaling pathway, providing novel insights to the development of treatments for osteoporosis induced by oxidative injury.

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.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.

BACKGROUND: With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis. METHODS: Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. RESULTS: A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets. CONCLUSION: We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.

Based on CT at the third lumbar spine level, the skeletal muscle index and psoas muscle index can predict osteoporosis.

BACKGROUND: With the increasing number of studies on osteoporosis and muscle adipose tissue, existing studies have shown that skeletal muscle tissue and adipose tissue are closely related to osteoporosis by dual-energy x-ray absorptiometry (DXA) measurement. However, few studies have explored whether the skeletal muscle and adipose tissue index measured at the lumbar spine 3 (L3) level are closely related to bone mineral density (BMD) and can even predict osteoporosis. Therefore, this study aimed to prove whether skeletal muscle and adipose tissue index measured by computed tomography (CT) images based on a single layer are closely related to BMD. METHODS: A total of 180 participants were enrolled in this study to obtain skeletal muscle index (SMI), psoas muscle index (PMI), subcutaneous fat index (SFI), visceral fat index (VFI), and the visceral-to-subcutaneous ratio of the fat area (VSR) at L3 levels and divide them into osteoporotic and normal groups based on the T-score of DXA. Spearman rank correlation was used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and BMD. Similarly, spearman rank correlation was also used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and the fracture risk assessment tool (FRAX). Receiver operating characteristic (ROC) was used to analyze the efficacy of SMI, PMI, SFI, VFI, and VSR in predicting osteoporosis. RESULTS: BMD of L1-4 was closely correlated with SMI, PMI, VFI and VSR (r = 0.199 p = 0.008, r = 0.422 p < 0.001, r = 0.253 p = 0.001, r = 0.310 p < 0.001). BMD of the femoral neck was only correlated with PMI and SFI (r = 0.268 p < 0.001, r = - 0.164 p-0.028). FRAX (major osteoporotic fracture) was only closely related to PMI (r = - 0.397 p < 0.001). FRAX (hip fracture) was closely related to SMI and PMI (r = - 0.183 p = 0.014, r = - 0.353 p < 0.001). Besides, FRAX (major osteoporotic fracture and hip fracture) did not correlate with VFI, SFI, and VSR. SMI and PMI were statistically significant, with the area under the curve (AUC) of 0.400 (95% confidence interval 0.312-0.488 p = 0.024) and 0.327 (95% confidence interval 0.244-0.410 p < 0.001), respectively. VFI, SFI, and VSR were not statistically significant in predicting osteoporosis. CONCLUSIONS: This study demonstrated that L3-based muscle index could assist clinicians in the diagnosis of osteoporosis to a certain extent, and PMI is superior to SMI in the diagnosis of osteoporosis. In addition, VFI, SFI, and VSR do not help clinicians to diagnose osteoporosis well.

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.

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.

Therapeutic effect of SIRT3 on glucocorticoid-induced osteonecrosis of the femoral head via intracellular oxidative suppression.

Glucocorticoid-induced osteonecrosis of the femoral head (GIONFH) is a serious complication after long-term or excess administration of clinical glucocorticoids intervention, and the pathogenic mechanisms underlying have not been clarified yet. Oxidative stress is considered as a major cause of bone homeostasis disorder. This study is aimed to explore the potential relevance between SIRT3 and GIONFH, as well as the effect of resveratrol, which has been reported for its role in SIRT3 activation, on dexamethasone-induced oxidative stress and mitochondrial compromise in bone marrow stem cells (BMSCs). In this study, our data showed that SIRT3 level was declined in GIONFH rat femoral head, corresponding to a resultant decrease of SIRT3 expression in dexamethasone-treated BMSCs in vitro. We also found that dexamethasone could result in oxidative injury in BMSCs, and resveratrol treatment reduced this deleterious effect via a SIRT3-dependent manner. Moreover, our results demonstrated that rewarding effect of resveratrol on BMSCs osteogenic differentiation was via activation of AMPK/PGC-1α/SIRT3 axis. Meanwhile, resveratrol administration prevented the occurrence of GIONFH, enhanced SIRT3 expression and reduced oxidative level in GIONFH model rats. Therefore, our study provides basic evidence that SIRT3 may be a promising therapeutic target for GIONFH treatment and resveratrol could be an ideal agent for clinical uses.

Genetic diversity of Y-chromosome microsatellites in the Fujian Han and the Sichuan Han populations of China.

Y-chromosome short tandem repeats (STRs) are potentially useful for forensic, anthropological and evolutionary studies. In this study we chose the loci DYS 19, DYS 388, DYS 389 I, DYS 389 II, DYS 390, DYS 391, DYS 392, DYS 393, DYS 425 and DYS 426. Blood samples were taken from 46 unrelated male individuals from Fujian Han and 43 unrelated males from Sichuan Han in China. DNA was extracted by conventional chelex extraction procedure. PCR was carried out in two multiplex reactions. Fragment analysis was conducted on an ABI PRISM 310 Genetic Analyzer. Allele frequency distributions and discrimination indices were calculated, and the two populations were tested for genetic differences by means of analysis of molecular variance (AMOVA). Here we obtained 75 Y-STR haplotypes and the haplotype diversity for the complete haplotype was 0.9884 in Fujian Han and 0.9967 in Sichuan Han. A larger genetic difference became apparent between the two populations that belong to the Sino-Tibetan speaking populations.

[Y chromosome variations and hyplogroups from 15 biallelic markers in six Chinese populations].

By using 15 biallelic markers, 342 male individuals from six populations in China were genotyped with ASPCR (allele specific PCR). The 15 biallelic markers included M1 (YAP), M15 (9 bp insertion), M89 (C-->T), M9 (C-->G), M119 (A-->C), M50 (T-->C), M110 (T-->C), M103 (C-->T), M95 (C-->T), M88 (A-->G), M111 (2-bp deletion), M45 (G-->A), M122 (T-->C), M7 (C-->G) and M134 (1 bp deletion). The distribution of variation frequencies of 15 biallelic markers in six populations showed that with the extremely high frequencies of M9G (96.20% & 96.43%) and Han nationality displayed higher diversity than the four minority populations. It's noteworthy that M95T (82.14%) in Sichuan Han and M45A (18.57%) in Hui gave prominace to the two populations. The six populations displayed 34 (Fujian Han), 21 (Sichuan Han), 14 (Mongol), 26 (Hui), 10 (Xibo) and 8 (Hezhe) haplogroups respectively with 2, 1, 2, 1, 2 and 2 prominent haplogroups among them. Furthermore, the haplogroup analysis revealed that one predominant haplogroup was shared in the four minority populations and even two predominant haplogroups were shared in Mongol, Hezhe and Xibo. Unlike Han populations, the minority populations showed strikingly different haplogroups which were close to the ancestral pattern. However, the two Han populations exhibited divergence between them with the distinct frequencies of M89T and M95T. With the comparison of the number of people sharing the common haplogroups between any two of the four minority populations, relative genetic distance among them was deduced.