A Precise Prediction of the Chemical and Thermal Shrinkage during Curing of an Epoxy Resin.

A precise prediction of the cure-induced shrinkage of an epoxy resin is performed using a finite element simulation procedure for the material behaviour. A series of experiments investigating the cure shrinkage of the resin system has shown a variation in the measured cure-induced strains. The observed variation results from the thermal history during the pre-cure. A proposed complex thermal expansion model and a conventional chemical shrinkage model are utilised to predict the cure shrinkage observed with finite element simulations. The thermal expansion model is fitted to measured data and considers material effects such as the glass transition temperature and the evolution of the expansion with the degree of cure. The simulations accurately capture the exothermal heat release from the resin and the cure-induced strains across various temperature profiles. The simulations follow the experimentally observed behaviour. The simulation predictions achieve good accuracy with 2-6% discrepancy compared with the experimentally measured shrinkage over a wide range of cure profiles. Demonstrating that the proposed complex thermal expansion model affects the potential to minimise the shrinkage of the studied epoxy resin. A recommendation of material parameters necessary to accurately determine cure shrinkage is listed. These parameters are required to predict cure shrinkage, allow for possible minimisation, and optimise cure profiles for the investigated resin system. Furthermore, in a study where the resin movement is restrained and therefore able to build up residual stresses, these parameters can describe the cure contribution of the residual stresses in a component.

Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding.

Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.

Mechanical and Drying Shrinkage Performance Study of Ultra-High-Performance Concrete Prepared from Titanium Slag under Different Curing Conditions.

This research investigates the effects of various curing regimes, the incorporation of titanium slag, and the utilization of quartz sand on the strength properties and shrinkage behavior of ultra-high-performance concrete (UHPC). By using low-heat silicate cement to prepare UHPC, this study conducted standard curing and steam curing, and comprehensively analyzed the macro and micro performance of UHPC under different curing conditions. The findings indicate that the application of steam curing markedly enhances the mechanical attributes of UHPC while efficiently decreasing its drying shrinkage. In the comparative tests, we found that the compressive strength of concrete that had undergone 2 days of steam curing was 9.15% higher than that of concrete cured for 28 days under standard conditions. In addition, under the same curing conditions, titanium slag sand had higher mechanical properties than quartz sand. Under standard curing conditions, the 28-day compressive strength of UHPC using titaniferous slag aggregate was 12.64% higher than that of UHPC using standard sand. Through the data analysis of XRD, TG, and MIP, we found that the content of Ca(OH)2 in the hydration products after steam curing was reduced compared to the standard curing conditions, and the pore structure had been optimized. The UHPC prepared with titanium slag sand has greater advantages in mechanical properties and drying shrinkage, and has a smaller pore structure than the UHPC prepared with quartz sand. Moreover, the use of titanium slag sand offers ecological and economic benefits, making it a more sustainable and cost-effective option for high-performance construction applications.

Influence of Ambient Relative Humidity on the Shrinkage Strain of Engineered Geopolymer Composites Based on Orthogonal Experimental Design.

With the aim to systematically analyze the ambient relative humidity on the shrinkage strain of Engineered Geopolymer Composites (EGCs), this paper studied four variables (fly ash to ground granulated blast furnace slag mass ratio, alkali content, water-binder ratio, and fiber volume content) though orthogonal experimental design and three different relative humidity values (30%, 60%, and 100% RH). The results indicated that, for EGC specimens under 30% RH and 60% RH, the decrease in slag content and increase in alkali content both resulted in greater drying shrinkage. The addition of fibers effectively reduced the shrinkage strain, while a minor impact on shrinkage was presented by the W/B ratio. The first and second key factors affecting the drying shrinkage strain were the FA/GGBS ratio and the alkali content. The optimal ratio of FA/GGBS, alkali content, and fiber volume fraction were 0/100, 4%, and 1.5%, respectively. Dring shrinkage strain was decreased with the increase in ambient relative humidity. Compared with the shrinkage strain under 30% RH, the reduction in shrinkage strain under 60% RH and 100%RH was up to 46.1% and 107.5%, respectively. At last, a relationship between shrinkage strain and curing age under 30% and 60% RH was established with a fitting degree from 0.9492 to 0.9987, while no clear relationship was presented under 100% RH. The results in this paper provide a practical method for solving the shrinkage problem of EGCs.

