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Background: To evaluate the predictive value of sICAM-1 and sP-Selectins in the risk of death in a prospective cohort of adult acute respiratory distress syndrome (ARDS). Methods: Adult ARDS patients were included. Plasma sICAM-1, sP-Selectins, and inflammatory cytokines (TNF-α, IL-1b, IL-6, IL-8, and IL-17A) were detected in ARDS subjects. The correlation between different factors and the potential of sICAM-1 and sP-Selectins as endothelial markers to predict the risk of deathfrom ARDS was analyzed. Results: Plasma sICAM-1 and sP-Selectins were higher in ARDS patients and higher in non-survivors of ARDS. sICAM-1 was significantly correlated with TNF-α, IL-6 and IL-8, and sP-Selectins was also significantly correlated with IL-6 and IL-8. Above the critical values of 245.5 ng/mL (sICAM-1) and 482.5 ng/mL (sP-Selectins), the death of the ARDS population increased. High plasma sICAM-1 and sP-Selectins at ICU admission were independent predictors of death of ARDS patients. Conclusion: sICAM-1 and sP-Selectins can predict the risk of death in critically ill patients with ARDS.

This paper firstly investigates the visual scene testing method with image processing technique and predicts the number of scenes by UML structure. Secondly, the scene recognition of the transport supervision hall is performed by using image processing technology, and the ant colony optimization algorithm is proposed for local search to update the scene information and edge extraction. Then, the ED-AlexNet network model is constructed to detect and identify the target scenes. Finally, an error matrix is introduced to calculate the confidence of the sample model distribution in the test set, and the recognition extraction performance and recognition accuracy of the ED-AlexNet network model are analyzed. The study shows that when the error matrix is introduced, the highest value of ED-AlexNet F – measure is close to 0.9, and the end value is over 160, which has a good performance of scene target recognition extraction. The average recognition accuracy of ED-AlexNet is higher than 95%, with good compatibility and high accuracy of recognition.

Background: Detection of gastric cancer (GC) at early stages is an effective strategy for decreasing mortality. This study aimed to construct a prediction nomogram based on a questionnaire to assess the risk of developing GC. Methods: Our study comprised a total of 4379 participants (2326 participants from outpatient at Fengqing People's Hospital were considered for model development and internal validation, and 2053 participants from outpatients at the endoscopy center at the Seventh Affiliated Hospital of Sun Yat-Sen University were considered for independent external validation) and gastric mucosa status was determined by endoscopy and biopsies. The eligible participants in development cohort from Fengqing people's Hospital were randomly separated into a training cohort (n=1629, 70.0%) and an internal validation cohort (n=697, 30.0%). The relevant features were selected by a least absolute shrinkage and selection operator (LASSO), and the ensuing features were evaluated through multivariable logistic regression analysis. Subsequently, the variables were selected to construct a prediction nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated by the C-index and calibration plot, respectively. Decision curve analysis (DCA) curves were used for the assessment of clinical benefit of the model. This model was developed to estimate the risk of developing neoplastic lesions according to the "transparent reporting of a multivariable prediction model for individual prognosis or diagnosis" (TRIPOD) statement. Results: Six variables, including age, sex, alcohol consumption, cigarette smoking, education level, and Hp infection status, were independent risk factors for the development of neoplastic lesions. Thus, these variables were incorporated into the final nomogram. The AUC of the nomogram were 0.701, 0.657 and 0.699 in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed that the nomogram was in good agreement with the observed outcomes. Compared to treatment of all patients or none, our nomogram showed a notably higher clinical benefit. Conclusion: This nomogram proved to be a convenient, cost-effective tool to effectively predict an individual's risk of developing neoplastic lesions, and it can act as a prescreening tool before gastroscopy. [ABSTRACT FROM AUTHOR]

