Detection of minimal hepatic encephalopathy in patients with cirrhosis based on the Stroop-CN model (NCRCID-CHESS 2106): a prospective multicenter study.

Minimal hepatic encephalopathy (MHE) has a substantial impact on the clinical outcomes and quality of life (QOL) of patients with cirrhosis. However, timely diagnosis and intervention are challenging due to sophisticated diagnostic methods. In this study, 673 healthy controls and 905 patients with cirrhosis were screened, and 660 healthy controls and 757 patients with cirrhosis, divided into the test (292 patients) and validation (465 patients) cohort, were analyzed after screening. A diagnostic model of the Stroop test (Stroop-CN) was constructed by multivariate linear regression based on the results of healthy controls. The prevalence of MHE and the comparison results with psychometric hepatic encephalopathy score through the Stroop-CN model were stable in the test and validation cohorts. Moreover, the prevalence of MHE remained significantly higher in patients with worse disease conditions marked as high Child-Pugh grades and the Model for End-stage Liver Disease and Sodium (MELD-Na) scores in the test and validation cohort. The EuroQol 5-D questionnaire revealed that patients with MHE had a worse QOL than those without MHE both in the test and validation cohort. In conclusion, an easy and practical Stroop-CN model for MHE diagnosis based on the EncephalApp is established. It is found that a considerable number of Chinese patients with cirrhosis experience MHE, which significantly impacts their QOL.

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

Novel serum biomarker of Golgi protein 73 for the diagnosis of clinically significant portal hypertension in patients with compensated cirrhosis.

Hepatic venous pressure gradient (HVPG) is the gold standard for evaluating clinically significant portal hypertension (CSPH). However, reliable noninvasive methods are limited. Our study aims to investigate the diagnostic value of serum Golgi protein 73 (GP73) for CSPH in patients with compensated cirrhosis. The study enrolled 262 consecutive patients with compensated cirrhosis from three centers in China from February 2021 to September 2023, who underwent both serum GP73 tests and HVPG measurements. CSPH was defined as HVPG ≥ 10 mmHg. Diagnostic accuracy was evaluated using the areas under the receiver operating characteristic curve (AUC). The prevalence of CSPH was 56.9% (n = 149). There were significant differences between the CSPH and non-CSPH groups in the median serum GP73 level (126.8 vs. 73.1 ng/mL, p < 0.001). GP73 level showed a significant positive linear correlation with HVPG (r = 0.459, p < 0.001). The AUC for the diagnosis of CSPH using serum GP73 alone was 0.75 (95% confidence interval [CI] 0.68-0.81). Multivariate logistic regression analysis determined that the levels of GP73, platelets and international normalized ratio were independently associated with CSPH. The combination of these three markers was termed "IP73" score with an AUC value of 0.85 (95% CI 0.80-0.89) for CSPH. Using 0 as a cut-off value, the specificity and sensitivity of IP73 score were 77.9% and 81.9%, respectively. The IP73 score offers a novel, simple and noninvasive method of assessing CSPH in patients with compensated cirrhosis. A cut-off value of the IP73 score at 0 can distinguish patients with or without CSPH.

Preparation and Application of Polyrotaxane Cross-Linking Agent Based on Cyclodextrin in Gel Materials Field.

The cross-linking point of a conventional chemical cross-linking agent is fixed. Therefore, gels that are prepared with a conventional cross-linking agent have poor deformability, strength, shear resistance, and further properties. Some researchers have prepared a new cross-linking agent using cyclodextrin (CD). In a polyrotaxane cross-linking agent, the cross-linking points can slide freely along the molecule chain. The special "slide ring" structure can provide better elongation, strength, and other properties to gels, which can effectively expand the application of the gel's materials. This paper summarizes the preparation methods and applications from different types of CD and compares the improvements of properties (swelling, viscoelastic properties, etc.). In addition, the current results of our group are presented, and some ideas are provided for the development of polyrotaxane cross-linking agents.

Preparation and Degradation Performance Study of P(AM/GG/PEGDA) Nanocomposite Self-Degradation Gel Plugging Material.

