Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage.

BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

A coating strategy to achieve effective local charge separation for photocatalytic coevolution.

Semiconductors of narrow bandgaps and high quantum efficiency have not been broadly utilized for photocatalytic coevolution of H2 and O2 via water splitting. One prominent issue is to develop effective protection strategies, which not only mitigate photocorrosion in an aqueous environment but also facilitate charge separation. Achieving local charge separation is especially challenging when these reductive and oxidative sites are placed only nanometers apart compared to two macroscopically separated electrodes in a photoelectrochemical cell. Additionally, the driving force of charge separation, namely the energetic difference in the barrier heights across the two type of sites, is small. Herein, we used conformal coatings attached by nanoscale cocatalysts to transform two classes of tunable bandgap semiconductors, i.e., CdS and GaInP2, into stable and efficient photocatalysts. We used hydrogen evolution and redox-mediator oxidation for model study, and further constructed a two-compartment solar fuel generator that separated stoichiometric H2 and O2 products. Distinct from the single charge-transfer direction reported for conventional protective coatings, the coating herein allows for concurrent injection of photoexcited electrons and holes through the coating. The energetic difference between reductive and oxidative catalytic sites was regulated by selectivity and local kinetics. Accordingly, the charge separation behavior was validated using numerical simulations. Following this design principle, the CdS/TiO2/Rh@CrOx photocatalysts evolved H2 while oxidizing reversible polysulfide redox mediators at a maximum rate of 90.6 µmol⋅h-1⋅cm-2 by stacking three panels. Powered by a solar cell, the redox-mediated solar water-splitting reactor regenerated the polysulfide repeatedly and achieved solar-to-hydrogen efficiency of 1.7%.

Consolidated bioprocessing of lignocellulosic wastes in Northwest China for D-glucaric acid production by an artificial microbial consortium.

D-glucaric acid is a platform chemical of great importance and the consolidated bioprocessing (CBP) of lignocellulose by the microbial consortium of Trichoderma reesei C10 and Saccharomyces cerevisiae LGA-1C3S2 features prospects in biomanufacturing it. Here we compared some representative lignocelluloses in Northwest China including corn stover, wheat straw and switchgrass, and the leading pretreatments including steam explosion, subcritical water pretreatment, sodium hydroxide pretreatment, aqueous ammonia pretreatment, lime pretreatment, and diluted sulfuric acid pretreatment. It was found that sodium hydroxide pretreated switchgrass (SHPSG) was the best substrate for D-glucaric acid production, resulting in the highest D-glucaric acid titers, 11.69 ± 0.73 g/L in shake flask and 15.71 ± 0.80 g/L in 10L airlift fermenter, respectively. To the best of our knowledge, this is the highest D-glucaric acid production titer from lignocellulosic biomass. This work offers a paradigm of producing low-cost D-glucaric acid for low-carbon polyethylene 2,5-furandicarboxylate (PEF) and a reference on developing biorefinery in Northwest China.

Endoplasmic reticulum stress-related gene model predicts prognosis and guides therapies in lung adenocarcinoma.

BACKGROUND: The prognosis and survival of lung adenocarcinoma (LUAD) patients are still not promising despite recent breakthroughs in treatment. Endoplasmic reticulum stress (ERS) is a self-protective mechanism resulting from an imbalance in quality control of unfolded proteins when cells are stressed, which plays an active role in lung cancer development, but the relationship between ERS and the pathological characteristics and clinical prognosis of LUAD patients remains unclear. METHODS: LASSO and Cox regression were applied based on sequencing information to construct the model, which was validated to be robust. The risk scores of the patients were calculated using the formula provided by the model, and the patients were divided into high and low-risk groups according to the median cut-off of risk scores. Cox regression analysis identifies independent prognostic factors for these patients, and enrichment analysis of prognosis-related genes was also performed. The relationship between risk scores and tumor mutation burden (TMB), cancer stem cell index, and drug sensitivity was explored. RESULTS: We constructed a 13-gene prognostic model for LUAD patients. Patients in the high-risk group had worse overall survival, lower immune score and ESTIMATE score, higher TMB, higher cancer stem cell index, and higher sensitivity to conventional chemotherapeutic agents. In addition, we constructed a nomogram that predicts 5-year survival in LUAD patients, which helps clinicians to foresee the prognosis from a new perspective. CONCLUSIONS: Our results highlight the association of ERS with LUAD and the potential use of ERS in guiding treatment.

