Objective:To understand the characteristics of new infections in the newly reported HIV-1 infected population in Luzhou, to find out the characteristics of the high-risk population, which may provide a basis for developing precise prevention and control measures locally.Methods:HIV-1 LAg Avidity EIA test was applied for newly reported cases in Luzhou from 2018 to 2021. The chi-square test was used for univariate analysis, logistic regression model for multivariate analysis, and spatial autocorrelation and hotspot analysis were applied to explore the spatial distribution characteristics of new infections in Luzhou city.Results:A total of 4 494 cases reported in the odd-numbered months were selected for testing, with 673 newly infected cases and the proportion of newly infected cases was 14.98%. Newly infected cases were predominantly male (472/673, 70.13%), married (335/673, 49.78%), heterosexual transmission (621/673, 92.27%), age>50 years (520/673, 77.27%), and junior high school or lower education (599/673, 89.00%). The proportion of new infections in each year were 11.74% (129/1 099), 17.11% (247/1 444), 13.57% (154/1 134) and 17.50% (143/817), respectively, with a statistically significant difference ( χ2=20.024, P<0.001) and an upward trend ( χ2=5.997, P=0.014). There were statistically significant differences in different transmission routes, gender, education level, occupation, sample source and residence (all P<0.05). Logistic regression analysis showed that gender, marital status, transmission route, current address, and sample source were all influencing factors for new infections. There are spatial autocorrelation characteristics of new case incidence in 2018 and 2021, hotspot areas gradually shifted from dispersed to relatively concentrated, with the number fluctuating with reported year. Conclusions:The proportion of new HIV-1 infections in Luzhou is relatively low throughout the province, but it is on the rise. The proportion of new infections is higher among youth, students, homosexual transmission, and retired persons. Attention should be focused on hotspot areas and routine surveillance and testing of key populations in hotspot areas should be strengthened.

CRM 197(cross-reacting material 197),a naturally occurring mutant of diphtheria toxin,is a safe and effective vaccine vector and extensively used on developing conjugate or combined vaccines.The mutant loses its enzymatic activity,but fully retains its receptor-binding ability and immunogenicity.In current work,the diphtheria toxin mutant CRM 197 and its fusion proteins with the receptor-binding do-main of botulinum neurotoxin serotype E(EHc)were developed using genetic engineering technology.These recombinant proteins were confirmed by Western blotting and SDS-PAGE.BALB/c mice were im-munized with the CRM197-EHc and EHc-CRM197 fusion proteins,and their immunogenicity was evalua-ted.These two fusion protein molecules,CRM197-EHc and EHc-CRM197,as subunit vaccines,elicited a robust humoral immune response targeting both CRM197 and EHc antigens in the immunized mice.Compared to the mixture of CRM197 and EHc,the mice vaccinated with the fusion proteins(CRM197-EHc and EHc-CRM197)induced higher levels of anti-CRM197 antibodies,and the mice vaccinated with EHc-CRM197 also generated strongest anti-EHc antibodies.Consequently,as a carrier molecule in the fusion protein vaccine,EHc enhances the immunogenicity of CRM197 molecules.Likewise,CRM197 boosts the immunogenicity of EHc in the EHc-CRM197 fusion protein.

Objective To establish a rapid analysis method for cyanide based on a ratiometric fluores-cent probe,providing a quantitative strategy for on-site visual and rapid detection of cyanide.Methods A dual-emission ratiometric fluorescent probe(AuNCs-FL)was constructed by using bovine serum al-bumin(BSA)-stabilized gold nanoclusters(AuNCs,fluorescence emission at 660 nm)as the responsive signal unit and fluorescein(FL,emission at 515 nm)as the internal reference.Results The etching effect of cyanide on AuNCs resulted in fluorescence quenching at 660 nm,while the fluorescence inten-sity of FL at 515 nm remained unchanged,enabling a rapid response analysis of cyanide shift from red to green fluorescence.The developed probe enabled rapid analysis of cyanide within 3 min,with a limit of detection(LOD)of 3.4 mg/L and a visual detection range of 10-100 mg/L.Conclusion The AuNCs-FL fluorescent probe is structurally simple,low-cost,and easy to operate,delivering rapid and accurate results.It also avoids the interference from sulfides encountered in commercial cyanide test kits,making it suitable for the on-site rapid detection of suspected powder samples in cyanide poisoning cases.

CRM 197(cross-reacting material 197),a naturally occurring mutant of diphtheria toxin,is a safe and effective vaccine vector and extensively used on developing conjugate or combined vaccines.The mutant loses its enzymatic activity,but fully retains its receptor-binding ability and immunogenicity.In current work,the diphtheria toxin mutant CRM 197 and its fusion proteins with the receptor-binding do-main of botulinum neurotoxin serotype E(EHc)were developed using genetic engineering technology.These recombinant proteins were confirmed by Western blotting and SDS-PAGE.BALB/c mice were im-munized with the CRM197-EHc and EHc-CRM197 fusion proteins,and their immunogenicity was evalua-ted.These two fusion protein molecules,CRM197-EHc and EHc-CRM197,as subunit vaccines,elicited a robust humoral immune response targeting both CRM197 and EHc antigens in the immunized mice.Compared to the mixture of CRM197 and EHc,the mice vaccinated with the fusion proteins(CRM197-EHc and EHc-CRM197)induced higher levels of anti-CRM197 antibodies,and the mice vaccinated with EHc-CRM197 also generated strongest anti-EHc antibodies.Consequently,as a carrier molecule in the fusion protein vaccine,EHc enhances the immunogenicity of CRM197 molecules.Likewise,CRM197 boosts the immunogenicity of EHc in the EHc-CRM197 fusion protein.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

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.

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.