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Background Sepsis is a common critical condition caused by the body’s overwhelming response to certain infective agents. Many biomarkers, including the serum lactate level, have been used for sepsis diagnosis and guiding treatment. Recently, the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) recommended the Sequential Organ Failure Assessment (SOFA) and the quick SOFA (qSOFA) rather than lactate for screening sepsis and assess prognosis. Here, we aim to explore and compare the prognostic accuracy of the lactate level, the SOFA score and the qSOFA score for mortality in septic patients using the public Medical Information Mart for Intensive Care III database (MIMIC III). Methods The baseline characteristics, laboratory test results and outcomes for sepsis patients were retrieved from MIMIC III. Survival was analysed by the Kaplan-Meier method. Univariate and multivariate analysis was performed to identify predictors of prognosis. Receiver operating characteristic curve (ROC) analysis was conducted to compare lactate with SOFA and qSOFA scores. Results A total of 3713 cases were initially identified. The analysis cohort included 1865 patients. The 24-h average lactate levels and the worst scores during the first 24 h of ICU admission were collected. Patients in the higher lactate group had higher mortality than those in the lower lactate group. Lactate was an independent predictor of sepsis prognosis. The AUROC of lactate (AUROC, 0.664 [95% CI, 0.639–0.689]) was significantly higher than that of qSOFA (AUROC, 0.547 [95% CI, 0.521–0.574]), and it was similar to the AUROC of SOFA (AUROC, 0.686 [95% CI, 0.661–0.710]). But the timing of lactate relative to SOFA and qSOFA scores was inconsistent. Conclusion Lactate is an independent prognostic predictor of mortality for patients with sepsis. It has superior discriminative power to qSOFA, and shows discriminative ability similar to that of SOFA.

Community detection in dynamic networks has become an interesting and popular research direction in recent years, widely used in electronic commerce, social media, and other fields. Evolutionary clustering is a classical and effective framework for dynamic community detection. Most current evolutionary clustering frameworks do not directly model the evolutionary pattern of dynamic networks, but only discover their change points. Therefore, some researchers introduce graph-regularization to generalize the evolutionary clustering. However, the corresponding problem is that the effect of graph regularization depends too much on the quality of dynamic networks. If the dynamic networks have too much noise or their structural organization is not obvious, the graph-regularization may not improve the model effect, and it may lead to the problem of being too smooth. Consequently, the depiction of distinct node characteristics is too uniform and challenging to discern. To solve this problem, a dynamic community detection framework based on Graph and Symmetry Bi-regularized Non-negative Matrix Factorization (GrSrNMF) is proposed. GrSrNMF can successfully identify community structures and appropriately address variations in the number of communities within network snapshots. This is particularly crucial in dynamic networks. analysis, as the number and structure of communities can vary over time. GrSrNMF can not only learn the symmetric structure of an undirected network well but also can capture the local structure of the graph. It improves the over-smoothing problem caused by graph-regularization, to mine the evolution pattern of dynamic networks and explore their temporal changes. Our proposed GrSrNMF outperforms some state-of-the-art models, like those based on evolutionary clustering and graph regularization, as well as sophisticated methods in exploring community detection in dynamic networks, utilizing two synthetic networks and two real networks.

Background Nearly 80% of patients with pancreatic cancer suffer from glucose intolerance or diabetes. Pancreatic cancer complicated by diabetes has a more immunosuppressive tumor microenvironment (TME) and is associated with a worse prognosis. The relationship between glucose metabolism and programmed cell death-Ligand 1 (PD-L1) is close and complex. It is important to explore the regulation of high glucose on PD-L1 expression in pancreatic cancer and its effect on infiltrating immune effectors in the tumor microenvironment. Methods Diabetic murine models (C57BL/6) were used to reveal different immune landscape in euglycemic and hyperglycemic pancreatic tumor microenvironment. Bioinformatics, WB, iRIP [Improved RNA Binding Protein (RBP) Immunoprecipitation]-seq were used to confirm the potential regulating role of peptidyl-tRNA hydrolase 1 homolog (PTRH1) on the stability of the PD-L1 mRNA. Postoperative specimens were used to identify the expression of PD-L1 and PTRH1 in pancreatic cancer. Co-culturing T cells with pancreatic cancer cells to explore the immunosuppressive effect of pancreatic tumor cells. Results Our results revealed that a high dose of glucose enhanced the stability of the PD-L1 mRNA in pancreatic tumor cells by downregulating PTRH1 through RAS signaling pathway activation following epidermal growth factor receptor (EGFR) stimulation. PTRH1 overexpression significantly suppressed PD-L1 expression in pancreatic cells and improved the proportion and cytotoxic function of CD8 + T cells in the pancreatic TME of diabetic mice. Conclusions PTRH1, an RBP, plays a key role in the regulation of PD-L1 by high glucose and is closely related to anti-tumor immunity in the pancreatic TME.