LAZY3 interacts with LAZY2 to regulate tiller angle by modulating shoot gravity perception in rice.

Starch biosynthesis in gravity-sensing tissues of rice shoot determines the magnitude of rice shoot gravitropism and thus tiller angle. However, the molecular mechanism underlying starch biosynthesis in rice gravity-sensing tissues is still unclear. We characterized a novel tiller angle gene LAZY3 (LA3) in rice through map-based cloning. Biochemical, molecular and genetic studies further demonstrated the essential roles of LA3 in gravity perception of rice shoot and tiller angle control. The shoot gravitropism and lateral auxin transport were defective in la3 mutant upon gravistimulation. We showed that LA3 encodes a chloroplast-localized tryptophan-rich protein associated with starch granules via Tryptophan-rich region (TRR) domain. Moreover, LA3 could interact with the starch biosynthesis regulator LA2, determining starch granule formation in shoot gravity-sensing tissues. LA3 and LA2 negatively regulate tiller angle in the same pathway acting upstream of LA1 to mediate asymmetric distribution of auxin. Our study defined LA3 as an indispensable factor of starch biosynthesis in rice gravity-sensing tissues that greatly broadens current understanding in the molecular mechanisms underlying the starch granule formation in gravity-sensing tissues, and provides new insights into the regulatory mechanism of shoot gravitropism and rice tiller angle.

High-resolution magnetic resonance imaging-based radiomic features aid in selecting endovascular candidates among patients with cerebral venous sinus thrombosis.

OBJECTIVES: Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. MATERIALS AND METHODS: RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. RESULTS: We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. CONCLUSIONS: The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.

Cognitive behavior therapy for depression in people with epilepsy: A systematic review and meta-analysis.

BACKGROUND: Cognitive behavioral therapy (CBT) is the recommended treatment for depression in patients with epilepsy (PWE). However, there are no studies that calculate the effect size of CBT on depression and quality of life (QoL) in PWE. METHODS: We searched seven electronic databases (PubMed, Web of Science, Embase, Cochrane Library, Clinical Trials, Ovid Medline, and PsycINFO). We included 13 studies examining CBT for depression in PWE and calculated its effect size. RESULTS: A total of 13 studies met the criteria. After treatment, CBT improves depression in PWE (g = 0.36, 95%CI: 0.18 to 0.54, I2 = 50%), and the efficacy maintains during follow-up (g = 0.47, 95%CI: 0.04 to 0.89, I2 = 80%). Subgroup analysis has shown that individual CBT (g = 0.47, 95%CI: 0.20 to 0.73, I2 = 0%) had a greater effect size than group CBT (g = 0.30, 95%CI: 0.07 to 0.53, I2 = 62%) in the treatment of depression. Likewise, CBT has a positive effect on the QoL improvement of PWE (g = 0.34, 95%CI: 0.11 to 0.57, I2 = 64%). In controlling seizures, CBT did not differ from the control group (g = -0.06, 95%CI: -0.32 to 0.19, I2 = 0%). CONCLUSIONS: Cognitive behavioral therapy interventions were effective in improving depression and QoL in PWE, but not effective in controlling seizures. The efficacy of CBT interventions targeting seizure control seems to be uncertain.

The Effect of Behavior Couples Therapy on Alcohol and Drug Use Disorder: a Systematic Review and Meta-Analysis.

