Time-dependent Curves Research Articles

Vibration-Controlled Transient Elastography Scores to Predict Liver-Related Events in Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide. It is important to develop noninvasive tests to assess the disease severity and prognosis. To study the prognostic implications of baseline levels and dynamic changes of the vibration-controlled transient elastography (VCTE)-based scores developed for the diagnosis of advanced fibrosis (Agile 3+) and cirrhosis (Agile 4) in patients with MASLD. This cohort study included data from a natural history cohort of patients with MASLD who underwent VCTE examination at 16 tertiary referral centers in the US, Europe, and Asia from February 2004 to January 2023, of which the data were collected prospectively at 14 centers. Eligible patients were adults aged at least 18 years with hepatic steatosis diagnosed by histologic methods (steatosis in ≥5% of hepatocytes) or imaging studies (ultrasonography, computed tomography or magnetic resonance imaging, or controlled attenuation parameter ≥248 dB/m by VCTE). The primary outcome was liver-related events (LREs), defined as hepatocellular carcinoma or hepatic decompensation (ascites, variceal hemorrhage, hepatic encephalopathy, or hepatorenal syndrome), liver transplant, and liver-related deaths. The Agile scores were compared with histologic and 8 other noninvasive tests. A total of 16 603 patients underwent VCTE examination at baseline (mean [SD] age, 52.5 [13.7] years; 9600 [57.8%] were male). At a median follow-up of 51.7 (IQR, 25.2-85.2) months, 316 patients (1.9%) developed LREs. Both Agile 3+ and Agile 4 scores classified fewer patients between the low and high cutoffs than most fibrosis scores and achieved the highest discriminatory power in predicting LREs (integrated area under the time-dependent receiver-operating characteristic curve, 0.89). A total of 10 920 patients (65.8%) had repeated VCTE examination at a median interval of 15 (IQR, 11.3-27.7) months and were included in the serial analysis. A total of 81.9% of patients (7208 of 8810) had stable Agile 3+ scores and 92.6% of patients (8163 of 8810) had stable Agile 4 scores (same risk categories at both assessments). The incidence of LREs was 0.6 per 1000 person-years in patients with persistently low Agile 3+ scores and 30.1 per 1000 person-years in patients with persistently high Agile 3+ scores. In patients with high Agile 3+ score at baseline, a decrease in the score by more than 20% was associated with substantial reduction in the risk of LREs. A similar trend was observed for the Agile 4 score, although it missed more LREs in the low-risk group. Findings of this study suggest that single or serial Agile scores are highly accurate in predicting LREs in patients with MASLD, making them suitable alternatives to liver biopsy in routine clinical practice and in phase 2b and 3 clinical trials for steatohepatitis.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BackgroundThe recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.MethodsWe extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan–Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.ResultsSeven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675–0.690), 0.689 (95% CI: 0.677–0.701), and 0.823 (95% CI: 0.786–0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.ConclusionThe nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Switchable reductive amination of aldehydes over metal-encapsulated S-1 zeolites with tunable acid-base properties

Catalytic reductive amination of aldehydes is a valuable strategy to synthesize nitrogen-containing compounds but often suffers from low selectivity and complex reaction mechanisms. This study aims to develop a selective control method for the syntheses of primary amines and secondary imines, which are widely used in fine and bulk chemicals, materials, and pharmaceuticals. Metal-encapsulated zeolite is a special type of catalyst that combines the advantages of metal nanoparticles and zeolites, such as high activity and selectivity. Herein, metal-encapsulated zeolites (Pd-M@S-1, M=Ni, Mn, Co) were applied as catalysts for the reductive amination of aromatic aldehydes. Results showed that the reductive amination of aldehydes over Pd-M@S-1 catalysts can be selectively switched between primary amines over Pd0.6@S-1 and secondary imines over Pd0.6Ni@S-1 by tuning the acid-base properties of the catalysts. The effect of acid-base properties of catalysts on selectivity control was claimed by conducting control experiments, time-dependent curves, and detailed characterizations.

