Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.

BACKGROUND: There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC. AIM: To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. METHODS: We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses. RESULTS: Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively. CONCLUSION: Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.

Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy.

BACKGROUND: Postoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification. AIM: To develop an ML-aided model for PF risk stratification. METHODS: We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model. RESULTS: A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370-1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191-1.261) and 0.882 (95%CI: 0.321-1.443). CONCLUSION: Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

Identification of Glycolysis-Related lncRNAs and the Novel lncRNA WAC-AS1 Promotes Glycolysis and Tumor Progression in Hepatocellular Carcinoma.

BACKGROUND: High glycolysis efficiency in tumor cells can promote tumor growth. lncRNAs play an important role in the proliferation, metabolism and migration of cancer cells, but their regulation of tumor glycolysis is currently not well researched. METHODS: We analyzed the co-expression of glycolysis-related genes and lncRNAs in The Cancer Genome Atlas (TCGA) database to screen glycolysis-related lncRNAs. Further prognostic analysis and differential expression analysis were performed. We further analyzed the relationship between lncRNAs and tumor immune infiltration. Since WAC antisense RNA 1 (WAC-AS1) had the greatest effect on the prognosis among all screened lncRNAs and had a larger coefficient in the prognostic model, we chose WAC-AS1 for further verification experiments and investigated the function and mechanism of action of WAC-AS1 in hepatocellular carcinoma. RESULTS: We screened 502 lncRNAs that have co-expression relationships with glycolytic genes based on co-expression analysis. Among them, 112 lncRNAs were abnormally expressed in liver cancer, and 40 lncRNAs were related to the prognosis of patients. Eight lncRNAs (WAC-AS1, SNHG3, SNHG12, MSC-AS1, MIR210HG, PTOV1-AS1, AC145207.5 and AL031985.3) were used to established a prognostic model. Independent prognostic analysis (P<0.001), survival analysis (P<0.001), receiver operating characteristic (ROC) curve analysis (AUC=0.779) and clinical correlation analysis (P<0.001) all indicated that the prognostic model has good predictive power and that the risk score can be used as an independent prognostic factor (P<0.001). The risk score and lncRNAs in the model were found to be related to a variety of immune cell infiltration and immune functions. WAC-AS1 was found to affect glycolysis and promote tumor proliferation (P<0.01). WAC-AS1 affected the expression of several glycolysis-related genes (cAMP regulated phosphoprotein 19 (ARPP19), CHST12, MED24 and KIF2A) (P<0.01). Under hypoxic conditions, WAC-AS1 regulated ARPP19 by sponging miR-320d to promote glucose uptake and lactate production (P<0.01). CONCLUSION: We constructed a model based on glycolysis-related lncRNAs to evaluate the prognostic risk of patients. The risk score and lncRNAs in the model were related to immune cell infiltration. WAC-AS1 can regulate ARPP19 to promote glycolysis and proliferation by sponging miR-320d.

Decreased NSG3 enhances PD-L1 expression by Erk1/2 pathway to promote pancreatic cancer progress.

Inhibiting the functioning of PD-1/PD-L1 to activate human immune system and improve the prognosis of pancreatic cancer (PC) would provide a significant boost to handling the disease. One research found the expression level of NSG3 was reduced in pediatric pilocytic astrocytoma, so is PC and we found NSG3 could regulate the expression of PD-L1. So NSG3 could become a new target for enhancing the immune response to PC. The GEPIA website was employed to analyze the prognoses in PC patients with different NSG3 levels. Immunohistochemistry (IHC) analysis was applied to detect different levels of NSG3 in para-PC and PC tissues. Cell biological function tests (in vitro) were performed and a subcutaneous nude mice tumor model (in vivo) was established to verify the effect of NSG3 on PC. Immunoblotting and RT-qPCR were utilized to demonstrate the inhibiting effect of NSG3 on PD-L1 through regulating Erk1/2 phosphorylation. A subcutaneous C57BL/6 tumor mice model was established to assess the possibility of a synergistic effect of NSG3 expression and the use of an anti-PD-L1 antibody on PC. PC tissues had decreased NSG3 expression levels, which led to poor prognosis. Overexpressing NSG3 suppressed proliferation, invasion and migration capacities of PC cells. On the contrary, knocking-down NSG3 prompted PC malignancy whether in vivo or in vitro. Importantly, NSG3 prevented Erk1/2 phosphorylation to inhibit PD-L1 expression. Additionally, NSG3 and an immune checkpoint inhibitor anti-PD-1 antibody acted synergistically, which enhanced the efficacy of the inhibitor. NSG3 inhibited PD-L1 expression by suppressing Erk1/2 phosphorylation to improve the immune response to PC. NSG3 is, therefore, a potential new diagnostic and prognostic marker, particularly useful in immune checkpoint blockade therapy.

