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.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

BACKGROUND:Kaempferol has anti-osteoporotic effects,but the mechanisms by which kaempferol regulates gut microbiota and metabolites to prevent and treat osteoporosis remain unclear.OBJECTIVE:To exploring the potential mechanisms by which kaempferol inhibit osteoporosis based on gut microbiota and comprehensive targeted metabolomics.METHODS:Eighteen female Sprague-Dawley rats were randomly divided into three groups:sham operation group,model group,and kaempferol group,with 6 rats in each group.Animal models of osteoporosis were made in the latter two groups through removal of bilateral ovaries.Eight weeks after modeling,the sham operation and model groups were gavaged with distilled water,and the kaempferol group was gavaged with 40 mg/kg kaempferol.Continuous administration in each group was carried out for 12 weeks.Rat fecal samples were collected for 16S rDNA amplicon sequencing to observe changes in the gut microbiota structure.Serum samples were subjected to comprehensive targeted metabolomics analysis using ultra-high performance liquid chromatography-tandem mass spectrometry technology,along with a proprietary database and multivariate statistical analysis.RESULTS AND CONCLUSION:After 12 weeks of continuous intervention,the results of 16S rDNA amplicon sequencing showed that compared with the sham operation group,the abundance of gut microbiota increased in the model group.Compared with the model group,kaempferol group exhibited a statistically significant increase in the abundance of the genus Latilactobacillus(P=0.021),while the abundances of Pantoea(P=0.034),Enterorhabdus(P=0.000),Monoglobus(P=0.024),Butyricimonas(P=0.034),Rothia(P=0.043),and Clostridia(P=0.004)were significantly downregulated.After 12 weeks of continuous intervention,the results of the serum samples analyzed by broad-targeted metabolomics revealed that 120 and 79 metabolites were identified between the sham operation and model groups and between the model and kaempferol groups,respectively.Among the three groups,there were 17 overlapping differentially expressed metabolites,including Cis-aconitic acid,barbituric acid,L-homocitrulline,3,4,5-trimethoxycinnamic acid,L-3-phenyllactic acid,cyclo(pro-pro),L-phenylalanine-L-serine,proline-isoleucine,L-donoraminoacetic acid-L-phenylalanineacetic acid,and phenylalanine-aspartic acid.Most of them belong to amino acids and their metabolites,glycerophospholipids and fatty acyls.The Kyoto Encyclopedia of Genes and Genomes pathways involved in the differential metabolites were mainly enriched in D-amino acid metabolism,histidine metabolism,propionate metabolism,lysine degradation,fatty acid metabolism and sphingolipid metabolism.After 12 weeks of continuous intervention,combined analysis revealed that genera such as Enterorhabdus,Latilactobacillus,Rothia,and Ruminococcus were closely associated with differential serum metabolites.To conclude,kaempferol may exert its anti-osteoporotic effects by modulating the abundance,diversity,and structure of gut microbiota,thereby regulating the metabolism of amino acids,their metabolites,and fatty acids.