Predictors of portal vein thrombosis after splenectomy in patients with cirrhosis.

Portal vein thrombosis (PVT) is a commonthsn complication after splenectomy in patients with cirrhosis. However, the predictors of postoperative PVT are not known. To investigate the predictors of PVT after splenectomy in patient with cirrhosis. A total of 45 patients with cirrhosis who underwent splenectomy were consecutively enrolled from January 2017 to December 2018. The incidence of PVT at 1 months, 3 months, and 12 months after splenectomy in patients with cirrhosis was observed. The hematological indicators, biochemical and coagulation parameters, and imaging features were recorded at baseline and at each observation point. The univariable, multivariable, receiver operating characteristic curve and time-dependent curve analyses were performed. The cumulative incidence of PVT was 40.0%, 46.6%, and 48.9% at 1 months, 3 months, and 12 months after splenectomy. Multivariable analysis showed that portal vein diameter (PVD) ≥ 14.5 mm and monthsdel‎ end-stage liver disease (MELD) score > 10 were independent predictors of PVT at 1 months, 3 months, and 12 months after splenectomy (P < 0.05). Time-dependent curve showed that the cumulative incidence of PVT was significantly different between patients with MELD score ≤ 10 and > 10 (P < 0.05). In addition, the cumulative incidence of PVT in the PVD ≥ 14.5 mm group was significantly higher than that in the PVD < 14.5 mm group (P < 0.05). Wider PVD and MELD score > 10 were independent predictors of PVT at 1 months, 3 months, and 12 months after splenectomy in patient with cirrhosis.

Development and validation of comprehensive nomograms from the SEER database for predicting early mortality in metastatic rectal cancer patients

BackgroundMetastatic rectal cancer is an incurable malignancy, which is prone to early mortality. We aimed to establish nomograms for predicting the risk of early mortality in patients with metastatic rectal cancer.MethodsIn this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database.We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Significant independent risk factors for all-cause and cancer-specific early mortality were determined by the univariate and multivariate logistic regression analyses, then we construct two practical nomograms. In order to assess the predictive performance of nomograms, we performed calibration plots, time-dependent receiver-operating characteristic curve (ROC), decision curve analysis (DCA) and clinical impact curve (CIC).ResultsA total of 2570 metastatic rectal cancer patients were included in the study. Multivariate logistic regression analyses revealed that age at diagnosis, CEA level, tumor size, surgical intervention, chemotherapy, radiotherapy, and metastases to bone, brain, liver, and lung were independently associated with early mortality of metastatic rectal cancer patients in the training cohort. The area under the curve (AUC) values of nomograms for all-cause and cancer-specific early mortality were all higher than 0.700. Calibration curves indicated that the nomograms accurately predicted early mortality and exhibited excellent discrimination. DCA and CIC showed moderately positive net benefits.ConclusionsThis study successfully generated applicable nomograms that predicted the high-risk early mortality of metastatic rectal cancer patients, which can assist clinicians in tailoring more effective treatment regimens.

Light and voltage dual-modulated volatile resistive switching in single ZnO nanowires.

A single ZnO nanowire device with volatile resistive switching behavior has been prepared. Different from traditional resistive switching devices, such ZnO nanowire devices do not exhibit resistive switching behaviors under a single bias voltage, and appear resistive switching behavior under the combined action of light stimuli and bias voltage. Through the demonstration of the time-dependent hysteresis curve and atmosphere-dependent hysteresis loop of the resistive switching devices, it is believed that under the resistive switching process, ultraviolet illumination can increase the carrier concentration and modulate the barrier depletion structure, and external bias voltage can ionize the surface state. They work together to modulate the switching process of the devices. Such light stimuli and bias voltage dual-modulated resistive switching device enables optical control and may thus be considered for sensory applications or optically tunable memories.

Construction of a prediction model for postoperative prognosis in patients with resectable cholangiocarcinoma based on silence information regulator 2 expression.

