Guizhitongluo Tablet inhibits atherosclerosis and foam cell formation through regulating Piezo1/NLRP3 mediated macrophage pyroptosis.

BACKGROUND: Atherosclerosis (AS) is the main pathological basis for the development of cardiovascular diseases. Vascular inflammation is an important factor in the formation of AS, and macrophage pyroptosis plays a key role in AS due to its unique inflammatory response. Guizhitongluo Tablet (GZTLT) has shown clinically effective in treating patients with AS, but its mechanism is elusive. PURPOSE: This study was to determine the effects of GZTLT on atherosclerotic vascular inflammation and pyroptosis and to understand its underlying mechanism. MATERIALS AND METHODS: The active constituents of GZTLT were analysed by means of UPLC-HRMS. In vivo experiments were performed using ApoE-/- mice fed a high fat diet for 8 weeks, followed by treatment with varying concentrations of GZTLT orally by gavage and GsMTx4 (GS) intraperitoneally and followed for another 8 weeks. Oil red O, Haematoxylin-eosin (HE) and Masson staining were employed to examine the lipid content, plaque size, and collagen fibre content of the mouse aorta. Immunofluorescence staining was utilised to identify macrophage infiltration, as well as the expression of Piezo1 and NLRP3 proteins in aortic plaques. The levels of aortic inflammatory factors were determined using RT-PCR and ELISA. In vitro, foam cell formation in bone marrow-derived macrophages (BMDMs) was observed using Oil Red O staining. Intracellular Ca2+ measurements were performed to detect the calcium influx in BMDMs, and the expression of NLRP3 and its related proteins were detected by Western blot. RESULTS: The UPLC-HRMS analysis revealed 31 major components of GZTLT. Our data showed that GZTLT inhibited aortic plaque formation in mice and increased plaque collagen fibre content to stabilise plaques. In addition, GZTLT could restrain the expression of serum lipid levels and suppress macrophage foam cell formation. Further studies found that GZTLT inhibited macrophage infiltration in aortic plaques and suppressed the expression of inflammatory factors. It is noteworthy that GZTLT can restrain Piezo1 expression and reduce Ca2+ influx in BMDMs. Additionally, we found that GZTLT could regulate NLRP3 activation and pyroptosis by inhibiting Piezo1. CONCLUSION: The present study suggests that GZTLT inhibits vascular inflammation and macrophage pyroptosis through the Piezo1/NLRP3 signaling pathway, thereby delaying AS development. Our finding provides a potential target for AS treatment and drug discovery.

Model Selection for Exposure-Mediator Interaction.

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators M and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging.

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (ß=0.481, P<0.001) and practice (ß=0.412, P<0.001), and attitude had a direct effect on practice (ß=0.135, P<0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database.

Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan-Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients' overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery.

Using the sympathetic system, beta blockers and alpha-2 agonists, to address acute respiratory distress syndrome.

Acute Respiratory Distress Syndrome (ARDS) manifests as an acute inflammatory lung injury characterized by persistent hypoxemia, featuring a swift onset, high mortality, and predominantly supportive care as the current therapeutic approach, while effective treatments remain an area of active investigation. Adrenergic receptors (AR) play a pivotal role as stress hormone receptors, extensively participating in various inflammatory processes by initiating downstream signaling pathways. Advancements in molecular biology and pharmacology continually unveil the physiological significance of distinct AR subtypes. Interventions targeting these subtypes have the potential to induce specific alterations in cellular and organismal functions, presenting a promising avenue as a therapeutic target for managing ARDS. This article elucidates the pathogenesis of ARDS and the basic structure and function of AR. It also explores the relationship between AR and ARDS from the perspective of different AR subtypes, aiming to provide new insights for the improvement of ARDS.

Prognostic impact of the high-sensitivity modified glasgow prognostic score on patients undergoing radical surgery for hepatocellular carcinoma : Authorship.

