Identification of immunogenic cell death-associated subtypes and characterization of the tumor microenvironment in endometrial cancer.

Immunogenic cell death (ICD) is one of the mechanisms regulating cell death, which activates adaptive immunity in immunocompetent hosts and is associated with tumor progression, prognosis and therapeutic response. Endometrial cancer (EC) is one of the most common malignancies of the female genital tract, and the potential role of immunogenic cell death-related genes (IRGs) in the tumor microenvironment (TME) remains unclear. We describe the variation of IRGs and assess the expression patterns in EC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. Based on the expression of 34 IRGs, we identified two different ICD-related clusters and subsequently differentially expressed genes between the two ICD-related clusters were used for the identification of two ICD gene clusters. We identified the clusters and found that alterations in the multilayer IRG were associated with patient prognosis and TME cell infiltration characteristics. On this basis, ICD score risk scores were calculated, and ICD signatures were constructed and validated for their predictive power in EC patients. To help clinicians better apply the ICD signature, an accurate nomogram was constructed. The low ICD risk group was characterized by high microsatellite instability, high tumor mutational load, high IPS score and stronger immune activation. Our comprehensive analysis of IRGs in EC patients suggested a potential role in the tumor immune interstitial microenvironment, clinicopathological features and prognosis. These findings may improve our understanding of the role of ICDs, and provide a new basis for assessing prognosis and developing more effective immunotherapeutic strategies in EC.

Association between weight-adjusted-waist index and chronic kidney disease: a cross-sectional study.

AIMS: We aimed to investigate the potential association between weight-adjusted-waist index (WWI) and chronic kidney disease (CKD). DESIGN AND METHODS: This research examined data collected from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. CKD was defined as the low estimated glomerular filtration rate (eGFR) or the existence of albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30mg/g). Low-eGFR was described as eGFR < 60 mL/min/1.73m2. The associations between WWI with CKD, albuminuria, and low-eGFR were examined using generalized additive models and weighted multivariable logistic regression models. We also analyzed the associations of other obesity indicators with CKD, albuminuria, and low-eGFR, including body mass index (BMI), waist-to-height ratio (WHtR), waist circumference(WC), height, and weight. The receiver operating characteristic (ROC) curves were used to assess and compare their diagnostic abilities. RESULTS: Males made up 48.26% of the total 40,421 individuals that were recruited. The prevalences of CKD, albuminuria, and low-eGFR were 16.71%, 10.97%, and 7.63%, respectively. WWI was found to be positively linked with CKD (OR = 1.42; 95% CI: 1.26, 1.60). A nonlinear connection between WWI and CKD was found using smooth curve fitting. Additionally, a higher prevalence of albuminuria is linked to a higher level of WWI (OR = 1.60; 95% CI: 1.40, 1.82). Different stratifications did not substantially influence the connection between WWI and CKD, albuminuria, and low-eGFR, according to subgroup analysis and interaction tests. We observed higher height was related to higher low-eGFR prevalence (OR = 1.05; 95% CI: 1.03, 1.06). ROC analysis revealed that WWI had the best discrimination and accuracy for predicting CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height and weight). In addition, height had the highest area under the curve (AUC) value for predicting low-eGFR. CONCLUSION: WWI is the best obesity indicator to predict CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height, and weight). WWI and CKD and albuminuria were found to be positively correlated. Furthermore, height had the strongest ability to predict low-eGFR. Therefore, the importance of WWI and height in assessing kidney health in US adults should be emphasized.

Association between triglyceride-glucose index and chronic kidney disease: results from NHANES 1999-2020.