To develop a prediction model for postoperative prognosis in patients with cholangiocarcinoma (CCA) based on the expression of silence information regulator 2 (SIRT2). The differential expression of SIRT2 between CCA and normal tissues was analyzed using TCGA and GEO databases. Gene set enrichment analysis (GSEA) was used to explore potential mechanisms of SIRT2 in CCA. The expression of SIRT2 protein in CCA tissues and normal tissues (including 44 pairs of specimens) was also detected by immunohistochemistry (IHC) in 89 resectable CCA patients who underwent surgical treatment in the First Affiliated Hospital of Bengbu Medical College between January 2016 and December 2021. The relationship between SIRT2 expression and clinicopathological characteristics and prognosis of CCA patients was analyzed. A survival prediction model for patients with resectable CCA was constructed with COX regression results, the calibration curve and the time-dependent receiver operating characteristic curve (ROC) were used to evaluate the performance of the constructed model, and the predictive power between this model and the American Joint Committee on Cancer (AJCC)/TNM staging system (8th edition) was compared. SIRT2 mRNA was overexpressed in CCA tissues as shown in TCGA and GEO databases. IHC staining showed that SIRT2 protein expression in CCA tissues was significantly higher than that in adjacent non-tumor tissues. GSEA results showed that elevated SIRT2 expression may be involved in multiple metabolism-related signaling pathway, such as fatty acid metabolism, oxidative phosphorylation and amino acid metabolism. SIRT2 expression was related to serum triglycerides level, tumor size and lymph node metastasis (all P<0.05). The survival analysis results showed that patients with higher SIRT2 expression had a significantly lower overall survival (OS) than patients with lower SIRT2 expression (P<0.05). Univariate COX regression analysis suggested that pathological differentiation, clinical stage, postoperative treatment and SIRT2 expression level were associated with the prognosis of CCA patients (all P<0.05). Multivariate regression analysis confirmed that clinical stage and SIRT2 expression level were independent predictors of OS in postoperative CCA patients (both P<0.05). A nomogram based on SIRT2 for prediction of survival in postoperative CCA patients was constructed. The C-index of the model was 0.675, and the area under the time-dependent ROC curve (AUC) for predicting survival in the first, second, and third years was 0.879, 0.778, and 0.953, respectively, which were superior to those of AJCC/TNM staging system (8th Edition). SIRT2 is highly expressed in CCA tissues, which is associated with poor prognosis in patients with resectable CCA. The nomogram developed based on SIRT2 may have better predictive power than the AJCC/TNM staging system (8th edition) in prediction of survival of postoperative CCA patients.

Development and Validation of a Nomogram for Predicting Survival Based on Ferritin and Transferrin Ratio in Breast Cancer Patients.

Our objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients. We conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis. The final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort. The FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.

Prognostic value of 18F-FDG PET radiomics and sarcopenia in patients with oral squamous cell carcinoma.

Oral cancer is one of the most common malignancies in the head and neck region. Approximately 90%of oral cancers are oral squamous cell carcinomas (OSCC). 18F-FDG PET/CT has been used in OSCC patients for its high value in detecting metastatic lymph nodes and distant metastases. PET radiomics and sarcopenia can be measured on the PET and CT components of 18F-FDG PET/CT. This study aimed to investigate the prognostic value of radiomics and sarcopenia measured on the PET and CT components of pre-operation 18F-FDG PET/CT in OSCC. A total of 116 patients eventually enrolled in our study were randomly divided into two cohorts: training cohort (n=58) and validation cohort (n=58). The Cox model combined with the least absolute shrinkage and selection operator (LASSO) algorithm was applied to construct the radiomics score (Rad_score). The third lumber skeletal muscle index (L3 SMI) was calculated to identify sarcopenia. Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors. Based on the clinical factors, the clinical model was constructed, and the combined model was developed through the combination of the clinical model and Rad_score. C index, time-dependent C-index curves, receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis were used to evaluate the performance of prediction models. Three radiomics features constitute the Rad_score for overall survival (OS) and progression-free survival (PFS), respectively. Multivariate Cox regression analysis revealed that Rad_score was an independent prognostic factor, whereas sarcopenia was not. The combined models showed satisfactory performance in both the training cohort (C-index: OS:0.836, PFS:0.776) and the validation cohort (C-index: OS:0.744, PFS:0.712). The combined models were visualized as nomograms. Nomogram scores can realize the risk stratification of OSCC patients. Lower nomogram score is significantly related to the poorer OS (training cohort: p<0.0001, validation cohort: p<0.0001, overall cohort: p<0.0001) and PFS (training cohort: p<0.0001, validation cohort: p=0.00017, overall cohort: p<0.0001). Rad_score, but not sarcopenia, was an independent prognostic factor for patients with OSCC. The nomograms had a satisfactory performance, which might be helpful for OSCC patients and clinicians in personalized prognostic prediction and treatment decision-making.