OBJECTIVE: Our study aimed to assess the ability of high-sensitivity modified Glasgow prognostic Score (HS-mGPS) predicting survival in patients undergoing radical surgery for hepatocellular carcinoma (HCC) and to compare the impact with other Inflammation-Based prognostic scoring systems including Glasgow prognostic Score (GPS) and modified GPS (mGPS). METHODS: Our study evaluated 293 patients with HCC who had undergone hepatectomy at the Third Affiliated Hospital of Soochow University between 2010 and 2018. The HS-mGPS, mGPS, and GPS were calculated based on particular cut-off values of preoperative C-reactive protein and albumin, and the correlations between HS-mGPS and clinicopathological parameters were evaluated. Univariate and multivariate survival analyses were conducted by Kaplan-Meier method and Cox proportional hazards model. To evaluate the discrimination ability of each prognostic score, the receiver operating characteristic (ROC) curve were generated and the areas under the curve (AUC) were measured and compared. RESULT: The study results indicated a correlation between elevated HS-mGPS scores and adverse clinical factors, including higher BCLC stage, C-P grade, multiple tumors, and larger tumor diameter. Kaplan-Meier and univariate survival analyses revealed that higher scores of HS-mGPS, GPS, and mGPS were all associated with significantly reduced overall survival (OS) (all p < 0.001). In multivariate survival analysis, HS-mGPS emerged as an independent risk factor for poor OS in patients undergoing hepatectomy for HCC (p = 0.010), along with factors including maximal tumor diameter (p < 0.001), microvascular invasion (MVI) (p = 0.008), and BCLC stage (p = 0.001). The analysis of ROC curves and the AUC values indicated that HS-mGPS outperforms GPS and mGPS in predicting the long-term prognosis of patients with resectable HCC. CONCLUSION: Preoperative HS-mGPS proves superior in predicting adverse long-term outcomes in HCC patients undergoing radical surgery.

Development and validation of a prognostic immunoinflammatory index for patients with gastric cancer.

BACKGROUND: Studies have demonstrated the influence of immunity and inflammation on the development of tumors. Although single biomarkers of immunity and inflammation have been shown to be clinically predictive, the use of biomarkers integrating both to predict prognosis in patients with gastric cancer remains to be investigated. AIM: To investigate the prognostic and clinical significance of inflammatory biomarkers and lymphocytes in patients undergoing surgical treatment for gastric cancer. METHODS: Univariate COX regression analysis was performed to identify potential prognostic factors for patients with gastric cancer undergoing surgical treatment. Least absolute shrinkage and selection operator-COX (LASSO-COX) regression analysis was performed to integrate these factors and formulate a new prognostic immunoinflammatory index (PII). The correlation between PII and clinical characteristics was statistically analyzed. Nomograms incorporating the PII score were devised and validated based on the time-dependent area under the curve and decision curve analysis. RESULTS: Patients exhibiting elevated neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammatory index displayed inferior progression-free survival (PFS) and overall survival (OS). Conversely, low levels of CD3(+), CD3(+) CD8(+), CD4(+)CD8(+), and CD3(+)CD16(+)CD56(+) T lymphocytes were associated with improved PFS and OS, while high CD19(+) T lymphocyte levels were linked to worse PFS and OS. The PII score demonstrated associations with tumor characteristics (primary tumor site and tumor size), establishing itself as an independent prognostic factor for both PFS and OS. Time-dependent area under the curve and decision curve analysis affirmed the effectiveness of the PII-based nomogram as a robust prognostic predictive model. CONCLUSION: PII may be a reliable predictor of prognosis in patients with gastric cancer undergoing surgical treatment, and it offers insights into cancer-related immune-inflammatory responses, with potential significance in clinical practice.

Effects of the combined regulation of nitrogen, phosphorus, and potassium nutrients on the migration and transformation of arsenic species in paddy soil.

