Effects of Selenium on the Lignin Deposition Pattern and Stem Mechanical Properties of Alfalfa (Medicago sativa L.).

Lignin provides structural support to plants; however, it reduces their utilization rate. According to our previous studies, selenium (Se) reduces lignin accumulation in alfalfa, but the specific mechanism involved remains unclear. Therefore, at the seedling stage, four root irrigation treatments using 2.5, 50, and 5 µmol/L sodium selenite (S-RI), selenomethionine (SS-RI), Se nanoparticles (SSS-RI), and deionized water (CK-RI) were performed. At the branching stage, four treatments of foliar spraying with the three Se fertilizers described above at a concentration of 0.5 mmol/L (S-FS, SS-FS, and SSS-FS) and deionized water (CK-FS) were administered. The results revealed that all Se treatments chiefly reduced the level of deposition of syringyl (S) lignin in the first internode of alfalfa stems. SS-FS and SSS-FS treatments mainly reduced the deposition of S and guaiacyl (G) lignins in the sixth internode of alfalfa stems, respectively, while S-FS treatment only slightly reduced the deposition of G lignin. S, SS, and SSS-RI treatments reduced the level of deposition of S and G lignins in the sixth internode of alfalfa stems. Se application increased plant height, stem diameter, epidermis (cortex) thickness, primary xylem vessel number (diameter), and pith diameter of alfalfa but decreased primary xylem area and pith parenchyma cell wall thickness of the first internode, and SS(SSS)-FS treatment reduced the mechanical strength of alfalfa stems. Therefore, Se application could decrease lignin accumulation by regulating the organizational structure parameters of alfalfa stems and the deposition pattern of the lignin monomers.

Advances in rapid point-of-care virus testing.

Outbreaks of viral diseases seriously jeopardize people's health and cause huge economic losses. At the same time, virology provides a new perspective for biology, molecular biology and cancer research, and it is important to study the discovered viruses with potential applications. Therefore, the development of immediate and rapid viral detection methods for the prevention and treatment of viral diseases as well as the study of viruses has attracted extensive attention from scientists. With the continuous progress of science and technology, especially in the field of bioanalysis, a series of new detection techniques have been applied to the on-site rapid detection of viruses, which has become a powerful approach for human beings to fight against viruses. In this paper, the latest research progress of rapid point-of-care detection of viral nucleic acids, antigens and antibodies is presented. In addition, the advantages and disadvantages of these technologies are discussed from the perspective of practical application requirements. Finally, the problems and challenges faced by rapid viral detection methods and their development prospects are discussed.

Immune checkpoint inhibitor for different age patients with NSCLC in efficacy: A systematic review and meta-analysis.

OBJECTIVE: This article is a Meta-analysis aiming to systematically evaluate the difference in efficacy of immune checkpoint inhibitor in patients with non-small cell lung cancer (NSCLC) by age. METHODS: We performed a Meta-analysis of published randomized controlled trials concerning for patients with NSCLC by age. We compared overall survival among three groups (age <65 years, age 65-75 years, age ≥75 years). Hazard ratios (HRs) and 95% confidence intervals (CIs) were collected and pooled. RESULTS: A total of 10,291 patients from 17 RCTs were included. In the group under age 65 years, immune checkpoint inhibitor can significantly prolong the overall survival of patients with NSCLC (HR = 0.73, 95% CI: 0.66∼0.81, P < 0.00001). In the age 65-75 years group, immune checkpoint inhibitors prolonged overall survival in patients with NSCLC (HR = 0.78, 95% CI:0.71∼0.84, P < 0.00001). However, it has no significant effect on the overall survival of NSCLC patients (HR = 0.88, 95% CI:0.72∼1.08, P > 0.05) in the group older than 75 years. CONCLUSIONS: Immune checkpoint inhibitors prolonged the overall survival of NSCLC patients in the age <65 years group and the age 65-75 years group, but in the age ≥75 years group, there was no significant effect on overall survival. This may be related to innate immune and adaptive immune dysregulation due to "immunosenescence" in older patients.

Schiff base functionalized dialdehyde starch for enhanced removal of Cu (II): Preparation, performances, DFT calculations.