Lost circulation is a world-class problem, and the contradiction between plugging and unplugging in reservoirs is a problem that needs to be solved urgently. The traditional LCM is not suitable for reservoirs and the complex subsequent operations. Currently, a self-degrading plugging material is proposed. In this paper, a new self-degradation plugging material, CKS-DPPG, was prepared by AM, GG, nano silica, and PEGDA. The effects of reactant concentration, pH, mineralization, etc., on the swelling and degradation performance of CKS-DPPG were investigated. The plugging capacity was tested by fracture plugging equipment, and the mechanism of self-degradation was revealed. The results show that the CKS-DPPG reached a 50% degradation rate in 54 h and complete degradation in 106 h at 80 °C and pH = 8. Low temperatures, high mineralization, and weak alkaline conditions prolong the complete degradation time of CKS-DPPG, which facilitates subsequent operations. The simulation of the 3 mm opening fracture plugging experiment showed that the pressure-bearing capacity reached 6.85 MPa and that a 0.16 MPa pressure difference could unplug after degradation. The ester bond of PEGDA is hydrolyzed under high-temperature conditions, and the spatial three-dimensional structure of CKS-DPPG becomes linear. The CKS-DPPG can effectively reduce subsequent unplugging operations and lower production costs.

Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study.

[This corrects the article DOI: 10.3389/fonc.2022.986867.].

Diagnosis of Clinically Significant Portal Hypertension Using CT- and MRI-based Vascular Model.

Background Currently, the hepatic venous pressure gradient (HVPG) remains the reference standard for diagnosis of clinically significant portal hypertension (CSPH) but is limited by its invasiveness and availability. Purpose To investigate a vascular geometric model for noninvasive diagnosis of CSPH (HVPG ≥10 mm Hg) in patients with liver cirrhosis for both contrast-enhanced CT and MRI. Materials and Methods In this retrospective study, consecutive patients with liver cirrhosis who underwent HVPG measurement from August 2016 to April 2019 were included. Patients without hepatic diseases were included and marked as non-CSPH to balance the ratio of CSPH 1:1. A variety of vascular parameters were extracted from the portal vein, hepatic vein, aorta, and inferior vena cava and then entered into a vascular geometric model for identification of CSPH. Diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Results The model was developed and tested with retrospective data from 250 patients with liver cirrhosis and 273 patients without clinical evidence of hepatic disease at contrast-enhanced CT examination, including 213 patients with CSPH (mean age, 49 years ± 12 [SD]; 138 women) and 310 patients without CSPH (mean age, 50 years ± 9; 177 women). For external validation, an MRI data set with 224 patients with cirrhosis (mean age, 49 years ± 10; 158 women) and a CT data set with 106 patients with cirrhosis (mean age, 53 years ± 12; 71 women) were analyzed. Significant reductions in mean whole-vessel volumes were observed in the portal vein (ranging from 36.9 cm3 ± 16.0 to 29.6 cm3 ± 11.1; P < .05) and hepatic vein (ranging from 35.3 cm3 ± 21.5 to 22.4 cm3 ± 15.7; P < .05) when CSPH occurred. Similarly, the mean whole-vessel lengths were shorter in patients with CSPH (portal vein: 1.7 m ± 1.2 vs 3.0 m ± 2.4, P < .05; hepatic vein: 0.9 m ± 1.5 vs 1.8 m ± 1.5, P < .05) than in those without CSPH. The proposed vascular model performed well in the internal test set (mean AUC, 0.90 ± 0.02) and external test sets (mean AUCs, 0.84 ± 0.12 and 0.87 ± 0.11). Conclusion A contrast-enhanced CT- and MRI-based vascular model was proposed with good diagnostic consistency for hepatic venous pressure gradient measurement. ClinicalTrials.gov registration nos. NCT03138915 and NCT03766880 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roldán-Alzate and Reeder in this issue.

Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): an international multicenter study.