Nitrogen (N), phosphorus (P) and potassium (K) are three macroelements in agriculture production, but their combined effects on arsenic (As) toxicity and its translocation in rice plants are not clear. In this study, an orthogonal rotation combination based on different N, P and K (NPK) concentration was first designed to examine their combined effect on the As toxicity, its transformation and migration in rice plants based on the hydroponic culture and pot soil culture. The results showed that 2.0 mg/L arsenite (As(III)) had obvious toxicity on the growth of indica LuYouMingZhan (LYMZ) and the optimal NPK concentration was 28.41, 6 and 50 mg/L based on the quadratic regression of the recovery rate of chlorophyll SPAD value of indica LYMZ. The optimal NPK combination significantly alleviated the physiological toxicity of As(III) on indica LYMZ rice seedling and decreased the accumulation of inorganic As in their roots and shoots by 23.8±1.8 % and 33.4±2.4 % respectively; further pot culture from different As(III) polluted soil showed that the optimal NPK combination significantly increased the dry weight of roots, stems, sheaths and leaves of indica LYMZ rice plants as well as yield indicators by 6.4 %-61.7 % and 7.1 %-89.8 % respectively, decreased the accumulation of As(III) and arsenate by 6.25 %-100 % and 12.36 %-100 % respectively in their roots, stems, sheaths, leaves, brans and kernels except As(III) concentration in their sheaths, decreased the accumulation of dimethylarsenate in their sheaths, leaves, brans and kernels, and had the best repair effect on the translocation of As species in 50 mg/kg As(III)-added soil. Our study provided a desirable strategy for alleviating As toxicity in paddy soil and reducing As pollution in rice plants.

Socioeconomic support, quality of life, and prognosis of frailty among the older adults.

Background: Although socioeconomic support is recommended for frailty management, its association with the prognosis of frailty is unclear. Methods: Using data from participants aged ≥65 years in the Chinese Longitudinal Healthy Longevity Survey (2008-2018), the associations between socioeconomic support (source of income, medical insurance, community support, living status), onset of prefrailty/frailty, and worsening of prefrailty, were analyzed using multinominal logistic regression models. The associations between self-reported low quality of life (QoL) and reversion of prefrailty/frailty were analyzed using multivariate logistic regression models. Associations with mortality risk were analyzed using Cox proportional hazard regression models. Results: A total of 13,859 participants (mean age: 85.8 ± 11.1 years) containing 2056 centenarians were included. Financial dependence was a risk factor for low QoL among prefrail/frail individuals, but not among robust individuals. Having commercial or other insurance, and receiving social support from the community were protective factors for low QoL among prefrail/frail individuals and for the worsening of prefrailty. Continuing to work was a risk factor for low QoL, but a protective factor for worsening of prefrailty. A negative association between continuing to work and mortality existed in prefrail individuals aged <85 years and ≥85 years. Living alone was a risk factor for low QoL, but was not significantly associated with frailty prognosis. Conclusions: Prefrail and frail individuals were vulnerable to changes in socioeconomic support and more sensitive to it compared with robust individuals. Preferential policies regarding financial support, social support, and medical insurance should be developed for individuals with frailty.

Preparation of 4-Amino-3-hydrazino-1,2,4-triazol-5-thiol-Modified Graphene Oxide and Its Greatly Enhanced Selective Adsorption of Gallium in Aqueous Solution.

Efficient recovery of gallium (Ga) from vanadium slag processing residue (VSPR) solution is of great significance for environmental protection and resource utilization, but improving its selective adsorption against the coexisting Sc3+ and In3+ is still challenging. Herein, a novel adsorbent consisting of 4-amino-3-hydrazino-1,2,4-triazol-5-thiol (AHTZT)-modified graphene oxide (GO-AHTZT) was successfully synthesized that exhibits a higher adsorption selectivity for Ga3+ in VSPR solution with coexisting Sc3+ and In3+. Under optimal conditions, the adsorption capacity of GO-AHTZT for Ga3+ can reach 23.92 mg g-1, which is 4.9 and 12.6 times higher than that for Sc3+ (4.87 mg g-1) and In3+ (1.90 mg g-1) adsorption, indicating the excellent anti-interference ability of GO-AHTZT against Sc3+ and In3+. The process and mechanism of Ga3+ adsorption onto GO-AHTZT was also studied and discussed in detail. By measuring the adsorption process and by characterizing the adsorbent before and after adsorption, we demonstrate that the selective interaction between the Ga3+- and N-containing groups in AHTZT is the main reason for the improved adsorption selectivity. This work opens up an avenue for the design and synthesis of highly selective adsorbents for Ga3+ in complex VSPR solutions.

