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Abstract Objective This study aimed to investigate the diagnostic potential of serum CXC chemokine ligand 5 (CXCL5) in patients with chronic atrophic gastritis (CAG) and to establish a prediction model for better diagnosis of CAG. Methods A retrospective analysis was conducted, encompassing 570 cases of CAG patients admitted to the Department of Gastroenterology of the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, who underwent gastroscopy and received pathologically confirmed diagnoses between June 2018 and June 2023. Additionally, 570 cases without CAG who underwent health checkups were included and classified into the control group. Single-factor and multi-factorial logistic regression analyses were employed to identify risk factors of CAG, and a prediction model for diagnosing CAG was developed using R software. The predictive performance of the constructed model was verified and evaluated through ROC analysis, decision curve analysis (DCA), and prediction efficacy curve. Results Multi-factorial logistic regression analysis revealed that history of smoking, family history of tumurs, Pepsinogen I (PG I), Gastrin 17 (G-17), Helicobacter pylori infection, D-dimer, and CXCL5 were independent risk factors in CAG patients. A nomogram for the diagnosis of CAG was constructed using R software. The ROC curve demonstrated that CXCL5 showed the best predictive efficacy as a single indicator, with an AUC of 0.897, a sensitivity of 0.789, and a specificity of 0.999. Furthermore, the nomogram exhibited an AUC of 0.992, a sensitivity of 0.958, and a specificity of 0.970. Calibration and DCA curves indicated that the predicted values of the nomogram were highly concordant with the observed values, thus demonstrating a high predictive value. Conclusion In this study, we found a correlation between serum CXCL5 level and CAG, and developed a prediction model to assist the clinical diagnosis of CAG.

Abstract Background Lung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes. Methods The genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model. Results Based on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients’ prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD. Conclusion In our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation.

BackgroundAirway management failure is associated with increased perioperative morbidity and mortality. Airway-related complications can be significantly reduced if difficult laryngoscopy is predicted with high accuracy. Currently, there are no large-sample studies on difficult airway assessments in Chinese populations. An airway assessment model based on the Chinese population is urgently needed to guide airway rescue strategy.MethodsThis prospective nested case–control study took place in a tertiary hospital in Shanghai, China. Information on 10,549 patients was collected, and 8,375 patients were enrolled, including 7,676 patients who underwent successful laryngoscopy and 699 patients who underwent difficult laryngoscopy. The baseline characteristics, medical history, and bedside examinations were included as predictor variables. Laryngoscopy was defined as ‘successful laryngoscopy’ based on a Cormack–Lehane Grades of 1–2 and as ‘difficult laryngoscopy’ based on a Cormack–Lehane Grades of 3–4. A model was developed by incorporating risk factors and was presented in the form of a nomogram by univariate logistic regression, least absolute shrinkage and selection operator, and stepwise logistic regression. The main outcome measures were area under the curve (AUC), sensitivity, and specificity of the predictive model.ResultThe AUC value of the prediction model was 0.807 (95% confidence interval [CI]: 0.787–0.828), with a sensitivity of 0.730 (95% CI, 0.690–0.769) and a specificity of 0.730 (95% CI, 0.718–0.742) in the training set. The AUC value of the prediction model was 0.829 (95% CI, 0.800–0.857), with a sensitivity of 0.784 (95% CI, 0.73–0.838) and a specificity of 0.722 (95% CI, 0.704–0.740) in the validation set.ConclusionOur model had accurate predictive performance, good clinical utility, and good robustness for difficult laryngoscopy in the Chinese population.