AIMS: Behavior couples therapy (BCT) is widely considered to be effective in the treatment of substance use disorders. However, the effect size of BCT in different outcome measures, and at different time points requires further study to prove it. METHODS: Systematic searches were performed in various databases. Ultimately, we identified 12 studies, involving 19 randomized controlled trials. We used Hedges' g as the effect size, and all pooled analyses were performed using random-effects models. RESULTS: After treatment, BCT was superior to control conditions (either an active or inactive control group) in frequency of substance use (g = 0.17), substance use consequences (g = -0.28) and relationship satisfaction (g = 0.45). After a 12-month follow-up, BCT remained superior to control conditions in frequency of substance use (g = 0.32), substance use consequences (g = -0.34) and relationship satisfaction (g = 0.31). In addition, BCT was more effective in reducing the frequency of substance use than individual-based treatment (IBT) (g = 0.23). There was no significant relationship between the effect size of BCT and publication year (t = 0.92, P = 0.372), percentage of females (t = -0.02, P = 0.987) or the number of treatment sessions (t = -0.52, P = 0.609). CONCLUSIONS: BCT was superior to the control conditions in all three outcome measures after treatment and at follow-up, and showed a relatively large effect size for relationship satisfaction. Moreover, BCT was superior to IBT in reducing the frequency of substance use.

Antimicrobial effect of photodynamic therapy using sinoporphyrin sodium and 390-400 nm light-emitting diode on Porphyromonas gingivalis in vitro.

This study aims to investigate the effect of antimicrobial photodynamic therapy (a-PDT) using a novel combination of sinoporphyrin sodium (DVDMS) and light-emitting diode (LED) with a wavelength of 390-400 nm on Porphyromonas gingivalis in vitro. Absorption spectrum of DVDMS was determined by spectrometer for selecting suitable wavelength light source. The uptake of DVDMS by P. gingivalis was evaluated according to fluorescence intensity detected by a spectrometer. Then effects of DVDMS alone, 390-400 nm LED alone, and photodynamic therapy produced by 10, 20, 40, and 80 µg/mL DVDMS and 390-400 nm LED on the suspension of P. gingivalis were evaluated by counting the number of colony forming units (CFU) after incubation. In the experiment, the LED illumination time was 30, 60, 90, 120, 180, 240, and 360 s, respectively, and the corresponding energy density was 1, 2, 3, 4, 6, 8, and 12 J/cm2, respectively. According to the absorption spectrum of DVDMS, the 390-400-nm light emitted by the LED was selected as the light source. The fluorescence intensity of DVDMS on P. gingivalis increased significantly at 5 min, and with the extension of time, it decreased at 30 min. DVDMS alone did not produce a significant toxicity on P. gingivalis compared with PBS (p = 0.979). While 390-400 nm LED alone had a certain bactericidal effect on P. gingivalis, the bactericidal effect was more obvious as the light dose increased (p < 0.001). The effect of a-PDT produced by 20, 40, and 80 µg/mL DVDMS and 390-400 nm LED were significantly better than that of 390-400 nm LED alone (p < 0.05). Both DVDMS concentration and light dose could enchance the bactericidal effect. The strongest photo-killing effect was generated by 80 µg/mL DVDMS with 360 s illumination (energy density is 12 J/cm2), and the log reduction of bacteria was 5.69 ± 1.70. a-PDT using the combination of DVDMS with 390-400 nm LED shows promise as a new treatment modality for pathogens elimination in periodontal therapy.

Graph convolutional neural networks with global attention for improved materials property prediction.

The development of an efficient and powerful machine learning (ML) model for materials property prediction (MPP) remains an important challenge in materials science. While various techniques have been proposed to extract physicochemical features in MPP, graph neural networks (GNN) have also shown very strong capability in capturing effective features for high-performance MPP. Nevertheless, current GNN models do not effectively differentiate the contributions from different atoms. In this paper we develop a novel graph neural network model called GATGNN for predicting properties of inorganic materials. GATGNN is characterized by its composition of augmented graph-attention layers (AGAT) and a global attention layer. The application of AGAT layers and global attention layers respectively learn the local relationship among neighboring atoms and overall contribution of the atoms to the material's property; together making our framework achieve considerably better prediction performance on various tested properties. Through extensive experiments, we show that our method is able to outperform existing state-of-the-art GNN models while it can also provide a measurable insight into the correlation between the atoms and their material property. Our code can found on - https://github.com/superlouis/GATGNN.