Identification of a necroptosis-related gene signature for making clinical predictions of the survival of patients with lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major pathological subtype of malignant lung cancer with a poor prognosis. Necroptosis is a caspase-independent programmed cell death mode that plays a pivotal role in cancer oncogenesis and metastasis. Here, we explore the prognostic values of different necroptosis-related genes (NRGs) in LUAD. mRNA expression data and related clinical information for LUAD samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. NRGs were identified using the GeneCards database. Least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis were used to construct a prognostic risk model. Time-dependent receiver-operating characteristic curves and a nomogram were constructed to validate the predictive values of the prognostic signatures. A necroptosis-related protein-protein interaction network was visualised using the STRING database and Cytoscape software. Functional analyses, including Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway enrichment, gene set enrichment, and gene set variation analyses, were conducted to explore the underlying molecular mechanisms. Finally, the mRNA expression of the prognostic signatures in LUAD cell lines was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. A prognostic model was established for eight NRGs (CALM1, DDX17, FPR1, OGT, PGLYRP1, PRDX1, TUFM, and CPSF3) based on TCGA-cohort data and validated with the GSE68465 cohort. Patients with low-risk scores had better survival outcomes than those with high-risk scores (p = 0.00013). The nomogram was used to predict the prognosis of patients with LUAD. The prediction curves for 1-, 3-, and 5-year OS showed good predictive performance and the accuracy of the nomograms increased over time. RT-qPCR results demonstrated that these eight genes, especially CALM1, PRDX1, and PGLYRP1, were differentially expressed in LUAD cells. We constructed a reliable eight-NRG signature that provides new insights for guiding clinical practice in the prognosis and treatment of LUAD.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study

The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell’s C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.

Association of advanced lung cancer inflammation index with all-cause and cardiovascular mortality in US patients with rheumatoid arthritis.

As a systemic autoimmune disorder, the prognosis of rheumatoid arthritis (RA) is intricately linked to inflammation. This study aimed to investigate the association between the advanced lung cancer inflammation index (ALI), a comprehensive indicator of inflammation combined with nutritional status, and all-cause and cardiovascular mortality among patients diagnosed with RA. The 2,305 RA patients from NHANES (2001-2018) included in the analysis were categorized into three groups according to ALI tertiles. Weighted Kaplan-Meier and multivariate COX regression analyses evaluated the relationship between ALI and mortality. The time-dependent characteristic curve (ROC) was used to assess the prediction accuracy of ALI. During a median follow-up of 7.92 years, 591 participants died from all causes, including 197 from cardiovascular diseases. Increased ALI was associated with a decreased probability of death. The full COX model revealed lower all-cause mortality hazard risks in the T2 (HR: 0.67, 95%CI: 0.54-0.83) and T3 (HR: 0.47 95%CI: 0.33-0.67, p for tend <0.001) groups compared to T1, and the risk of cardiovascular mortality was also lower in the groups of T2 (HR: 0.47, 95%CI: 0.31-0.70) and T3 (HR: 0.34, 95%CI: 0.19-0.62, p for trend <0.001). Furthermore, the ROC analysis underscored the strong predictive capability of ALI (AUC for 1-year all-cause and cardiovascular mortality were 0.73 and 0.79, respectively). This cohort study demonstrated the higher accuracy of ALI in predicting mortality in RA patients, highlighting the important clinical value of ALI in risk assessment and prognosis evaluation.

