Prognostic analysis of rectal cancer patients after neoadjuvant chemoradiotherapy: different prognostic factors in patients with different TRGs.

PURPOSE: The extent of tumor regression varies widely among locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME). The purpose of this retrospectively study is to assess prognostic factors in LARC patients with NCRT, and further to analyze survival outcomes in patients with different tumor regression grades (TRGs). METHODS: This study includes LARC patients who underwent NCRT and TME at our institution. We retrospectively analyzed the clinicopathological characteristics and survival of all patients, and performed subgroup analysis for patients with different TRGs. Survival differences were compared using the Kaplan-Meier method and the log rank test. Additionally, a multiple Cox proportional hazard model was used to identify independent prognostic factors. RESULTS: The study included 393 patients, with 21.1%, 26.5%, 45.5%, and 6.9% achieving TRG 0, TRG 1, TRG 2, and TRG 3, respectively. The overall survival (OS) rate and disease-free survival (DFS) rate for all patients were 89.4% and 70.7%, respectively. Patients who achieved TRG 0-3 had different 5-year OS rates (96.9%, 91.1%, 85.2%, and 68.8%, P = 0.001) and 5-year DFS rates (80.8%, 72.4%, 67.0%, 55.8%, P = 0.031), respectively. Multivariate analyses showed that the neoadjuvant rectal (NAR) score was an independent prognostic indicator for both overall survival (OS) (HR = 4.040, 95% CI = 1.792-9.111, P = 0.001) and disease-free survival (DFS) (HR = 1.971, 95% CI = 1.478-2.628, P ˂ 0.001). In the subgroup analyses, the NAR score was found to be associated with DFS in patients with TRG 1 and TRG 2. After conducting multivariate analysis, it was found that ypT stage was a significant predictor of DFS for TRG 1 patients (HR = 4.384, 95% CI = 1.721-11.168, P = 0.002). On the other hand, ypN stage was identified as the dominant prognostic indicator of DFS for TRG 2 patients (HR = 2.795, 95% CI = 1.535-5.091, P = 0.001). However, none of these characteristics was found to be correlated with survival in patients with TRG 0 or TRG 3. CONCLUSION: NAR score, in particular, appears to be the most powerful prognostic factor. It is important to consider various prognostic predictors for patients with different TRGs.

Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy.

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

The intervention of cannabinoid receptor in chronic and acute kidney disease animal models: a systematic review and meta-analysis.

AIM: Cannabinoid receptors are components of the endocannabinoid system that affect various physiological functions. We aim to investigate the effect of cannabinoid receptor modulation on kidney disease. METHODS: PubMed, Web of Science databases, and EMBASE were searched. Articles selection, data extraction and quality assessment were independently performed by two investigators. The SYRCLE's RoB tool was used to assess the risk of study bias, and pooled SMD using a random-effect model and 95% CIs were calculated. Subgroup analyses were conducted in preselected subgroups, and publication bias was evaluated. We compared the effects of CB1 and CB2 antagonists and/or knockout and agonists and/or genetic regulation on renal function, blood glucose levels, body weight, and pathological damage-related indicators in different models of chronic and acute kidney injury. RESULTS: The blockade or knockout of CB1 could significantly reduce blood urea nitrogen [SMD,- 1.67 (95% CI - 2.27 to - 1.07)], serum creatinine [SMD, - 1.88 (95% CI - 2.91 to - 0.85)], and albuminuria [SMD, - 1.60 (95% CI - 2.16 to - 1.04)] in renal dysfunction animals compared with the control group. The activation of CB2 group could significantly reduce serum creatinine [SMD, - 0.97 (95% CI - 1.83 to - 0.11)] and albuminuria [SMD, - 2.43 (95% CI - 4.63 to - 0.23)] in renal dysfunction animals compared with the control group. CONCLUSIONS: The results suggest that targeting cannabinoid receptors, particularly CB1 antagonists and CB2 agonists, can improve kidney function and reduce inflammatory responses, exerting a renal protective effect and maintaining therapeutic potential in various types of kidney disease.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

Short-term duration of diabetic retinopathy as a predictor for development of diabetic kidney disease.

