Risk factors of pneumothorax in computed tomography guided lung nodule marking using autologous blood: a retrospective study.

BACKGROUND: To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules. METHODS: In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient's positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established. RESULTS: Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax. CONCLUSION: The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue.

Effects of Polymers on Cocrystal Growth in a Drug-Drug Coamorphous System: Relations between Glass-to-Crystal Growth and Surface-Enhanced Crystal Growth.

Coamorphous and cocrystal drug delivery systems provide attractive crystal engineering strategies for improving the solubilities, dissolution rates, and oral bioavailabilities of poorly water-soluble drugs. Polymeric additives have often been used to inhibit the unwanted crystallization of amorphous drugs. However, the transformation of a coamorphous phase to a cocrystal phase in the presence of polymers has not been fully elucidated. Herein, we investigated the effects of low concentrations of the polymeric excipients poly(ethylene oxide) (PEO) and poly(vinylpyrrolidone) (PVP) on the growth of carbamazepine-celecoxib (CBZ-CEL) cocrystals from the corresponding coamorphous phase. PEO accelerated the growth rate of the cocrystals by increasing the molecular mobility of the coamorphous system, while PVP had the opposite effect. The coamorphous CBZ-CEL system exhibited two anomalously fast crystal growth modes: glass-to-crystal (GC) growth in the bulk and accelerated crystal growth at the free surface. These two fast growth modes both disappeared after doping with PEO (1-3% w/w) but were retained in the presence of PVP, indicating a potential correlation between the two fast crystal growth modes. We propose that the different effects of PEO and PVP on the crystal growth modes arose from weaker effects of the polymers on cocrystallization at the surface than in the bulk. This work provides a deep understanding of the mechanisms by which polymers influence the cocrystallization kinetics of a multicomponent amorphous phase and highlights the importance of polymer selection in stabilizing coamorphous systems or preparing cocrystals via solid-based methods.

Predicting Colloidal Stability of High-Concentration Monoclonal Antibody Formulations in Common Pharmaceutical Buffers Using Improved Polyethylene Glycol Induced Protein Precipitation Assay.

Colloidal stability is an important consideration when developing high concentration mAb formulations. PEG-induced protein precipitation is a commonly used assay to assess the colloidal stability of protein solutions. However, the practical usefulness and the current theoretical model for this assay have yet to be verified over a large formulation space across multiple mAbs and mAb-based modalities. In the present study, we used PEG-induced protein precipitation assays to evaluate colloidal stability of 3 mAbs in 24 common formulation buffers at 20 and 5 °C. These prediction assays were conducted at low protein concentration (1 mg/mL). We also directly characterized high concentration (100 mg/mL) formulations for cold-induced phase separation, turbidity, and concentratibility by ultrafiltration. This systematic study allowed analysis of the correlation between the results of low concentration assays and the high concentration attributes. The key findings of this study include the following: (1) verification of the usefulness of three different parameters (Cmid, µB, and Tcloud) from PEG-induced protein precipitation assays for ranking colloidal stability of high concentration mAb formulations; (2) a new method to implement PEG-induced protein precipitation assay suitable for high throughput screening with low sample consumption; (3) improvement in the theoretical model for calculating robust thermodynamic parameters of colloidal stability (µB and εB) that are independent of specific experimental settings; (4) systematic evaluation of the effects of pH and buffer salts on colloidal stability of mAbs in common formulation buffers. These findings provide improved theoretical and practical tools for assessing the colloidal stability of mAbs and mAb-based modalities during formulation development.

Triptolide attenuates irritable bowel syndrome via inhibiting ODC1.

BACKGROUND: Irritable bowel syndrome (IBS) is a chronic disorder of the gut-brain axis with significant morbidity. Triptolide, an active compound extracted from Tripterygium wilfordii Hook F (TwHF), has been widely used as a major medicinal herb in the treatment of inflammatory disease. METHODS: The chronic-acute combined stress (CAS) stimulation was used to establish IBS rat model. The model rats were then gavaged with triptolide. Forced swimming, marble-burying, fecal weight and abdominal withdrawal reflex (AWR) score were recorded. Pathologic changes in the ileal and colonic tissues were validated by hematoxylin and eosin staining. The inflammatory cytokines and Ornithine Decarboxylase-1 (ODC1) in the ileal and colonic tissues were performed by ELISA and WB. RESULTS: Triptolide didn't have antidepressant- and antianxiety- effects in rats caused by CAS, but decreased fecal weight and AWR score. In addition, Triptolide reduced the release of IL-1, IL-6, and TNF-α and the expression of ODC1 in the ileum and colon. CONCLUSION: The therapeutic efficacy of triptolide for IBS induced by CAS was revealed in this study, which may be related to the reduction of ODC1.