AIMS: Examining the connection between the triglyceride-glucose (TyG) index and chronic kidney disease (CKD) was the aim of this investigation. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) covering the years 1999-2020 were analyzed in this study. The TyG index was calculated as Ln (triglycerides (mg/dl) * fasting glucose (mg/dl)/2). The two criteria used to diagnose CKD were low estimated glomerular filtration rate (eGFR) (eGFR < 60 mL/min/1.73m2) or albuminuria (urine albumin-to-creatinine ratio (ACR) ≥ 30 mg/g). To look into the independent associations between TyG index levels with CKD, albuminuria, and low-eGFR, weighted multivariable logistic regression and generalized additive models were employed. To assess and contrast the diagnostic ability, receiver operating characteristic (ROC) curves were employed. RESULTS: Out of 18,078 total participants recruited, 48.54% were male. 8.48 + 0.68 was the mean value of the TyG index. CKD, albuminuria, and low-eGFR were common, with respective prevalences of 17.06%, 11.26%, and 8.03%, respectively. The TyG index and CKD were observed to positively correlate (OR = 4.03; 95% CI 1.81, 8.96). In US adults between the ages of 41 and 60, a J-shaped connection was found between the two. Furthermore, a higher TyG index is associated with a higher prevalence of albuminuria (OR = 6.11; 95% CI 2.64, 14.14). Subgroup analyses and interaction tests revealed that different stratifications did not significantly affect the relationship between TyG index and CKD, albuminuria, and low-eGFR. Comparing the TyG index to other indicators [lipid accumulation product (LAP), Visceral adiposity index (VAI), and the triglyceride glucose-body mass index (TyG-BMI)], it may be more accurate and discriminative in predicting CKD and albuminuria. CONCLUSION: When predicting CKD and albuminuria, the TyG index may be a more useful marker when compared to other markers (LAP, VAI, and TyG-BMI index). In addition, in American adults aged 41-60, the TyG index shows a J-shaped relationship with CKD. As a result, when assessing the kidney health of US adults, we must pay close attention to the significance of the TyG index.

Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study.

Introduction: Monocyte-to-lymphocyte ratio (MLR) is a convenient and noninvasive inflammatory biomarker, and inflammation has been reported to be associated with prostate cancer (PCa). Our objective was to ascertain any possible correlation between PCa and MLR. Methods: We utilized data from the 1999-2020 cycles of the National Health and Nutrition Examination Survey (NHANES) regarding MLR and PCa. The independent associations of MLR and other inflammatory biomarkers (platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)) with PCa was investigated using weighted multivariate logistic regression and generalized additive models. Receiver operating characteristic (ROC) curves were conducted to evaluate and contrast their diagnostic capabilities. Results: The analysis we conducted comprised 25,367 persons in total. The mean MLR was 0.31 ± 0.14. The prevalence of PCa was 3.1%. A positive association was found between MLR and PCa (OR = 2.28; 95% CI: 1.44, 3.62). According to the interaction tests, age, body mass index (BMI), hypertension, diabetes, and smoking status did not significantly impact the relationship between MLR and PCa (all p for interaction >0.05). ROC analysis showed that MLR had a stronger discriminative ability and accuracy in predicting PCa than other inflammatory biomarkers (NLR, SII, AISI, PLR, and SIRI). Conclusion: MLR might be better than other inflammatory biomarkers (NLR, SIRI, AISI, PLR, and SII) in predicting PCa. American adults who have elevated levels of MLR, NLR, PLR, SII, and AISI should be aware that they have a greater risk of PCa.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

Aims: This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Methods: Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves. Results: 44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR. Conclusion: Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.

Background: Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response. Methods: The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts. Results: In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy. Conclusion: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis.

Background: Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment. Methods: Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models. Results: In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection. Conclusion: The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.

INTRODUCTION: Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. METHODS: This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. RESULTS: Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. CONCLUSION: The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.

Co-implantation of magnesium and zinc ions into titanium regulates the behaviors of human gingival fibroblasts.

Soft tissue sealing around implants acts as a barrier between the alveolar bone and oral environment, protecting implants from the invasion of bacteria or external stimuli. In this work, magnesium (Mg) and zinc (Zn) are introduced into titanium by plasma immersed ion implantation technology, and their effects on the behaviors of human gingival fibroblasts (HGFs) as well as the underlying mechanisms are investigated. Surface characterization confirms Mg and Zn exist on the surface in metallic and oxidized states. Contact angle test suggests that surface wettability of titanium changes after ion implantation and thus influences protein adsorption of surfaces. In vitro studies disclose that HGFs on Mg ion-implanted samples exhibit better adhesion and migration while cells on Zn ion-implanted samples have higher proliferation rate and amounts. The results of immunofluorescence staining and real-time reverse-transcriptase polymerase chain reaction (RT-PCR) suggest that Mg mainly regulates the motility and adhesion of HGFs through activating the MAPK signal pathway whereas Zn influences HGFs proliferation by triggering the TGF-ß signal pathway. The synergistic effect of Mg and Zn ions ensure that HGFs cultured on co-implanted samples possessed both high proliferation rate and motility, which are critical to soft tissue sealing of implants.

