Spexin Diminishes Atrial Fibrillation Vulnerability by Acting on Galanin Receptor 2.

BACKGROUND: G protein-coupled receptors play a critical role in atrial fibrillation (AF). Spexin is a novel ligand of galanin receptors (GALRs). In this study, we investigated the regulation of spexin and GALRs on AF and the underlying mechanisms. METHODS: Global spexin knockout (SPX-KO) and cardiomyocyte-specific GALRs knockout (GALR-cKO) mice underwent burst pacing electrical stimulation. Optical mapping was used to determine atrial conduction velocity and action potential duration. Atrial myocyte action potential duration and inward rectifying K+ current (IK1) were recorded using whole-cell patch clamps. Isolated cardiomyocytes were stained with Fluo-3/AM dye, and intracellular Ca2+ handling was examined by CCD camera. A mouse model of AF was established by Ang-II (angiotensin II) infusion. RESULTS: Spexin plasma levels in patients with AF were lower than those in subjects without AF, and knockout of spexin increased AF susceptibility in mice. In the atrium of SPX-KO mice, potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) and sarcolipin (SLN) were upregulated; meanwhile, IK1 current was increased and Ca2+ handling was impaired in isolated atrial myocytes of SPX-KO mice. GALR2-cKO mice, but not GALR1-cKO and GALR3-cKO mice, had a higher incidence of AF, which was associated with higher IK1 current and intracellular Ca2+ overload. The phosphorylation level of CREB (cyclic AMP responsive element binding protein 1) was upregulated in atrial tissues of SPX-KO and GALR2-cKO mice. Chromatin immunoprecipitation confirmed the recruitment of p-CREB to the proximal promoter regions of KCNJ2 and SLN. Finally, spexin treatment suppressed CREB signaling, decreased IK1 current and decreased intracellular Ca2+ overload, which thus reduced the inducibility of AF in Ang-II-infused mice. CONCLUSIONS: Spexin reduces atrial fibrillation susceptibility by inhibiting CREB phosphorylation and thus downregulating KCNJ2 and SLN transcription by GALR2 receptor. The spexin/GALR2/CREB signaling pathway represents a novel therapeutic avenue in the development of agents against atrial fibrillation.

Creation of Room-Temperature Sub-100 nm Antiferromagnetic Skyrmions in an Antiferromagnet IrMn through Interfacial Exchange Coupling.

Antiferromagnetic (AFM) skyrmions are magnetic vortices composed of antiparallell-aligned neighboring spins. In stark contrast to conventional skyrmions based on ferromagnetic order, AFM skyrmions have vanished stray fields, higher response frequencies, and rectified translational motion driven by an external force. Therefore, AFM skyrmions promise highly efficient spintronics devices with high bit mobility and density. Nevertheless, the experimental realization of intrinsic AFM skyrmions remains elusive. Here, we show that AFM skyrmions can be nucleated via interfacial exchange coupling at the surface of a room-temperature AFM material, IrMn, exploiting the particular response from uncompensated moments to the thermal annealing and imprinting effects. Further systematic magnetic characterizations validate the existence of such an AFM order at the IrMn/CoFeB interfaces. Such AFM skyrmions have a typical size of 100 nm, which presents pronounced robustness against field and temperature. Our work opens new pathways for magnetic topological devices based on AFM skyrmions.

Silanol-Stabilized Atomically Dispersed Ptδ+-Ox-Sn Active Sites in Protozeolite for Propane Dehydrogenation.

Crystalline zeolites have been proven to be excellent supports for confining subnanometric metal catalysts to boost the propane dehydrogenation (PDH) reaction. However, the introduced metallic species may suffer from severe sintering and limited stability during the catalytic process, especially when utilizing an industrial impregnation method for metal incorporation. In this study, we developed a new type of support based on amorphous protozeolite (PZ), taking advantage of its adjustable silanol chemistry and zeolitic microporous characteristic for stabilizing atomically dispersed PtSn catalyst via a simple, cost-effective coimpregnation process. The combination of X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, in situ diffuse reflectance infrared Fourier transform spectroscopy under CO atmosphere, and density functional theory calculations confirmed the formation of highly dispersed active Ptδ+-Ox-Sn species in PtSn/PZ. The PtSn/PZ catalyst exhibited a high propane conversion of 45.4% and a high propylene selectivity of 99% (WHSV= 3.6 h-1, 550 °C), with a high apparent rate coefficient of 565 molC3H6·gPt-1·h-1·bar-1 at a high WHSV of 108 h-1, presenting a top-level performance among the state-of-the-art Pt-based catalysts prepared by in situ synthesis and impregnation methods. The silanol density determined the chemical state of PtSn species, showing a change from atomically dispersed Ptδ+-Ox-Sn sites to PtSn alloy with decreasing silanol density of supports. This work provides a general strategy using silanol-rich amorphous protozeolite as support for stabilizing various metal catalysts by the simple impregnation method and also offers an effective way for fine tailoring the chemical state of metallic species via a silanol-engineered approach.