Shrinkage and Creep Properties of Low-Carbon Hybrid Cement.

Hybrid cements combine clinker with large amount of supplementary cementitious materials while utilizing hydration and alkali activation processes. This paper summarizes shrinkage and creep properties of industrially produced H-cement, containing only 20% of Portland clinker. In comparison with a reference cement CEM II/B-S 32.5 R, autogenous shrinkage is smaller after 7 days, and drying shrinkage is similar at similar times. A different capillary system of H-cement leads to faster water mass loss during drying. Basic and total creep of concrete remains in the standard deviation of B4 and EC2 creep models. The results demonstrate that shrinkage and creep properties of concrete made from H-cement have similar behavior as conventional structural concrete or high-volume fly ash concrete.

Development and validation of a nomogram for premature coronary artery disease patients in Guangzhou.

Background: Data regarding risk factors for premature coronary artery disease (PCAD) is scarce given that few research focus on it. This study aimed to develop and validate a clinical nomogram for PCAD patients in Guangzhou. Methods: We recruited 108 PCAD patients (female ≤65 years old and male ≤55 years old) and 96 healthy controls from Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 01/01/2021 and 31/12/2022. Twenty potentially relevant indicators of PCAD were extracted. Next, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. The nomogram was developed based on the selected variables visually. Results: Independent risk factors, including body mass index (BMI), history of PCAD, glucose, Apolipoprotein A1(ApoA1), high density lipoprotein 2-cholesterol (HDL2-C), total cholesterol and triglyceride, were identified by LASSO and logistic regression analysis. The nomogram showed accurate discrimination (area under the receiver operator characteristic curve, ROC, 87.45 %, 95 % CI: 82.58 %-92.32 %). Decision curve analysis (DCA) suggested that the nomogram was clinical beneficial. HDL2, one risk factor, was isolated by a two-step discontinuous density-gradient ultracentrifugation method. And HDL2 from PCAD patients exhibited less 3H-cholesterol efflux (22.17 % vs 26.64 %, P < 0.05) and less delivery of NBD-cholesterol detecting by confocal microscope compared with healthy controls. Conclusions: In conclusion, the seven-factor nomogram can achieve a reasonable relationship with PCAD, and a large cohort were needed to enhance the credibility and effectiveness of our model in future.

A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures.

Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.

Apoptosis-Cell Cycle-Autophagy Molecular Mechanisms Network in Heterogeneous Aggressive Phenotype Prostate Hyperplasia Primary Cell Cultures Have a Prognostic Role.

Our study highlights the apoptosis, cell cycle, DNA ploidy, and autophagy molecular mechanisms network to identify prostate pathogenesis and its prognostic role. Caspase 3/7 expressions, cell cycle, adhesion glycoproteins, autophagy, nuclear shrinkage, and oxidative stress by flow-cytometry analysis are used to study the BPH microenvironment's heterogeneity. A high late apoptosis expression by caspases 3/7 activity represents an unfavorable prognostic biomarker, a dependent predictor factor for cell adhesion, growth inhibition by arrest in the G2/M phase, and oxidative stress processes network. The heterogeneous aggressive phenotype prostate adenoma primary cell cultures present a high S-phase category (>12%), with an increased risk of death or recurrence due to aneuploid status presence, representing an unfavorable prognostic biomarker, a dependent predictor factor for caspase 3/7 activity (late apoptosis and necrosis), and cell growth inhibition (G2/M arrest)-linked mechanisms. Increased integrin levels in heterogenous BPH cultures suggest epithelial-mesenchymal transition (EMT) that maintains an aggressive phenotype by escaping cell apoptosis, leading to the cell proliferation necessary in prostate cancer (PCa) development. As predictor biomarkers, the biological mechanisms network involved in apoptosis, the cell cycle, and autophagy help to establish patient prognostic survival or target cancer therapy development.