Dialdehyde starch modified by 2-hydrazinopyridine (HYD-DAS) based on the reaction of dialdehyde starch (DAS) and 2-hydrazinopyridine was synthesized and characterized by FT-IR spectra, element analysis and SEM. HYD-DAS can efficiently adsorb Cu (II) ion to demonstrate visual color changes from yellow to dark brown in aqueous solutions. The influence on HYD-DAS to Cu (II) adsorption including pH value of solution, isotherm, kinetics, thermodynamics and possible mechanism had also been examined. Batch experiments indicate that HYD-DAS's to Cu (II) adsorption reaches equilibrium within 250 min, and its adsorption capacity and rate are 195.75 mg/g and 98.63 %, respectively. Moreover, HYD-DAS to Cu (II) adsorption remains robust and underscoring after five cycles to exhibit good selectivity and reusability. Kinetics studies suggest the absorption process follows a quasi-second-order with isotherms aligning to the Langmuir monolayer model, and thermodynamics reveals that it is a spontaneous endothermic nature of adsorption. Based on the analyses of XPS and DFT calculations, a possible mechanism for HYD-DAS to Cu (II) adsorption is that Cu (II) combined with nitrogen atoms from Schiff base and hydrazine pyridine ring in HYD-DAS.

Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization.

Background and purpose: Postinterventional rupture of intracranial aneurysms (IAs) remains a severe complication after flow diverter treatment. However, potential hemodynamic mechanisms underlying independent predictors for postinterventional rupture of IAs remain unclear. In this study, we employed arteriography-derived radiomic features to predict this complication. Methods: We included 64 patients who underwent pipeline flow diversion for intracranial aneurysms, distinguishing between 16 patients who experienced postinterventional rupture and 48 who did not. We performed propensity score matching based on clinical and morphological factors to match these patients with 48 patients with postinterventional unruptured IAs at a 1:3 ratio. Postinterventional digital subtraction angiography were used to create five arteriography-derived perfusion parameter maps and then radiomics features were obtained from each map. Informative features were selected through the least absolute shrinkage and selection operator method with five-fold cross-validation. Subsequently, radiomics scores were formulated to predict the occurrence of postinterventional IA ruptures. Prediction performance was evaluated with the training and test datasets using area under the curve (AUC) and confusion matrix-derived metrics. Results: Overall, 1,459 radiomics features were obtained, and six were selected. The resulting radiomics scores had high efficacy in distinguishing the postinterventional rupture group. The AUC and Youden index were 0.912 (95% confidence interval: 0.767-1.000) and 0.847 for the training dataset, respectively, and 0.938 (95% confidence interval, 0.806-1.000) and 0.800 for the testing dataset, respectively. Conclusion: Radiomics scores generated using arteriography-derived radiomic features effectively predicted postinterventional IA ruptures and may aid in differentiating IAs at high risk of postinterventional rupture.

An MRI radiomics approach to discriminate hemorrhage prone intracranial tumors before stereotactic biopsy.

PURPOSE: To explore imaging biomarkers predictive of intratumoral hemorrhage 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. 34 lesions were defined as "hemorrhage-prone tumors" as hemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1WI and T2WI images. Features informative of hemorrhage were then selected by the LASSO algorithm and an SVM model was built with selected features. The SVM model was further simplified by discarding features with low importance calculated using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and AUC value on the independent test dataset. RESULTS: Nine radiomics features were selected as hemorrhage related features of intracranial tumors 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 T1WI and T2WI sequences were predictive of future hemorrhage of intracranial tumors with favorable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.

Efficacy and safety of biologic agents for IgA nephropathy: A protocol for systematic review and meta-analysis.

BACKGROUND: IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis worldwide and a leading cause of chronic kidney failure. There are currently no definitive therapeutic regimens to treat or prevent the progression of IgAN. However, biologic agents offer novel therapeutic approaches that target immunological mechanisms to slow or halt disease progression. The objective of this study is to evaluate the efficacy and safety of biologic agents in patients with IgA nephropathy. METHODS: We will systematically search PubMed, EMbase, Web of Science, Cochrane Library, and www.clinicaltrials.gov for randomized controlled trials of biologic agents for the treatment of IgA nephropathy. The search period will span from the establishment of each database until October 2023. The quality assessment of included studies will be performed individually using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2), and meta-analysis will be conducted using Revman 5.4.1 software. CONCLUSIONS: The results of this study will provide evidence-based medical evidence for the clinical application of biologic agents in patients with IgA nephropathy. PROSPERO REGISTRATION NUMBER: CRD42023400450.