ZnO@ZnS nanorod-array coated titanium: Good to fibroblasts but bad to bacteria.

Cell-selective toxic titanium is highly desired in clinical dental practice. Herein, based on the in situ conversion of ZnO to ZnO@ZnS, nanorod-array structured coatings with a controllable release features of zinc (Zn), has been successfully fabricated by a two-step hydrothermal method to endow titanium surface with cell-selectivity, i.e. boosting the functions (attachment and migration) of human gingival fibroblasts (HGnFs) while acting against the invasion of pathogenic bacteria. The improved functions of HGnFs over the ZnO@ZnS nanorod-array were attributed to the material's optimized zinc release, which was decreased from an order of 3.5 mg L-1 to about 0.3 mg L-1 (within the first week). But more importantly, this concentration still had a high antibacterial efficacy up to 100% (against both the S. aureus and E. coli, 107 CFU mL-1). This study demonstrated that a ZnO@ZnS nanorod-array coating could be a promising strategy to endow titanium dental implants with improved soft tissue sealing and effectively reduce peri-implantitis.

Functional phosphoproteomic profiling of phosphorylation sites in membrane fractions of salt-stressed Arabidopsis thaliana.

BACKGROUND: Under conditions of salt stress, plants respond by initiating phosphorylation cascades. Many key phosphorylation events occur at the membrane. However, to date only limited sites have been identified that are phosphorylated in response to salt stress in plants. RESULTS: Membrane fractions from three-day and 200 mM salt-treated Arabidopsis suspension plants were isolated, followed by protease shaving and enrichment using Zirconium ion-charged magnetic beads, and tandem mass spectrometry analyses. From this isolation, 18 phosphorylation sites from 15 Arabidopsis proteins were identified. A unique phosphorylation site in 14-3-3-interacting protein AHA1 was predominately identified in 200 mM salt-treated plants. We also identified some phosphorylation sites in aquaporins. A doubly phosphorylated peptide of PIP2;1 as well as a phosphopeptide containing a single phosphorylation site (Ser-283) and a phosphopeptide containing another site (Ser-286) of aquaporin PIP2;4 were identified respectively. These two sites appeared to be novel of which were not reported before. In addition, quantitative analyses of protein phosphorylation with either label-free or stable-isotope labeling were also employed in this study. The results indicated that level of phosphopeptides on five membrane proteins such as AHA1, STP1, Patellin-2, probable inactive receptor kinase (At3g02880), and probable purine permease 18 showed at least two-fold increase in comparison to control in response to 200 mM salt-stress. CONCLUSION: In this study, we successfully identified novel salt stress-responsive protein phosphorylation sites from membrane isolates of abiotic-stressed plants by membrane shaving followed by Zr4+-IMAC enrichment. The identified phosphorylation sites can be important in the salt stress response in plants.

Regulating the Behavior of Human Gingival Fibroblasts by sp2 Domains in Reduced Graphene Oxide.

