Full electrical manipulation of perpendicular exchange bias in ultrathin antiferromagnetic film with epitaxial strain.

Achieving effective manipulation of perpendicular exchange bias effect remains an intricate endeavor, yet it stands a significance for the evolution of ultra-high capacity and energy-efficient magnetic memory and logic devices. A persistent impediment to its practical applications is the reliance on external magnetic fields during the current-induced switching of exchange bias in perpendicularly magnetized structures. This study elucidates the achievement of a full electrical manipulation of the perpendicular exchange bias in the multilayers with an ultrathin antiferromagnetic layer. Owing to the anisotropic epitaxial strain in the 2-nm-thick IrMn3 layer, the considerable exchange bias effect is clearly achieved at room temperature. Concomitantly, a specific global uncompensated magnetization manifests in the IrMn3 layer, facilitating the switching of the irreversible portion of the uncompensated magnetization. Consequently, the perpendicular exchange bias can be manipulated by only applying pulsed current, notably independent of the presence of any external magnetic fields.

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 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.

Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen (Populus rotundifolia) in the Hengduan Mountains.

Biogeographical barriers to gene flow are central to plant phylogeography. In East Asia, plant distribution is greatly influenced by two phylogeographic breaks, the Mekong-Salween Divide and Tanaka-Kaiyong Line, however, few studies have investigated how these barriers affect the genetic diversity of species that are distributed across both. Here we used 14 microsatellite loci and four chloroplast DNA fragments to examine genetic diversity and distribution patterns of 49 populations of Populus rotundifolia, a species that spans both the Mekong-Salween Divide and the Tanaka-Kaiyong Line in southwestern China. Demographic and migration hypotheses were tested using coalescent-based approaches. Limited historical gene flow was observed between the western and eastern groups of P. rotundifolia, but substantial flow occurred across both the Mekong-Salween Divide and Tanaka-Kaiyong Line, manifesting in clear admixture and high genetic diversity in the central group. Wind-borne pollen and seeds may have facilitated the dispersal of P. rotundifolia following prevalent northwest winds in the spring. We also found that the Hengduan Mountains, where multiple genetic barriers were detected, acted on the whole as a barrier between the western and eastern groups of P. rotundifolia. Ecological niche modeling suggested that P. rotundifolia has undergone range expansion since the last glacial maximum, and demographic reconstruction indicated an earlier population expansion around 600 Ka. The phylogeographic pattern of P. rotundifolia reflects the interplay of biological traits, wind patterns, barriers, niche differentiation, and Quaternary climate history. This study emphasizes the need for multiple lines of evidence in understanding the Quaternary evolution of plants in topographically complex areas.

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes.

PURPOSE: To evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes. DESIGN: Longitudinal cohort study METHODS: We analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010-2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state. RESULTS: The median assessment interval was 12 months, with most patients having 3 assessments. Annual probabilities for DR progression (no-to-mild, mild-to-moderate and moderate-to-severe/proliferative) were 6.1 %, 7.0 % and 19.3 %, respectively; and for regression (mild-to-no, moderate-to-mild and severe-to-moderate) were 55.4 %, 17.3 % and 4.4 %, respectively. Annual mortality rates from each DR state were 1.2 %, 2.0 %, 18.7 %, and 30.0 %. The sojourn time in each state were 8.2, 0.8, 0.8 and 2.2 years. Higher HbA1c and SBP levels were associated with progression of no-mild and mild-moderate DR, and diabetes duration with no-to-mild and moderate-to-severe/proliferative DR. Lower HbA1c levels were associated with regression from mild-to-no and moderate-to-mild, and higher BMI with mild-to-no DR. CONCLUSIONS: Our results suggest a prolonged duration (∼8 years) in developing mild DR, with faster transitions (within a year) from mild or moderate states. Moderate/above DR greatly increases the probability of progression and death as compared to mild DR/below. HbA1c was associated with both progression as well as regression.

Effect of Mo and Cr on the Microstructure and Properties of Low-Alloy Wear-Resistant Steels.