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.

Progressive coronal caudal curve after corrective osteotomies for congenital cervicothoracic scoliosis: incidence and predictors.

OBJECTIVE: Postoperative progressive coronal caudal curve (PCC) was characterized by a postoperative de novo caudal S-curve ≥ 20° following congenital cervicothoracic scoliosis (CTS) corrective osteotomies, and at least 20° greater than the preoperative measurement, while the incidence was uncertain and the pathogenesis was equivocal. The objective of this study was to investigate the morbidity and potential factors contributing to PCC following CTS surgery. METHODS: This study reviewed 72 CTS patients between 2005 and 2021. Patients were categorized into two groups according to the absence or presence of PCC at last follow-up, namely the nonprogressive curve group (NPC-group) and the progressive curve group (PC-group). Demographics, radiographic data and the Scoliosis Research Society-22 (SRS-22) questionnaire results were reviewed. Multivariate linear regression analyses were utilized to determine possible predictors for PCC. RESULTS: PCC was observed in 11 (15%) of the total 72 patients. Compared with the NPC-group, the PC-group exhibited greater postoperative residual local curve (24.0 ± 9.7° vs. 9.1 ± 4.4°, P < 0.001), upper instrumented vertebra (UIV) tilt (16.9 ± 7.4° vs. 6.2 ± 3.7°, P < 0.001), T1 tilt (14.3 ± 9.4° vs. 6.6 ± 3.9°, P = 0.022) and neck tilt (10.1 ± 6.7° vs. 3.7 ± 2.5, P = 0.009). The multivariable linear regression demonstrated that the larger postoperative UIV tilt, residual local curve and neck tilt were associated with PCC. In addition, patients with PCC showed lower SRS-22 scores in terms of pain, mental health, self-image and satisfaction (P < 0.05). CONCLUSIONS: The morbidity of PCC was 15% in CTS patients who underwent corrective osteotomies. Greater residual local curve, postoperative UIV tilt and neck tilt were identified as predictors for PCC.

Phase Transformation Temperature Prediction in Steels via Machine Learning.

The phase transformation temperature plays an important role in the design, production and heat treatment process of steels. In the present work, an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation temperatures, namely Ac1, Ac3, the martensite transformation start (MS) temperature and the bainitic transformation start (BS) temperature. The effects of the alloying element were discussed in detail by comparing their influencing mechanisms on different phase transformation temperatures. The training accuracy was significantly improved by further introducing appropriate features related to atomic parameters. The melting temperature and coefficient of linear thermal expansion of the pure metals corresponding to the alloying elements, atomic Waber-Cromer pseudopotential radii and valence electron number were the top four among the eighteen atomic parameters used to improve the trained model performance. The training and prediction processes were analyzed using a partial dependence plot (PDP) and Shapley additive explanation (SHAP) methods to reveal the relationships between the features and phase transformation temperature.

Dietary Provision, GLIM-Defined Malnutrition and Their Association with Clinical Outcome: Results from the First Decade of nutritionDay in China.

Malnutrition is a common and serious issue that worsens patient outcomes. The effects of dietary provision on the clinical outcomes of patients of different nutritional status needs to be verified. This study aimed to identify dietary provision in patients with eaten quantities of meal consumption and investigate the effects of dietary provision and different nutritional statuses defined by the GLIM criteria on clinical outcomes based on data from the nutritionDay surveys in China. A total of 5821 adult in-patients from 2010 to 2020 were included in this study's descriptive and Cox regression analyses. Rehabilitation and home discharge of 30-day outcomes were considered a good outcome. The prevalence of malnutrition defined by the GLIM criteria was 22.8%. On nutritionDay, 51.8% of all patients received dietary provisions, including hospital food and a special diet. In multivariable models adjusting for other variables, the patients receiving dietary provision had a nearly 1.5 higher chance of a good 30-day outcome than those who did not. Malnourished patients receiving dietary provision had a 1.58 (95% CI [1.36-1.83], p < 0.001) higher chance of having a good 30-day outcome and had a shortened length of hospital stay after nutritionDay (median: 7 days, 95% CI [6-8]) compared to those not receiving dietary provision (median: 11 days, 95% CI [10-13]). These results highlight the potential impacts of the dietary provision and nutritional status of in-patients on follow-up outcomes and provide knowledge on implementing targeted nutrition care.