Background: Diabetic retinopathy (DR) is a risk factor for diabetic kidney disease (DKD). Whether the duration, especially the short-term duration, of DR is associated with the development and progression of DKD remains unclear. Materials and Methods: A retrospective study and two-sample Mendelian randomization (MR) analysis were conducted. Kidney disease was defined by the urinary albumin-to-creatinine ratio (ACR) and the estimated glomerular filtration rate (eGFR). DR was diagnosed by an expert ophthalmologist by using a digital fundus camera. Binary and ordinal logistic regression analyses were performed. A restricted cubic spline was utilized to detect nonlinear associations. Summary statistics for DR- and DKD-associated single-nuclear polymorphisms (SNPs) were extracted from the FinnGen and the UK Biobank consortia. Results: A total of 2674 patients with type 2 diabetes mellitus (T2DM) and type 2 diabetic kidney disease (T2DKD) were included. The prevalence and mean duration of DR increased with elevation of ACR and decline in eGFR. Renal function was significantly reduced in patients with DR in the fifth year of life. Binary and ordinal logistic regression showed that each 1-year increase in DR duration was associated with a 19% risk increase in the development of DKD, 16% in the elevation of ACR, and 21% in the decline of renal function. MR estimates indicated that DR was causally associated with DKD development, with an odds ratio of 2.89. Conclusions: DR and the duration of DR were independent risk factors for the development and progression of DKD. The short-term duration of DR may be associated with DKD development. DR had a statistically significant effect on DKD.

Identification of urinary extracellular vesicles differentially expressed RNAs in diabetic nephropathy via whole-transcriptome integrated analysis.

BACKGROUND: Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes and a leading cause of chronic kidney disease worldwide. Urinary extracellular vesicles (uEVs), which are natural nanoscale vesicles that protect RNA from degradation, have the potential to serve as an invasive diagnostic biomarker for DN. METHODS: We enrolled 24 participants, including twelve with renal biopsy-proven T2DN and twelve with T2DM, and isolated uEVs using ultracentrifugation. We performed microarrays for mRNAs, lncRNAs, and circRNAs in parallel, and Next-Generation Sequencing for miRNAs. Differentially expressed RNAs (DE-RNAs) were subjected to CIBERSORTx, ssGSEA analysis, GO enrichment, PPI network analysis, and construction of the lncRNA/circRNA-miRNA-mRNA regulatory network. Candidate genes and potential biomarker RNAs were validated using databases and machine learning models. RESULTS: A total of 1684 mRNAs, 126 lncRNAs, 123 circRNAs and 66 miRNAs were found in uEVs in T2DN samples compared with T2DM. CIBERSORTx revealed the involvement of uEVs in immune activity and ssGSEA explored possible cell or tissue sources of uEVs. A ceRNA co-expression and regulation relationship network was constructed. Candidate genes MYO1C and SP100 mRNA were confirmed to be expressed in the kidney using Nephroseq database, scRNA-seq dataset, and Human Protein Atlas database. We further selected 2 circRNAs, 2 miRNAs, and 2 lncRNAs from WGCNAs and ceRNAs and demonstrated their efficacy as potential diagnostic biomarkers for T2DN using machine learning algorithms. CONCLUSIONS: This study reported, for the first time, the whole-transcriptome genetic resources found in urine extracellular vesicles of T2DN patients. The results provide additional support for the possible interactions, and regulators between RNAs from uEVs themselves and as potential biomarkers in DN.

Full-color persistent room temperature phosphorescent elastomers with robust optical properties.

Persistent room temperature phosphorescent materials with unique mechanical properties and robust optical properties have great potential in flexible electronics and photonics. However, developing such materials remains a formidable challenge. Here, we present highly stretchable, lightweight, and multicolored persistent luminescence elastomers, produced by incorporating ionic room temperature phosphorescent polymers and polyvinyl alcohol into a polydimethylsiloxane matrix. These prepared elastomers exhibit high optical transparency in daylight and emit bright persistent luminescence after the removal of 365 nm excitation. The homogeneous distribution of polymers within the matrix has been confirmed by confocal fluorescence microscopy, scanning electron microscopy, and atomic force microscopy. Mechanical property investigations revealed that the prepared persistent luminescence elastomers possess satisfactory stretchability. Impressively, these elastomers maintain robust optical properties even under extensive and repeated mechanical deformations, a characteristic previously unprecedented. These fantastic features make these persistent luminescence elastomers ideal candidates for potential applications in wearable devices, flexible displays, and anti-counterfeiting.

