A novel nomogram based on inflammation biomarkers for predicting radiation cystitis in patients with local advanced cervical cancer.

BACKGROUNDS: Platelet-to-albumin ratio (PAR) is a new systemic inflammatory prognostic indicator associated with many inflammatory diseases. However, its role in radiation cystitis (RC) is obscure. This study aimed to explore whether PAR could be used as an effective parameter for predicting the RC risk in local advanced cervical cancer (CC) treated with radiotherapy. METHODS: A total of 319 local advanced CC patients who received radical radiotherapy at Fujian Cancer Hospital were enrolled between December 2018 and January 2021. Demographics and clinical parameters were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for RC. Backward and stepwise regression was applied to construct two monograms-one with primary significant factors and the other with extra inflammatory biomarkers. A DeLong test was applied to compare the prediction abilities of two nomograms. Calibration curves and decision curve analysis (DCA) evaluated its prediction consistency, discrimination ability, and clinical net benefit. RESULTS: Univariate analysis showed that age, tumor size, stage, total radiation dose, pelvic radiation dose, Systemic Immune-Inflammation Index (SII), platelet-to-lymphocyte ratio (PLR), and PAR were significantly associated with RC occurrence (all p < 0.05). Multivariate analyses indicated that age, tumor size, stage, total radiation dose, and PAR were independent factors (all p < 0.05). Then, the area under curve (AUC) value of the nomogramSII+PAR was higher (AUC = 0.774) compared to that of the baseline nomogram (AUC = 0.726) (pDelong = 0.02). Also, the five-cross validation confirmed the stability of the nomogramSII+PAR. Moreover, the calibration curve and DCA exhibited the nomograms' good prediction consistency and clinical practicability. CONCLUSIONS: PAR and SII could be valued for CC patients who are treated with radiation therapy. The nomogram based on PAR and SII could stratify patients who need extra intervention and nursing care to prevent bladder radiation damage and improve patients' quality of life.

CT radiomics analysis discriminates pulmonary lesions in patients with pulmonary MALT lymphoma and non-pulmonary MALT lymphoma.

PURPOSE: The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes. METHODS: Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile, individuals diagnosed with primary pulmonary MALT lymphoma and lung infections at Fujian Provincial Cancer Hospital were chosen as the external test group. We employed ITK-SNAP software for delineating the Region of Interest (ROI) within the images. Subsequently, we extracted radiomics features and convolutional neural networks using PyRadiomics, a component of the Onekey AI software suite. Relevant radiomic features were selected to build an intelligent diagnostic prediction model utilizing CT images, and the model's efficacy was assessed in both the validation group and the external test group. RESULTS: Leveraging radiomics, ten distinct features were carefully chosen for analysis. Subsequently, this study employed the machine learning techniques of Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) to construct models using these ten selected radiomics features within the training groups. Among these, SVM exhibited the highest performance, achieving an accuracy of 0.868, 0.870, and 0.90 on the training, validation, and external testing groups, respectively. For LR, the accuracy was 0.837, 0.863, and 0.90 on the training, validation, and external testing groups, respectively. For KNN, the accuracy was 0.884, 0.859, and 0.790 on the training, validation, and external testing groups, respectively. CONCLUSION: We established a noninvasive radiomics model utilizing CT imaging to diagnose pulmonary MALT lymphoma associated with pulmonary lesions. This model presents a promising adjunct tool to enhance diagnostic specificity for pulmonary MALT lymphoma, particularly in populations where pulmonary lesion changes may be attributed to other causes.

Pathogen species are the risk factors for postoperative infection of patients with transurethral resection of the prostate: a retrospective study.