Causal association of polyunsaturated fatty acids with biliary tract diseases: A Mendelian randomization study.

OBJECTIVES: The evidence connecting polyunsaturated fatty acids (PUFAs) to biliary problems is still highly contested and speculative despite the fact that biliary diseases are common and PUFAs have long been studied for their potential health benefits. This work used Mendelian randomization (MR) techniques in conjunction with genome-wide association study (GWAS) data to clarify the causal relationships between PUFAs and biliary tract diseases. METHODS: We compiled data on PUFAs, including Omega-3 fatty acids, Omega-6 fatty acids, and the ratio of Omega-6 to Omega-3 fatty acids (Omega-6:Omega-3), using GWAS. MR was used to examine biliary tract problems (cholecystitis, cholelithiasis, gallbladder cancer, primary biliary cholangitis, primary sclerosing cholangitis, and disorders of gallbladder, biliary tract and pancreas). Single nucleotide polymorphisms significantly associated with PUFAs were selected as instrumental variables to estimate causal effects on biliary tract diseases. The final results were analyzed using five MR analysis techniques. Inverse variance weighting (IVW) was used as the primary outcome. And IVW was utilized in conjunction with the other MR analysis techniques (MR-Egger, weighted median, simple mode, and weighted mode). Additionally, we evaluated heterogeneity and horizontal multiplicity using the MR-Egger intercept test and Cochrane's Q test, respectively. Finally, to increase the accuracy and precision of the study outcomes, we carried out a number of sensitivity analyses. RESULTS: We found that Omega-3 fatty acids reduced the risk of cholecystitis (OR: 0.851, P = 0.009), cholelithiasis (OR: 0.787, P = 8.76e-5), and disorders of gallbladder, biliary tract and pancreas (OR: 0.842, P = 1.828e-4) but increased the primary biliary cholangitis (OR: 2.220, P = 0.004). There was no significant association between Omega-3 fatty acids and risk of gallbladder cancer (OR: 3.127, P = 0.530) and primary sclerosing cholangitis (OR: 0.919, P = 0.294). Omega-6 fatty acids were associated with a reduced risk of cholecystitis (OR: 0.845, P = 0.040). However, they were not linked to an increased or decreased risk of cholelithiasis (OR: 0.878, P = 0.14), gallbladder cancer (OR: 4.670, P = 0.515), primary sclerosing cholangitis (OR: 0.993, P = 0.962), primary cholestatic biliary cholangitis (OR: 1.404, P = 0.509), or disorders of gallbladder, biliary tract and pancreas. Omega-6:Omega-3 fatty acids were linked to a greater risk of cholecystitis, cholelithiasis, and disorders of gallbladder, biliary tract and pancreas (OR:1.168, P = 0.009, OR:1.191, P = 1.60e-6, and OR:1.160, P = 4.11e-6, respectively). But (OR: 0.315, P = 0.010) was linked to a decreased risk of primary biliary cholangitis. Not linked to risk of primary sclerosing cholangitis (OR: 1.079, P = 0.078) or gallbladder cancer (OR: 0.046, P = 0.402). According to the MR-Egger intercept, our MR examination did not appear to be impacted by any pleiotropy (all P > 0.05). Additionally, sensitivity studies validated the accuracy of the calculated causation. CONCLUSION: Inconsistent causative relationships between PUFAs and biliary tract diseases were revealed in our investigation. However, Omega-3 fatty acids were found to causally lower the risk of cholecystitis, cholelithiasis, and disorders of gallbladder, biliary tract and pancreas. Omega-3 fatty acids increased the risk of primary biliary cholangitis in a causative way. Omega-3 fatty acids with the risk of gallbladder cancer and primary sclerosing cholangitis did not have any statistically significant relationships. Omega-6 fatty acids were not significantly causally connected with the risk of cholelithiasis, gallbladder cancer, primary sclerosing cholangitis, or disorders of gallbladder, biliary tract and pancreas. However, they did play a causative role in lowering the risk of cholecystitis. Omega-6:Omega-3 fatty acids decreased the risk of primary biliary cholangitis but increased the risk of cholecystitis, gallstone disease, and disorders of gallbladder, biliary tract and pancreas. They had no effect on the risk of gallbladder cancer or primary sclerosing cholangitis. Therefore, additional research should be done to examine the probable processes mediating the link between polyunsaturated fatty acids and the risk of biliary tract diseases.