The Road from AKI to CKD: Molecular Mechanisms and Therapeutic Targets of Ferroptosis.

Acute kidney injury (AKI) is a prevalent pathological condition that is characterized by a precipitous decline in renal function. In recent years, a growing body of studies have demonstrated that renal maladaptation following AKI results in chronic kidney disease (CKD). Therefore, targeting the transition of AKI to CKD displays excellent therapeutic potential. However, the mechanism of AKI to CKD is mediated by multifactor, and there is still a lack of effective treatments. Ferroptosis, a novel nonapoptotic form of cell death, is believed to have a role in the AKI to CKD progression. In this study, we retrospectively examined the history and characteristics of ferroptosis, summarized ferroptosis's research progress in AKI and CKD, and discussed how ferroptosis participates in regulating the pathological mechanism in the progression of AKI to CKD. Furthermore, we highlighted the limitations of present research and projected the future evolution of ferroptosis. We hope this work will provide clues for further studies of ferroptosis in AKI to CKD and contribute to the study of effective therapeutic targets to prevent the progression of kidney diseases.

Corrigendum: Relationship between serum iPTH and peritonitis episodes in patients undergoing continuous ambulatory peritoneal dialysis.

[This corrects the article DOI: 10.3389/fendo.2023.1081543.].

Relationship between serum iPTH and peritonitis episodes in patients undergoing continuous ambulatory peritoneal dialysis.

Background: Peritonitis is considered as one of the most serious complications that cause hospitalization in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). There is limited evidence on the impact of the parathyroid hormone (PTH) on the first peritoneal dialysis (PD)-associated peritonitis episode. We aimed to investigate the influence of serum intact parathyroid hormone (iPTH) on peritonitis in patients undergoing PD. Methods: This was a retrospective cohort study. Patients undergoing initial CAPD from a single center in China were enrolled. The baseline characteristics and clinical information were recorded. The primary outcome of interest was the occurrence of the first PD-associated peritonitis episode. Five Cox proportional hazard models were constructed in each group set. In group set 1, all participants were divided into three subgroups by tertiles of the serum concentration of iPTH; in group set 2, all participants were divided into three subgroups based on the serum concentration of iPTH with 150 pg/ml interval (<150, 150-300, and >300 pg/ml). Hazard ratios and 95% confidence intervals (CIs) were calculated for each model. The multivariate linear regression analysis elimination procedure assessed the association between the clinical characteristics at baseline and the iPTH levels. Restricted cubic spline models were constructed, and stratified analyses were also conducted. Results: A total of 582 patients undergoing initial PD (40% women; mean age, 45.1 ± 11.5 years) from a single center in China were recruited. The median follow-up duration was 25.3 months. Multivariate Cox regression analysis showed that, in the fully adjusted model, a higher serum iPTH level (tertile 3, iPTH >300 pg/ml) was significantly associated with a higher risk of PD-associated peritonitis at 3 years [tertile 3: hazard ratio (HR) = 1.53, 95%CI = 1.03-2.55, p = 0.03; iPTH > 300 pg/ml: HR = 1.57, 95%CI = 1.08-2.27, p = 0.02]. The hazard ratio for every 100 pg/ml increase in serum iPTH level was 1.12 (95%CI = 1.05-1.20, p < 0.01) in the total cohort when treating iPTH as a continuous variable. Conclusions: An elevated iPTH level was significantly associated with an increased risk of peritonitis in patients undergoing CAPD.

Integrated analysis for treatment scheme of sodium-glucose cotransporter 2 inhibitors in patients with diabetic kidney disease: a real-world study.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are recommended for type 2 diabetes mellitus patients with impaired renal function, but the actual situation of SGLT2i using is unclear. Therefore, in this real-world study, we analyzed the treatment scheme and clinical characteristics of SGLT2i in patients with diabetic kidney disease (DKD). We included DKD patients hospitalized in the First Affiliated Hospital of Zhengzhou University from October 2017 to March 2020. The Apriori algorithm of association rules was used to analysis treatment scheme prescribing SGLT2i and other different combinations of hypoglycemic drugs. SGLT2i was used in 781 (12.3%) of 6336 DKD patients, both number and proportion of patients using SGLT2i increased from 2017 to 2020 (1.9% to 33%). Nighty-eight percent of all DKD patients using SGLT2i were combined with other glucose-lowering agents, and insulin, metformin and alpha-glucosidase inhibitors are most commonly used in combination with hypoglycemic drugs. Multivariate analysis showed that compared with non-SGLT2i group, patients using SGLT2i were associated with younger age, higher BMI, higher HbA1c, preserved kidney function, dyslipidemia and combined with ACEI/ARB and statins. In this real-world study, use of SGLT2i in DKD patients is still low. Most patients performed younger age and in the early stages of chronic kidney disease with poor glycemic control. Clinical inertia should be overcome to fully exert the cardiorenal protective effects of SGLT2 inhibitors, with attention to rational drug use.

