In this work,with tris(4-aminophenyl)amine(TAPA)and 1,3,5-tris(4-formylphenyl)benzene(TFPB)as monomers,an imine-type porous organic polymer,TAPA-TFPB,was synthesized using a simple method under the catalysis of acetic acid.The material TAPA-TFPB was used as solid-phase extraction adsorbent and combined with ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry(UHPLC-QTOF-MS)to establish a detection method for four kinds of nitrofuran metabolites(NFMs)residues in meat samples.The parameters of the adsorbent dosage,the pH value and volume of sample,and the type and volume of washing and eluent solvents were optimized,respectively.Under the optimal extraction conditions,low detection limits(0.11-1.60 μg/kg)were achieved for four kinds of NFMs.At three different spiked levels,the intra-day and inter-day precisions(Relative standard deviations)were 2.8% -10.9% and 4.3% -16.2%,respectively,and the spiked recoveries were 72.0% -107.2%.The results showed that the method chould be used for efficient extraction and analysis of trace NFMs residues in meat samples,indicating that TAPA-TFPB was a kind of promising SPE adsorbent.

Through evidence mapping, this paper systematically summarized the research evidence on the use of traditional Chinese medicine(TCM) in treating renal anemia, displaying the distribution of evidence in this field. A systematic search was conducted across databases, including CNKI, Wanfang, VIP, SinoMed, Springner, PubMed, Engineering Village, and Web of Science, targeting studies published up to June 30, 2024. The research evidence was summarized and displayed through a combination of graphs, tables, and text. A total of 264 interventional studies, 37 observational studies, and 7 systematic reviews were included. The annual publication volumes related to TCM treatment in renal anemia showed an overall upward trend, with most studies involving sample sizes between 60 and 120 participants(224 articles, 74.42%). Intervention measures were categorized into 21 types, with oral TCM decoctions being the most common medicine(171 times, 56.81%). The use of self-made prescriptions was the most common TCM intervention method. The intervention duration was mainly between 8 weeks and 3 months(239 articles, 79.40%). The most frequently reported TCM syndrome was spleen and kidney Qi deficiency. The top 2 outcome indicators were the anemia indicators and renal injury/renal function markers. However, several issues were identified in these studies, such as insufficient attention to the sources, social/geographical information, and temporal continuity of research subjects in observational research. Randomized controlled trials mostly had a high risk of bias, mainly due to issues such as randomization bias, blinding bias, and failure to register research protocols. The methodology quality of systematic reviews was generally low, mainly due to inadequate inclusion of literature, failure to specify funding sources, and lack of pre-registrations. While the report quality of systematic review was acceptable, there were significant gaps in the reporting of protocols, registration, and funds. The results show that these issues affect the quality of research and the reliability of findings on TCM in treating renal anemia, underscoring the need to address them to conduct higher-quality research and provide more reliable medical evidence for TCM in treating renal anemia.
Humans ; Anemia/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Kidney Diseases/drug therapy*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

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.

Objective To investigate the key factors influencing the implementation of long-acting injection-promoting policies and propose effective improving paths.Methods Qualitative interviews were carried out for stakeholders involved in the promotion of long-acting injections,based on the consolidated framework for implementation research.Additionally,countermeasures for identified barriers were proposed based on expert recommendations for implementation changes.Results A total of 46 health administrators,healthcare workers,and patients in Beijing were interviewed.The study identified several barriers in the strength and quality of evidence,adaptability,relative advantage,complexity and cost,patient needs and resources,external collaboration,external policies and incentives,organizational structural characteristics,and self-efficacy.Conclusions From the perspectives and experiences of stakeholders,the promotion of long-acting injections has shown initial success but still faces multiple obstacles.It is recommended that efforts should be made to coordinate and adapt policies,improve and incentivize relative organizations,and continuously strengthen the advocacy and education for individuals.
Humans ; Beijing ; Delayed-Action Preparations ; Health Personnel ; Health Policy ; Injections

Objective:To investigate the impact of ginseng alcohol extract(GEE)on improving sleep quality in the aged Drosophila model by regulating the redox balance,and to elucidate its associated mechanism.Methods:Thirty-two male drosophila melanogaster(7-days-old)were randomly selected as young group,while 64 male Drosophila melanogaster flies(35-days-old)were randomly assigned to aged model group(n=32)and GEE group(n=32).The sleep parameters,including total sleep duration,daytime sleep duration,night sleep duration,0-4 h of sleep duration after lights off(ZT0-4 sleep duration),deep sleep duration,sleep episodetimes,sleep fragmentation,and the activity parameters such as the total number of locomotor activity daytime locomotor activity amount and nighttime locomotor activity amount were analyzed using the DAM2 Drosophila behavioral analysis system 7 d after administration.The grouping of the drosophila was as above,and there were 100 drosophila ineach group.The differentially expressed proteins in drosophila brain tissue were screened,identified,and functionally analyzed using two-dimensional fluorescence difference gel electrophoresis(2D-DIGE)and matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF/TOF-MS)proteomic methods.The grouping of the drosophila was as above,and there were 100 drosophila in each group.The activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)and the levels of lipid peroxidation product(MDA)in brain tissue of the drosophila were determined using assay kits.Results:Compared with young group,the total sleep duration daytime sleep duration and night sleep cluration of the drosophila in agaed group were decreased(P<0.05 or P<0.01);and the sleep rhythm amplitude was shortened.Compared with aged group,the total sleep duration and daytime and nighttime sleep durations of the drosphila in GEE group were lengthened(P<0.01).Compared with young group,the ZT0-4 sleep duration deep sleep duration and sleep fragment of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the sleep rhythm amplitude was shortened.Compared with young group,the ZT0-4 sleep duration,deep sleep duration,and single sleep fragment of the drosphila in GEE group were significantly prolonged(P<0.01),and the sleep amplitude was increased.Compared with young group,there was no significant difference in diurnal spontaneous activity or total spontaneous activity of the drosophila in aged group(P>0.05),while the nocturnal spontaneous activity was significantly increased(P<0.05).Compared with aged group,the diurnal spontaneous activity,nocturnal spontaneous activity,and total spontaneous activity of the drosophila in GEE group were significantly decreased(P<0.05 or P<0.01).A total of 47 differentially expressed proteins were selected in the 2D-DIGE electrophoretic mapping.Compared with young group,the expressions of 47 differentially expressed protein sites in aged group were down-regulated mainly including glutathione S-transferase,peroxiredoxin 1 and dihydrolipoic dehydrogenase,which were related to redox balance.Compared with young group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the level of MDA was increased(P<0.01);compared with aged group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosphila in GEE group were increased(P<0.05 or P<0.01),and the MDA level was decreased(P<0.05).Conclusion:GEE has improvement effect on the sleep quality of aged drosophila,and its possible mechanism may be related to upregulating the activities of antioxidant enzymes,inhibiting the accumulation of lipid peroxidation products,and maintaining redox balance.