Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Aim To investigate the protective effect of Vaccarin(Va)on epithelial-mesenchymal transition(EMT)in renal fibrosis model mice through regulating STAT3,and the underlying mechanism.Methods Left ureter ligation was used to establish a mouse model of unilateral ureteral obstruction(UUO);human kid-ney tubular epithelial(HK2)cells were induced to differentiate by transforming growth factor-β(TGF-β)in vitro.HE and Masson staining were used to observe the morphological changes of renal tissue;kits were used to detect the levels of BUN,Cr,IL-1β and IL-7 in mouse serum;CCK-8 was used to detect the effect of Va on the viability of HK2 cells;RT-PCR was used to detect the levels of inflammatory factors in HK2 cells;Western blot was used to detect the expression of STAT3,p-STAT3,E-cadherin,and α-SMA proteins in renal tissue and HK2 cells;to further investigate the regulation of Va on STAT3,JAK/STAT3 pathway acti-vator RO8191 was used to treat TGF-β-induced HK2 cells,and functional loss was detected.Results Va improved the pathological damage in UUO mice,inhibi-ted the levels of BUN,Cr and inflammatory factors;Va inhibited the phosphorylation of STAT3,upregulated E-cadherin,and downregulated α-SMA protein expres-sion;RO8191 counteracted the inhibitory effect of Va on the phosphorylation of STAT3.Conclusions Va inhibits the phosphorylation of STAT3 and the release of inflammatory factors,improves EMT,thus exerting an anti-renal fibrosis effect.

Ovulatory dysfunction (OD) is one of the main causes of infertility in women of childbearing age, which not only affects their reproductive ability, but also physical and mental health. Traditional treatment strategies have limited efficacies, and the emergence of biomedicines provides a promising alternative solution via the strategies of combining engineered design with modern advanced technology. This review explores the pathophysiological characteristics and related induction mechanisms of OD, and evaluates the current cutting-edge advances in its treatments. It emphasizes the potentials of biomedicines strategies such as hydrogels, nanoparticles and extracellular vesicles in improving therapeutic precision and efficacy. By mimicking natural physiological processes, and achieving controlled drug release, these advanced drug carriers are expected to address the challenges in ovarian microenvironment reprogramming, tissue repair, and metabolic and immune regulation. Despite the promising progress, there are still challenges in terms of biomedical complexity, differences between animal models and human physiology, and the demand for intelligent drug carriers in the therapy of OD. Future researches are mainly dedicated to developing precise personalized biomedicines in OD therapy through interdisciplinary collaboration, promoting the development of reproductive regenerative medicine.

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

OBJECTIVE: We aimed to investigate the patterns of fasting blood glucose (FBG) trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers. METHODS: The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort (TGOC) between 2017 and 2022. A group-based trajectory model was used to identify the FBG trajectories. Environmental risk scores (ERS) were constructed using regression coefficients from the occupational hazard model as weights. Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories. RESULTS: FBG trajectories were categorized into three groups. An association was observed between high temperature, noise exposure, and FBG trajectory ( P < 0.05). Using the first quartile group of ERS1 as a reference, the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90 and 2.21 times, respectively (odds ratio [ OR] = 1.90, 95% confidence interval [ CI]: 1.17-3.10; OR = 2.21, 95% CI: 1.09-4.45). CONCLUSION An association was observed between occupational hazards based on ERS and FBG trajectories. The risk of FBG trajectory levels increase with an increase in ERS.
Humans ; Male ; Adult ; Blood Glucose/analysis* ; China ; Prospective Studies ; Occupational Exposure/adverse effects* ; Risk Factors ; Middle Aged ; Steel ; Fasting/blood* ; Metal Workers ; East Asian People

OBJECTIVE: Pseudomonas aeruginosa( P. aeruginosa) is a prevalent pathogenic bacterium involved in meningitis; however, the virulence factors contributing to this disease remain poorly understood. METHODS: The virulence of the P. aeruginosa A584, isolated from meningitis samples, was evaluated by constructing in vitro blood-brain barrier and in vivo systemic infection models. qPCR, whole-genome sequencing, and drug efflux assays of A584 were performed to analyze the virulence factors. RESULTS: Genomic sequencing showed that A584 formed a phylogenetic cluster with the reference strains NY7610, DDRC3, Pa58, and Pa124. Its genome includes abundant virulence factors, such as hemolysin, the Type IV secretion system, and pyoverdine. A584 is a multidrug-resistant strain, and its wide-spectrum resistance is associated with enhanced drug efflux. Moreover, this strain caused significantly more severe damage to the blood-brain barrier than the standard strain, PAO1. qPCR assays further revealed the downregulation of the blood-brain barrier-associated proteins Claudin-5 and Occludin by A584. During systemic infection, A584 exhibited a higher capacity of brain colonization than PAO1 (37.1 × 10 ⁶ CFU/g brain versus 2.5 × 10 ⁶ CFU/g brain), leading to higher levels of the pro-inflammatory factors IL-1β and TNF-α. CONCLUSION This study sheds light on the virulence factors of P. aeruginosa involved in meningitis.
Pseudomonas aeruginosa/genetics* ; Virulence Factors/metabolism* ; Animals ; Virulence ; Mice ; Pseudomonas Infections/microbiology* ; Blood-Brain Barrier/microbiology* ; Humans ; Female

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Objective:To assess the relationship between basophil levels and mortality in patients with intra-abdominal infection.Methods:Information on patients with intraperitoneal infection admitted to the intensive care unit were extracted from the MIMIC database.A time-dependent Cox regression model was used to adjust for confounders associated with 28-day mortality.Propensity score matching(PSM)was used to balance the baseline differences be-tween groups with different basophil levels,and a restricted cube chart(RCS)was used to show the relationship between basophil count and 28-day mortality in patients with intra-abdominal infection.Results:A total of 4403 patients with intra-abdominal infection were enrolled in the MIMIC database.Patients with high basophil levels have lower mortality than those with low basophil levels.There was an L-shaped curve between basophil level and 28-day mortality,with a cut-off value of 0.47×109/L.Cox regression analysis showed that basophil levels were an independent protective factor for mortal-ity in patients with intra-abdominal infection after adjusting for potential confounders(HR=0.586,95%CI:0.443-0.769).Protective factors for death at basophil levels remained after PSM adjusted for potential confounders(HR=0.628,95%CI:0.470-0.832).Conclusion:Basophil level is an independent protective factor for mortality in patients with intra-abdominal infection,and basophil levels should be dynamically monitored to better evaluate the prognosis of patients.