Insights of Chinese herbal medicine for mitochondrial dysfunction in chronic cerebral hypoperfusion induced cognitive impairment: Existed evidences and potential directions.

Chronic cerebral hypoperfusion (CCH) is one of the main pathophysiological markers of cognitive impairment in central nervous system diseases. Mitochondria are cores of energy generation and information process. Mitochondrial dysfunction is the key upstream factors of CCH induced neurovascular pathology. Increasing studies explored the molecular mechanisms of mitochondrial dysfunction and self-repair for effective targets to improve CCH-related cognitive impairment. The clinical efficacy of Chinese herbal medicine in the treatment of CCH induced cognitive impairment is definite. Existed evidences from pharmacological studies have further proved that, Chinese herbal medicine could improve mitochondrial dysfunction and neurovascular pathology after CCH by preventing calcium overload, reducing oxidative stress damage, enhancing antioxidant capacity, inhibiting mitochondria-related apoptosis pathway, promoting mitochondrial biogenesis and preventing excessive activation of mitophagy. Besides, CCH mediated mitochondrial dysfunction is one of the fundamental causes for neurodegeneration pathology aggravation. Chinese herbal medicine also has great potential therapeutic value in combating neurodegenerative diseases by targeting mitochondrial dysfunction.

Resistance to targeted therapy in metastatic colorectal cancer: Current status and new developments.

Colorectal cancer (CRC) is one of the most lethal and common malignancies in the world. Chemotherapy has been the conventional treatment for metastatic CRC (mCRC) patients. However, the effects of chemotherapy have been unsatisfactory. With the advent of targeted therapy, the survival of patients with CRC have been prolonged. Over the past 20 years, targeted therapy for CRC has achieved substantial progress. However, targeted therapy has the same challenge of drug resistance as chemotherapy. Consequently, exploring the resistance mechanism and finding strategies to address the resistance to targeted therapy, along with searching for novel effective regimens, is a constant challenge in the mCRC treatment, and it is also a hot research topic. In this review, we focus on the current status on resistance to existing targeted therapies in mCRC and discuss future developments.

Conformation-dependent dynamic organic phosphorescence through thermal energy driven molecular rotations.

Organic room-temperature phosphorescent (RTP) materials exhibiting reversible changes in optical properties upon exposure to external stimuli have shown great potential in diverse optoelectronic fields. Particularly, dynamic manipulation of response behaviors for such materials is of fundamental significance, but it remains a formidable challenge. Herein, a series of RTP polymers were prepared by incorporating phosphorescent rotors into polymer backbone, and these materials show color-tunable persistent luminescence upon excitation at different wavelengths. Experimental results and theoretical calculations revealed that the various molecular conformations of monomers are responsible for the excitation wavelength-dependent (Ex-De) RTP behavior. Impressively, after gaining insights into the underlying mechanism, dynamic control of Ex-De RTP behavior was achieved through thermal energy driven molecular rotations of monomers. Eventually, we demonstrate the practical applications of these amorphous polymers in anti-counterfeiting areas. These findings open new opportunities for the control of response behaviors of smart-responsive RTP materials through external stimuli rather than conventional covalent modification method.

Integrated analysis of RNA methylation regulators crosstalk and immune infiltration for predictive and personalized therapy of diabetic nephropathy.

BACKGROUND: RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear. METHODS: The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro. RESULTS: We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose. CONCLUSION: Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.

Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy.

Background: Growing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN. Methods: Gene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein-protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed. Results: Four gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases. Conclusions: This study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.

[Full-cycle Health Management of Multiple Myeloma].