A risk score based on baseline risk factors for predicting mortality in COVID-19 patients.

BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.

The Neutrophil-to-Lymphocyte Ratio Determines Clinical Efficacy of Corticosteroid Therapy in Patients with COVID-19.

Corticosteroid therapy is now recommended as a treatment in patients with severe COVID-19. But one key question is how to objectively identify severely ill patients who may benefit from such therapy. Here, we assigned 12,862 COVID-19 cases from 21 hospitals in Hubei Province equally to a training and a validation cohort. We found that a neutrophil-to-lymphocyte ratio (NLR) > 6.11 at admission discriminated a higher risk for mortality. Importantly, however, corticosteroid treatment in such individuals was associated with a lower risk of 60-day all-cause mortality. Conversely, in individuals with an NLR ≤ 6.11 or with type 2 diabetes, corticosteroid treatment was not associated with reduced mortality, but rather increased risks of hyperglycemia and infections. These results show that in the studied cohort corticosteroid treatment is associated with beneficial outcomes in a subset of COVID-19 patients who are non-diabetic and with severe symptoms as defined by NLR.

Kidney Function Indicators Predict Adverse Outcomes of COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a recently emerged respiratory infectious disease with kidney injury as a part of the clinical complications. However, the dynamic change of kidney function and its association with COVID-19 prognosis are largely unknown. METHODS: In this multicenter retrospective cohort study, we analyzed clinical characteristics, medical history, laboratory tests, and treatment data of 12,413 COVID-19 patients. The patient cohort was stratified according to the severity of the outcome into three groups: non-severe, severe, and death. FINDINGS: The prevalence of elevated blood urea nitrogen (BUN), elevated serum creatinine (Scr), and decreased blood uric acid (BUA) at admission was 6.29%, 5.22%, and 11.66%, respectively. The trajectories showed the elevation in BUN and Scr levels, as well as a reduction in BUA level for 28 days after admission in death cases. Increased all-cause mortality risk was associated with elevated baseline levels of BUN and Scr and decreased levels of BUA. CONCLUSIONS: The dynamic changes of the three kidney function markers were associated with different severity and poor prognosis of COVID-19 patients. BUN showed a close association with and high potential for predicting adverse outcomes in COVID-19 patients for severity stratification and triage. FUNDING: This study was supported by grants from the National Key R&D Program of China (2016YFF0101504), the National Science Foundation of China (81630011, 81970364, 81970070, 81970011, 81870171, and 81700356), the Major Research Plan of the National Natural Science Foundation of China (91639304), the Hubei Science and Technology Support Project (2019BFC582, 2018BEC473, and 2017BEC001), and the Medical Flight Plan of Wuhan University.

In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19.

Statins are lipid-lowering therapeutics with favorable anti-inflammatory profiles and have been proposed as an adjunct therapy for COVID-19. However, statins may increase the risk of SARS-CoV-2 viral entry by inducing ACE2 expression. Here, we performed a retrospective study on 13,981 patients with COVID-19 in Hubei Province, China, among which 1,219 received statins. Based on a mixed-effect Cox model after propensity score-matching, we found that the risk for 28-day all-cause mortality was 5.2% and 9.4% in the matched statin and non-statin groups, respectively, with an adjusted hazard ratio of 0.58. The statin use-associated lower risk of mortality was also observed in the Cox time-varying model and marginal structural model analysis. These results give support for the completion of ongoing prospective studies and randomized controlled trials involving statin treatment for COVID-19, which are needed to further validate the utility of this class of drugs to combat the mortality of this pandemic.

Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes.

Type 2 diabetes (T2D) is a major comorbidity of COVID-19. However, the impact of blood glucose (BG) control on the degree of required medical interventions and on mortality in patients with COVID-19 and T2D remains uncertain. Thus, we performed a retrospective, multi-centered study of 7,337 cases of COVID-19 in Hubei Province, China, among which 952 had pre-existing T2D. We found that subjects with T2D required more medical interventions and had a significantly higher mortality (7.8% versus 2.7%; adjusted hazard ratio [HR], 1.49) and multiple organ injury than the non-diabetic individuals. Further, we found that well-controlled BG (glycemic variability within 3.9 to 10.0 mmol/L) was associated with markedly lower mortality compared to individuals with poorly controlled BG (upper limit of glycemic variability exceeding 10.0 mmol/L) (adjusted HR, 0.14) during hospitalization. These findings provide clinical evidence correlating improved glycemic control with better outcomes in patients with COVID-19 and pre-existing T2D.

Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network.

This paper investigates the performance of a full-duplex (FD) relaying-enabled satellite sensor network under residual loop interference, where the satellite uplink and the downlink transmissions simultaneously occur over the same frequency band. Specifically, the closed-form expressions for the outage probability and ergodic capacity of the FD relaying satellite sensor network are derived by considering residual loop interference, channel statistical property, propagation loss, geometric satellite antenna pattern, and terminal elevation angle. Simulation results show the achieved performance gains of a full-duplex relaying satellite sensor network over traditional half-duplex relaying, and highlight the impact of key system parameters on the performance of the considered FD relaying satellite sensor network.

[The effects of lipiodol-hydroxyapatite nanoparticle on apoptosis, proliferation, and angiogenesis in hepatic tumor: experiment with rabbits].

OBJECTIVE: To investigate the effects of lipiodol-hydroxyapatite nanoparticle (lipi-nHAP) on the growth, necrosis, apoptosis, proliferation, and angiogenesis of hepatic tumor. METHODS: Ultrasound-emulsification was used to make lipi-nHAP Eighty New Zealand white rabbits underwent implantation of carcinoma cells of the line VX2 into the left lobe of liver. Two weeks later the rabbits underwent catheterization into the gastroduodenal artery so that, and then the rabbits were randomly divided into four equal groups to receive infusion via the hepatic artery of different drugs: physiological saline (Group A), lipiodol (Group B), adriamycin + lipiodol (Group C), and lipi-nHAP (Group D). Seven and 14 days after the treatment the size of tumor was observed by spiral CT scan, and the volume and growth rate of tumor were calculated. Two weeks after the treatment 8 rabbits from each group were killed and their liver tumors were taken out and the survival rates of remaining rabbits were observed. The necrosis rate of the liver tumor was assessed by measuring the area of the tumor and the necrosis. The apoptotic rate was examined by TUNEL method. Mcrovessel density (MVD) was examined by immunohistochemistry anti-CD31 antibody. Anti-proliferating cell nuclear antigen (PCNA) monoclonal antibody was used to detect the expression of PCNA so as to calculate the proliferation index of the cells. RESULTS: The tumor volume and growth rate of Group D 7 and 14 days after treatment were both significantly lower than those of other groups (all P < 0.05) and the necrosis rate and apoptotic index of Group D were both significantly higher than those of other groups (all P < 0.05). The values of MVD were higher in Groups C and D compared with those of Group A. Compared with those in other groups, the values of MVD and expression level of PCNA were significantly lower in group D (all P < 0.05). The survival time of Group D was longer than those of other groups (all P < 0.05). CONCLUSION: lipi-nHAP can suppress the growth of tumor, increase the tumor's necrosis rate and apoptotic index, inhibit the development of neovascularization, decrease the expression level of PCNA of residual tumor, and prolong the surviving time of the animals with hepatic tumor. It may become an effective embolization material to treat liver cancer.