Background: Surgical treatment has been widely controversial for gastric cancer accompanied by liver metastasis (GCLM). This paper aims to develop and validate a nomogram to predict the survival and estimate surgical benefits for GCLM patients. Methods: A total of 616 GCLM patients from the Surveillance, Epidemiology, and End Results Program (SEER) database and 74 GCLM patients receiving primary tumor resection (PTR) from the Chinese center were included in this study. Patients from the SEER database were divided into training set (with PTR) (n=493) and non-operative set (without PTR) (n=123). Patients undergoing PTR from China were included as external validation set. Independent risk factors associated with the overall survival of GCLM patients undergoing PTR were identified in the training set via log-rank test and Cox regression analysis. Afterwards, a comprehensive model and corresponding nomogram were constructed and validated by validation set. Results: The survival of patients undergoing PTR (n=493) was longer than that without PTR (n=123) (log-rank test, p <0.0001) in SEER cohort. T stage (HR=1.40, 95% CI=1.14, 1.73), differentiation grade (HR=1.47, 95% CI=1.17, 1.85), non-hepatic metastases (HR=1.69, 95% CI=1.29, 2.21), and adjuvant therapy (HR=0.34, 95% CI= 0.28, 0.42) were closely related with the survival of GCLM with PTR, and thus, a four-factor nomogram was established. However, GCLM patients receiving PTR in the high-risk subgroup (n=255) screened out by the nomogram did not have better survival outcomes compared with patients without PTR (n=123) (log-rank test, p =0.25). Conclusions: The nomogram could predict survival of GCLM patients receiving PTR with acceptable accuracy. In addition, although PTR did improve the survival of whole GCLM patients, patients in the high-risk subgroup were unable to benefit from PTR, which could assist clinicians to make decisions for the treatment of GCLM. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common malignancies worldwide, with non-small cell lung cancer (NSCLC) being the most common type. As understanding of precise treatment options for NSCLC deepens, circulating tumor DNA (ctDNA) has emerged as a potential biomarker that has become a research hotspot and may represent a new approach for the individualized diagnosis and treatment of NSCLC. This article reviews the applications of ctDNA for the early screening of patients with NSCLC, guiding targeted therapy and immunotherapy, evaluating chemotherapy and postoperative efficacy, assessing prognosis and monitoring recurrence. With the in-depth study of the pathogenesis of NSCLC, plasma ctDNA may become an indispensable part of the precise treatment of NSCLC, which has great clinical application prospects. Article highlights Background Non-small cell lung cancer (NSCLC) has a high morbidity and mortality in the world today. There is a lack of effective means for clinical diagnosis and monitoring of NSCLC recurrence. Circulating tumor DNA (ctDNA) is a hot research direction in the precise treatment of NSCLC. Biological characteristics of circulating tumor DNA Biological characteristics of cell-free circulating DNA. Biological characteristics and genetic information sources of ctDNA. Detecting ctDNA The traditional method of ctDNA detection. The current ctDNA detection method. The advantages and disadvantages of current ctDNA detection methods. Clinical use of ctDNA detection for the diagnosis & treatment of NSCLC Early screening and diagnosis of lung cancer ctDNA detection is helpful to improve the detection rate of clinical lung cancer. ctDNA methylation was combined with imaging examination. Guiding immunotherapy and targeted therapy in patients with NSCLC ctDNA detection was used to evaluate the effect of immunotherapy in patients with NSCLC. ctDNA detection was used to evaluate the effect of targeted therapy in NSCLC patients. Evaluation of chemotherapy efficacy The advantage of ctDNA in evaluating chemotherapy in NSCLC patients. ctDNA monitoring can evaluate the sensitivity of NSCLC patients to chemotherapy. Evaluation of postoperative efficacy Postoperative ctDNA detection can evaluate the efficacy of surgery. Postoperative ctDNA monitoring can predict the risk of recurrence of NSCLC. Assessing prognosis and monitoring recurrence Expert consensus on minimal residual disease. ctDNA monitoring can evaluate the prognosis and recurrence of lung cancer after treatment. Discussion The advantages of ctDNA as a new tumor marker for NSCLC. Limitations of ctDNA detection technology. Challenges and opportunities for clinical application of ctDNA. [ABSTRACT FROM AUTHOR]