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.

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 explore the safety and effectiveness of the robot-assisted system for transforaminal percutaneous endoscopic in the treatment of lumbar disc herniation with lumbar instability.Methods From October 2021 to March 2023,26 patients with single-segment lumbar disc herniation and lumbar spinal instability were treated with robot-assisted system for transforaminal percutaneous endoscopic.The operation time,intraoperative blood loss,incision length,postoperative drainage volume,postoperative ambulation activity time,postoperative hospitalization time were record.The intervertebral space height and the lumbar lordosis angle before and after surgery were observed and compared.Pain level was evaluated using the visual analogue scale(VAS).The clinical efficacy was evaluated by Oswestry disability index(ODI).The interbody fusion was evalu-ated by Brantigan Steffee criteria.Results All patients successfully completed the operation,the operation time ranged form 105 to 109 min with an average of(150.8±24.1)min.Intraoperative blood loss ranged form 35 to 88 ml with an average of(55.5±16.4)ml.Incision length ranged form 1.4 to 3.5 cm with an average of(2.3±0.8)cm.Postoperative drainage volume ranged form 15 to 40 ml with an average of(28.5±7.8)ml.Postoperative ambulation time ranged form 15 to 30 h with an aver-age of(22.8±4.5)h.Postoperative hospitalization time was 3 to 7 d with an average of(4.2±1.3)d.Total of 26 patients were followed up,the duration ranged from 12 to 16 months with an average of(14.0±1.3)months.The VAS and ODI at 1 week[(2.96±0.72)points,(41.63±4.79)％]and 12 months[(1.27±0.60)points,(13.11±2.45)％]were significantly different from those before surgery[(6.69±0.93)points,(59.12±5.92)％],P＜0.0 1.The height of the intervertebral space(11.95±1.47)mm and lumbar lordosis(57.46±7.59)° at 12 months were significantly different from those before surgery[(6.67±1.20)mm,(44.08±7.79)°],P＜0.01.At 12 months after surgery,all patients had no pedicle screw rupture or dislocation of the fusion cage,and the intervertebral fusion was successful.According to Brantigan-Steffee classification,17 cases were grade D and 9 cases were grade E.Conclusion Robot-assisted system for transforaminal percutaneous endoscopic for the treatment of single-segment lumbar disc herniation with lumbar instability improved the accuracy and safety of the operation,and the clinical effect of early follow-up is accurate.

Aim To investigate the effects of multi-gly-cosides of Tripterygium wilfordii(GTW)on mitochon-drial dynamics-related proteins and the mechanism of nephroprotective effects in IgA nephrophathy(IgAN)rats.Methods SPF grade male SD rats were random-ly divided into the Control group,modelling group,prednisone group(6.25 mg·kg·d-1)and GTW group(6.25 mg·kg·d-1).The IgAN rat model was established by the method of"bovine serum albumin(BSA)+carbon tetrachloride(CCl4)+lipopolysac-charide(LPS)".The total amount of urinary protein(24 h-UTP)and erythrocyte count in urine were meas-ured in 24 h urine.Blood biochemistry of serum albu-min(ALB),alanine aminotransferase(ALT),urea ni-trogen(BUN),and creatinine(Scr)were measured in abdominal aorta of the rats;immunofluorescence and HE staining were used to observe the histopathology of the kidneys;RT-PCR and Western blotting were used to detect the mRNA and protein expression levels of key proteins regulating mitochondrial division and fu-sion:dynamin-related protein 1(Drp1),mitochondrial fusion protein 1(Mfn1),and mitochondrial fusion pro-tein 2(Mfn2),and PTEN-induced putative kinase 1(Pink1),in the kidney tissue of rats.Results GTW significantly reduced urinary erythrocyte count and 24 h-UTP,decreased serum ALT,BUN and Scr levels,in-creased serum ALB levels,improved renal histopatho-logical status in IgAN rats,increased the protein and mRNA expression levels of Mfn1,Mfn2,and Pink1,and decreased the protein and mRNA expression levels of Drp1 in renal tissues.Conclusions GTW may regu-late mitochondrial structure and maintain the dynamic balance of mitochondrial dynamics by promoting the ex-pression of Mfn1,Mfn2,Pink1 and decreasing Drp1.This may result in a reduction in urinary erythrocyte counts and proteinuria,and an improvement in renal function.

Aim To explore the mechanism of action of Tripterygium glycosides tablets on kidney of rats with IgA nephropathy based on inflammation-related path-ways.Methods Forty-five male SD rats of SPF grade were randomly divided into control group and modeling group.In addition to the blank group,the modeling group used the combination of bovine serum albumin(BSA)+carbon tetrachloride(CC14)+lipopolysac-charide(LPS)to establish the IgA nephropathy rat model.Successfully modeled rats were randomly divid-ed into the model group,the prednisone group and Tripterygium glycosides tablets group,and the treat-ment group was given the drug by gavage from the 13 th week,and the 24 hours urine,blood and kidney tis-sues of the rats were collected and examined after 4 weeks of the administration of the drug.Urine erythro-cyte count,quantitative 24-h urine protein(24 h-UTP),urea nitrogen(BUN),and blood creatinine(Scr)were detected in each group;serum interleukin 1β(IL-1β)and tumor necrosis factor α(TNF-α)were detected by enzyme-linked immunosorbent assay(Elisa);the pathological changes in the renal tissues of the rats in each group were observed by horizontal hematoxylin-eosin(HE)staining;and the renal tis-sues in each group were observed by Western blotting.The expressions of LIGHT,HVEM,LTβR proteins and their mRNAs in rat kidney tissue were detected by Western blot and real-time fluorescence quantitative polymerase chain reaction(RT-PCR).Results Tripterygium glycosides tablets significantly reduced the levels of urinary erythrocyte count,24 h-UTP,BUN,and Scr in IgA nephropathy rats(P＜0.01),improved renal histopathology,lowered the levels of se-rum inflammatory factors IL-1β and TNF-α(P＜0.01),and lowered the levels of LIGHT,HVEM,LTβR proteins and their mRNA expression in renal tis-sues(P＜0.01).Conclusions Tripterygium glyco-sides tablets may inhibit the immune response and re-duce the release of inflammatory factors by down-regu-lating the LIGHT-HVEM/LT(3R pathway,thus reduc-ing the inflammatory response,lowering the urinary e-rythrocytes and urinary proteins,improving the renal nephron pathologic injury,and protecting the renal function.

RNA fluorescence aptamers are RNA sequences that can specifically bind to non-toxic,cell permeable,and self-fluorescent target molecules and activate their luminescent properties.These aptamers provide powerful tools for biosensing and imaging researches due to their simple structure,easy synthesis,and easy transfection.This article summarized the characteristics and development history of various RNA fluorescent aptamers,including Malachite Green,Spinach,Broccoli,Mango,Corn,and Pepper family,as well as their corresponding fluorescent groups.The applications of RNA fluorescent aptamers were also reviewed from two aspects:extracellular detection and cell imaging.This review might provide guidance for labeling,detection and interactions of molecules from proof of concept and clinical assessment to practical clinical and biomedical applications.