This study aimed to analyze the infection risk factors for transurethral resection of the prostate (TURP) and establish predictive models to help make personalized treatment plans. Our study was designed one-center and retrospectively enrolled 1169 benign prostatic hyperplasia (BPH) patients. Risk factors were explored for postoperative infection. A TURP-postoperative infection (TURP-PI) model with infection prediction values was created. The improved-TURP-PI (I-TURP-PI) model, including extra new factors (pathogens species), was also built to see whether it could optimize the prediction abilities. At last, we developed a nomogram for better clinical application. Operation time, preoperative indwelling urinary catheter (PIUC), and positive preoperative urine culture were independent risk factors (all P < 0.05). Interestingly, pathogens species in pre-surgery urine (PEnterococcus faecium = 0.014, PPseudomonas aeruginosa = 0.086) were also independent risk factors. Patients with positive Enterococcus faecium (37.50%) were most likely to have postoperative infection. We built two models with AUCTURP-PI = 0.709 (95% CI 0.656-0.763) and AUCI-TURP-PI = 0.705 (95% CI 0.650-0.760). The nomogram could help improve the prediction ability. To our knowledge, our study is the first to use pathogen species in urine before surgery as risk factors for infection prediction after TURP. TURP-PI and I-TURP-PI models have essential roles in predicting patients' postoperative infections and in better postoperative antibiotic decision-making.

Development and validation of a model based on immunogenic cell death related genes to predict the prognosis and immune response to bladder urothelial carcinoma.

Background: Immunogenic cell death (ICD) has been categorized as a variant of regulated cell death that is capable of inducing an adaptive immune response. A growing body of evidence has indicated that ICD can modify the tumor immune microenvironment by releasing danger signals or damage-associated molecular patterns (DAMPs), potentially enhancing the efficacy of immunotherapy. Consequently, the identification of biomarkers associated with ICD that can classify patients based on their potential response to ICD immunotherapy would be highly advantageous. Therefore the goal of the study is to better understand and identify what patients with bladder urothelial carcinoma (BLCA) will respond to immunotherapy by analyzing ICD signatures and investigate ICD-related prognostic factors in the context of BLCA. Methods: The data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples was categorized based on ICD-related genes (IRGs). Specifically, we conducted an immunohistochemical (IHC) experiment to validate the expression levels of Calreticulin (CALR) in both tumor and adjacent tissues, and evaluated its prognostic significance using the Kaplan-Meier (KM) curve. Subsequently, the samples from TCGA were divided into two subtypes using consensus clustering. To obtain a more comprehensive comprehension of the biological functions, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The calculation of immune landscape between two subtypes was performed through ESTIMATE and CIBERSORT. Risk models were constructed using Cox and Lasso regression and their prognosis predictive ability was evaluated using nomogram, receiver operating characteristic (ROC), and calibration curves. Finally, Tumor Immune Dysfunction and Exclusion (TIDE) algorithms was utilized to predict the response to immunotherapy. Results: A total of 34 IRGs were identified, with most of them exhibiting upregulation in BLCA samples. The expression of CALR was notably higher in BLCA compared to the adjacent tissue, and this increase was associated with an unfavorable prognosis. The differentially expressed genes (DEGs) associated with ICD were linked to various immune-related pathways. The ICD-high subtypes exhibited an immune-activated tumor microenvironment (TME) compared to the ICD-low subtypes. Utilizing three IRGs including CALR, IFNB1, and IFNG, a risk model was developed to categorize BLCA patients into high- and low-risk groups. The overall survival (OS) was considerably greater in the low-risk group compared to the high-risk group, as evidenced by both the TCGA and GEO cohorts. The risk score was identified as an independent prognostic parameter (all p < 0.001). Our model demonstrated good predictive ability (The area under the ROC curve (AUC), AUC1-year= 0.632, AUC3-year= 0.637, and AUC5-year =0.653). Ultimately, the lower risk score was associated with a more responsive immunotherapy group. Conclusion: The potential of the ICD-based risk signature to function as a marker for evaluating the prognosis and immune landscape in BLCA suggests its usefulness in identifying the suitable population for effective immunotherapy against BLCA.