Marketable 1,3-dimethylamylamine and caffeine-based thermogenic supplements: Regulatory genotoxicity assessment through in vitro and in silico approaches

ABSTRACT The consumption of dietary supplements to enhance physical performance has increased significantly in the last century, especially thermogenic pre-workout supplements. Nevertheless, this industry has faced criticism for inadequate safety measures surveillance in regulatory issues regarding their products. The aims of our study were to investigate two pre-workout supplements with respect to (1) mutagenicity utilizing Salmonella/microsome assay; (2) genotoxicity employing cytokinesis-block micronucleus (CBMN) assay protocols; and (3) hepatocytoxicity using WST cell proliferation, activities of lactate dehydrogenase (LDH) and alkaline phosphatase using human liver carcinoma (HepG2) and mouse fibroblast (F C3H) cells. Oxidative stress was determined through glutathione (GSH) measurement and in silico for predictions of pharmacokinetics and toxicity for the most abundant isolated substances present in these supplements. Both supplements induced mutagenicity in all examined bacterial strains, especially in the presence of exogenous metabolism. Further, tested supplements significantly elevated the formation of micronuclei (MN) as well as other cellular phenomena. Concentration- and time-dependent curves were observed for hepatotoxicity in both studied cell lines. In addition, both supplements decreased levels of intracellular and extracellular GSH. In silico predictions showed that the isolated individual compounds failed to induce the observed outcomes. Our findings provide contributions to the molecular mechanisms underlying two pre-workout supplement-induced toxicity and the need for surveillance.

Dynamical behavior of a particle-doped multi-segment dielectric elastomer minimal energy structure

The dynamic behavior of dielectric elastomers (DEs) has significant influence on their performance. The present study investigates the nonlinear dynamics of particle-doped multi-segmented DE minimum energy structures (DEMESs). To simulate the multi-segment DEMES, we consider each segment as a combination of hyperelastic film and elastic beam and obtain the ordinary differential equations governing the system dynamics based on the Euler–Lagrange equations. Due to the difficulty in measuring various physical parameters of DEs in practice, we utilize experimental data from a single-segment DE and employ a physics-informed neural network to predict the unknown parameters of the DE and the framework, such as stiffness K bb and doping volume fraction ϕ. Based on these predictions, nonlinear analysis is performed for the multi-segment system. Stability analyses of the motion equations reveal that the system exhibits a supercritical pitchfork bifurcation with hyperelastic thin film pre-stretching as the bifurcation parameter. For the three-segment DEMES, there are eight stable modes, but only four are illustrated in the bifurcation diagram due to the identical parameter settings for each segment. The amplitude-frequency curves under different AC voltage loads indicate the presence of harmonic, superharmonic, and subharmonic resonances in the system, with varying frequencies and magnitudes depending on the applied load. The Poincaré maps of the time response demonstrate that the system response is predominantly quasiperiodic. Under low voltage loads, the system exhibits periodic oscillations, while under certain high voltage loads, chaotic behavior emerges, characterized by strong nonlinearity in the time-dependent curves and non-periodicity in the Poincaré maps. This study provides insights into the present mathematical model in the motion control of DEMES.

Retracted: A nomogram for predicting in-hospital overall survival of hypertriglyceridemia-induced severe acute pancreatitis: A single center, cross-sectional study