Low-alloy wear-resistant steel often requires the addition of trace alloy elements to enhance its performance while also considering the cost-effectiveness of production. In order to comparatively analyze the strengthening mechanisms of Mo and Cr elements and further explore economically feasible production processes, we designed two types of low-alloy wear-resistant steels, based on C-Mn series wear-resistant steels, with individually added Mo and Cr elements, comparing and investigating the roles of the alloying elements Mo and Cr in low-alloy wear-resistant steels. Utilizing JMatPro software to calculate Continuous Cooling Transformation (CCT) curves, conducting thermal simulation quenching experiments using a Gleeble-3800 thermal simulator, and employing equipment such as a metallographic microscope, transmission electron microscope, and tensile testing machine, this study comparatively investigated the influence of Mo and Cr on the microstructural transformation and mechanical properties of low-alloy wear-resistant steels under different cooling rates. The results indicate that the addition of the Mo element in low-alloy wear-resistant steel can effectively suppress the transformation of ferrite and pearlite, reduce the martensitic transformation temperature, and lower the critical cooling rate for complete martensitic transformation, thereby promoting martensitic transformation. Adding Cr elements can reduce the austenite transformation zone, decrease the rate of austenite formation, and promote the occurrence of low-temperature phase transformation. Additionally, Mo has a better effect on improving the toughness of low-temperature impact, and Cr has a more significant improvement in strength and hardness. The critical cooling rates of C-Mn-Mo steel and C-Mn-Cr steel for complete martensitic transition are 13 °C/s and 24 °C/s, respectively. With the increase in the cooling rate, the martensitic tissues of the two experimental steels gradually refined, and the characteristics of the slats gradually appeared. In comparison, the C-Mn-Mo steel displays a higher dislocation density, accompanied by dislocation entanglement phenomena, and contains a small amount of residual austenite, while granular ε-carbides are clearly precipitated in the C-Mn-Cr steel. The C-Mn-Mo steel achieves its best performance at a cooling rate of 25 °C/s, whereas the C-Mn-Cr steel only needs to increase the cooling rate to 35 °C/s to attain a similar comprehensive performance to the C-Mn-Mo steel.

CureAuxSP: An R package for estimating mixture cure models with auxiliary survival probabilities.

BACKGROUND AND OBJECTIVE: There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset. METHODS: The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively. RESULTS: The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results. CONCLUSIONS: Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes.

Upcycling of polyethylene to gasoline through a self-supplied hydrogen strategy in a layered self-pillared zeolite.

Conversion of plastic wastes to valuable carbon resources without using noble metal catalysts or external hydrogen remains a challenging task. Here we report a layered self-pillared zeolite that enables the conversion of polyethylene to gasoline with a remarkable selectivity of 99% and yields of >80% in 4 h at 240 °C. The liquid product is primarily composed of branched alkanes (selectivity of 72%), affording a high research octane number of 88.0 that is comparable to commercial gasoline (86.6). In situ inelastic neutron scattering, small-angle neutron scattering, solid-state nuclear magnetic resonance, X-ray absorption spectroscopy and isotope-labelling experiments reveal that the activation of polyethylene is promoted by the open framework tri-coordinated Al sites of the zeolite, followed by ß-scission and isomerization on Brönsted acids sites, accompanied by hydride transfer over open framework tri-coordinated Al sites through a self-supplied hydrogen pathway to yield selectivity to branched alkanes. This study shows the potential of layered zeolite materials in enabling the upcycling of plastic wastes.

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.

The coupling coordination characteristics of China's health production efficiency and new urbanization and its influencing factors.

Urbanization leads to dramatic changes in habitat quality, which significantly affects population health. Research on the coupling coordination relationship between new urbanization and health production efficiency is conducive to improving residents' well-being and urban sustainable development. In this article, we adopted the super-efficient SBM model and entropy value method separately to evaluate the spatiotemporal variation characteristics of health production efficiency and new urbanization in China. Then, we used the coupling coordination degree model to investigate the interactive coercing relationship between new urbanization and health production efficiency. Finally, the panel Tobit model is used to analyze the factors influencing the coupled coordination of the two systems. The results showed that the new urbanization levels of 31 provinces in China have all steadily increased from 2003 to 2018. Health production efficiency exhibited a fluctuating but increasing trend, and its regional differences are gradually narrowing. Health production efficiency and new urbanization have developed in a more coordinated direction, with a spatial pattern of "high in the southeast and low in the northwest." Meanwhile, the relative development characteristics between the two systems have constantly changed, from the new urbanization lagged type to the two systems synchronized type and the health production efficiency lagged type. Population density, economic development level, government financial investment, and government health investment positively impact the coupling coordination degree of the two systems. In comparison, individual health investment harms the harmonization of the two systems.