In Situ-Grown Al2O3 Nanoflowers and Hydrophobic Modification Enable Superhydrophobic SiC Ceramic Membranes for Membrane Distillation.

Membrane distillation (MD) is considered a promising technology for desalination. In the MD process, membrane pores are easily contaminated and wetted, which will degrade the permeate flux and salt rejection of the membrane. In this work, SiC ceramic membranes were used as the supports, and an Al2O3 micro-nano structure was constructed on its surface. The surface energy of Al2O3@SiC micro-nano composite membranes was reduced by organosilane grafting modification. The effective deposition of Al2O3 nanoflowers on the membrane surface increased membrane roughness and enhanced the anti-fouling and anti-wetting properties of the membranes. Simultaneously, the presence of nanoflowers also regulated the pore structures and thus decreased the membrane pore size. In addition, the effects of Al2(SO4)3 concentration and sintering temperature on the surface morphology and performance of the membranes were investigated in detail. It was demonstrated that the water contact angle of the resulting membrane was 152.4°, which was higher than that of the pristine membrane (138.8°). In the treatment of saline water containing 35 g/L of NaCl, the permeate flux was about 11.1 kg⋅m-2⋅h-1 and the salt rejection was above 99.9%. Note that the pristine ceramic membrane cannot be employed for MD due to its larger membrane pore size. This work provides a new method for preparing superhydrophobic ceramic membranes for MD.

An MRI radiomics approach to discriminate haemorrhage-prone intracranial tumours before stereotactic biopsy.

PURPOSE: To explore imaging biomarkers predictive of intratumoral haemorrhage for lesions intended for elective stereotactic biopsy. METHOD: This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset ( n =121) and a test dataset ( n =54) at a ratio of 7:3. Thirty-four lesions were defined as "hemorrhage-prone tumors" as haemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging images. Features informative of haemorrhage were then selected by the LASSO algorithm, and an Support Vector Machine model was built with selected features. The Support Vector Machine model was further simplified by discarding features with low importance and calculating them using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and area under curve (AUC) values on the independent test dataset. RESULTS: Nine radiomics features were selected as haemorrhage-related features of intracranial tumours by the LASSO algorithm. The simplified model's sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95% CI: 0.865-1.000) on the test dataset in the discrimination of "hemorrhage-prone tumors". The permutation method rated feature "T2_gradient_firstorder_10Percentile" as the most important, the absence of which decreased the model's accuracy by 10.9%. CONCLUSION: Radiomics features extracted on contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging sequences were predictive of future haemorrhage of intracranial tumours with favourable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.

Tuning Optical Properties of Plasmonic Aerosols through Ligand-Solvent Interactions.

Plasmonic nanoparticles are highly tunable light-harvesting materials with a wide array of applications in photonics and catalysis. More recently, there has been interest in using aerosolized plasmonic nanoparticles for cloud formation, airborne photocatalysts, and molecular sensors, all of which take advantage of the large scattering cross sections and the ability of these particles to support intense local field enhancement ("hot spots"). While extensive research has investigated properties of plasmonic particles in the solution phase, surfaces, and films, aerosolized plasmonics are relatively unexplored. Here, we demonstrate how the capping ligand, suspension solvent, and atomization conditions used for aerosol generation control the steady-state optical properties of aerosolized Silica@Au plasmonic nanoshells. Our experimental results, supported with spectral simulations, illustrate that ligand coverage and atomization conditions control the degree of solvent retention and thus the spectral characteristics and potential access to surfaces for catalysis in the aerosol phase, opening a new regime for tunable applications of plasmonic metamaterials.