Background Modulation of cell surface expression of MHC class I chain-related protein A/B (MICA/B) has been proven to be one of the mechanisms by which tumor cells escape from NK cell-mediated killing. Abnormal metabolic condition, such as high glucose, may create a cellular stress milieu to induce immune dysfunction. Hyperglycemia is frequently presented in the majority of pancreatic cancer patients and is associated with poor prognosis. In this study, we aimed to detect the effects of high glucose on NK cell-mediated killing on pancreatic cancer cells through reduction of MICA/B expression. Methods The lysis of NK cells on pancreatic cancer cells were compared at different glucose concentrations through lactate dehydrogenase release assay. Then, qPCR, Western Blot, Flow cytometry and Immunofluorescence were used to identify the effect of high glucose on expression of MICA/B, Bmi1, GATA2, phosphorylated AMPK to explore the underlying mechanisms in the process. Moreover, an animal model with diabetes mellitus was established to explore the role of high glucose on NK cell-mediated cytotoxicity on pancreatic cancer in vivo. Results In our study, high glucose protects pancreatic cancer from NK cell-mediated killing through suppressing MICA/B expression. Bmi1, a polycomb group (PcG) protein, was found to be up-regulated by high glucose, and mediated the inhibition of MICA/B expression through promoting GATA2 in pancreatic cancer. Moreover, high glucose inhibited AMP-activated protein kinase signaling, leading to high expression of Bmi1. Conclusion Our findings identify that high glucose may promote the immune escape of pancreatic cancer cells under hyperglycemic tumor microenvironment. In this process, constitutive activation of AMPK-Bmi1-GATA2 axis could mediate MICA/B inhibition, which may serve as a therapeutic target for further intervention of pancreatic cancer immune evasion.

Pancreatic ductal adenocarcinoma has a dismal prognosis. A comprehensive analysis of single-cell multi-omic data from matched tumour-infiltrated CD45+ cells and peripheral blood in 12 patients, and two published datasets, reveals a complex immune infiltrate. Patients have either a myeloid-enriched or adaptive-enriched tumour microenvironment. Adaptive immune cell-enriched is intrinsically linked with highly distinct B and T cell clonal selection, diversification, and differentiation. Using TCR data, we see the largest clonal expansions in CD8 effector memory, senescent cells, and highly activated regulatory T cells which are induced within the tumour from naïve cells. We identify pathways that potentially lead to a suppressive microenvironment, including investigational targets TIGIT/PVR and SIRPA/CD47. Analysis of patients from the APACT clinical trial shows that myeloid enrichment had a shorter overall survival compared to those with adaptive cell enrichment. Strategies for rationale therapeutic development in this disease include boosting of B cell responses, targeting immunosuppressive macrophages, and specific Treg cell depletion approaches. Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis involving evasion of immune control. Here, the authors perform a comprehensive analysis of single-cell multi-omic data revealing either a myeloid-enriched or adaptive-enriched tumour microenvironment, linked to distinct B and T cell clonal selection and differentiation, distinct overall survival, and potential therapeutic approaches.

In order to improve the precise control level of power grid infrastructure investment, this paper proposes a monitoring and early warning method of power grid infrastructure investment progress based on building information model and blockchain. By capturing the project construction progress images, the features of the power grid infrastructure are extracted automatically. Combined with the technical characteristics of distributed, tamper‐proof, and traceable blockchain, statistical indicators are generated automatically, monitoring and early warning of the investment progress execution deviation are triggered by the rules running on the smart contracts. The case study results show that the mean absolute error of the target image recognition method based on the image features is 4.32%, and the prediction accuracy of the incoming line is better than that of the engineering civil and substation. The early warning model of investment statistics based on smart contracts can automatically monitor the investment progress and generate early warnings, which provides a basis for the dynamic adjustment of the investment plan, and effectively improves the refined management level of power grid infrastructure investment projects. This paper proposes a monitoring and early warning method of power grid infrastructure investment progress based on building information model and blockchain. The results show that the mean absolute error of the target image recognition method based on the image features is 4.32%, and the prediction accuracy of the incoming line is better than that of the engineering civil and substation.