Ceratophyllum demersum alleviates microplastics uptake and physiological stress responses in aquatic organisms, an overlooked ability.

It has been demonstrated that microplastics (MPs) may be inadvertently ingested by aquatic animals, causing harm to their physiological functions and potentially entering the food chain, thereby posing risks to human food safety. To achieve an environmentally friendly and efficient reduction of MPs in freshwater environments, this experiment investigates the depuration effect of C. demersum on MPs using three common aquatic animals: Macrobrachium nipponense, Corbicula fluminea, and Bellamya aeruginosa as research subjects. The amounts of MPs, digestive enzyme activity, oxidative stress index, and energy metabolism enzyme activity in the digestive and non-digestive systems of three aquatic animals were measured on exposure days 1, 3, and 7 and on depuration days 1 and 3. The results indicated that the depuration effect of C. demersum and the species interaction were significant for the whole individual. Concerning digestive tissue, C. demersum was the most effective in purifying B. aeruginosa. When subjected to short-term exposure to MPs, C. demersum displayed a superior depuration effect. Among non-digestive tissues, C. demersum exhibited the earliest purifying effect on C. fluminea. Additionally, C. demersum alleviated physiological responses caused by MPs. In conclusion, this study underscores C. demersum as a promising new method for removing MPs from aquatic organisms.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Background: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). Methods: One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. Results: We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. Conclusion: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Hepatocyte-specific loss of DDB1 attenuates hepatic steatosis but aggravates liver inflammation and fibrosis in MASH.

BACKGROUND: MASH is a common clinical disease that can lead to advanced liver conditions, but no approved pharmacotherapies are available due to an incomplete understanding of its pathogenesis. Damaged DNA binding protein 1 (DDB1) participates in lipid metabolism. Nevertheless, the function of DDB1 in MASH is unclear. METHODS: Clinical liver samples were obtained from patients with MASH and control individuals by liver biopsy. Hepatocyte-specific Ddb1-knockout mice and liver Hmgb1 knockdown mice were fed with a methionine-and choline-deficient diet to induce MASH. RESULTS: We found that the expression of DDB1 in the liver was significantly decreased in MASH models. Hepatocyte-specific ablation of DDB1 markedly alleviated methionine-and choline-deficient diet-induced liver steatosis but unexpectedly exacerbated inflammation and fibrosis. Mechanistically, DDB1 deficiency attenuated hepatic steatosis by downregulating the expression of lipid synthesis and uptake genes. We identified high-mobility group box 1 as a key candidate target for DDB1-mediated liver injury. DDB1 deficiency upregulated the expression and extracellular release of high-mobility group box 1, which further increased macrophage infiltration and activated HSCs, ultimately leading to the exacerbation of liver inflammation and fibrosis. CONCLUSIONS: These data demonstrate the independent regulation of hepatic steatosis and injury in MASH. These findings have considerable clinical implications for the development of therapeutic strategies for MASH.

Patient care in complex Sociotechnological ecosystems and learning health systems.

The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report "Crossing the Quality Chasm," the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.

Strategic assessment model of smart stadiums based on genetic algorithms and literature visualization analysis: A case study from Chengdu, China.

This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.

Prognostic impact of tumor size in ureteral cancer following segmental ureterectomy.