BackgroundHypertriglyceridemia-induced severe acute pancreatitis (HTG-SAP) is a type of pancreatitis characterized by an abnormal elevation of plasma triglyceride. HTG-SAP has been associated with various complications and a high mortality rate. In this study, we established a nomogram for predicting the overall survival (OS) of HTG-SAP patients during hospitalization. Methods128 HTG-SAP cases hospitalized at the Affiliated Huadu Hospital, Southern Medical University, from 2019 to 2022 were analyzed retrospectively. A nomogram including prognostic factors correlated with OS during hospitalization was established by multivariate Cox regression analysis. We internally validated the nomogram using time-dependent (at 1-, 2-, and 3- months) survival receiver operating characteristic (SROC) and calibration curve with 500 iterations of bootstrap resampling. Time-dependent decision curve analysis (DCA) was employed to validate the clinical value of the nomogram. ResultsMultivariate Cox regression indicated that serum triglyceride, red blood cell distribution width (RDW), lactic acid, and interleukin-6 (IL6) were independent prognostic factors for OS of HTG-SAP patients during hospitalization and were used to construct a nomogram. The time-dependent area under the curve (AUC) values at 1-, 2-, and 3- months were 0.946, 0.913, and 0.929, respectively, and the Concordance index (C-index) of the nomogram was 0.916 (95%CI 0.871–0.961). The time-dependent calibration curves indicated good consistency between the observed and predicted outcomes. The time-dependent DCAs also revealed that the nomogram yielded a high clinical net benefit. After stratifying the included cases into two risk groups based on the risk score obtained from the nomogram, the high-risk group exhibited a significantly inferior overall survival (OS) compared to the low-risk group (p < 0.0001). ConclusionsOur nomogram exhibited good performance in predicting the overall survival of HTG-SAP patients during hospitalization.

A pyroptosis-related gene expression signature predicts immune microenvironment and prognosis in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive malignancy with a poor prognosis. Pyroptosis triggered by gasdermins family proteins is reported vital for tumor microenvironment and cancer progression. However, pyroptosis-related gene expression and its relationship with immune infiltration and prognosis of HNSCC have not been fully defined. RNA-sequencing data of HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. A pyroptosis-related gene expression signature and infiltrated immune cells were analyzed. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and nomogram analyses were used to construct a clinical-molecular risk model for survival prognosis. HNSCC was classified into three different molecular subtypes based on the expression information of pyroptosis-related genes. Immune cell infiltration was demonstrated to be distinct between the three subtypes. The segregation of patients into the high-risk group and low-risk group, were carried out using the signature of differential expression genes (DEGs) signature among the three molecular subtypes. The precision of this signature was corroborated by Receiver operating characteristic curve (ROC) analysis with the 3-year area under time-dependent ROC curve (AUC) reaching 0.711. The risk model was validated in another dataset from the Gene Expression Omnibus (GEO) database. Subsequently we established a clinical-molecular nomogram which combined the risk score with age and stage. The calibration plots for predicting the overall survival rate of 1-, 3-, and 5-years indicated that the nomogram performs well. The expression signature that encompasses pyroptosis-related genes could be used as molecular classification for HNSCC and pyroptosis might be a promising therapeutic target in HNSCC.

Systematic study of stability, loading efficiency and release mechanisms, and cellular interaction of vaterite with various sizes

Porous vaterite particles are regarded as promising candidates for drug-carrying system due to their good biocompatibility, degradability, physicochemical properties. However, different particle sizes can significantly affect their drug loading efficiency (LE) and release mechanism through adsorption-desorption mechanism. Herein, we successfully synthesized CaCO3 with high content of nanoscale vaterite at subzero temperatures. The results of six different sizes of vaterite in water, PBS, SBF and NaCl revealed that particle size and medium composition had important effects on its stability. The dissolution and recrystallization kinetics of the particles were explored through the time-dependent curves of pH and Ca2+ concentration induced by the particles in PBS and SBF solutions, which provided support for the drugs release mechanism. The LE of these vaterites for Rhodamine-6G and the release mechanism in PBS and SBF media were systematically studied, indicating that smaller vaterites had higher LE and faster drug release rate, which were attributed to their higher overall surface area and free energy, and the release model supported that the drug release not only depended on diffusion mechanism but was affected by self-dissolution and recrystallization. Furthermore, vaterite exhibited good cytocompatibility through the co-culture of particles and cells, and the cells showed stronger interaction behavior to small vaterites compare to larger vaterites.