Enhancement of de novo lipogenesis by the IDH1 and IDH2-dependent reverse TCA cycle maintains the growth and angiogenic capacity of bone marrow-derived endothelial progenitor cells under hypoxia.

BACKGROUND: Bone marrow-derived endothelial progenitor cells (EPCs) play a dynamic role in maintaining the structure and function of blood vessels. But how these cells maintain their growth and angiogenic capacity under bone marrow hypoxic niche is still unclear. This study aims to explore the mechanisms from a perspective of cellular metabolism. METHODS: XFe96 Extracellular Flux Analyzer was used to analyze the metabolic status of EPCs. Gas Chromatography-Mass Spectrometry (GC-MS) was used to trace the carbon movement of 13C-labeled glucose and glutamine under 1 % O2 (hypoxia) and ∼20 % O2 (normoxia). Moreover, RNA interference, targeting isocitrate dehydrogenase-1 (IDH1) and IDH2, was used to inhibit the reverse tricarboxylic acid (TCA) cycle and analyze metabolic changes via isotope tracing as well as changes in cell growth and angiogenic potential under hypoxia. The therapeutic potential of EPCs under hypoxia was investigated in the ischemic hindlimb model. RESULTS: Compared with normoxic cells, hypoxic cells showed increased glycolysis and decreased mitochondrial respiration. Isotope metabolic tracing revealed that under hypoxia, the forward TCA cycle was decreased and the reverse TCA cycle was enhanced, mediating the conversion of α-ketoglutarate (α-KG) into isocitrate/citrate, and de novo lipid synthesis was promoted. Downregulation of IDH1 or IDH2 under hypoxia suppressed the reverse TCA cycle, attenuated de novo lipid synthesis (DNL), elevated α-KG levels, and decreased the expression of hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor A (VEGFA), eventually inhibiting the growth and angiogenic capacity of EPCs. Importantly, the transplantation of hypoxia-cultured EPCs in a mouse model of limb ischemia promoted new blood vessel regeneration and blood supply recovery in the ischemic area better than the transplantation of normoxia-cultured EPCs. CONCLUSIONS: Under hypoxia, the IDH1- and IDH2-mediated reverse TCA cycle promotes glutamine-derived de novo lipogenesis and stabilizes the expression of α-KG and HIF-1α, thereby enhancing the growth and angiogenic capacity of EPCs.

Enhancing soil health and strawberry disease resistance: the impact of calcium cyanamide treatment on soil microbiota and physicochemical properties.

Introduction: Continuous strawberry cropping often causes soil-borne diseases, with 20 calcium cyanamide being an effective soil fumigant, pig manure can often be used as soil organic fertilizer. Its impact on soil microorganisms structure, however, remains unclear. Methods: This study investigated the effectiveness of calcium cyanamide and pig manure in treating strawberry soil, specifically against strawberry anthracnose. We examined the physical and chemical properties of the soil and the rhizosphere microbiome and performed a network analysis. Results: Results showed that calcium cyanamide treatment significantly reduces the mortality rate of strawberry in seedling stage by reducing pathogen abundance, while increasing actinomycetes and Alphaproteobacteria during the harvest period. This treatment also enhanced bacterial network connectivity, measured by the average connectivity of each Operational Taxonomic Unit (OTU), surpassing other treatments. Moreover, calcium cyanamide notably raised the levels of organic matter, available potassium, and phosphorus in the soil-key factors for strawberry disease resistance and yield. Discussion: Overall, applying calcium cyanamide to soil used for continuous strawberry cultivation can effectively decrease anthracnose incidence. It may be by changing soil physical and chemical properties and enhancing bacterial network stability, thereby reducing the copy of anthracnose. This study highlights the dual benefit of calcium cyanamide in both disease control and soil nutrient enhancement, suggesting its potential as a valuable tool in sustainable strawberry farming.