Metagenomic insights into carbon and nitrogen cycling in the water-land transition zone of inland alkaline wetlands.

Inland alkaline wetlands play a crucial role in maintaining ecological functions. However, these wetlands are becoming more vulnerable to the effects of water level fluctuations caused by global climate change, especially concerning carbon (C) and nitrogen (N) cycling. Here, metagenomics sequencing was used to investigate microorganism diversity, C and N cycling gene abundance at three water level types (D (dry), MF (middle flooded), HF (high flooded)) along an inland alkaline wetland. Our findings reveal that water level was the most important factor in regulating the microbial communities. Distinct shifts in community composition were found along the water level increases, without fundamentally altering their composition. With the increase of water level, the relative abundance of pmoA decreased from 2.5 × 10-5 to 5.1 × 10-6. The C cycling processes shift from predominantly CO2-generated processes under low water levels to CO2 and CH4 co-generated processes under high water levels. The relative abundance of nosZ reached 4.9 × 10-5 in HF, while in D and MF, it is recorded at 4.5 × 10-5 and 3.4 × 10-5, respectively. Water levels accelerate N cycling and generating N2O intermediates. Furthermore, our study highlights the dynamic competition and cooperation between C and N cycling processes. This research provides a comprehensive biological understanding of the influence of varying water levels on soil C and N cycling processes in wetland.

Early enteral nutrition versus early supplemental parenteral nutrition in patients undergoing major abdominal surgery: a secondary analysis of 2 randomized clinical trials.

BACKGROUND: The effect of early isoenergetic feeding routes [early enteral nutrition (E-EN) or early supplemental parenteral nutrition (E-SPN)] on the outcome of patients undergoing major abdominal surgery is controversial. OBJECTIVES: The aim of this study was to investigate the impact of early isoenergetic EN compared with early isoenergetic SPN on nosocomial infections in patients undergoing major abdominal surgery. METHODS: This study is a secondary, post hoc analysis of data from 2 open-label randomized clinical trials. Participants were recruited from the general surgery department of 11 academic hospitals in China undergoing major abdominal surgery and with Nutritional Risk Screening 2002 score ≥3. All eligible patients were categorized into 2 groups based on their achievement of the 100% energy target on postoperative day (POD) 3: the E-EN group (n = 199) and the E-SPN group (n = 115). The primary outcome was the incidence of nosocomial infections between POD 3 and hospital discharge. RESULTS: In total, 314 patients [mean (SD) age, 59.2 (11.4) y; 113 (36.0%) females] were included. Patients in the E-EN group showed no significant difference in nosocomial infections compared with those in the E-SPN group {17/199 [8.5%] compared with 10/115 [8.7%], risk difference, 0.2% [95% confidence interval (CI): -6.3, 6.6]}. The hematological nutritional status of the E-EN group showed a significant improvement at discharge compared with the E-SPN group (albumin: 38.0 ± 6.0 g/L compared with 35.5 ± 7.6 g/L; mean difference, -2.5 g/L; 95% CI: -4.0, -1.0 g/L; prealbumin: 200.0 ± 8.0 mg/L compared with 158.4 ± 38.1 mg/L; mean difference, -41.6 mg/L; 95% CI: -41.7, -36.1 mg/L). Other indicators were comparable between groups. CONCLUSION: E-EN compared with isoenergetic SPN may not be associated with a reduced rate of nosocomial infection in patients undergoing major abdominal surgery, but may be associated with improved hematological nutritional status. TRIAL REGISTRATION NUMBER: This trial was registered at clinicaltrials.gov as NCT03115957 (https://clinicaltrials.gov/ct2/show/NCT03115957) and NCT03117348 (https://clinicaltrials.gov/ct2/show/NCT03117348).

Sol-Gel Synthesized Amorphous (InxGa1-x)2O3 for UV Photodetection with High Responsivity.