BACKGROUND AND AIMS: The prevalence of high-risk varices (HRV) is low among compensated cirrhotic patients undergoing EGD. Our study aimed to identify a novel machine learning (ML)-based model, named ML EGD, for ruling out HRV and avoiding unnecessary EGDs in patients with compensated cirrhosis. METHODS: An international cohort from 17 institutions from China, Singapore, and India were enrolled (CHESS2001). The variables with the top 3 importance scores (liver stiffness, platelet count, and total bilirubin) were selected by the Shapley additive explanation and input into a light gradient-boosting machine algorithm to develop ML EGD for identification of HRV. Furthermore, we built a web-based calculator for ML EGD, which is free with open access (http://www.pan-chess.cn/calculator/MLEGD_score). Unnecessary EGDs that were not performed and the rates of missed HRV were used to assess the efficacy and safety for varices screening. RESULTS: Of 2794 enrolled patients, 1283 patients formed a real-world cohort from 1 university hospital in China used to develop and internally validate the performance of ML EGD for varices screening. They were randomly assigned into the training (n = 1154) and validation (n = 129) cohorts with a ratio of 9:1. In the training cohort, ML EGD spared 607 (52.6%) unnecessary EGDs with a missed HRV rate of 3.6%. In the validation cohort, ML EGD spared 75 (58.1%) EGDs with a missed HRV rate of 1.4%. To externally test the performance of ML EGD, 966 patients from 14 university hospitals in China (test cohort 1) and 545 from 2 hospitals in Singapore and India (test cohort 2) comprised the 2 test cohorts. In test cohort 1, ML EGD spared 506 (52.4%) EGDs with a missed HRV rate of 2.8%. In test cohort 2, ML EGD spared 224 (41.1%) EGDs with a missed HRV rate of 3.1%. When compared with the Baveno VI criteria, ML EGD spared more screening EGDs in all cohorts (training cohort, 52.6% vs 29.4%; validation cohort, 58.1% vs 44.2%; test cohort 1, 52.4% vs 26.5%; test cohort 2, 41.1% vs 21.1%, respectively; P < .001). CONCLUSIONS: We identified a novel model based on liver stiffness, platelet count, and total bilirubin, named ML EGD, as a free web-based calculator. ML EGD could efficiently help rule out HRV and avoid unnecessary EGDs in patients with compensated cirrhosis. (Clinical trial registration number: NCT04307264.).

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.

Introduction: Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods: A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results: The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion: A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

Thermal Stability, Smoke Density, and Flame Retardance of Ecotype Bio-Based Flame Retardant Agricultural Waste Bagasse/Epoxy Composites.

In the study, agricultural waste bagasse was used as a bio-based flame retardant for reducing the flammability of epoxy. Specifically, an interpenetrating network (IPN) was formed through a ring opening reaction between the hydroxyl functional group of bagasse and the epoxy group of triglycidyl isocyanurate (TGIC), forming Bagasse@TGIC. Next, 9, 10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO) was mixed with Bagasse@TGIC, inducing a reaction between the active hydrogen of DOPO and the epoxy group of TGIC, ultimately forming Bagasse@TGIC@DOPO with an IPN structure. Finally, the novel flame retardant was added to epoxy to create a composite. The integral procedural decomposition temperature (IPDT) of pure epoxy is 619 °C; after the introduction of the 30 wt% flame retardant, the IPDT of the resultant composite material increased to 799 °C, greatly increasing the thermal stability by 29%. After the addition of the Bagasse@TGIC@DOPO flame retardant, the limiting oxygen index increased from 21% for the pure epoxy to 29% for the composite, and the UL-94 rating improved from failing rating for the pure epoxy and V-0 rating for the composite. The Raman spectrum indicated that the addition of Bagasse@TGIC@DOPO IPN substantially increased the biochar yield during the burning process, increasing thermal stability. These results confirmed that the epoxy/Bagasse@TGIC@DOPO composite had substantial flame retarding effects.

A Study on Circular Economy Material Using Fish Scales as a Natural Flame Retardant and the Properties of Its Composite Materials.