KCNQ1 rs2237895 gene polymorphism increases susceptibility to type 2 diabetes mellitus in Asian populations.

BACKGROUND: The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus (T2DM) is currently controversial. It is unknown whether this association can be gene realized across different populations. AIM: To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library, Medline, Baidu Academic, China National Knowledge Infrastructure, China Biomedical Liter-ature Database, and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12, 2022. Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature. RESULTS: Twelve case-control studies (including 11273 cases and 11654 controls) met our inclusion criteria. In the full population, allelic model [odds ratio (OR): 1.19; 95% confidence interval (95%CI): 1.09-1.29; P < 0.0001], recessive model (OR: 1.20; 95%CI: 1.11-1.29; P < 0.0001), dominant model (OR: 1.27. 95%CI: 1.14-1.42; P < 0.0001), and codominant model (OR: 1.36; 95%CI: 1.15-1.60; P = 0.0003) (OR: 1.22; 95%CI: 1.10-1.36; P = 0.0002) indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM. In stratified analysis, this association was confirmed in Asian populations: allelic model (OR: 1.25; 95%CI: 1.13-1.37; P < 0.0001), recessive model (OR: 1.29; 95%CI: 1.11-1.49; P = 0.0007), dominant model (OR: 1.35; 95%CI: 1.20-1.52; P < 0.0001), codominant model (OR: 1.49; 95%CI: 1.22-1.81; P < 0.0001) (OR: 1.26; 95%CI: 1.16-1.36; P < 0.0001). In non-Asian populations, this association was not significant: Allelic model (OR: 1.06, 95%CI: 0.98-1.14; P = 0.12), recessive model (OR: 1.04; 95%CI: 0.75-1.42; P = 0.83), dominant model (OR: 1.06; 95%CI: 0.98-1.15; P = 0.15), codominant model (OR: 1.08; 95%CI: 0.82-1.42; P = 0.60. OR: 1.15; 95%CI: 0.95-1.39; P = 0.14). CONCLUSION: KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population. Carriers of the C allele had a higher risk of T2DM. This association was not significant in non-Asian populations.

A tear-free and edible dehydrated vegetables packaging film with enhanced mechanical and barrier properties from soluble soybean polysaccharide blending carboxylated nanocellulose.

The aim of the study was to develop soybean polysaccharide (SSPS) -carboxylated nanocellulose (CNC) blending films with enhanced mechanical and barrier properties to be used as a tear-free and edible packaging materials. The films were formed by casting method, with CNC as the strengthening unit and glycerol as the plasticizer. The effect of CNC on structural and physical performances of the SSPS-CNC films were studied. SEM indicated that the film will stratify with excess CNC (10 %), but the film remains intact and compact. Incorporation of CNC into SSPS films did not change peak position in the XRD pattern significantly. Hydrogen bonds among SSPS, glycerol and CNC were indicated by the FTIR spectra. The compounding of CNC greatly lessened the light transmittance and hydrophilicity (CA increased from 55.42° to 70.67°), but perfected the barrier (WVP decreased from 3.595 × 10-10 to 2.593 × 10-10 g m-1 s-1 Pa-1) and mechanical properties (TS improved from 0.806 to 1.317 MPa). The results of packaging dehydrated vegetable indicated that the SSPS-8CNC film can effectively inhibit the packaged cabbage absorption water vapor. As a consequence, SSPS film perfected by CNC is hopeful to pack dehydrated vegetables in instant foods.

Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials.

Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here a Blank-filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network-based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank-filling language model for text generation and has demonstrated unique advantages in learning the "materials grammars" together with high-quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo-random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user-friendly web app for tinkering materials design has been developed and can be accessed freely at www.materialsatlas.org/blmtinker.

Decoding Wilson disease: a machine learning approach to predict neurological symptoms.

Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods. Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms. Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur. Conclusions: To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.