ß-Ga2O3 photodetectors have the advantages of low dark current and strong radiation resistance in UV detection. However, the limited photocurrent has restricted their applications. Herein, MSM UV photodetectors based on (InxGa1-x)2O3 (x = 0, 0.1, 0.2, 0.3) by a sol-gel method were fabricated and studied. The doping of indium ions in Ga2O3 leads to lattice distortion and promotes the formation of oxygen vacancies. The oxygen vacancies in (InxGa1-x)2O3 can be modulated by various proportions of indium, and the increased oxygen vacancies contribute to the enhancement of electron concentration. The results show that the amorphous In0.4Ga1.6O3 photodetector exhibited improved performances, including a high light-to-dark current ratio (2.8 × 103) and high responsivity (739.2 A/W). This work provides a promising semiconductor material In0.4Ga1.6O3 for high-performance MSM UV photodetectors.

Predictive value of serum fibroblast growth factor 19 and liver stiffness for intestinal failure associated liver disease-cholestasis.

BACKGROUND: Intestinal failure associated liver disease (IFALD)-cholestasis is a common complication of long-term parenteral nutrition (PN) in patients with intestinal failure (IF). The lack of effective early identification indicators often results in poor clinical outcomes. The objective of this study was to evaluate the predictive value of serum FGF19 and liver stiffness in IFALD-cholestasis. METHODS: Eligible adults diagnosed with IF were identified from Jinling Hospital in China. Diagnostic criteria for IFALD-cholestasis: total bilirubin >1 mg/dL and conjugated bilirubin >0.3 mg/dL for ≥6 months. Fasting blood specimens were prospectively collected and serum FGF19 concentrations were determined using ELISA and liver stiffness was measured by Two-dimensional shear wave elastography. Binary logistic regression analysis identified predictors of IFALD-cholestasis. Receiver operating characteristic (ROC) curves and areas under the ROC curves (AUROC) were used to evaluate the accuracy of serum FGF19 and liver stiffness in identifying IFALD-cholestasis. RESULTS: Of 203 study patients with IF, 70 (34.5%) were diagnosed with IFALD-cholestasis. The serum FGF19 levels in those with IFALD-cholestasis were significantly decreased compared with those in patients without, and liver stiffness was significantly increased (p < 0.001). Multivariate logistic regression analyses suggested that intestinal discontinuity, dependence on PN, liver stiffness >6.5 kPa, and serum FGF19 ≤107 pg/mL were independent risk factors for IFALD-cholestasis. The AUROC for serum FGF19 and liver stiffness, which indicate the occurrence of IFALD-cholestasis, were 0.810 and 0.714, respectively. Serum FGF19 had a superior predictive performance than liver stiffness (p < 0.05). CONCLUSION: Both low circulating serum FGF19 concentration and increased liver stiffness are excellent predictors of IFALD-cholestasis, but serum FGF19 is superior to increased liver stiffness in predicting IFALD-cholestasis.

Accurate Cancer Screening and Prediction of PD-L1-Guided Immunotherapy Efficacy Using Quantum Dot Nanosphere Self-Assembly and Machine Learning.

Accurate quantification of exosomal PD-L1 protein in tumors is closely linked to the response to immunotherapy, but robust methods to achieve high-precision quantitative detection of PD-L1 expression on the surface of circulating exosomes are still lacking. In this work, we developed a signal amplification approach based on aptamer recognition and DNA scaffold hybridization-triggered assembly of quantum dot nanospheres, which enables bicolor phenotyping of exosomes to accurately screen for cancers and predict PD-L1-guided immunotherapeutic effects through machine learning. Through DNA-mediated assembly, we utilized two aptamers for simultaneous ultrasensitive detection of exosomal antigens, which have synergistic roles in tumor diagnosis and treatment prediction, and thus, we achieved better sample classification and prediction through machine-learning algorithms. With a drop of blood, we can distinguish between different cancer patients and healthy individuals and predict the outcome of immunotherapy. This approach provides valuable insights into the development of personalized diagnostics and precision medicine.

Switchable Pickering Emulsions Stabilized via Synergistic Nanoparticles-Superamphiphiles Effects and Rapid Response to CO2/N2.