Quantum random number generation based on phase reconstruction.

Quantum random number generator (QRNG) utilizes the intrinsic randomness of quantum systems to generate completely unpredictable and genuine random numbers, finding wide applications across many fields. QRNGs relying on the phase noise of a laser have attracted considerable attention due to their straightforward system architecture and high random number generation rates. However, traditional phase noise QRNGs suffer from a 50% loss of quantum entropy during the randomness extraction process. In this paper, we propose a phase-reconstruction quantum random number generation scheme, in which the phase noise of a laser is reconstructed by simultaneously measuring the orthogonal quadratures of the light field using balanced detectors. This enables direct discretization of uniform phase noise, and the min-entropy can achieve a value of 1. Furthermore, our approach exhibits inherent robustness against the classical phase fluctuations of the unbalanced interferometer, eliminating the need for active compensation. Finally, we conducted experimental validation using commercial optical hybrid and balanced detectors, achieving a random number generation rate of 1.96 Gbps at a sampling rate of 200 MSa/s.

Outcomes of covered vs bare metal stents for the treatment of aortoiliac occlusive disease.

OBJECTIVE: We retrospectively compared the clinical outcomes of self-expanding covered stents (CSs) and bare metal stents (BMSs) in the treatment of aortoiliac occlusive disease (AIOD) at a single center between 2016 and 2022. METHODS: All patients with AIOD receiving endovascular therapy at a single center from January 2016 to October 2022 were continuously analyzed, including patients with lesions of all classes according to the Trans-Atlantic Inter-Society Consensus II (TASC-II). Relevant clinical and baseline data were collected, and propensity score matching was performed to compare CSs and BMSs in terms of baseline characteristics, surgical factors, 30-day outcomes, 5-year primary patency, and limb salvage. The follow-up results were analyzed by Kaplan-Meier curves. Cox proportional hazard models were used to identify predictors of primary patency. RESULTS: A total of 209 patients with AIOD were enrolled in the study, including 135 patients (64.6%) in the CS group and 74 patients (35.4%) in the BMS group. Surgical success rates (100% vs 100%; P = 1.00), early (<30-day) mortality rates (0% vs 0%; P = 1.00), cumulative surgical complication rate (12.0% vs 8.0%; P = .891), 5-year primary patency rate (83.4% vs 86.9%; P = .330), secondary patency rate (96% vs 100%; P = .570), and limb salvage rate (100% vs 100%; P = 1.00) did not exhibit significant differences between the two groups. Patients in the CS group had a lower preoperative ankle-brachial index (0.48 ± 0.26 vs 0.52 ± 0.19; P = .032), more cases of complex AIOD (especially TASC D) (47.4% vs 9.5%; P < .001), more chronic total occlusive lesions (77.0% vs 31.1%; P < .001), and more severe calcification (20.7% vs 14.9%; P < .036). After propensity score matching, 50 patients (25 with CS and 25 with BMS) were selected. The results showed that only severe calcification (32.0% vs 8.0%; P = .034) and ankle-brachial index increase (0.45 ± 0.15 vs 0.41 ± 0.22; P = .038) were significantly different between the groups. In terms of surgical factors, patients in the CS group had more use of bilateral femoral or combined brachial artery percutaneous access (60.0% vs 12.0%; P < .001), more number of stents used (2.3 ± 1.2 vs 1.3 ± 0.7; P < .001), longer mean stent length (9.3 ± 3.3 vs 5.8 ± 2.6 cm; P < .001), and more catheter-directed thrombolysis treatment (32.0% vs 4.0%; P = .009). Multivariate Cox survival analysis showed that severe calcification (hazard ratio, 1.32; 95% confidence interval, 1.04-1.85; P = .048) was the only independent predictor of the primary patency rate. CONCLUSIONS: All patients with AIOD who underwent endovascular therapy were included and achieved good outcomes with both CSs and BMSs. The influence of confounding factors in the two groups was minimized by propensity score matching, and the 5-year patency rates were generally similar in the unmatched and matched cohorts. Postoperative hemodynamic improvement was more obvious in patients in the CS group. For more complex lesions, CS is recommended to be preferred. Especially for severe calcification lesions, which is the only independent predictor of primary patency, CS showed obvious advantages. Further studies with more samples are needed to investigate the role of stent types in AIOD treatment.