Nearly instantaneous stem diameter response to fluctuations in the atmospheric water demand.

Changes in vapour pressure deficit (VPD) can lead to the depletion and replenishment of stem water pools to buffer water potential variations in the xylem. Yet, the precise velocity at which stem water pools track environmental cues remains poorly explored. Nine eucalyptus seedlings grown in a glasshouse experienced high-frequency environmental oscillations, and their stem radial variations (ΔR) were monitored at a 30-second temporal resolution in upper and lower stem locations and on the bark and xylem. The stem ΔR response to VPD changes was nearly instantaneous (< 1 minute), while temperature lagged behind stem ΔR. No temporal differences in the stem ΔR response were observed between locations. Punctual gravimetric measurements confirmed the synchrony between transpiration and stem ΔR dynamics. These results indicate (i) that stem-stored water can respond to the atmospheric evaporative demand much faster than commonly assumed and (ii) that the origin of the water released to the transpiration stream seems critical in determining time lags in stem water pool dynamics. Near-zero time lags may be explained by the high elasticity of eucalyptus woody tissues and the predominant water use from the xylem, circumventing the hydraulic radial barriers to water flow from/to the outer tissues.

A nomogram and heat map based on LASSO-Cox regression for predicting the risk of early-stage severe fever with thrombocytopenia syndrome patients developing into critical illness at 7-day and 14-day.

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7-day and 14-day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X-tile software. The performance of this risk stratification system was assessed using the Kaplan-Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C-reactive protein (CRP), and neurological symptoms. The AUCs for 7-day and 14-day were 0.802 [95% confidence interval (CI): 0.707-0.897] and 0.859 (95% CI: 0.794-0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high-risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance.

Construction and validation of a prediction model for inguinal lymph node metastasis of penile malignancy.

Background: Penile squamous cell carcinoma is a relatively rare malignancy among male malignancies, there are more than 30,000 new cases and more than 10,000 deaths of penile cancer annually. In patients with penile malignancy, inguinal lymph node metastasis (ILNM) significantly reduces patient survival. Thus, we identified the risk factors for ILNM in penile malignancies, aiming to develop a precise prediction model. Methods: We retrospectively analyzed 112 male patients with penile cancer. All subjects underwent penile surgery and inguinal lymphadenectomy at the same time, and postoperative pathology confirmed ILNM. Fisher's exact test, t-test, and Wilcoxon rank sum test were used to assess differences in demographic information and clinical features between the two groups, followed by logical least absolute shrinkage and selection operator (LASSO) regression analysis to determine risk factors of ILNM. The prediction model was constructed using nomogram. Results: LASSO regression revealed that age [ß=-0.005, odds ratio (OR) =0.995], smoking history (ß=-0.006, OR =0.994) and interleukin 2 (IL-2) level (ß=-0.0112, OR =0.989) were protective against ILNM. However, lymph node diameter (ß=0.3117, OR =1.366), T-stage (ß=0.1254, OR =1.134), fibrinogen (ß=0.0377, OR =1.038), IL-4 level (ß=0.004, OR =1.001), and neutrophil-to-lymphocyte ratio (ß=0.0355, OR =1.034) were risk factors for developing ILNM. When assessing the risk of metastasis, it is crucial to balance these factors. The aforementioned characteristics were utilized to establish the predictive model, which demonstrated a good predictive ability with an area under the curve (AUC) value of 0.81. Moreover, internal leave-one-way cross-validation was used to construct a nomogram showing consistency, with an AUC of 0.75. Conclusions: The diagnosis of ILNM in penile malignant tumors can be predicted through clinicopathological features, biochemical tests, and prediction models based on tumor markers.

