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.

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.

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.

ObjectiveTo analyze the expression level of triggering receptor expressed on myeloid cells-1 （TREM-1） in serum and ascites of patients with cirrhotic ascites， and to investigate its correlation with clinical features and inflammatory markers and its role in the diagnosis of infection and prognostic evaluation. MethodsA total of 110 patients with cirrhotic ascites who were hospitalized in The Fifth Hospital of Shijiazhuang from January 2019 to December 2020 were enrolled， and according to the presence or absence of intra-abdominal infection， they were divided into infection group with 72 patients and non-infection group with 38 patients. The patients with infection were further divided into improvement group with 38 patients and non-improvement group with 34 patients. Clinical data and laboratory markers were collected from all patients. Serum and ascites samples were collected， and ELISA was used to measure the level of TREM-1. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the Kruskal-Wallis H test was used for comparison between multiple groups； the chi-square test was used for comparison of categorical data between two groups. A Spearman correlation analysis was used to investigate the correlation between indicators. A multivariate Logistic regression analysis was used to identify the influencing factors for the prognosis of patients with cirrhotic ascites and infection. The receiver operating characteristic （ROC） curve was used to evaluate the diagnostic and prognostic efficacy of each indicator， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsThe level of TREM-1 in ascites was significantly positively correlated with that in serum （r=0.50， P<0.001）. Compared with the improvement group， the non-improvement group had a significantly higher level of TREM-1 in ascites （Z=-2.391， P=0.017） and serum （Z=-2.544， P=0.011）， and compared with the non-infection group， the infection group had a significantly higher level of TREM-1 in ascites （Z=-3.420， P<0.001）， while there was no significant difference in the level of TREM-1 in serum between the two groups （P>0.05）. The level of TREM-1 in serum and ascites were significantly positively correlated with C-reactive protein （CRP）， procalcitonin （PCT）， white blood cell count， and neutrophil-lymphocyte ratio （r=0.288， 0.344， 0.530， 0.510， 0.534， 0.454， 0.330， and 0.404， all P<0.05）. The ROC curve analysis showed that when PCT， CRP， and serum or ascitic TREM-1 were used in combination for the diagnosis of cirrhotic ascites with infection， the AUCs were 0.715 and 0.740， respectively. The multivariate Logistic regression analysis showed that CRP （odds ratio ［OR］=1.019， 95% confidence interval ［CI］： 1.001‍ ‍—‍ ‍1.038， P=0.043） and serum TREM-1 （OR=1.002， 95%CI： 1.000‍ ‍—‍ ‍1.003， P=0.016） were independent risk factors for the prognosis of patients with cirrhotic ascites and infection， and the combination of these two indicators had an AUC of 0.728 in predicting poor prognosis. ConclusionThe level of TREM-1 is closely associated with the severity of infection and prognosis in patients with cirrhotic ascites， and combined measurement of TREM-1 and CRP/PCT can improve the diagnostic accuracy of infection and provide support for prognostic evaluation.

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

OBJECTIVES: This study aims to use 3D prin-ting technology based on the principle of stereo lithography apparatus (SLA) to shape dental lithium disilicate ceramics and study the effects of different slurry proportions on the microstructure and properties of heat-treated samples. METHODS: The experimental group comprised lithium disilicate ceramics manufactured through SLA 3D printing, and the control group comprised lithium disilicate ceramics (IPS e.max CAD) fabricated through commercial milling. An array of different particle sizes of lithium disilicate ceramic powder materials (nano and micron) was selected for mixing with photocurable acrylate resin. The proportion of experimental raw materials was adjusted to prepare five groups of ceramic slurries for 3D printing (Groups S1-S5) on the basis of rheological properties, stability, and other factors. Printing, debonding, and sintering were conducted on the experimental group with the optimal ratio, followed by measurements of microstructure, crystallographic information, shrinkage, and mechanical properties. RESULTS: Five groups of lithium disilicate ceramic slurries were prepared, of which two groups with high solid content (75%) (Groups S2 and S3) were selected for 3D printing. X-ray diffraction and scanning electron microscopy results showed that lithium disilicate was the main crystalline phase in Groups S2 and S3, and its microstructure was slender, uniform, and compact. The average grain sizes of Groups S2 and S3 were (559.79±84.58) nm and (388.26±61.49) nm, respectively (P<0.05). Energy spectroscopy revealed that the samples in the two groups contained a high proportion of Si and O elements. After heat treatment, the shrinkage rate of the two groups of ceramic samples was 18.00%-20.71%. Test results revealed no statistical difference in all mechanical properties between Groups S2 and S3 (P>0.05). The flexural strengths of Groups S2 and S3 were (231.79±21.71) MPa and (214.86±46.64) MPa, respectively, which were lower than that of the IPS e.max CAD group (P<0.05). The elasticity modulus of Groups S2 and S3 were (87.40±12.99) GPa and (92.87±19.76) GPa, respectively, which did not significantly differ from that of the IPS e.max CAD group (P>0.05). The Vickers hardness values of Groups S2 and S3 were (6.53±0.19) GPa and (6.25±0.12) GPa, respectively, which were higher than that of the IPS e.max CAD group (P<0.05). The fracture toughness values of Groups S2 and S3 were (1.57±0.28) MPa·m^0.5 and (1.38±0.17) MPa·m^0.5, respectively, which did not significantly differ from that of the IPS e.max CAD group (P>0.05). CONCLUSIONS The combination of lithium disilicate ceramic powders with different particle sizes can yield a slurry with high solid content (75%) and suitable viscosity and stability. The dental lithium disilicate ceramic material is successfully prepared by using 3D printing technology. The 3D-printed samples show a small shrinkage rate after heat treatment. Their microstructure conforms to the crystal phase of lithium disilicate ceramics, and their mechanical properties are close to those of milled lithium disilicate ceramics.
Printing, Three-Dimensional ; Dental Porcelain/chemistry* ; Ceramics/chemistry* ; Materials Testing ; Particle Size

