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BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Summary: Background: Endoscopic retrograde cholangiopancreatography (ERCP) is an established treatment for common bile duct (CBD) stones. Post- ERCP cholecystitis (PEC) is a known complication of such procedure and there are no effective models and clinical applicable tools for PEC prediction. Methods: A random forest (RF) machine learning model was developed to predict PEC. Eligible patients at The First Hospital of Lanzhou University in China with common bile duct (CBD) stones and gallbladders in-situ were enrolled from 2010 to 2019. Logistic regression analysis was used to compare the predictive discrimination and accuracy values based on receiver operation characteristics (ROC) curve and decision and clinical impact curve. The RF model was further validated by another 117 patients. This study was registered with ClinicalTrials.gov, NCT04234126. Findings: A total of 1117 patients were enrolled (90 PEC, 8.06%) to build the predictive model for PEC. The RF method identified white blood cell (WBC) count, endoscopic papillary balloon dilatation (EPBD), increase in WBC, residual CBD stones after ERCP, serum amylase levels, and mechanical lithotripsy as the top six predictive factors and has a sensitivity of 0.822, specificity of 0.853 and accuracy of 0.855, with the area under curve (AUC) value of 0.890. A separate logistic regression prediction model was built with sensitivity, specificity, and AUC of 0.811, 0.791, and 0.864, respectively. An additional 117 patients (11 PEC, 9.40%) were used to validate the RF model, with an AUC of 0.889 compared to an AUC of 0.884 with the logistic regression model. Interpretation: The results suggest that the proposed RF model based on the top six PEC risk factors could be a promising tool to predict the occurrence of PEC. Funding: This work was supported by National Natural Science Foundation of China (8187103130; 32160255); Gansu Competitive Foundation Projects for Technology Development and Innovation (1602FKDA001); Gansu Province Science and Technology Planning Project (20YF8WA085); Science and Technology Planning Project of Chengguan District in Lanzhou (2020JSCX0043). The Foundation of The First Hospital of Lanzhou University (ldyyyn-2018-16).

Abstract Background Cholangiocarcinoma (CCA) is an invasive malignancy arising from biliary epithelial cells; it is the most common primary tumour of the bile tract and has a poor prognosis. The aim of this study was to screen prognostic biomarkers for CCA by integrated multiomics analysis. Methods The GSE32225 dataset was derived from the Gene Expression Omnibus (GEO) database and comprehensively analysed by using R software and The Cancer Genome Atlas (TCGA) database to obtain the differentially expressed RNAs (DERNAs) associated with CCA prognosis. Quantitative isobaric tags for relative and absolute quantification (iTRAQ) proteomics was used to screen differentially expressed proteins (DEPs) between CCA and nontumour tissues. Through integrated analysis of DERNA and DEP data, we obtained candidate proteins APOF, ITGAV and CASK, and immunohistochemistry was used to detect the expression of these proteins in CCA. The relationship between CASK expression and CCA prognosis was further analysed. Results Through bioinformatics analysis, 875 DERNAs were identified, of which 10 were associated with the prognosis of the CCA patients. A total of 487 DEPs were obtained by using the iTRAQ technique. Comprehensive analysis of multiomics data showed that CASK, ITGAV and APOF expression at both the mRNA and protein levels were different in CCA compared with nontumour tissues. CASK was found to be expressed in the cytoplasm and nucleus of CCA cells in 38 (45%) of 84 patients with CCA. Our results suggested that patients with positive CASK expression had significantly better overall survival (OS) and recurrence-free survival (RFS) than those with negative CASK expression. Univariate and multivariate analyses demonstrated that negative expression of CASK was a significantly independent risk factor for OS and RFS in CCA patients. Conclusions CASK may be a tumour suppressor; its low expression is an independent risk factor for a poor prognosis in CCA patients, and so it could be used as a clinically valuable prognostic marker.

Background: Gallbladder drainage plays a key role in the management of acute cholecystitis (AC) patients. Percutaneous transhepatic gallbladder drainage (PTGBD) is commonly used while endoscopic naso-gallbladder drainage (ENGBD) serves as an alternative. Methods: A single center, prospective randomized controlled trial was performed. Eligible AC patients were randomly assigned to ENGBD or PTGBD group. Randomization was a computer-generated list with 1:1 allocation. All patients received cholecystectomy 2–3 months after drainage. The primary endpoint was abdominal pain score, and the intention-to-treat population was analyzed. (ClinicalTrials.gov: NCT03701464). Findings: Between Oct 1, 2018 and Feb 29, 2020, 22 out of 61 consecutive AC patients were enrolled in the final analysis. The mean abdominal pain scores before drainage, and at 24, 48, and 72 h after drainage in ENGBD were 6.9 ± 1.1, 4.3 ± 1.2, 2.2 ± 0.8 and 1.5 ± 0.5, respectively, while those of PTGBD were 7.4 ± 1.2, 6.2 ± 1.2, 5.3 ± 1.0 and 3.7 ± 0.9; and the mean gallbladder area tenderness scores were 8.4 ± 1.2, 5.7 ± 0.9, 3.5 ± 0.7, 2.5 ± 0.5 for ENGBD and 8.6 ± 0.9, 7.3 ± 1.0, 7.4 ± 0.5, 4.8 ± 0.9 for PTGBD. The mean abdominal pain and gallbladder area tenderness scores of the ENGBD significantly decreased than the PTGBD (group × time interaction P

Dipeptidyl peptidase-IV (DPP-4) enzyme inhibitors are a promising category of diabetes medications. Bioactive peptides, particularly those derived from bovine milk proteins, play crucial roles in inhibiting the DPP-4 enzyme. This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques. Five machine learning models, including GBDT, XGBoost, LightGBM, CatBoost, and RF, were trained. Notably, LightGBM demonstrated superior performance with an AUC value of 0.92 ± 0.01. Subsequently, LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins. Through a series of in silico screening process and in vitro experiments, GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity. Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides. Through retracing the virtual proteolysis steps, it was found that GPVRGPF can be obtained from β-casein through enzymatic hydrolysis by chymotrypsin, while HPHPHL can be obtained from κ-casein through enzymatic hydrolysis by stem bromelain or papain. In summary, the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.