Tumor enhancement and shrinkage pattern in dynamic contrast-enhanced magnetic resonance imaging for predicting pathologic complete response after human epidermal growth factor receptor 2 (HER2)-targeted therapy in breast cancer.

Background: Targeted therapy with neoadjuvant chemotherapy for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer has increased the rates of pathological complete response (pCR) and breast preservation surgery and improved the overall disease-free survival rate. This study aimed to determine whether tumor enhancement and shrinkage patterns in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the efficacy of targeted therapy in patients with HER2-positive breast cancer and differentiate pCR from non-pCR. Methods: The data of 64 patients with HER2-positive breast cancer who received targeted therapy prior to surgery were retrospectively collected. All patients had complete postoperative pathological data. The pretreatment evaluation of the tumor enhancement pattern and the shrinkage pattern after two treatment cycles were assessed. The difference in the enhancement and shrinkage patterns between the pCR and non-pCR groups was evaluated via the χ2 test. Logistic regression analysis was used to assess the value of enhancement and shrinkage patterns for predicting pCR in patients with HER2-positive breast cancer. Results: There were statistically significant differences in tumor size, estrogen receptor (ER) status, lymph node metastasis, enhancement pattern, and shrinkage pattern between the pCR and non-pCR cases. Patients with a tumor size ≤20 mm were likely to achieve pCR. ER status, lymph node metastasis, and enhancement and shrinkage patterns each had good precision for predicting pCR, and the combination of enhancement and shrinkage patterns had the highest prediction accuracy. Multivariate logistic regression analysis indicated that only enhancement pattern had a significant predictive value. Conclusions: Among patients with HER2-positive breast cancer, those with tumor size ≤20 mm, ER-negative status, no lymph node metastases, and mass enhancement and concentric shrinkage patterns are more likely to achieve pCR. Mass enhancement combined with concentric shrinkage had the highest accuracy in predicting pCR, indicating that preoperative imaging may be useful for guiding clinical decisions regarding targeted treatments.

3D micro-CT and O-PTIR spectroscopy bring new understanding of the influence of filler content in dental resin composites.

BACKGROUND: Dental resin composites' performance is intricately linked to their polymerisation shrinkage characteristics. This study compares polymerisation shrinkage using advanced 3D micro-computed tomography (micro-CT) and traditional 2D linear assessments. It delves into the crucial role of filler content on shrinkage and the degree of conversion in dental resin composites, providing valuable insights for the field. METHODS: Five experimental dental composite materials were prepared with increasing filler contents (55-75 wt%) and analysed using either 3D micro-CT for volumetric shrinkage or a custom-designed linometer for 2D linear shrinkage. The degree of conversion was assessed using Optical Photothermal Infrared (O-PTIR) and Fourier-Transform Infrared (FTIR) spectroscopy. Light transmittance through a 2-mm layer was evaluated using a NIST-calibrated spectrometer. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) examined surface morphology and elemental distribution. Correlation between the investigated parameters was determined using Spearman correlation analyses. RESULTS: The study found significant differences in polymerisation-related properties among different filler content categories, with volumetric shrinkage consistently demonstrating higher mean values than linear shrinkage across most groups. Volumetric shrinkage decreased with increasing curing depth, showing no direct correlation between filler content and shrinkage levels at different curing depths. The results highlighted a strong negative correlation between filler content and degree of conversion, volumetric and linear shrinkage, as well as maximum shrinkage rate. Light transmittance showed a moderate correlation with the filler content and a weak correlation with other tested parameters. CONCLUSIONS: This study underscores the importance of considering both volumetric and linear shrinkage in the design and analysis of dental composite materials. The findings advocate optimising filler content to minimise shrinkage and enhance material performance. Integrating micro-CT and O-PTIR techniques offers novel insights into dental composites' polymerisation behaviour, providing a foundation for future research to develop materials with improved clinical outcomes.

Emerging technologies for the evaluation of spatio-temporal polymerisation changes in flowable vs. sculptable dental resin-based composites.