Ectodermal dysplasia is a group of hereditary diseases characterized by developmental defects of ectodermal structures. Its oral manifestations mainly center on congenital missing teeth, abnormal tooth morphology, and maxillofacial bone developmental disorders, which seriously affect the masticatory function, maxillofacial development, and mental health of affected children. In this article, the multidimensional diagnostic strategy system for children with ectodermal dysplasia and the related progress of early oral prosthodontic treatment methods were systematically reviewed to provide references for clinicians in the diagnosis and treatment of children with ectodermal dysplasia.
Child ; Humans ; Anodontia ; Ectodermal Dysplasia/diagnosis* ; Prosthodontics ; Tooth Abnormalities/therapy*

Objective: To investigate the protective effects of the combined concentrated liquid extract of Pueraria lobata (Willd.) Ohwi root (P. lobata, Ge Gen) and Hovenia dulcis Thunb. (H. dulcis, Zhi Ju Zi) against ethanol-induced liver damage in vitro, using a human hepatoma cell line G2 (HepG2) cell model. Methods: HepG2 cells were cultured in medium containing 4% ethanol to establish a model of alcoholic liver damage. The cells were then treated with the combined extract obtained via cryogenic extraction. Biochemical assays and Western blot analyses were performed to assess the levels of oxidative stress markers, antioxidant enzymes, and inflammatory cytokines. In addition, activation of the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway was examined to elucidate the mechanisms underlying the effects of the extract. Results: Treatment with the extract contributed to a significant reduction in the release of nitric oxide and reactive oxygen species in the ethanol-treated HepG2 cells; promoted the elevated expression of superoxide dismutase, catalase, and glutathione, indicating enhanced antioxidant defenses; and showed strong free radical-scavenging activity against 1,1-diphenyl-2-picrylhydrazyl radicals. In addition, by activating the PI3K/AKT pathway, treatment promoted increases in the expression of nuclear factor erythroid 2-related factor 2 and its downstream targets, subsequently inhibiting apoptosis. Moreover. inflammatory responses were mitigated, as indicated by reductions in the expression of tumor necrosis factor-alpha and interleukin-6, and we detected reduction in the levels of alanine aminotransferase and aspartate aminotransferase, thereby indicating hepatoprotective effects. Conclusion The combined P. lobata root and H. dulcis extract was established to have notable antioxidative and anti-inflammatory properties, effectively alleviating ethanol-induced liver damage in vitro. These findings highlight the potential applicability of this extract as a candidate for treating alcoholic liver disease.

Objective:To identify the factors influencing the diagnostic accuracy of endoscopic ultrasonography (EUS) for colorectal submucosal tumors (SMT).Methods:A retrospective analysis was conducted on 330 colorectal SMT lesions (from 323 patients) diagnosed by EUS at Shenzhen Hospital of Southern Medical University from December 2015 to October 2023. Pathological diagnosis were confirmed through endoscopic resection, EUS-guided fine needle aspiration (EUS-FNA) or surgical resection. Diagnostic accuracy was calculated for each type of colorectal SMT. Univariate and multivariate logistic regression analysis were performed to identify factors affecting EUS diagnostic accuracy.Results:The overall diagnostic accuracy of EUS for colorectal SMT was 73.6% (243/330). Among 19 SMT subtypes enrolled, neuroendocrine neoplasms (51.2%, 169/330) and lipomas (15.5%, 51/330) were most prevalent, while 17 rare subtypes each accounted for <6%. Seven rare SMT (mucosal chronic inflammation, colorectal schwannoma, xanthogranulomatous inflammation, capillary hemangioma, colonic xanthoma, lymphadenoid complex, and angiomyolipoma) showed 0% diagnostic accuracy. Seven other subtypes (granular cell tumor, leiomyoma, rectal tonsil, intestinal schistosomiasis, fibrous tissue hyperplasia, gastrointestinal stromal tumor, and lymphangioma) showed accuracy <30%, whereas five subtypes (cyst, bowel endometriosis, neuroendocrine neoplasm, lipoma, and pneumatosis cystoides intestinalis) achieved >60% accuracy. Multivariate logistic regression analysis confirmed that the lesion location (left colon VS rectum: OR=0.06, 95% CI: 0.02-0.17, P<0.001; right colon VS rectum: OR=0.04, 95% CI: 0.01-0.13, P<0.001; ileocecal valve VS rectum: OR=0.09, 95% CI: 0.02-0.42, P=0.002); echogenicity (anechoic VS hypoechoic: OR=6.26, 95% CI: 1.31-29.97, P=0.022; hyperechoic VS hypoechoic: OR=13.39, 95% CI: 4.16-43.09, P<0.001) and ultrasonic layer (layer 4 VS layer 3: OR=0.22, 95% CI: 0.06-0.81, P=0.023) were independent influencing factors of EUS diagnostic accuracy for colorectal SMT. Conclusion:Neuroendocrine neoplasms and lipomas represent the most common colorectal SMT, whereas rare and uncommon SMT exhibit low EUS diagnostic accuracy. Lesion location, echogenicity, and ultrasonic layer significantly influence EUS diagnostic accuracy for colorectal SMT.