BackgroundUnanticipated difficult mask ventilation (DMV) is a potentially life-threatening event in anesthesia. Nevertheless, predicting DMV currently remains a challenge. This study aimed to verify whether three dimensional (3D) facial scans could predict DMV in patients scheduled for general anesthesia.MethodsThe 3D facial scans were taken on 669 adult patients scheduled for elective surgery under general anesthesia. Clinical variables currently used as predictors of DMV were also collected. The DMV was defined as the inability to provide adequate and stable ventilation. Spatially dense landmarks were digitized on 3D scans to describe sufficient details for facial features and then processed by 3D geometric morphometrics. Ten different machine learning (ML) algorithms, varying from simple to more advanced, were introduced. The performance of ML models for DMV prediction was compared with that of the DIFFMASK score. The area under the receiver operating characteristic curves (AUC) with its 95% confidence interval (95% CI) as well as the specificity and sensitivity were used to evaluate the predictive value of the model.ResultsThe incidence of DMV was 35/669 (5.23%). The logistic regression (LR) model performed best among the 10 ML models. The AUC of the LR model was 0.825 (95% CI, 0.765–0.885). The sensitivity and specificity of the model were 0.829 (95% CI, 0.629–0.914) and 0.733 (95% CI, 0.532–0.819), respectively. The LR model demonstrated better predictive performance than the DIFFMASK score, which obtained an AUC of 0.785 (95% CI, 0.710–0.860) and a sensitivity of 0.686 (95% CI, 0.578–0.847). Notably, we identified a significant morphological difference in the mandibular region between the DMV group and the easy mask ventilation group.ConclusionOur study indicated a distinct morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. 3D geometric morphometrics with ML could be a rapid, efficient, and non-invasive tool for DMV prediction to improve anesthesia safety.

Background: Gastric intestinal metaplasia (IM) is the key link of gastric precancerous lesions. Ferroptosis is a novel form of programmed cell death. However, its impact on IM is unclear. The focus of this study is to identify and verify ferroptosis-related genes (FRGs) that may be involved in IM by bioinformatics analysis.Materials and methods: Differentially expressed genes (DEGs) were obtained from microarray dataset GSE60427 and GSE78523 downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed ferroptosis-related genes (DEFRGs) were obtained from overlapping genes of DEGs and FRGs got from FerrDb. DAVID database was used for functional enrichment analysis. Protein-protein interaction (PPI) analysis and Cytoscape software were used to screen hub gene. In addition, we built a receiver operating characteristic (ROC) curve and verified the relative mRNA expression by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the CIBERSORT algorithm was used to analyze the immune infiltration in IM.Results: First, a total of 17 DEFRGs were identified. Second, a gene module identified by Cytoscape software was considered as hub gene: PTGS2, HMOX1, IFNG, and NOS2. Third, ROC analysis showed that HMOX1 and NOS2 had good diagnostic characteristics. qRT-PCR experiments confirmed the differential expression of HMOX1 in IM and normal gastric tissues. Finally, immunoassay showed that the proportion of T cells regulatory (Tregs) and macrophages M0 in IM was relatively higher, while the proportion of T cells CD4 memory activated and dendritic cells activated was lower.Conclusion: We found significant associations between FRGs and IM, and HMOX1 may be diagnostic biomarkers and therapeutic targets for IM. These results may enhance our understanding of IM and may contribute to its treatment.

Nasopharyngeal carcinoma occurs in many parts of the pars nasalis pharyngis, and the pathological type is mainly squamous cell carcinoma. Because of the special position of nasopharynx, breathing, pronunciation and daily life will be seriously affected. At present, the research direction of nasopharyngeal carcinoma is mainly to explore the law of tumor cell proliferation and migration, study the molecular mechanism, master its biological behavior and clinical significance, try to find therapeutic targets, and further improve the level of tumor treatment. However, the pathologic structure and molecular mechanism of nasopharyngeal carcinoma have not been fully elucidated. In this study, the Lentivirus-mediated EIF3C shRNA vector (L.V-shEIF3C) was constructed to down-regulate the expression of EIF3C in human pharyngeal squamous carcinoma cell FaDu and the human nasopharyngeal carcinoma cell 5-8F, it was found that down-regulation of EIF3C could significantly inhibit the cell proliferation, promote cell apoptosis, induce cell cycle arrest, and inhibit the formation and growth of tumors in mouse models. This study provides strong evidence that EIF3C is a key gene driving the development and progression of head and neck cancer, which is of great significance for the diagnosis, prognosis or treatment of tumors, suggesting that EIF3C may become a valuable therapeutic development and intervention target.