Hypothesis tests in ordinal predictive models with optimal accuracy.

In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$-statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings.

Renewable risk assessment of heterogeneous streaming time-to-event cohorts.

The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.

BACKGROUND: Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. OBJECTIVE: This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. METHODS: We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI. RESULTS: Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). CONCLUSIONS: This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Deterministic Magnetization Switching via Tunable Noncollinear Spin Configurations in Canted Magnets.

Spin obit torque (SOT) driven magnetization switching has been used widely for encoding consumption-efficient memory and logic. However, symmetry breaking under a magnetic field is required to realize the deterministic switching in synthetic antiferromagnets with perpendicular magnetic anisotropy (PMA), which limits their potential applications. Herein, we report all electric-controlled magnetization switching in the antiferromagnetic Co/Ir/Co trilayers with vertical magnetic imbalance. Besides, the switching polarity could be reversed by optimizing the Ir thickness. By using the polarized neutron reflection (PNR) measurements, the canted noncollinear spin configuration was observed in Co/Ir/Co trilayers, which results from the competition of magnetic inhomogeneity. In addition, the asymmetric domain walls demonstrated by micromagnetic simulations result from introducing imbalance magnetism, leading to the deterministic magnetization switching in Co/Ir/Co trilayers. Our findings highlight a promising route to electric-controlled magnetism via tunable spin configuration, improve our understanding of physical mechanisms, and significantly promote industrial applications in spintronic devices.

Impact of residual stenosis on clinical outcomes when performing carotid artery stenting without postdilation.

OBJECTIVE: Many centers consider postdilation if the final angiography after carotid artery stenting (CAS) shows residual stenosis of >30% to 40%. Postdilation has been demonstrated to potentially increase the risk of developing neurologic events. This study aimed to investigate the safety of CAS without postdilation regardless of the degree of residual stenosis. METHODS: We retrospectively investigated 191 patients who underwent transfemoral CAS without postdilation intendedly. All cases underwent mild predilation and self-expanding stent implantation. We divided the patients into a residual stenosis of ≥40% group (n = 69 [36.1%]) and a residual stenosis of <40% group (n = 122 [63.9%]) according to their final angiography. We compared the procedural (within 30 days after CAS) and nonprocedural (afterward) adverse cardiovascular events and in-stent restenosis between the two groups. We also investigated the incidence of perioperative hemodynamic depression between the groups and the changes in residual stenosis over the follow-up time. RESULTS: Patients in the residual stenosis of ≥40% group had a higher preoperative stenosis rate and a greater proportion of severely calcified lesions than those in the <40% group. There was one procedural cardiac death (0.5%), five strokes (2.6%), and four myocardial infarctions (2.1%). A total of 2.9% had stroke or death procedurally in the residual stenosis of ≥40% group and 3.2% in the residual stenosis of <40% group (P > .950). The median nonprocedural follow-up time was 22 months, with a total of six deaths and four strokes. The cumulative 2-year death or stroke rate was 6.2%, with 5.9% in the residual stenosis of ≥40% group versus 6.7% in the residual stenosis of <40% group (P = .507). There were two cases of in-stent restenosis in the residual stenosis of ≥40% group and three in the residual stenosis of <40% group (P = .927). The difference in the peak systolic velocity of the target lesion between groups at 3 months after CAS was no longer present, and residual stenosis stabilized at 10% to 20% at 6 months in both groups. The patients showed an association between increasing hemodynamic depression incidence and residual stenosis in a significantly graded response (P = .021). CONCLUSIONS: Residual stenosis after carotid stenting without postdilation is not associated with a risk of postoperative adverse events. This study provides evidence for the feasibility of a no postdilation strategy for CAS.