Nanoscale Insight into the Effect of Calcium on Early-Age Polymerization of CNASH Gels.

Sodium-containing calcium-alumino-silicate-hydrate (CNASH) gels, the primary binder phase of alkali-activated materials (AAMs), significantly impact the performance of the AAM. Although the effect of the calcium content on the AAM has been extensively studied in the past, few studies focus on the effect of calcium on the structure and performance of gels at a molecular scale. As an important element in gels, the effect of calcium in gels on its atomic-scale properties remains unclear. This study establishes a molecular model of the CNASH gel via reactive molecular dynamics (MD) simulation and verifies the feasibility of the gel model. By employing the reactive MD, the effect of calcium on the physicochemical properties of gels in the AAM is investigated. The simulation highlights that the condensation process of the system containing Ca is accelerated dramatically. This phenomenon is explained from the perspective of thermodynamics and kinetics. The increased calcium content enhances the thermodynamic stability and reduces the energy barrier of the reaction. Then, the phenomenon is further analyzed through the nanosegregation in the structure. It is proved that this behavior is driven by the weaker affinity of calcium for aluminosilicate chains than the particles in the aqueous environment. The difference in affinity leads to nanosegregation in the structure, making Si(OH)4 and Al(OH)3 monomers and oligomers closer for better polymerization.

Variables Impacting Silicone Oil Migration and Biologics in Prefilled Syringes.

Prefilled syringes (PFS) as a primary container for parenteral drug products offer significant advantages, such as fast delivery time, ease of self-administration and fewer dosing errors. Despite the benefits that PFS can provide to patients, the silicone oil pre-coated on the glass barrels has shown migration into the drug product, which can impact particle formation and syringe functionality. Health authorities have urged product developers to better understand the susceptibility of drug products to particle formation in PFS due to silicone oil. In the market, there are multiple syringe sources provided by various PFS suppliers. Due to current supply chain shortages and procurement preferences for commercial products, the PFS source may change in the middle of development. Additionally, health authorities require establishing source duality. Therefore, it is crucial to understand how different syringe sources and formulation compositions impact the drug product quality. Here, several design of experiments (DOE) are executed that focus on the risk of silicone oil migration induced by syringe sources, surfactants, protein types, stress, etc. We utilized Resonant Mass Measurement (RMM) and Micro Flow Imaging (MFI) to characterize silicone oil and proteinaceous particle distribution in both micron and submicron size ranges, as well as ICP-MS to quantify silicon content. The protein aggregation and PFS functionality were also monitored in the stability study. The results show that silicone oil migration is impacted more by syringe source, siliconization process and surfactant (type & concentration). The break loose force and extrusion force across all syringe sources increase significantly as protein concentration and storage temperature increase. Protein stability is found to be impacted by its molecular properties and is less impacted by the presence of silicone oil, which is the same inference drawn in other literatures. A detailed evaluation described in this paper enables a thorough and optimal selection of primary container closure and de-risks the impact of silicone oil on drug product stability.

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.

A nomogram for predicting post-operative wound complications after open reduction and internal fixation for calcaneal fractures.

The purpose of our study was to determine the risk factors for post-operative wound complications (PWCs) after open reduction and internal fixation (ORIF) for calcaneal fracture and establish a nomogram prediction model. We retrospectively analysed the clinical data of patients who suffered from calcaneal fractures and had been surgically treated for ORIF in our institution between January 2010 and January 2020. Perioperative information was obtained through the electronic medical record system, univariate and multivariate analyses were performed to determine the risk factors of PWCs, and a nomogram model was constructed to predict the risk of PWCs. The predictive performance and consistency of the model were evaluated by the Hosmer -Lemeshow (H-L) test and the calibration curve. In total, 444 patients were enrolled in our study. Multivariate analysis results showed that smoking, limb swelling, angle of incision, and CRP were independent risk factors for skin necrosis. The AUC value for skin necrosis risk was 0.982 (95%CI 0.97-0.99). The H-L test revealed that the normogram prediction model had good calibration ability (P = .957). Finally, we found a correlation between PWCs and smoking, limb swelling, angle of incision, and CRP after ORIF for calcaneal fracture patients. Our nomogram prediction model might be helpful for clinicians to identify high-risk patients, as interventions could be taken early to reduce the incidence of PWCs.

Polymorphic selectivity in crystal nucleation.