In this paper, the surface activated crumb rubber with waste cooking oil (WCO) was studied to improve the performance of crumb rubber modified asphalt. The activated waste crumb rubber modified asphalt (OCRMA) with different amount of crumb rubber was prepared to study the microscopic appearance of OCRMA by scanning electron microscope and fluorescence microscope and analyze the surface performance. The rheological properties and microscopic mechanism of OCRMA were characterized by dynamic shear rheological test, multiple stress creep recovery (MSCR) test, BBR test and infrared spectroscopy. The results show that the dissolution degree of waste crumb rubber is improved after WCO activation, and the compatibility with asphalt components is enhanced, and the stable cross-linking structure is formed, which improves the asphalt performance. The several new absorption peaks, which were obvious, were all caused by the composition of WCO, that is, there was no significant chemical change during the interaction between the activated crumb rubber and base asphalt. Compared with the common waste crumb rubber modified asphalt (CRMA), activation with WCO can significantly reduce the viscosity of CRMA, decrease the difference of segregation softening point by 27%, and enhance the low temperature performance by 30%. The aging degree is greatly reduced, and the anti-aging performance of OCRMA is increased by about 20% with the same dosage. The high temperature performance, though higher than that of base asphalt, decreases to some extent. After comprehensive analysis, the optimal dosage of crumb rubber for OCRMA is 30%.

Hydrogels are widely used in wound dressings due to their moisturizing properties and biocompatibility. However, traditional hydrogel dressings cannot monitor wounds and provide accurate treatment. Recent advancements focus on hydrogel dressings with integrated monitoring and treatment functions, using sensors or intelligent materials to detect changes in the wound microenvironment. These dressings enable responsive treatment to promote wound healing. They can carry out responsive dynamic treatment in time to effectively promote wound healing. However, there is still a lack of comprehensive reviews of hydrogel wound dressings that incorporate both wound micro-environment monitoring and treatment functions. Therefore, this review categorizes hydrogel dressings according to wound types and examines their current status, progress, challenges, and future trends. It discusses various wound types, including infected wounds, burns, and diabetic and pressure ulcers, and explores the wound healing process. The review presents hydrogel dressings that monitor wound conditions and provide tailored treatment, such as pH-sensitive, temperature-sensitive, glucose-sensitive, pressure-sensitive, and nano-composite hydrogel dressings. Challenges include developing dressings that meet the standards of excellent biocompatibility, improving monitoring accuracy and sensitivity, and overcoming obstacles to production and commercialization. Furthermore, it provides the current status, progress, challenges, and future trends in this field, aiming to give a clear view of its past, present, and future.

Adzuki bean is widely consumed in East Asia. Although the positive effects of its biologically active ingredients on obesity have been confirmed, the role of whole cooked adzuki bean in preventing obesity and the relationship between the effects and gut microbiota remain unclear. Mice were fed either a low-fat diet (LFD) or high-fat diet (HFD) with or without 15% cooked adzuki bean for 12 weeks. Cooked adzuki bean significantly inhibited weight gain and hepatic steatosis, reduced high levels of serum triacylglycerol (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), and alleviated systemic inflammation and metabolic endotoxemia in mice fed a HFD. Importantly, cooked adzuki bean regulated gut microbiota composition, decreased the abundance of lipopolysaccharide (LPS)-producing bacteria (Desulfovibrionaceae,Helicobacter,and Bilophila), and HFD-dependent taxa (Deferribacteraceae, Ruminiclostridium_9, Ruminiclostridium, Mucispirillum, Oscillibacter, Enterorhabdus, Tyzzerella, Anaerotruncus, Intestinimonas, unclassified_f_Ruminococcaceae, Ruminiclostridium_5, and Ruminococcaceae), and enriched Muribaculaceae, norank_f_Muribaculaceae, Anaeroplasma, Lachnospiraceae_NK4A136_group, and Lachnospiraceae to alleviate inflammation and metabolic disorders induced by HFD. These findings provide new evidence for understanding the anti-obesity effect of cooked adzuki bean.

Air filtration materials such as protective masks can protect humans from airborne pathogens; however, most of the existing protective filtration materials are aimed to intercept bacteria. Therefore, in this work, modified polypropylene- (PP-) based melt-blown nonwovens with antibacterial property were prepared for reducing the infection rate during the filtering process. Firstly, an N-halamine precursor, 2,4-diamino-6-diallylamino-1,3,5-triazine (NDAM) monomer, was grafted with PP polymers (PP-g-NDAM) by reactive extrusion method, and the grafting effect was confirmed by nitrogen analysis and FTIR spectra. Then, the obtained PP-g-NDAM was mixed with pristine PP resins in different ratios to prepare the filter materials by melt-blown technology. Finally, the new PP-g-NDAM melt-blown filter materials were finishing treated by the chlorination and electrostatic process, which showed a high filtration efficiency with low pressure drop and a potent antibacterial effect against Escherichia coli (E. coli). This work provides an innovative method for manufacturing antibacterial filtration nonwovens, which can improve the quality of conventional filtration products.