A Bayesian network meta-analysis for acute thrombosis after lower extremity artery endovascular treatment.

BACKGROUND: Various endovascular treatment devices have been widely used in the lower extremity arterial disease (LEAD). Their patency efficiency for target lesions has been well studied and reported. Comparison of the risk of acute thrombosis events between the different endovascular treatment devices is unclear. AIMS: To rank the risk of acute thrombosis events when bare metal stents (BMSs), covered stents (CSs), drug-eluting stents (DESs), drug-coated balloons (DCBs), and conventional percutaneous transluminal balloon angioplasty (PTA) are used to treat LEAD through Bayesian network meta-analysis. METHODS: We performed a network meta-analysis of randomized controlled trials comparing the risk of 1-year postoperative acute thrombosis between BMSs, CSs, DESs, DCBs, and PTA for treating LEAD. Bayesian random models were used for pooled endovascular treatment modality comparisons. We ranked these treatment modalities via the Bayesian method according to their surface under the cumulative ranking curve (SUCRA) and estimated probabilities. RESULTS: Nineteen studies (38 study arms; 2758 patients) were included. The Bayesian network ranking of treatments indicated that DCB had the lowest risk of acute thrombosis, PTA had the second-lowest risk of thrombosis, and CS, BMS, and DES had the highest risk of thrombosis. Regarding the treatment efficacy, the OR values of the loss of primary patency were significantly lower for DCB (OR = 0.44, 95% CI: 0.30-0.62), DES (OR = 0.36, 95% CI: 0.14-0.94), and CS (OR = 0.31, 95% CI: 0.18,0.56) than for PTA. When BMS was used as a reference, only the OR for CS was significantly lower (OR = 0.41, 95% CI = 0.21-0.82). Correspondingly, the Bayesian ranking of treatments from better to worse target lesion primary patency was CS, DES, DCB, BMS, and PTA. CONCLUSION: With the available research evidence and according to the network analysis ranking, DES appears to have the highest risk of acute thrombosis and DCB appears to have the lowest risk.

Association of gut microbiome and primary liver cancer: A two-sample Mendelian randomization and case-control study.

BACKGROUND AND AIMS: Observational epidemiology studies suggested a relationship between the gut microbiome and primary liver cancer. However, the causal relationship remains unclear because of confounding factors and reverse causality. We aimed to explore the causal role of the gut microbiome in the development of primary liver cancer, including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS: Mendelian randomization (MR) study was conducted using summary statistics from genome-wide association studies (GWAS) of the gut microbiome and liver cancer, and sequencing data from a case-control study validated the findings. A 5-cohort GWAS study in Germany (N = 8956) served as exposure, whilst the UK biobank GWAS study (N = 456 348) served as an outcome. The case-control study was conducted at the First Affiliated Hospital of Wenzhou Medical University from December 2018 to October 2020 and included 184 HCC patients, 63 ICC patients and 40 healthy controls. RESULTS: A total of 57 features were available for MR analysis, and protective causal associations were identified for Family_Ruminococcaceae (OR = 0.46 [95% CI, 0.26-0.82]; p = .009) and Genus_Porphyromonadaceae (OR = 0.59 [95% CI, 0.42-0.83]; p = .003) with HCC, and for Family_Porphyromonadaceae (OR = 0.36 [95% CI, 0.14-0.94]; p = .036) and Genus_Bacteroidetes (OR = 0.55 [95% CI, 0.34-0.90]; p = .017) with ICC respectively. The case-control study results showed that the healthy controls had a higher relative abundance of Family_Ruminococcaceae (p = .00033), Family_Porphyromonadaceae (p = .0055) and Genus_Bacteroidetes (p = .021) than the liver cancer patients. CONCLUSIONS: This study demonstrates that Ruminococcaceae, Porphyromonadaceae and Bacteroidetes are related to a reduced risk of liver cancer (HCC or ICC), suggesting potential significance for the prevention and control of liver cancer.

Goodness-of-fit test for a parametric mixture cure model with partly interval-censored data.