Multiple myeloma is a hematologic tumor characterized by clonal proliferation of plasma cells.The development of novel agents and immunotherapy have substantially improved the prognosis of multiple myeloma,with an expectable median survival beyond ten years.Therefore,there is an urgent need to improve the management of this disease.Health management is effective in controlling chronic diseases and full-cycle management should be implemented from the early to the end stage of the disease.Implanting the full-cycle concept into the health management of multiple myeloma will guide and standardize the advances in this field.This review focuses on the full-cycle concept of multiple myeloma and the corresponding application of health management at each stage of the cycle.

The Application of Focused Care Model in the Management of Hepatitis B Patients in a Tertiary Care Hospital and the Impact on Patients' Quality of Life.

Chronic hepatitis B is one of the common infectious diseases in the world, with a wide epidemic range and strong contagiousness, which is difficult to completely eradicate. From July 2020 to April 2022, 142 patients with hepatitis B who were admitted to tertiary hospitals were selected and randomly divided into 2 cases, with 71 cases per case. The control group adopts a conventional model, and the observation group implements a nursing model based on the control group. The management capacity and quality of life before and after the two groups of care were compared. 94 patients with chronic hepatitis B who were admitted to the hospital from July 2020 to April 2022 were selected, and 47 cases were divided into the control group and observation group according to the admission time. The control group is given a conventional model, and the observation group is given a nursing model on the basis of the control group. Both groups of patients observed anxiety self-assessment scale (SAS), depression self-assessment scale (SDS), short life span scale (SF-36) scores, and treatment conditions before and after the intervention, to explore the application of nursing model in the management of hepatitis B patients in tertiary hospitals and its impact on patient quality of life. Based on study's precare outcomes, there was no statistically significant difference between the self-administered scale (PIH) score and the quality of life measurement summary scale (QOL-BREF) score (P > 0.05) between the two groups. After one month of care, both groups had lower PIH scores than before care, and QOL-BREF scores were higher than before care (P < 0.05). After one month of treatment, the PIH score of the observation group was significantly reduced, and the QOL-BREF score after one month of treatment was significantly higher than that of the control group (P < 0.05). The SAS and SDS scores in the observation group were lower than those in the control group, and the quality of life scores and treatment compliance were higher than those in the control group, and the difference was statistically significant (P < 0.05). Conclusions. The nursing model can effectively improve the management and quality of life of hepatitis B patients. It also had a significant positive effect on the therapeutic capacity and quality of life of patients with hepatitis B and improved patient compliance behavior and quality of life.

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients.

Background: Diabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management. Methods: A retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS). Results: We recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring. Conclusion: A deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Structure, preparation and properties of liquid fluoroelastomers with different end groups.

In order to design and prepare liquid fluoroelastomers with different end groups, and reveal the relationship between the molecular chain structure and properties, we studied on the oxidation degradation method and functional group conversion method to prepare carboxyl-terminated and hydroxyl-terminated liquid fluoroelastomers, respectively. The reaction mechanisms were also deduced. Furthermore, the curing system was created for liquid fluoroelastomers, and systematically analyzed their properties. The sequence type and content of the -C[double bond, length as m-dash]C- and oxygen-containing groups in the samples were measured and characterized by attenuated total reflectance/Fourier transform infrared (ATR-FTIR) spectroscopy, 1H nuclear magnetic resonance (1H-NMR), 19F-NMR spectroscopy and chemical titration, the molecular weights of liquid fluoroelastomers were measured by gel permeation chromatography (GPC). Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to examine the thermal properties, while a viscometer was used to measure the dynamic viscosity of the liquid fluoroelastomers. Then the mechanical and surface properties of the cured samples were examined by universal testing machine and contact angle measurement instrument, respectively. The results show that carboxyl-terminated liquid fluoroelastomer with 2.71 wt% carboxyl terminal groups can be prepared by oxidation degradation method. When lithium aluminium hydride (LiAlH4) was used as the reducing agent, it can efficiently convert carboxyl group to hydroxyl group with a conversion rate of more than 95%. In addition, it can be seen that the dynamic viscosity of the liquid fluoroelastomers were all decreased with the increase of temperature, and it is similar to about 10 Pa s at 70 °C. Compared with carboxyl-terminated liquid fluoroelastomers, hydroxyl-terminated liquid fluoroelastomers has higher curing reactivity, higher glass transition temperature (T g) and thermal decomposition temperature (T d), and better mechanical properties of cured samples. The two types of liquid fluoroelastomers with distinct end groups presented distinct hydrophilicity.