ObjectiveTo investigate the risk factors and construct a prediction model of chronic atrophic gastritis (CAG) patients with intestinal metaplasia or dysplasia.MethodThe clinical data of 450 patients with CAG who were diagnosed and treated in the Department of Gastroenterology of the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine from June 2016 to February 2022 were collected. Single and multiple factors logistic regression analysis were used to explore the risk factors of intestinal metaplasia or dysplasia in patients of training cohort. Then, we constructed a model to predict the onset of intestinal metaplasia or dysplasia based on the data of training cohort, following which we tested the model in an external validation cohort of 193 patients from a local university teaching hospital. The ROC curve, calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model.ResultHelicobacter pylori (H. pylori, HP) infection, pepsinogen I, gastrin-17, and the number of lesions were found to be independent rick factors of the model. The liner prediction model showed excellent predictive value in both training cohort and validation cohort.ConclusionHP infection, pepsinogen I, gastrin-17, and the number of lesions are independent risk factors for intestinal metaplasia or dysplasia in patients with CAG. The prediction model constructed based on these factors has a high accuracy and excellent calibration, which can provide a great basis for condition assessment and individualized treatment of the patients.

Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on “public key infrastructure” are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on “logic key hierarchy” need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.

A perceptual image hashing method based on the image content and color distribution was proposed.The image was scaled to a fixed size and divided into blocks.Each block was then classified semantically into complex or plain according to the ratio of the first two singular values of its luminance matrix so as to give a table of complexity indices.RGB components of each complex block were used to form a color-feature vector,which was combined with the complexity table and encrypted to produce the image hash.Experiments show that the hash is robust against content-preserving image manipulations such as JPEG compression,smoothing and re-scaling,and sensitive to tampering of the image content.

In order to study the inhibition effect of gas⁻solid two-phase inhibitors on a methane explosion, the influence of these parameters was investigated and compared with that of single-phase inhibitors. The results show that the inhibition effect of gas⁻solid two-phase inhibitors on a methane explosion is better than the added effect of two single-phase inhibitors, indicating that a synergistic effect can be obtained by gas⁻solid two-phase inhibitors. The two-phase inhibitors which are composed of NaHCO3 (BC) powders and inert gas have a better suppressing property than those composed of NH4H2PO4 (ABC) powders and inert gas. The two-phase inhibitors composed of CO2 and powders have a better suppressing property than those composed of N2 and powders. The 9.5% premixed methane⁻air mixture can be completely inhibited by 0.10 g/L BC powders mixed with 8% CO2. The suppression mechanisms of the gas⁻solid two-phase inhibitors on the methane explosion were discussed.

Targeted therapy has been standardized in front-line therapies for metastatic colorectal cancer (mCRC), while explicit recommendations for third- or later-line are still lacking. This study evaluated the efficacy and safety of combining targeted therapy with chemotherapy in the third- or later-line treatment for mCRC via meta-analysis, providing evidence-based guidance for clinical or research practice. Comprehensive retrieval of related studies was conducted according to the PRISMA guideline. Studies were stratified with patient characteristics and pharmacological classification of the drugs. For the data available for quantitative analysis, pooled overall response rate, disease control rate, hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), and adverse events rate with respective 95% confidence intervals (CIs) were calculated. A total of 22 studies (1,866 patients) were included in this meta-analysis. Data from 17 studies (1,769 patients) involving targets of epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) were extracted for meta-analyses. The overall response rates for monotherapy and combined therapy were 4% (95% CI: 3%, 5%) and 20% (95% CI: 11%, 29%). The pooled HRs (combined therapy vs. mono) for OS and PFS were 0.72 (95% CI: 0.53, 0.99) and 0.34 (95% CI: 0.26, 0.45). Another five studies were included in narrative depiction, involving targets of BRAF, HER-2, ROS1, and NTRK. The findings of this meta-analysis indicate that VEGF and EGFR inhibitors manifest promising clinical response rates and prolonged survival in the treatment of mCRC with acceptable adverse events. [ABSTRACT FROM AUTHOR]