BACKGROUND: This study presents a novel multi-technique approach that integrates micro-CT and optical photothermal infrared spectroscopy (O-PTIR) to evaluate polymerisation differences, so-called spatio-temporal polymerisation properties, between flowable and sculptable dental resin-based composites. METHODS: Ten commercially available dental composites were investigated, including flowable and sculptable counterparts from the same manufacturer. Eight parameters were evaluated: short-term polymerisation characteristics (degree of conversion after 5 min, maximum polymerisation rate, time to reach maximum polymerisation rate) was measured using ATR-FTIR with real-time monitoring; changes in the degree of conversion with depth were evaluated with O-PTIR, 3D visualisation of shrinkage patterns, overall volumetric shrinkage, depth-specific shrinkage, and porosity were measured using micro-CT; surface morphology with detailed measurements of elemental composition was characterised using SEM/EDX; light transmittance was analysed with a NIST-referenced spectrometer. RESULTS: The study found that the increase in filler weight and volume ratio reduced the degree of conversion and polymerisation shrinkage, while moderately influencing the maximum polymerisation rates. The time to reach maximum polymerisation rates and light transmittance were not dependent on the filler amount. O-PTIR assessed a depth-dependent decrease in the degree of conversion for both composite types, with flowable composites generally showing a greater decrease in the degree of conversion than sculptable composites, except for bulk-fill composites. Micro-CT scans showed significantly higher flowable shrinkage values than their sculptable counterparts, highlighting the performance differences between the two types of composites. CONCLUSIONS: The findings of this study have practical implications for the selection and use of dental composites. Flowable composites, despite their higher degrees of conversion and polymerisation rates, also exhibit higher volumetric shrinkage, which can be detrimental for clinical applications. The new measurement methods used in this study provide a comprehensive overview of the polymerisation behaviour of commercially available dental composites, offering valuable insights for material optimisation.

Investigating the Heterogeneity of "Study Twins".

Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favor of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phase III of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often be misleading.

Cyclic Acetals as Expanding Monomers to Reduce Shrinkage.

Polarity-reversal catalysts (PRCs) for hydrogen-atom transfer reactions have been known in radical chemistry for more than 60 years but are rarely described and utilized in the field of photopolymerization up to now. Herein, we present the use of thiols in a unique dual function as thiol-ene click reagents and as polarity-reversal catalyst (PRC) for the radical-mediated redox rearrangements of benzylidene acetals. During the rearrangement reaction, cyclic benzylidene acetals are transformed into benzoate esters leading to a significant volumetric expansion to reduce thermoset shrinkage. We were able to show that this expansion on a molecular level reduces shrinkage and polymerization stress but does not significantly affect the (thermo-)mechanical properties of the cross-linked networks. One of the key advantages of this process lies in its simplicity. No additives like sensitizers or combinations of different initiators (radical and cationic) are needed. Furthermore, the same light source can be used for both the polymerization reaction and expansion through rearrangement. Additionally, the applied photoinitiator enables spatial and temporal control of the polymerization; thus, the developed system can be an excellent platform for additive manufacturing processes.

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.

We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.

The role of VEGF and TGF-ß blood levels for fibroid shrinkage, menorrhagia score, and quality of life improvement after uterine artery embolization for uterine fibroids: a study protocol.