Effect of Mechanical Microenvironment on Collagen Self-Assembly In Vitro.

Collagen, as a structural protein, is widely distributed in the human body. Many factors influence collagen self-assembly in vitro, including physical-chemical conditions and mechanical microenvironment, and play a key role in driving the structure and arrangement. However, the exact mechanism is unknown. The purpose of this paper is to investigate the changes in the structure and morphology of collagen self-assembly in vitro under mechanical microenvironment, as well as the critical role of hyaluronic acid in this process. Using bovine type I collagen as the research object, collagen solution is loaded into tensile and stress-strain gradient devices. The morphology and distribution of collagen is observed using an atomic force microscope while changing the concentration of collagen solution, mechanical loading strength, tensile speed, and ratio of collagen to hyaluronic acid. The results demonstrate that the mechanics field governs collagen fibers and changes their orientation. Stress magnifies the differences in results caused by different stress concentrations and sizes, and hyaluronic acid improves collagen fiber orientation. This research is critical for expanding the use of collagen-based biomaterials in tissue engineering.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining.

BACKGROUND: Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications. METHODS: We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes. RESULTS: We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC1-year = 0.714, ROC3-year = 0.750, ROC5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response. CONCLUSION: The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

Potential Diagnostic and Prognostic Values of CBX8 Expression in Liver Hepatocellular Carcinoma, Kidney Renal Clear Cell Carcinoma, and Ovarian Cancer: A Study Based on TCGA Data Mining.

Background: Chromobox protein homolog 8 (CBX8), a transcriptional repressor, participates in many biological processes in various carcinomas. Cell differentiation, aging, and cell cycle progression are examples of such processes. It is critical to investigate CBX8 expression and its relationship with clinicopathological characteristics in liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), and ovarian cancer (OV) to investigate CBX8's potential diagnostic and prognostic values. Methods: TCGA and CPTAC databases were used to compare the data between cancer and matched normal tissues on RNA and protein expression profiles and their relevant clinical information to determine the relationship between CBX8 and clinicopathological features. Kaplan-Meier analyses were used to assess CBX8 relationship's with disease-free survival (DFS), relapse-free survival (RFS), and overall survival (OS). The multivariate Cox regression analysis was used to identify independent risk factors which affect prognosis. DNA methylation and genetic changes and their impact on prognoses were evaluated by cBioPortal and MethSurv websites. Spearman's correlation was used to determine the relationship of CBX8 expression with somatic mutation. Tumor immune estimation resource (TIMER) was adopted to investigate the relationship between CBX8 and immune cell infiltration (ICI). CBX8-relevant genes and proteins are analyzed by EnhancedVolcano and STRING databases. The gene set enrichment analysis (GSEA) was performed to investigate the potential functions of CBX8. Results: CBX8 RNA and protein overexpression were confirmed in LIHC, KIRC, and OV (p < 0.05). High CBX8 was significantly related to the clinical features and poor prognoses. The CBX8 genetic alteration rate was 3%. DNA methylation was also associated with prognoses. CBX8 closely interacted with ICI, TMB, MSI, purity, and ploidy. GO analyses revealed that CBX8-associated genes were enriched in biological processes, cell cycle regulation, and molecular functions. KEGG analyses exhibited that CBX8 was gathered in signaling pathway regulation. GSEA revealed that cell cycle, DNA replication, and Wnt signaling pathways were differentially enriched in the high CBX8 expression group. Conclusions: CBX8 could be a potential diagnostic and prognostic biomarker for LIHC, KIRC, and OV cancers.

A Novel Necroptosis-Related lncRNA Signature for Osteosarcoma.