BACKGROUND:Kaempferol has anti-osteoporotic effects,but the mechanisms by which kaempferol regulates gut microbiota and metabolites to prevent and treat osteoporosis remain unclear.OBJECTIVE:To exploring the potential mechanisms by which kaempferol inhibit osteoporosis based on gut microbiota and comprehensive targeted metabolomics.METHODS:Eighteen female Sprague-Dawley rats were randomly divided into three groups:sham operation group,model group,and kaempferol group,with 6 rats in each group.Animal models of osteoporosis were made in the latter two groups through removal of bilateral ovaries.Eight weeks after modeling,the sham operation and model groups were gavaged with distilled water,and the kaempferol group was gavaged with 40 mg/kg kaempferol.Continuous administration in each group was carried out for 12 weeks.Rat fecal samples were collected for 16S rDNA amplicon sequencing to observe changes in the gut microbiota structure.Serum samples were subjected to comprehensive targeted metabolomics analysis using ultra-high performance liquid chromatography-tandem mass spectrometry technology,along with a proprietary database and multivariate statistical analysis.RESULTS AND CONCLUSION:After 12 weeks of continuous intervention,the results of 16S rDNA amplicon sequencing showed that compared with the sham operation group,the abundance of gut microbiota increased in the model group.Compared with the model group,kaempferol group exhibited a statistically significant increase in the abundance of the genus Latilactobacillus(P=0.021),while the abundances of Pantoea(P=0.034),Enterorhabdus(P=0.000),Monoglobus(P=0.024),Butyricimonas(P=0.034),Rothia(P=0.043),and Clostridia(P=0.004)were significantly downregulated.After 12 weeks of continuous intervention,the results of the serum samples analyzed by broad-targeted metabolomics revealed that 120 and 79 metabolites were identified between the sham operation and model groups and between the model and kaempferol groups,respectively.Among the three groups,there were 17 overlapping differentially expressed metabolites,including Cis-aconitic acid,barbituric acid,L-homocitrulline,3,4,5-trimethoxycinnamic acid,L-3-phenyllactic acid,cyclo(pro-pro),L-phenylalanine-L-serine,proline-isoleucine,L-donoraminoacetic acid-L-phenylalanineacetic acid,and phenylalanine-aspartic acid.Most of them belong to amino acids and their metabolites,glycerophospholipids and fatty acyls.The Kyoto Encyclopedia of Genes and Genomes pathways involved in the differential metabolites were mainly enriched in D-amino acid metabolism,histidine metabolism,propionate metabolism,lysine degradation,fatty acid metabolism and sphingolipid metabolism.After 12 weeks of continuous intervention,combined analysis revealed that genera such as Enterorhabdus,Latilactobacillus,Rothia,and Ruminococcus were closely associated with differential serum metabolites.To conclude,kaempferol may exert its anti-osteoporotic effects by modulating the abundance,diversity,and structure of gut microbiota,thereby regulating the metabolism of amino acids,their metabolites,and fatty acids.

Background: We investigated osteoporosis management strategies and clinical outcomes following the occurrence of medicationrelated osteonecrosis of the jaw (MRONJ). Methods: We retrospectively studied individuals diagnosed with MRONJ during osteoporosis treatment who were managed in the Osteoporosis Center or the Oral Maxillofacial Surgery & Dental Unit at Queen Mary Hospital in Hong Kong between 2010 and 2022. We examined subsequent osteoporosis management plans, fracture events, and bone mineral density (BMD). Results: Thirty-six individuals were included (mean age, 78.5 years; 94.4% women). The estimated prevalence of MRONJ was 0.26%. All patients had been exposed to bisphosphonates, and seven had also received denosumab before MRONJ. Following MRONJ, only 14 individuals continued anti-osteoporosis treatment, a decision influenced by a higher fracture probability at MRONJ onset. The most common regimen was a teriparatide-raloxifene sequence (n=8): three patients achieved stable BMD, four achieved improving BMD, and one exhibited a mixed response. The patient with a mixed BMD response had also been treated with denosumab. Six patients sustained incident fractures after MRONJ, and these patients had lower BMD T-scores than their counterparts. Two patients experienced MRONJ recurrence, which was associated with the resumption of bisphosphonate or denosumab therapy after MRONJ. These patients had higher BMD T-scores than those who did not experience MRONJ recurrence. Conclusion MRONJ is challenging because high fracture risk necessitates discontinuation of antiresorptive agents. Teriparatide followed by raloxifene may be a reasonable regimen. Individualised decisions in osteoporosis management after MRONJ are required to balance fracture risk reduction with minimising MRONJ recurrence.

This paper discusses the mechanism of acupuncture for diabetes mellitus from the perspective of molecular biology, aiming to reveal the potential rules of restoring the balance in the body and fighting against diabetes mellitus (DM). By searching the basic research literature of acupuncture treatment for DM, it is found that the molecular biological mechanism of acupuncture treatment for DM is closely related to the regulation of insulin signaling pathway, the modulation of inflammatory response, the protection and regeneration of islet β-cells, fat metabolism and energy balance. It points out that there are few studies of acupuncture on the type 1 diabetes mellitus and the large-scale randomized controlled trials, as well as the studies on the upstream regulation mechanism, the specific cellular molecules and the interaction mechanism of various molecules. It needs to deepen the multi-level, multi-target and multi-dimensional exploration on the molecular biological mechanism of acupuncture for diabetes mellitus.
Humans ; Acupuncture Therapy ; Diabetes Mellitus/genetics* ; Animals ; Insulin/metabolism* ; Signal Transduction