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.

Conservation genomic investigation of an endangered conifer, Thuja sutchuenensis, reveals low genetic diversity but also low genetic load.

Endangered species generally have small populations with low genetic diversity and a high genetic load. Thuja sutchuenensis is an endangered conifer endemic to southwestern China. It was once considered extinct in the wild, but in 1999 was rediscovered. However, little is known about its genetic load. We collected 67 individuals from five wild, isolated T. sutchuenensis populations, and used 636,151 SNPs to analyze the level of genetic diversity and genetic load in T. sutchuenensis to delineate the conservation units of T. sutchuenensis, based on whole transcriptome sequencing data, as well as target capture sequencing data. We found that populations of T. sutchuenensis could be divided into three groups. These groups had low levels genetic diversity and were moderately genetically differentiated. Our findings also indicate that T. sutchuenensis suffered two severe bottlenecks around the Last Glaciation Period and Last Glacial Maximum. Among Thuja species, T. sutchuenensis presented the lowest genetic load and hence might have purged deleterious mutations efficiently through purifying selection. However, distribution of fitness effects analysis indicated a high extinction risk for T. sutchuenensis. Multiple lines of evidence identified three management units for T. sutchuenensis. Although T. sutchuenensis possesses a low genetic load, low genetic diversity, suboptimal fitness, and anthropogenic pressures all present an extinction risk for this rare conifer. This might also hold true for many endangered plant species in the mountains all over the world.

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.

A meta-analysis of the efficacy and safety of the traditional Chinese medicine formula Kaixinsan decoction for depression.

BACKGROUND: Kaixinsan (KXS) decoction is a traditional Chinese herbal formulation commonly used to treat depression. This study aimed to evaluate the efficacy and safety of KXS, which is widely used, alone and in combination with other therapies, for the treatment of depression. The main objective was to assess the efficacy and safety of KXS in the treatment of depression as a single agent or in combination with other methods. METHODS: Randomized controlled trials of KXS in the treatment of depression were systematically searched from several Chinese and English databases with no language restriction. Patients in these studies met the relevant diagnostic criteria for depression. Data on HAMD, SDS, practical situations, and occurrence of side effects in the studies were extracted. Finally, the methodological quality and risk of bias of the included studies were assessed using the Jadad scale and Cochrane bias evaluation tool. RESULTS: Twelve studies with 1034 patients were included after screening. The Jadad scale and Cochrane bias evaluation tool indicated that the quality of the studies ranged from fair to good, with 41.7% categorized as good and 58.3% as poor. Egger test and funnel plots showed that the publication bias remain low. CONCLUSION: The results showed that the frequency of side effects in the control group was higher than that in the treatment group, and there was a statistically significant difference. KXS was comparable or superior to antidepressants in treating depression and has fewer side effects. The data analysis showed that effectiveness and other indicators differed significantly by geographic area and dosage form, which has implications for future clinical work.

Integrated evaluation of biomechanical and biological properties of the biomimetic structural bone scaffold: Biomechanics, simulation analysis, and osteogenesis.