Backgrounds: Osteosarcoma (OS) is easy to metastasis. Necroptosis-related long noncoding RNA (lncRNA) (NRlncRNA) plays a vital role in the tumorigenesis of many malignant tumors. Nonetheless, there have been few studies investigating the relations between NRlncRNA and OS. During the investigation, NRlncRNAs in OS were confirmed and characterized and their relationships with prognoses were investigated. Methods: NRlncRNAs were downloaded from The Cancer Genome Atlas (TCGA) OS expression data and clinical-pathological information. First, univariate Cox regression and LASSO regression analyses were used to screen for prognostic-related NRlncRNAs. Second, multivariate regression analyses were used to establish a prognostic nomogram for predicting individual survival probability. Survival analyses demonstrated that high-risk patients (HRPs) had a poor prognosis. In addition, gene set enrichment analyses (GSEA) were used to identify gene function in high- and low-risk groups based on the survival mode. Results: The 7 NRlncRNAs (AC004812.2, AC022915.1, AC073073.2, AC090559.1, AL512330.1, DDN-AS1, and SENCR) were shown to have a distinct difference and were used to construct an NRlncRNA signature. Using the signature as a risk score was an independent factor for OS patients. The signature divided OS patients into the high- and low-risk groups. Furthermore, the seven lncRNAs were significantly enriched in cell migration and metabolism. Conclusions: The 7 NRlncRNA survival models have the potential to serve as therapeutic targets and molecular biomarkers for patients with OS, as well as to precisely predict OS prognoses.

The effect of preoperative urine culture and bacterial species on infection after percutaneous nephrolithotomy for patients with upper urinary tract stones.

To study the relationship between preoperative urine culture, bacterial species and infection after percutaneous nephrolithotomy in patients with upper urinary tract stones, and summarize the clinical characteristics of different bacterial infections. From January 2014 and January 2020, 963 patients with upper urinary tract stones who underwent PCNL in the department of urology of Fujian provincial hospital were included in the study. Information included the patient's age, gender, weight, diabetes, chronic disease history, urine routine, preoperative urine culture results, stone size, number of stones, hydronephrosis level, operation time, body temperature, heart rate, blood pressure, breathing rate, hemoglobin, serum creatinine, bilirubin, platelets and whether there was preoperative infection were recorded. 141 patients (14.6%) had a positive urine culture before surgery, and 7 of them had multiple bacterial infections. The most common pathogenic bacteria was Escherichia coli, followed by Enterococcus and Klebsiella pneumoniae. A total of 74 cases (7.7%) of 963 patients with infection after PCNL occurred, 24 cases (32.4%) of infected patients progressed to urinary septic shock. Univariate analysis shown that the probability of infection in patients with long operation time and positive urine culture was significantly higher, and the difference was statistically significant. Further multivariate logistic regression analysis shown that positive urine culture before operation and long operation time were independent risk factors for infection after PCNL. Among the 29 patients with septic shock, 18 cases (62.1%) had a positive urine culture before surgery. The incidence (43.9%) of postoperative infection in Escherichia coli positive patients was significantly higher than that in the negative group, and the difference was statistically significant. The rate of patients with Escherichia coli infection progressing to septic shock was 9 cases (60%). 2 patients with Enterococcus faecium infection and 2 patients with Klebsiella pneumoniae infection all progressed to septic shock. The age of patients with post-PCNL infection caused by Escherichia Coli, Enterococcus faecium and Klebsiella pneumoniae were 58.53 ± 11.73 years, 76.5 years and 74 years.The body temperature of patients with post-PCNL infection caused by Escherichia Coli, Enterococcus faecium and Klebsiella pneumoniae were 39.10 ± 0.25 °C, 39.45 °C and 38.65 °C. The highest pct value of patients with post-PCNL infection caused by Escherichia Coli, Enterococcus faecium and Klebsiella pneumoniae were 80.62 ± 31.45 ng/mL, 24.32 ng/mL and 8.45 ng/mL. The nitrite positive rate of patients with post-PCNL infection caused by Escherichia Coli, Enterococcus faecium and Klebsiella pneumoniae were 64.51%, 16.6% and 0. Postoperative infection of PCNL is significantly correlated with positive preoperative urine culture, and positive preoperative urine culture is an independent risk factor for postoperative infection. The most common pathogen of postoperative infection of PCNL is Escherichia coli, followed by Enterococcus and Klebsiella pneumoniae. Patients with Escherichia coli infection are often positive for nitrite before surgery, mainly manifested by high fever, and PCT is significantly increased (often exceeded 100 ng/ml). Enterococcus faecium and Klebsiella pneumoniae infections mostly occur in elderly patients and often progress to septic shock. Patients with Enterococcus faecium infection have a high fever, and the PCT value is significantly higher (often exceeded 20 ng/ml). Patients with Klebsiella pneumoniae infection have a moderate fever, and the PCT value generally does not exceeded 10 ng/ml. Long operation time is another independent risk factor for PCNL infection.

