Development and validation of a predictive nomogram for lower extremity deep vein thrombosis dislodgement in orthopedic patients

ObjectiveTo analyze the risk factors of lower extremity deep venous thrombosis (DVT) detachment in orthopedic patients, and to establish a risk nomogram prediction model.MethodsThe clinical data of 334 patients with orthopedic DVT admitted to the Third Hospital of Hebei Medical University from January 2020 to July 2021 were retrospectively analyzed. General statistics included gender, age, BMI, thrombus detachment, inferior vena cava filter window type, filter implantation time, medical history, trauma history, operation, use of tourniquet, thrombectomy, anesthesia mode, anesthesia grade, operative position, blood loss during operation, blood transfusion, immobilization, use of anticoagulants, thrombus side, thrombus range, D-dimer content before filter implantation and during removal of inferior vena cava filter. Logistic regression was used to perform univariate and multivariate analysis on the possible factors of thrombosis detachment, screen out independent risk factors, establish a risk nomogram prediction model by variables, and internally verify the predictability and accuracy of the model.ResultsBinary logistic regression analysis showed that Short time window filter (OR = 5.401, 95% CI = 2.338–12.478), lower extremity operation (OR = 3.565, 95% CI = 1.553–8.184), use of tourniquet (OR = 3.871, 95% CI = 1.733–8.651), non-strict immobilization (OR = 3.207, 95% CI = 1.387–7.413), non-standardized anticoagulation (OR = 4.406, 95% CI = 1.868–10.390), distal deep vein thrombosis (OR = 2.212, 95% CI = 1.047–4.671) were independent risk factors for lower extremity DVT detachment in orthopedic patients (P < 0.05). Based on these six factors, a prediction model for the risk of lower extremity DVT detachment in orthopedic patients was established, and the risk prediction ability of the model was verified. The C-index of the nomogram model was 0.870 (95% CI: 0.822–0.919). The results indicate that the risk nomogram model has good accuracy in predicting the loss of deep venous thrombosis in orthopedic patients.ConclusionThe nomogram risk prediction model based on six clinical factors, including filter window type, operation condition, tourniquet use, braking condition, anticoagulation condition, and thrombosis range, has good predictive performance

Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release

Thrombosis and its complications are the leading cause of death in patients with diabetes. Metformin, a first-line therapy for type 2 diabetes, is the only drug demonstrated to reduce cardiovascular complications in diabetic patients. However, whether metformin can effectively prevent thrombosis and its potential mechanism of action is unknown. Here we show, metformin prevents both venous and arterial thrombosis with no significant prolonged bleeding time by inhibiting platelet activation and extracellular mitochondrial DNA (mtDNA) release. Specifically, metformin inhibits mitochondrial complex I and thereby protects mitochondrial function, reduces activated platelet-induced mitochondrial hyperpolarization, reactive oxygen species overload and associated membrane damage. In mitochondrial function assays designed to detect amounts of extracellular mtDNA, we found that metformin prevents mtDNA release. This study also demonstrated that mtDNA induces platelet activation through a DC-SIGN dependent pathway. Metformin exemplifies a promising new class of antiplatelet agents that are highly effective at inhibiting platelet activation by decreasing the release of free mtDNA, which induces platelet activation in a DC-SIGN-dependent manner. This study has established a novel therapeutic strategy and molecular target for thrombotic diseases, especially for thrombotic complications of diabetes mellitus

Screening of alternative solvent ionic liquids for artemisinin: COSMO-RS prediction and experimental verification

Organic solvents are usually used to extract artemisinin from Artemisia annua L., and they can also be the solvents for the subsequent purification or derivatization to produce compounds with more efficient antimalarial effect. However, these solvents are volatile, explosive and toxic. The designable material ionic liquids (ILs) are alternative solvents to replace traditional ones. In this work, a reliable method for screening ILs with high solvation capability for artemisinin was developed. The infinite dilution activity coefficients of artemisinin in 903 ILs, composed by 43 cations and 21 anions, were calculated by COSMO-RS, and the results implied that the solubility of artemisinin in ILs mainly depends on the anions. Solubilities of artemisinin in 14 representative ILs were tested, and the results were in good accordance with those obtained in COSMO-RS calculation. The stability of artemisinin in some typical ILs was also studied, which indicated that this drug was stable in [EMIM] [BE4], [EMIM][CF3Ac], [EMIM][NTF2], [BPY] [NTF2], [EMIM][SCN], and [EMIM][Ac]. The excess enthalpy analysis demonstrated that artemisinin interacted with ILs mainly through hydrogen bond. Extraction of artemisinin using the optimal IL indicated that more artemisinin could be extracted from the leaves when compared with petroleum ether (254.73 mg/mol IL vs. 14.16 mg/mol solvent), further verifying accuracy of the simulation results. Therefore, structures of ILs with high solvation capacity for artemisinin can be obtained by the COSMO-RS method. (C) 2021 Elsevier B.V. All rights reserved

Penalized spline estimation for functional coefficient regression models

The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter [lambda], which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter [lambda]: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty [lambda] accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.

Protic Ionic Liquid‐Based Deep Eutectic Solvents with Multiple Hydrogen Bonding Sites for Efficient Absorption of NH3

The emerging of ionic liquids (ILs) provides an efficient and sustainable way to separate and recover NH3 due to their unique properties. However, the solid or highly viscous ILs are not suitable for traditional scrubbing. Therefore, an effective strategy was proposed by combining the protic ILs (PILs) with acidic H and low viscous ethylene glycol (EG) to form IL‐based deep eutectic solvents (DESs) for NH3 absorption. The results indicated that these PIL‐based DESs not only have fast absorption rate, but also exhibit exceptional NH3 capacity and excellent recyclability. The highest mass capacity of 211 mg NH3/g DES was achieved by [Im][NO3]/EG with molar ratio of 1:3, and was higher than all the reported ILs and IL‐based DESs, which was originated from multiple hydrogen bonding between acidic H and hydroxyl groups of the DESs and NH3. This work will provide useful idea for designing IL‐based solvents for NH3 separation applications.Validerad;2020;Nivå 2;2020-08-17 (johcin)</p

Broadband microwave spectrum sensing based on photonic RF channelization and compressive sampling

A novel approach to realize broadband microwave spectrum sensing based on photonic RF channelization and compressive sampling (CS) is proposed. The photonic RF channelization system is used to slice the input broadband signal into multiple sub-channel signals with narrow bandwidth in parallel and thus the rate of pseudo-random binary sequence (PRBS) and the bandwidth of the MZM for CS can be largely decreased. It is shown that a spectrally sparse signal within a wide bandwidth can be captured with a sampling rate far lower than the Nyquist rate thanks to both photonic RF channelization and CS. In addition, the influence of the non-ideal filtering of the photonic channelizer is evaluated and a novel approach based on measuring twice is proposed to overcome the problem of frequency aliasing induced by the non-ideal filtering. It is demonstrated that a system with 20 Gbit/s PRBS and 2.5 GS/s digitizer can be used to capture a signal with multiple tones within a 40 GHz bandwidth, which means a sampling rate 32 times lower than the Nyquist rate