A CO2/N2-responsive emulsion provides milder reaction conditions, nontoxicity, and economic feasibility compared to other switchable surfactants. In this study, CO2/N2-responsive pickering emulsions were fabricated by using a compounded dispersion containing SiO2 nanoparticles (NPs) and superamphiphiles as the emulsifying agents. The synergistic effects of the SiO2 NPs and superamphiphiles significantly stabilized the emulsion at all of the tested concentrations and prevented complete phase separation of oil and water. The electrostatic interaction between the SiO2 NPs and superamphiphiles was disrupted after bubbling with CO2 for 30 s, resulting in the breaking of the emulsion. However, the dispersion recovered its interfacial activity after the introduction of N2 and again emulsified the emulsion. This reversible switching behavior was validated through three consecutive cycles of bubbling CO2/N2. The protonation and deprotonation of the SiO2 NPs and superamphiphiles in response to CO2/N2 facilitated reversible assembly and disassembly, which enabled the switching of the emulsions between inactive and active forms. The novel highly stable Pickering emulsions demonstrated rapid demulsification and emulsification in response to CO2/N2 and are promising for a wide range of applications.

Prediction of Venous Trans-Stenotic Pressure Gradient Using Shape Features Derived From Magnetic Resonance Venography in Idiopathic Intracranial Hypertension Patients.

OBJECTIVE: Idiopathic intracranial hypertension (IIH) is a condition of unknown etiology associated with venous sinus stenosis. This study aimed to develop a magnetic resonance venography (MRV)-based radiomics model for predicting a high trans-stenotic pressure gradient (TPG) in IIH patients diagnosed with venous sinus stenosis. MATERIALS AND METHODS: This retrospective study included 105 IIH patients (median age [interquartile range], 35 years [27-42 years]; female:male, 82:23) who underwent MRV and catheter venography complemented by venous manometry. Contrast enhanced-MRV was conducted under 1.5 Tesla system, and the images were reconstructed using a standard algorithm. Shape features were derived from MRV images via the PyRadiomics package and selected by utilizing the least absolute shrinkage and selection operator (LASSO) method. A radiomics score for predicting high TPG (≥ 8 mmHg) in IIH patients was formulated using multivariable logistic regression; its discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC). A nomogram was constructed by incorporating the radiomics scores and clinical features. RESULTS: Data from 105 patients were randomly divided into two distinct datasets for model training (n = 73; 50 and 23 with and without high TPG, respectively) and testing (n = 32; 22 and 10 with and without high TPG, respectively). Three informative shape features were identified in the training datasets: least axis length, sphericity, and maximum three-dimensional diameter. The radiomics score for predicting high TPG in IIH patients demonstrated an AUROC of 0.906 (95% confidence interval, 0.836-0.976) in the training dataset and 0.877 (95% confidence interval, 0.755-0.999) in the test dataset. The nomogram showed good calibration. CONCLUSION: Our study presents the feasibility of a novel model for predicting high TPG in IIH patients using radiomics analysis of noninvasive MRV-based shape features. This information may aid clinicians in identifying patients who may benefit from stenting.

Application and Properties of Polyglycolic Acid as a Degradation Agent in MPU/HNBR Degradable Elastomer Composites for Dissolvable Frac Plugs.

In this research, fully degradable elastomeric sealing materials were developed to enhance the environmental sustainability of oil and gas extraction. The modification of millable polyurethane rubber (MPU) with polyglycolic acid/hydrogenated nitrile butadiene rubber (PGA/HNBR) led to the synthesis of PGA@MPU/HNBR composite materials. The impact of varying monomer quantities on the mechanical properties, degradation behavior, degradation mechanisms, and thermal stability of these materials was investigated. Our findings illustrate that an increasing proportion of HNBR in the PGA@MPU/HNBR composite materials resulted in a decreased degradation rate. Simultaneously, higher HNBR content improved the thermal stability of the materials, while the inclusion of PGA reduced material hardness, rendering the composites more susceptible to swelling. At an HNBR content of 40 phr, MPU at 60 phr, and PGA at 6 phr, the composite material demonstrated the highest retention of mechanical properties at 31.3% following 168 h of hydrolysis at 100 °C. The degradation of the composite materials in 100 °C water primarily resulted from the hydrolysis of MPU's ester groups, with HNBR remaining unaffected.

AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks.

Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.