Long-term function of dental implants relies on not only stable osseointegration but also strong soft tissue-sealing ability. Ideal soft tissue sealing around implants is an effective protective barrier between the external environment and alveolar bone, preventing the invasion of bacteria that is considered as a vital trigger of irreversible marginal bone loss. Carbon-based materials have been reported to be beneficial to soft tissue sealing, which can be regulated through the hybridization type of carbon atoms (sp2 or sp3), but its internal mechanism is still not clear. In this work, graphene oxide with both sp2- and sp3-hybridized carbons was electrophoretic deposited on titanium and reduced to regulate the hybridization type of carbon atoms to investigate its effect and possible mechanism on human gingival fibroblasts (HGFs). X-ray photoelectron spectroscopy and Raman mapping test show the increase of sp2 domain content and the decrease of their size after reduction. Through computer simulation, the possible mechanism of the decrease of sp2 domain size was proposed. In vitro studies disclose that the HGFs exhibit higher proliferation rate, better adhesion, and migration ability with the increase of sp2 domains and the decrease of their sizes. It may be due to the amount and size of sp2 domains that synergistically regulate the amount and properties of adsorbed proteins, thereby influencing the cellular behaviors of HGFs. Our results may offer a different perspective on material designing and academic research to enhance the soft tissue integration of implants.

Comparative phosphoproteomic analysis of microsomal fractions of Arabidopsis thaliana and Oryza sativa subjected to high salinity.

Plants respond to salt stress by initiating phosphorylation cascades in their cells. Many key phosphorylation events take place at membranes. Microsomal fractions from 400 mM salt-treated Arabidopsis suspension plants were isolated, followed by trypsin shaving, enrichment using Zirconium ion-charged or TiO(2) magnetic beads, and tandem mass spectrometry analyses for site mapping. A total of 27 phosphorylation sites from 20 Arabidopsis proteins including photosystem II reaction center protein H PsbH were identified. In addition to Arabidopsis, microsomal fractions from shoots of 200 mM salt-treated rice was carried out, followed by trypsin digestion using shaving or tube-gel, and enrichment using Zirconium ion-charged or TiO(2) magnetic beads. This yielded identification of 13 phosphorylation sites from 8 proteins including photosystem II reaction center protein H PsbH. Label-free quantitative analysis suggests that the phosphorylation sites of PsbH were regulated by salt stress in Arabidopsis and rice. Sequence alignment of PsbH phosphorylation sites indicates that Thr-2 and Thr-4 are evolutionarily conserved in plants. Four conserved phosphorylation motifs were predicted, and these suggest that a specific unknown kinase or phosphatase is involved in high-salt stress responses in plants.

Flow-through sampling for electrophoresis-based microchips and their applications for protein analysis.

This work presents a model behind the operation of a flow-through sampling chip and its application for immunoseparation, as well as its integration with a wash/elution bed for protein purification, concentration, and detection. This device used hydrodynamic pressure to drive the sample flow, and a gating voltage was applied to the electrophoretic channel on the microchip to control the sample loading for the separation and to inhibit sample leakage. The deduced model indicates that the critical gating voltage (VC) that is defined as the minimum gating voltage applied to the microchip for sampling is a function of the pump flow rate, the configuration of the microchannel on the chip, and the electroosmosis of the buffer solution. It was found that the theoretical V(C) values calculated from the measured electroosmotic mobilities and flow split ratios were comparable to those experimentally obtained from two microchips with different sampling channel sizes. This had an error percentage ranging from 1 to 20%. Because the hydrodynamic flow is insensitive to electrophoretic mobility, this electrophoresis-based microchip device was free of injection bias due to different ionic strength and electrophoretic mobility in the sample. Additionally, the usefulness of this device was demonstrated for the study of affinity interactions. Mixtures of Cy5-labeled bovine serum albumin (Cy5-BSA) and anti-BSA in various proportions were introduced into the microchip via a syringe pump, and the immunocomplex was electrophoretically separated from the free Cy5-BSA on the microchip. Based on the relative intensity of the free and complex BSA, the binding constant of BSA and anti-BSA was estimated as 3.3 x 10(7) M(-1). Furthermore, a C18 microcartridge (20 microL) was connected to the hydrodynamic inlet of the microchip. Using this device, the wash/elution step can be integrated on-line with the electrophoretic separation and detection on the microchip. Results show that the calibration curve of Cy5-BSA obtained from this integrated device has an R2 value greater than 0.99 and a minimum of quantitation at approximately 10 ng. This direct sampling method is another means of subfractionation, resulting in a relatively greater concentration factor than the average concentration of the whole fraction. Moreover, the electrical field-free bed ensures that the protein interaction will not be affected by the electric field during the wash/elution step.