Crystal nucleation rates have been measured in the supercooled melts of two richly polymorphic glass-forming liquids: ROY and nifedipine (NIF). ROY or 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile is known for its crystals of red, orange, and yellow colors and many polymorphs of solved structures (12). Of the many polymorphs, ON (orange needles) nucleates the fastest with the runner up (Y04) trailing by a factor of 103 when compared under the same mobility-limited condition, while the other unobserved polymorphs are slower yet by at least 5 orders of magnitude. Similarly, of the six polymorphs of NIF, γ' nucleates the fastest, ß' is slower by a factor of 10, and the rest are slower yet by at least 5 decades. In both systems, the faster-nucleating polymorphs are not built from the lowest-energy conformers, while they tend to have higher energies and lower densities and thus greater similarity to the liquid phase by these measures. The temperature ranges of this study covered the glass transition temperature Tg of each system, and we find no evidence that the nucleation rate is sensitive to the passage of Tg. At the lowest temperatures investigated, the rates of nucleation and growth are proportional to each other, indicating that a similar kinetic barrier controls both processes. The classical nucleation theory provides an accurate description of the observed nucleation rates if the crystal growth rate is used to describe the kinetic barrier for nucleation. The quantitative rates of both nucleation and growth for the competing polymorphs enable prediction of the overall rate of crystallization and its polymorphic outcome.

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.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Coating Polyelectrolyte Multilayers Loaded with Quercetin on Titanium Surfaces by Layer-By-Layer Assembly Technique to Improve Surface Osteogenesis Under Osteoporotic Condition.

Titanium (Ti) and its alloy implants are widely used in the field of orthopedics, and osteoporosis is an important reason for implantation failure. This study aimed to establish a quercetin (QTN) controlled release system on the surface of titanium implants and to study its effects on osteogenesis and osseointegration on the surface of implants. Polyethylenimine (PEI) was first immobilized on a titanium substrate as the base layer, and then, hyaluronic acid/chitosan-quercetin (HA/CS-QTN) multilayer films were assembled on the PEI layer by a self-assembly technique. Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and contact angle measurements were used to characterize and analyze the samples. The release characteristics of QTN were studied by release assays. The osteogenic ability of the samples was evaluated by experiments on an osteoporosis rat model and MC3T3-E1 cells. The FTIR, SEM, and contact angle measurements all showed that the PEI substrate layer and HA/CS-QTN multilayer film were successfully immobilized on the titanium matrix. The drug release test showed the successful establishment of a QTN controlled release system. The in vitro results showed that osteoblasts exhibited higher adhesion, proliferation and differentiation ability on the coated titanium matrix than on the pure titanium surface. In addition, the in vivo results showed that the HA/CS-QTN coating significantly increased the new bone mass around the implant. By depositing a PEI matrix layer and HA/CS-QTN multilayer films on titanium implants, a controlled release system of QTN was established, which improved implant surface osseointegration under osteoporotic conditions. This study proposes a new implant therapy strategy for patients with osteoporosis.

Exploring the Pharmacological Mechanisms of Tripterygium wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking.

BACKGROUND: Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach. MATERIALS AND METHODS: The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD-related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein-protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by the Cytoscape software. RESULTS: The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components, and molecular functions) and KEGG pathways were screened with a P value ≤0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG. CONCLUSIONS: These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.

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.

Structures of glass-forming liquids by x-ray scattering: Glycerol, xylitol, and D-sorbitol.

Synchrotron x-ray scattering has been used to investigate three liquid polyalcohols of different sizes (glycerol, xylitol, and D-sorbitol) from above the glass transition temperatures Tg to below. We focus on two structural orders: the association of the polar OH groups by hydrogen bonds (HBs) and the packing of the non-polar hydrocarbon groups. We find that the two structural orders evolve very differently, reflecting the different natures of bonding. Upon cooling from 400 K, the O⋯O correlation at 2.8 Å increases significantly in all three systems, indicating more HBs, until kinetic arrests at Tg; the increase is well described by an equilibrium between bonded and non-bonded OH with ΔH = 9.1 kJ/mol and ΔS = 13.4 J/mol/K. When heated above Tg, glycerol loses the fewest HBs per OH for a given temperature rise scaled by Tg, followed by xylitol and by D-sorbitol, in the same order the number of OH groups per molecule increases (3, 5, and 6). The pair correlation functions of all three liquids show exponentially damped density modulations of wavelength 4.5 Å, which are associated with the main scattering peak and with the intermolecular C⋯C correlation. In this respect, glycerol is the most ordered with the most persistent density ripples, followed by D-sorbitol and by xylitol. Heating above Tg causes faster damping of the density ripples with the rate of change being the slowest in xylitol, followed by glycerol and by D-sorbitol. Given the different dynamic fragility of the three liquids (glycerol being the strongest and D-sorbitol being the most fragile), we relate our results to the current theories of the structural origin for the difference. We find that the fragility difference is better understood on the basis of the thermal stability of HB clusters than that of the structure associated with the main scattering peak.