Uterine leiomyoma is a common condition affecting women that occurs in more than 70% of females. Women with leiomyomas overall have lower quality of life and deficiency in many specific spheres of life including work-related productivity, sexuality, relationships, social-emotional health, and physical well-being that might be present even in pre-and extended throughout early postmenopausal life. Choices for symptomatic fibroid management include medical, interventional radiology procedures, surgical, and expectant management. The uterine artery embolization (UAE) procedure has gained justified popularity for myoma management. Growth factors, vascular endothelial growth factor (VEGF) and tumor growth factor ß (TGF-ß), hold an important role in leiomyoma progression. However, blood levels of VEGF and TGF-ß in patients before and after UAE are not fully investigated and the possible relationship with myoma shrinkage has not been evaluated. Therefore, this study aims to assess menorrhagia score and quality of life improvement after UAE for uterine fibroids and compare blood levels of VEGF and TGF-ß in patients with uterine leiomyoma before and after UAE. This cross-sectional study will be performed at the University Medical Center, Astana, Kazakhstan. Women undergoing the UAE procedure for uterine leiomyoma will be involved in the study following the precisely defined inclusion/exclusion criteria. Uterine leiomyoma nodules' structural changes after UAE will be assessed along with the blood levels of growth factors (VEGF and TGF-ß), menorrhagia score, and quality of life. An important outcome of this project will be an investigation of the blood levels of growth factors (VEGF and TGF-ß) before and after the procedure and their association with leiomyoma shrinkage in correlation with the menorrhagia score and quality of life alterations among patients undergoing UAE.

Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients.

Objective: This study aimed to identify prognostic signatures to predict the prognosis of patients with stomach adenocarcinoma (STAD), which is necessary to improve poor prognosis and offer possible treatment strategies for STAD patients. Methods: The overlapping genes between the key model genes that were screened by the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) whose expression was different with significance between normal and tumor tissues were extracted to serve as co-expression genes. Then, enrichment analysis was performed on these genes. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression was performed to screen the hub genes among overlapping genes. Finally, we constructed a model to explore the influence of polygenic risk scores on the survival probability of patients with STAD, and interaction effect and mediating analyses were also performed. Results: DEGs included 2,899 upregulated genes and 2,896 downregulated genes. After crossing the DEGs and light-yellow module genes that were obtained by WGCNA, a total of 39 overlapping genes were extracted. The gene enrichment analysis revealed that these genes were enriched in the prion diseases, biosynthesis of unsaturated fatty acids, RNA metabolic process, hydrolase activity, etc. PIP5K1P1, PTTG3P, and SNORD15B were determined by LASSO-Cox. The prognostic prediction of the three-gene model was established. The Cox regression analysis showed that the comprehensive risk score for three genes was an independent prognosis factor. Conclusion: PIP5K1P1, PTTG3P, and SNORD15B are related to the prognosis and overall survival of patients. The three-gene risk model constructed has independent prognosis predictive ability for STAD.

Depth of response and treatment outcomes of immune checkpoint inhibitor-based therapy in patients with advanced non-small cell lung cancer and high PD-L1 expression: An exploratory analysis of retrospective multicenter cohort.

The association between depth of response (DpR) and treatment outcomes has been documented across various types of cancer. Immune checkpoint inhibitor (ICI)-based treatment is globally used as first-line treatment for non-small cell lung cancer (NSCLC) with programmed death-ligand 1 (PD-L1) expression ≥ 50%. However, in this population, the significance of DpR is not elucidated. Patients with advanced NSCLC and PD-L1 expression ≥ 50% who received ICI-monotherapy or ICI plus chemotherapy were retrospectively enrolled into this study. Treatment responses were grouped into DpR 'quartiles' by percentage of maximal tumor reduction (Q1 = 1-25%, Q2 = 26-50%, Q3 = 51-75%, and Q4 = ≥ 76%), and no tumor reduction (NTR). The association between DpR and survival rates were determined using hazard ratios (HR) generated by the Cox proportional hazards model. The Kaplan-Meier method was used to determine survival outcomes. A total of 349 patients were included, of which 214 and 135 patients received pembrolizumab monotherapy and ICI plus chemotherapy, respectively, as first-line treatments. The majority of the patients were male. All DpR quartiles, especially Q4, showed an association with progression-free survival (PFS)/overall survival (OS). In the Q4 cohort, patients who received pembrolizumab had a longer PFS than those who received ICI plus chemotherapy. High DpR was associated with longer PFS and OS, with a more pronounced effect observed with pembrolizumab monotherapy than with ICI plus chemotherapy.