Background The current guidelines of the American Joint Committee on Cancer (AJCC) for the staging of exocrine pancreatic tumors seem inapplicable to malignant pancreatic intraductal papillary mucinous neoplasms (IPMN). Therefore, we aimed to improve the accuracy of clinical staging and prognosis for malignant IPMN by modifiing current AJCC system. Methods We extracted data of 2001 patients with malignant IPMN from the Surveillance, Epidemiology, and End Results database between 2000 and 2016. Of these, 1401 patients were assigned to the primary cohort and 600 patients to the validation cohort. Results In Kaplan–Meier analysis of the primary cohort, the current AJCC guidelines were unable to distinguish between certain tumor substages (IA and IB in the 7th, IB and IIA in the 8th). The modified system that we regrouped based on the median overall survival and hazard ratios, was superior in tumor stage classifications. Age > 70 years, tumors located in the body or tail, high-grade differentiated tumors, surgery, chemotherapy, and tumor, lymph node, and metastasis (TNM) stage were identified as independent predictive factors for overall survival. Compared to that of TNM-based systems, the concordance index of the clinical predictive nomogram significantly improved (0.819; 95% confidence interval, 0.805–0.833), with excellent area under the receiver operating characteristic curves (1-, 3-, and 5-year: 0.881, 0.889, and 0.879, respectively). The calibration curves also showed good agreement between prediction and actual observation. The analysis of treatment modalities revealed that surgery resulted in better survival for all resectable malignant IPMN. The analysis of chemotherapy data reveals its potential in improving the prognosis of treatment for patients with locally advanced or distant metastases. Conclusions Our modified staging system improves the distinction of tumor stages. The nomogram was a more accurate and clinically reliable tool for prognosis prediction of patients with malignant IPMN.

Background The latest 8th edition of the AJCC staging system emphasizes the importance of tumor size however, the clinical significance of the combination of tumor location with tumor size remains unknown. Methods We conducted this study to investigate the prognostic role of tumor location in T1 resectable pancreatic ductal adenocarcinoma (PDAC). Resectable PDAC patients from Surveillance, Epidemiology, and End Results (SEER) database (2004–2014) were selected for the propensity score matching analysis. We used matched cohort to analyze the relationship between clinicopathologic features and survival of patients. Result Eight thousand, four hundred nine patients were included in the propensity score matching analysis and 4571 patients were selected for final analysis. In T1 patients, the patients with pancreatic head tumor had worse prognosis compared to the patients with body/tail tumors. Multivariate analysis result showed that pancreatic body/tail location was an independent indicator for better chances of survival in T1 PDAC patients (hazard ratio, 0.69; 95%CI, 0.52–0.93; P  = 0.01). The modified staging system was more efficient than the AJCC 8th staging system. Conclusion Modified staging system exhibited a good assessment of the survival rate. The tumor location is a good prognostic indicator for T1 resectable PDAC patients. Modification of T1 subgroup according to tumor location exhibited favorable survival prediction effects.

Remote sensing technology has shown its irreplaceable advantages in the identification of urban black and odorous water body. However, the universality of the remote sensing recognition algorithm for the black and odorous water body is not clear in different regions. Thus, two typical cities, Shenyang and Nanjing, were selected from northern and southern China as the study areas. Four forms of recognition models of the black and odorous water body are built based on Gaofen-2 images, including the single-band model, difference model, ratio model, and water quality parameter model. Combined with the remote sensing interpretation marks of black and odorous water body, the recognition precision of black and odorous water body is analyzed by the methods of the confusion matrix and Kappa coefficient. The results show that the overall accuracy of the normalized difference black-odorous water index (NDBWI) model is higher than 80% in the northern and southern cities of China, with good consistency of spatial distribution. The accuracy of the black and odorous water index model and G model is higher than 75% only in the special areas of Nanjing or Shenyang. The accuracy of other models is generally low. Research show that the NDBWI model has better universality in the identification of urban black and odorous water body, and it is suitable for promotion and application. The remote sensing interpretation marks of black and odorous water body have shown a good auxiliary identification function. The research provides a technical foundation for large-scale and rapid identification of urban black and odorous water body.

Carbon nanotubes (CNTs) have attracted considerable attention as nanomechanical resonators because of their exceptional mechanical properties and nanoscale dimensions. In this study, a novel CNT-based probe is proposed as an efficient nanoforce sensing nanomaterial that detects external pressure. The CNT probe was designed to be fixed by clamping tunable outer CNTs. By using the mobile-supported outer CNT, the position of the partially clamped outer CNT can be controllably shifted, effectively tuning its resonant frequency. This study comprehensively investigates the modeling and vibration analysis of gigahertz frequencies with loaded CNTs used in sensing applications. The vibration frequency of a partially clamped CNT probe under axial loading was modeled using continuum mechanics, considering various parameters such as the clamping location, length, and boundary conditions. In addition, the interaction between external forces and CNT resonators was investigated to evaluate their sensitivity for force sensing. Our results provide valuable insights into the design and optimization of CNT-based nanomechanical resonators for high-performance force sensing applications.