Partly interval-censored event time data arise naturally in medical, biological, sociological and demographic studies. In practice, some patients may be immune from the event of interest, invoking a cure model for survival analysis. Choosing an appropriate parametric distribution for the failure time of susceptible patients is an important step to fully structure the mixture cure model. In the literature, goodness-of-fit tests for survival models are usually restricted to uncensored or right-censored data. We fill in this gap by proposing a new goodness-of-fit test dealing with partly interval-censored data under mixture cure models. Specifically, we investigate whether a parametric distribution can fit the susceptible part by using a Cramér-von Mises type of test, and establish the asymptotic distribution of the test . Empirically, the critical value is determined from the bootstrap resamples. The proposed test, compared to the traditional leveraged bootstrap approach, yields superior practical results under various settings in extensive simulation studies. Two clinical data sets are analyzed to illustrate our method.

Instrumental variable model average with applications in Mendelian randomization.

Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.

A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers.

Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However the original definition of HUM involves multiple integration and thus a medical investigation for multi-class receiver operating characteristic (ROC) analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this article. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

Fitting additive risk models using auxiliary information.

There has been a growing interest in incorporating auxiliary summary information from external studies into the analysis of internal individual-level data. In this paper, we propose an adaptive estimation procedure for an additive risk model to integrate auxiliary subgroup survival information via a penalized method of moments technique. Our approach can accommodate information from heterogeneous data. Parameters to quantify the magnitude of potential incomparability between internal data and external auxiliary information are introduced in our framework while nonzero components of these parameters suggest a violation of the homogeneity assumption. We further develop an efficient computational algorithm to solve the numerical optimization problem by profiling out the nuisance parameters. In an asymptotic sense, our method can be as efficient as if all the incomparable auxiliary information is accurately acknowledged and has been automatically excluded from consideration. The asymptotic normality of the proposed estimator of the regression coefficients is established, with an explicit formula for the asymptotic variance-covariance matrix that can be consistently estimated from the data. Simulation studies show that the proposed method yields a substantial gain in statistical efficiency over the conventional method using the internal data only, and reduces estimation biases when the given auxiliary survival information is incomparable. We illustrate the proposed method with a lung cancer survival study.

Midterm Outcomes of Kissing Covered Self-Expanding Stents for Reconstruction of Complex Aortoiliac Occlusive Disease.

BACKGROUND: We sought to investigate the midterm results of kissing self-expanding covered stents (SECSs) for the reconstruction of aortic bifurcation in complex aortoiliac occlusive disease. METHODS: Data of consecutive patients who had undergone endovascular treatment for aortoiliac occlusive disease were screened. Only patients with TransAtlantic Inter-Society Consensus (TASC) class C and D lesions treated by bilateral iliac kissing stents (KSs) were included. Midterm primary patency, risk factors, and limb salvage rates were analyzed. Follow-up results were analyzed using the Kaplan-Meier curves. Cox proportional hazards models were used to identify the predictors of primary patency. RESULTS: A total of 48 patients (95.8% men; mean age, 65.3 ± 10.2 years) were treated with kissing SECSs. Of them, 17 patients had TASC-II class C lesions and 31 had class D lesions. There were 38 total occlusive lesions, with a mean occlusive lesion length of 108.2 ± 57.3 mm. The overall mean lesion length was 140.3 ± 60.5 mm, and the mean length of implanted stents in the aortoiliac arteries was 141.9 ± 59.9 mm. The mean diameter of the deployed SECSs was 7.8 ± 0.5 mm. The mean follow-up time was 36.5 ± 15.8 months, and the follow-up rate was 95.8%. At 36 months, the overall primary patency, assisted primary patency, secondary patency, and limb salvage rates were 92.2%, 95.7%, 97.8%, and 100%, respectively. Univariate Cox regression analysis revealed that stent diameter ≤7 mm (hazard ratio [HR]: 9.53; 95% confidence interval [CI] 1.56-57.94, P = 0.014) and severe calcification (HR: 12.66; 95% CI 2.04-78.45, P = 0.006) were significantly associated with restenosis. Multivariate analysis showed severe calcification to be the only significant determinant of restenosis (HR: 12.66; 95% CI 2.04-78.45, P = 0.006). CONCLUSIONS: Kissing SECSs provide good midterm results for the treatment of aortoiliac occlusive disease. A stent diameter >7 mm is a potent protective factor against restenosis. Because severe calcification appears to be the only significant determinant of restenosis, patients with severe calcification require close follow-up.

Circ_0000566 contributes oxygen-glucose deprivation and reoxygenation (OGD/R)-induced human brain microvascular endothelial cell injury via regulating miR-18a-5p/ACVR2B axis.