OBJECTIVE: To evaluate the classification and prognostic effects of a 2 cm tumor size in patients with ureteral cancer (UC) undergoing segmental ureterectomy (SU). PATIENTS AND METHODS: A total of 75 patients with UC who underwent SU in our hospital between April 2013 and April 2023 were included in this study. The study population was grouped based on tumor size, which was defined as the maximum diameter of the pathological specimens, resulting in 30 patients (40.0%) with tumor size <2 cm and 45 patients (60.0%) with tumor size ≥2 cm. The clinicopathological variables, perioperative parameters, and oncological outcomes were compared between the 2 groups. The endpoints were recurrence-free survival (RFS), and cancer-specific survival (CSS). RESULTS: A tumor ≥2 cm was related to a higher positive rate of urine exfoliative cytology (P = 0.049) and fewer preoperative ureteroscopies (P = 0.033) than tumors <2 cm. After a follow-up of 6.3 to 128.7 months (median 40.2 months), 23 cases (30.7%) experienced recurrence and 11 patients (14.7%) succumbed to UC in the end. Compared to those with tumor size <2 cm, patients with tumor size ≥2 cm experienced more urothelial recurrence (P = 0.032). Kaplan-Meier analysis demonstrated that patients with tumor size ≥2 cm displayed inferior urothelial RFS than those with tumor size <2 cm (P = 0.026). Multivariate Cox analysis identified tumor size ≥2 cm, and pathological stage ≥T2 were significant prognostic factors of poor urothelial RFS (all P < 0.05). CONCLUSION: Tumor size ≥2 cm was associated with a high rate of urothelial recurrence and served as an independent prognostic factor of adverse urothelial RFS in SU-treated patients with UC. Patients are advised to select surgical treatments for UC following the EAU guidelines.

Association between household size and risk of incident dementia in the UK Biobank study.

Currently, the relationship between household size and incident dementia, along with the underlying neurobiological mechanisms, remains unclear. This prospective cohort study was based on UK Biobank participants aged ≥ 50 years without a history of dementia. The linear and non-linear longitudinal association was assessed using Cox proportional hazards regression and restricted cubic spline models. Additionally, the potential mechanisms driven by brain structures were investigated by linear regression models. We included 275,629 participants (mean age at baseline 60.45 years [SD 5.39]). Over a mean follow-up of 9.5 years, 6031 individuals developed all-cause dementia. Multivariable analyses revealed that smaller household size was associated with an increased risk of all-cause dementia (HR, 1.06; 95% CI 1.02-1.09), vascular dementia (HR, 1.08; 95% CI 1.01-1.15), and non-Alzheimer's disease non-vascular dementia (HR, 1.09; 95% CI 1.03-1.14). No significant association was observed for Alzheimer's disease. Restricted cubic splines demonstrated a reversed J-shaped relationship between household size and all-cause and cause-specific dementia. Additionally, substantial associations existed between household size and brain structures. Our findings suggest that small household size is a risk factor for dementia. Additionally, brain structural differences related to household size support these associations. Household size may thus be a potential modifiable risk factor for dementia.

Pangenome analyses of Clostridium butyricum provide insights into its genetic characteristics and industrial application.

Clostridium butyricum is a Gram-positive anaerobic bacterium known for its ability to produce butyate. In this study, we conducted whole-genome sequencing and assembly of 14C. butyricum industrial strains collected from various parts of China. We performed a pan-genome comparative analysis of the 14 assembled strains and 139 strains downloaded from NCBI. We found that the genes related to critical industrial production pathways were primarily present in the core and soft-core gene categories. The phylogenetic analysis revealed that strains from the same clade of the phylogenetic tree possessed similar antibiotic resistance and virulence factors, with most of these genes present in the shell and cloud gene categories. Finally, we predicted the genes producing bacteriocins and botulinum toxins as well as CRISPR systems responsible for host defense. In conclusion, our research provides a desirable pan-genome database for the industrial production, food application, and genetic research of C. butyricum.