A porous structure is essential for bone implants because it increases the bone ingrowth space and improves mechanical and biological properties. The biomimetically designed porous Voronoi scaffold can reconstruct the structure and function of cancellous bone; however, its comprehensive properties need to be investigated further. In this study, algorithms based on scaling factors were used to design the Voronoi scaffolds. Classic approaches, such as computer-aided design and the implicit surface method, have been used to design Diamond, Gyroid, and I-WP scaffolds as controls. All scaffolds were prepared by selective laser melting of titanium alloys and three-dimensional printing. Mechanical tests, finite element analysis, and in vitro and in vivo experiments were performed to investigate the biomechanical, cytologic, and osteogenic performance of the scaffolds, while computational fluid dynamics simulations were used to explore the underlying mechanisms. Diamond scaffolds have a better loading capacity, and the mechanical behaviors and fluid flow of Voronoi scaffolds are similar to those of the human trabecular bone. Cells showed more proliferation and distribution on the Diamond and Voronoi scaffolds and exhibited evident differentiation on Gyroid and Voronoi scaffolds. Bone formation was apparent on the inner part of the Gyroid, the outer part of the I-WP, and the entire Diamond and Voronoi scaffolds. The hydrodynamic properties and stimulus response of cells influenced by the porous structure account for the varied biological performance of the scaffolds. The Voronoi scaffolds with bionic mechanical behavior and an appropriate hydrodynamic response exhibit evident cell growth and osteogenesis, making them preferable for porous structural bone implants.

Tough and Self-Healing Waterborne Polyurethane Elastomers via Dynamic Hydrogen Bonds Design for Flexible Conductive Substrate Applications.

Balancing the mechanical strength and self-healing performance of polyurethane (PU) remains a significant challenge in achieving excellent self-repairing PU materials. In this study, a self-healing waterborne PU elastomer was designed from a bionic concept by incorporating 2'-deoxythymidine (2'-dT) and isophorone diamine (IPDA) into the polymer chain. The loose stacking of IPDA's irregular cycloaliphatic structure resulted in the irregular arrangement of urethane bonds in the hard domain. The formation of sextuple hydrogen bonds between 2'-dT and urethane bonds, as well as quadruple hydrogen bonds between urethane bonds themselves, enhanced the mechanical properties of the material. The multiple hydrogen bonds can dissociate, recombine, and dissipate energy, thereby improving the material's repair capability. The hierarchical self-assembly of hydrogen bonds enabled the PU to achieve a tensile strength of 15.3 MPa and toughness of 100.75 MJ/m3. The prepared PU film is highly transparent and has a transmittance of more than 90%. Additionally, it can undergo rapid repair under high temperatures or under trace solvent conditions. When used as a flexible conductive substrate, it quickly restored the conductivity and enhanced the material's lifespan after surface damage. This environmentally friendly and self-healing waterborne PU elastomer will hold broad application prospects in the field of flexible electronic devices.

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.

Alterations in brain network functional connectivity and topological properties in DRE patients.

Objective: The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people. Methods: We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons. Results: In the full frequency band (0.5-45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05). Conclusion: The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.

Tongue texture may contribute to the assessment of malignant risk of thyroid nodules.

In the present study, it was aimed to evaluate whether there is an objective tongue image indicator that could be used to evaluate malignant risk of thyroid nodules through a cross sectional study. From December 2018 to December 2020, the TFDA-1 digital tongue-face diagnostic instrument was used to collect the tongue images. TDAS 2.0 software was used for tongue image analysis. A standardized database was constructed by combining patient physical examination results and tongue image analysis results. The relationship between tongue image index and TI-RADS classification of thyroid nodules was tested. A total of 5,900 cases were collected and 4,615 cases were included in the present study after excluding 154 cases due to incomplete information, 1,221 cases with thyroid nodules were separated into 417 cases TI-RADS 2 group, 693 cases in TI-RADS 3 group and 111 cases in TI-RADS 4 group. Without considering confounding factors, tongue image indexes zhiCon, zhiASM, zhiENT, zhiMEAN, zhiClrB, zhiClrR, zhiClrG, zhiClrI, zhiClrL and zhiClrY were significantly different among the three groups (P<0.05). Excluding the influence of age, sex, body mass index, smoking and drinking, the results of one-way variance linear trend analysis showed that the values of zhiCon, zhiENT and zhiMEAN increased with the increasing TI-RADS category, while the values of zhiASM decreased with the increase of TI-RADS category. Tongue texture index may be helpful for differentiating the benign and malignant of thyroid nodules.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.

Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). Methods: We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC). Results: ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies. Conclusions: Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. Funding: This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.