Circular RNAs (circRNAs) exert regulatory roles in cerebrovascular disease. Human brain microvascular endothelial cells (HBMECs) participated in brain vascular dysfunction in ischemic stroke. Herein, the functions of circ_0000566 in oxygen-glucose deprivation and reoxygenation (OGD/R)-induced HBMECs were investigated. The expression of circ_0000566, miR-18a-5p, and Activin receptor type 2B (ACVR2B) was measured via quantitative real-time PCR (qRT-PCR). Cell Counting Kit-8 (CCK-8) and flow cytometry assays were utilized to detect cell viability and cell apoptosis. Western blot assay was employed to measure the levels of apoptotic-related proteins and ACVR2B. The secretion of IL-1ß, IL-6, and TNF-α was detected via corresponding kits. The relationship between miR-18a-5p and circ_0000566 or ACVR2B was examined via dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Circ_0000566 and ACVR2B were highly expressed, while miR-18a-5p was down-regulated in OGD/R-treated HBMECs. OGD/R treatment promoted HBMECs apoptosis and inflammation and suppressed cell viability, which could be attenuated by silencing of circ_0000566. Circ_0000566 acted as a miR-18a-5p sponge to contribute to OGD/R-induced HBMECs injury. ACVR2B served as a direct target of miR-18a-5p, and ACVR2B overexpression might abolish the inhibitory role of miR-18a-5p on OGD/R-treated HBMEC injury. Circ_0000566 sponged miR-18a-5p to regulate OGD/R-induced HBMECs injury via regulating ACVR2B expression.

Phylotranscriptomics of Swertiinae (Gentianaceae) reveals that key floral traits are not phylogenetically correlated.

Establishing how lineages with similar traits are phylogenetically related remains critical for understanding the origin of biodiversity on Earth. Floral traits in plants are widely used to explore phylogenetic relationships and to delineate taxonomic groups. The subtribe Swertiinae (Gentianaceae) comprises more than 350 species with high floral diversity ranging from rotate to tubular corollas and possessing diverse nectaries. Here we performed phylogenetic analysis of 60 species from all 15 genera of the subtribe Swertiinae sensu Ho and Liu, representing the range of floral diversity, using data from the nuclear and plastid genomes. Extensive topological conflicts were present between the nuclear and plastome trees. Three of the 15 genera represented by multiple species are polyphyletic in both trees. Key floral traits including corolla type, absence or presence of lobe scales, nectary type, nectary position, and stigma type are randomly distributed in the nuclear and plastome trees without phylogenetic correlation. We also revealed the likely ancient hybrid origin of one large clade comprising 10 genera with diverse floral traits. These results highlight the complex evolutionary history of this subtribe. The phylogenies constructed here provide a basic framework for further exploring the ecological and genetic mechanisms underlying both species diversification and floral diversity.

Incomplete lineage sorting and local extinction shaped the complex evolutionary history of the Paleogene relict conifer genus, Chamaecyparis (Cupressaceae).

Inferring accurate biogeographic history of plant taxa with an East Asia (EA)-North America (NA) is usually hindered by conflicting phylogenies and a poor fossil record. The current distribution of Chamaecyparis (false cypress; Cupressaceae) with four species in EA, and one each in western and eastern NA, and its relatively rich fossil record, make it an excellent model for studying the EA-NA disjunction. Here we reconstruct phylogenomic relationships within Chamaecyparis using > 1400 homologous nuclear and 61 plastid genes. Our phylogenomic analyses using concatenated and coalescent approaches revealed strong cytonuclear discordance and conflicting topologies between nuclear gene trees. Incomplete lineage sorting (ILS) and hybridization are possible explanations of conflict; however, our coalescent analyses and simulations suggest that ILS is the major contributor to the observed phylogenetic discrepancies. Based on a well-resolved species tree and four fossil calibrations, the crown lineage of Chamaecyparis is estimated to have originated in the upper Cretaceous, followed by diversification events in the early and middle Paleogene. Ancestral area reconstructions suggest that Chamaecyparis had an ancestral range spanning both EA and NA. Fossil records further indicate that this genus is a relict of the "boreotropical" flora, and that local extinctions of European species were caused by global cooling. Overall, our results unravel a complex evolutionary history of a Paleogene relict conifer genus, which may have involved ILS, hybridization and the extinction of local species.