Development and validation of two nomograms for predicting overall survival and Cancer-specific survival in prostate cancer patients with bone metastases: a population-based study

BackgroundProstate cancer with bone metastasis has significant invasiveness and markedly poorer prognosis. The purpose of this study is to establish two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of prostate cancer patients with bone metastasis.MethodsFrom January 2000 to December 2018, a total of 2683 prostate adenocarcinoma with bone metastasis patients were identified from the Surveillance, Epidemiology, and End Results Program (SEER) database. These patients were then divided into a training cohort and a validation cohort, with OS and CSS as the study endpoints. Correlation analyses were employed to assess the relationship between variables. Univariate and multivariate Cox analyses were utilized to ascertain the independent prognostic factors. Calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were employed to evaluate discrimination and calibration of the nomogram. DCA was applied to examine accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Lastly, the risk stratifications of the nomogram and the AJCC Stage System were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that seven variables (age, surgery, brain metastasis, liver metastasis, lung metastasis, Gleason score, marital status) and six variables (age, surgery, lung metastasis, liver metastasis, Gleason score, marital status) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of PABM patients.

Comparative Analysis of Inventory Management System with Two-Phase Time-dependent Demand and Shortages under Tolerable Slack of Payment for Deteriorating Goods

In actual commercial transactions, the supplier frequently gives the merchant a maximum permitted settlement delay in order to boost sales. In order to account for these variations and assume time-varying holding costs and shortages, this research analyses a time-dependent two-phase ramp curve demand under the allowable slack of payment model for goods with a limited life span. The demand rate for the nonlinear ramp curve is deterministic, changes over time until a pivotal moment, and remains constant. It is presumed that the breakthrough point happened within the cycle duration. Two scenarios are considered: One is that the slack of the payment period is shorter than the demand turning point, and on the other hand, the payment defer period is higher than the demand turning point. The outcomes of numerical results are used to compare the best optimality. The sensitivity analysis is then employed to exhibit the extent of the applicability of the inventory parameters.

Abstract 18359: Association of Depressive Symptoms and Risk of 30- and 90-day Hospital Admissions in Patients With a New or Worsening Diagnosis of Peripheral Artery Disease

Introduction: Patients diagnosed with Peripheral Artery Disease (PAD) often present with a complex profile of comorbidities, including a high mental health burden. Depressive symptoms are associated with worse long-term PAD outcomes, but it is unknown whether they translate into a higher short term hospital admission. Methods: Patients with a new or worsening PAD diagnosis (Rutherford 1-3) enrolled at US in the PORTRAIT registry from 2011-2015 were included. The 8-item Patient Health Questionnaire assessed depressive symptoms (scores 0 to 24, higher scores more depressive symptoms). The Youden Criterion based on time-dependent receiver operator curves was used to describe high vs. low-risk admission groups. Adjusted Cox regression models generated risk estimates associated with continuous PHQ-8 scores and all-cause, cumulative 30- and 90-day admissions based on patients’ medical records. Results: We included 669 patients in the analysis (mean age 68.6 ± 9.6 years, 57.6% male, 73.0% white). The mean PHQ-8 Score was 4.8 ± 5.2; 61.9% was classified as low- (PHQ-8 &lt;5) vs. 38.1% high-risk (PHQ-8 ≥ 5) for admissions based on the Youden Criterion. Patients in the low-risk vs. high-risk group had admission rates of 7.7% [95% CI 5.5-10.7] vs. 12.9% [95% CI 9.5-17.5] at 30 days (p-value: 0.023) and 16.2 [95% CI 13.1-20.0] vs. 25.6% [95% CI 21.0-31.0] at 90 days (p=0.002). In the adjusted model, depressive symptoms (per 1-unit increase) were independently and proportionally associated with 30-day (HR=1.06, 95% CI 1.01-1.11; p=0.026) and 90-day (HR=1.04, 95% CI 1.01-1.08; p=0.021) admissions following PAD diagnosis (e.g. those with scores &gt;20 had a &gt;20% or &gt;30% 30- and 90-day admission risk, respectively) (Figure 1). Conclusion: Thirty- and 90-day admission risk for patients with a new or worsening PAD diagnosis is high, especially for those with depressive symptoms. Value-based PAD care should include screening and integrated care pathways for depression to mitigate this risk.