Fish scales (FSs) are fishery wastes that can cause environmental pollution. This study aimed to solve this environmental problem. FSs were used as a flame retardant for polymer materials, making them valuable. Fish scales were combined with a commercial flame retardant, ammonium polyphosphate (APP), through synergistic effects to reduce the amount of commercial flame retardant. The use of FSs conforms to the concept of a circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt.% (pure EP) to 36.07 wt.%; IPDT from 685.6 °C (pure EP) to 1143.1°C; LOI from 21% (pure EP) to 30%; and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

[Clinical Characteristics of Patients with Myeloid Sarcoma].

OBJECTIVE: To investigate the clinical characteristics, diagnosis and treatment methods of patients with myeloid sarcoma(MS)． Methods: The clinical data, laboratory examination, clinical pathology and treatment methods of 15 patients with MS treated in the First Affiliated Hospital of Wannan Medical College from June 2012 to January 2020 were retrospectively analyzed. RESULTS: Among the 15 cases of MS, including eight males and seven females, the middle age of patients were 53（19 to 72）. Among the 15 patients with MS, 4 showed solitary MS, while 11 showed secondary MS. Immunohistochemical results showed that MPO+（12/15）、CD68+（3/6）、Lys+（3/3）、CD34+（6/14）、TdT+（0/9）、CD43+（13/13）、CD117+（6/10）、CD15+（7/10）、CD3+（1/15）、CD20+（0/15）. 6 of 13 patients were survival till follow-up date．The median overall survival (OS) time was 16 months (1-88 months)．Conclusion: Myeloid sarcoma is rare and often secondary from acute myeloid leukemia(AML) and chronic myeogenous leukemia(CML). Isolated MS can easily be misdiagnosed as lymphoma. Treatment response should be evaluated in combination with bone marrow examination, PET/CT and other imagines．Systematic chemotherapy and hematopoietic stem cell transplantation are the main method to treat MS.

Thermal hazard investigation and hazardous scenarios identification using thermal analysis coupled with numerical simulation for 2-(1-cyano-1-methylethyl)azocarboxamide.

Polymers are salient participants in the current world, and roughly more than 40%-45% of all industrial polymers were produced by free radical polymerization. Azo-initiators now have been the foremost radical initiator with the virtue of low tendency to undergo chain transfer reactions. Nevertheless, azo-initiators are readily to decompose and release an immense amount of heats and gases under elevated ambient temperature. 2-(1-Cyano-1-methylethyl)azocarboxamide (CABN) was deliberately picked as an example for identifying the hazardous scenarios in the application of azo-initiators. Initially, thermal analysis technologies were used to investigate the thermal decomposition characteristics of CABN, and selected decomposition mechanism functions were verified for the best-fitting thermokinetic model. Subsequently, thermokinetic-based numerical simulations were implemented to evaluate the thermal hazards of CABN under the ideal adiabatic scenario. Process safety parameters under adiabatic conditions including time to maximum rate as well as induction period were consequently retrieved. Furthermore, inherent safety recommendations for free-radical polymerization were established to forestall the process accidents in storage and the applications of azo-initiator.

Use of magnetic fields and nitrate concentration to optimize the growth and lipid yield of Nannochloropsis oculata.

Microalgae produce increased lipid content accompanied by a significant decrease in cell density with decreasing nitrate concentration. Magnetic fields (MF) have been reported as a factor that could accelerate metabolism and growth in microalgae culture. Thus, this study aimed to optimize the influence of MF and nitrate concentration (sodium nitrate, N) on the growth and lipid productivity of Nannochloropsis oculata. A single-factor experiment integrated with response surface methodology (RSM) via central composite design (CCD) was performed. The results showed that the maximum specific growth rate (0.24 d-1) and maximum lipid productivity (38 mg L-1 d-1) obtained in this study were higher than those of the control culture (by 166% and 103%, respectively). This study also found that the two-way interaction term MF × N had a significant effect on cell growth but not on lipid production. It was concluded that to design appropriate MF for enhanced lipid productivity due to cell growth, further research must focus on developing an understanding of the relationship between the bioeffects of the magnetic field and the proteomic changes involved in lipid accumulation strategies. This approach would enable the design of conditions to obtain inexpensive high-value products from N. oculata.