A new perspective: Periodontal ligament is a viscoelastic fluid biomaterial as evidenced by dynamic shear creep experiment.

Currently, Periodontal ligament (PDL) is considered as a viscoelastic solid biomaterial. However, we observed the steady-state rheological behavior of PDL through long time loading experiments, and suggested the theoretical definition of PDL as a viscoelastic fluid biomaterial. PDL specimens were prepared from the middle area of the mandibular central incisors in pigs. Dynamic force loading with frequencies of 0 (static load), 2, 5, and 10 Hz and amplitudes of 0.01, 0.02, and 0.03 MPa was adopted. The shear strain-time curve at the equilibrium position of PDL was obtained by a dynamic shear creep experiment. The results showed that the shear strain increased exponentially at first and then inclined toward an oblique line. The results showed that the PDL has viscoelastic fluid characteristics, independent of frequency and amplitude. The shear strain decreased with an increase in frequency and amplitude. To further analyze the viscoelastic characteristics of PDL, a 50000-s static shear creep experiment was re-designed. PDL exhibited viscoelastic fluid biomaterial characteristics according to the three aspects of the algebraic fitting, geometric characteristics, and physical results. For the first time, a viscoelastic fluid constitutive model was established to characterize the mechanical properties of PDL with high fitting accuracy. Furthermore, the shear viscosity coefficient of the dynamic load was larger than that of the static load, increasing with an increase in frequency and amplitude; compared with the static force, the dynamic force improved the viscosity of PDL, enhancing its function of fixing teeth, and introducing the new medical knowledge of "No tooth extraction after a meal."

Mechanical Roles in Formation of Oriented Collagen Fibers.

Collagen is a structural protein that is widely present in vertebrates, being usually distributed in tissues in the form of fibers. In living organisms, fibers are organized in different orientations in various tissues. As the structural base in connective tissue and load-bearing tissue, the orientation of collagen fibers plays an extremely important role in the mechanical properties and physiological and biochemical functions. The study on mechanics role in formation of oriented collagen fibers enables us to understand how discrete cells use limited molecular materials to create tissues with different structures, thereby promoting our understanding of the mechanism of tissue formation from scratch, from invisible to tangible. However, the current understanding of the mechanism of fiber orientation is still insufficient. In addition, existing fabrication methods of oriented fibers are varied and involve interdisciplinary study, and the achievements of each experiment are favorable to the construction and improvement of the fiber orientation theory. To this end, this review focuses on the preparation methods of oriented fibers and proposes a model explaining the formation process of oriented fibers in tendons based on the existing fiber theory. Impact statement As the structural base in connective tissue and load-bearing tissue, the orientation of collagen fibers plays an extremely important role in the mechanical properties and physiological and biochemical functions. However, the current understanding of the mechanism of fiber orientation is still insufficient, which is greatly responsible for the challenge of functional tissue repair and regeneration. Understanding the mechanism of fiber orientation can promote the successful application of fiber orientation scaffolds in tissue repair and regeneration, as well as providing an insight for the mechanism of tissue histomorphology.