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.

mRNA gene therapy has attracted much attention due to its advantages such as scalability, modification, no need to enter the nucleus and no integration of host genes. In gene therapy, safe and effective delivery of mRNA into cells is critical for the success of gene therapy. In this study, we designed and synthesized an amphiphilic cationic lipopeptide gene vector (dendritic arginine & disulfide bond-containing cationic lipopeptide, RLS) enriched with branched arginine. We achieved a 1.5-fold higher mRNA transfection efficiency in zebrafish compared to the commercial reagent Lipofectamine 2000, and confirmed its good biosafety by in vitro cytotoxicity and in vivo biosafety. First, we characterized the chemical composition of the cationic lipid peptides by nuclear magnetic resonance hydrogen spectroscopy (¹H NMR) and time-of-flight mass spectrometry (MS). The results of particle size and potential tested by dynamic light scattering particle size analysis showed that at a nitrogen/phosphorus (N/P) ratio of 20, the RLS/mRNA composite assemblies formed homogeneous nanoparticles with an average particle size of about 220 nm and a surface ζ potential of about +21 mV. In vitro gene transfection, the transfection experiments demonstrated that RLS exhibited 1.2-fold higher transfection efficiency in human embryonic kidney 293 cells (HEK293) and 3-fold higher transfection efficiency in rat mesenchymal stem cells (MSC) compared to Lipofectamine 2000. In addition, after microinjection of RLS into zebrafish embryos, we evaluated the survival, hatching, and teratogenicity rates, all of which confirmed its favorable in vivo safety profile. Thus, this amphiphilic cationic lipid peptide RLS, enriched with branched arginine, exhibits excellent mRNA delivery properties and safety. These findings highlight its potential as a promising gene therapy tool.

[Objective] To understand the resistance mechanisms, virulence characteristics, and pathogenicity of hypervirulent Klebsiella pneumoniae (hvKp), which causes pyogenic liver abscess (PLA), and to provide related data for clinical treatment of infection caused by this type of bacteria. [Methods] We collected three strains of Klebsiella pneumoniae isolated from the liver abscess fluid of patients with liver abscesses in various departments of The First Affiliated Hospital of Xi’an Jiaotong University. The hypervirulent phenotypes were determined by the wire test, and drug sensitivity was assessed using the VITEK 2 compact automatic microbiological analyzer. Molecular characteristics such as podocarp serotypes, multi-locus sequence typing (MLST), virulence genes, and drug resistance genes were identified through whole-genome sequencing. Additionally, a mouse infection model was established to evaluate pathogenicity. [Results] The isolates were sticky, with mucous thread pulling length >5 mm, all of which exhibited high viscosity phenotypes. Except 146007, which is a multidrug-resistant bacterium, the other two strains had higher antibiotic sensitivity. Whole genome sequencing revealed that the isolates were of high-virulence type, carrying the toxin plasmid rmpADC/rmpA2, iron uptake system, bacterial hairs, secretion system, and other virulence factors. All the three isolates tested positive for rmpA/rmpA2 combined with iucA/iutA, indicating they could be classified as hvKp. Multiple resistance genes were detected, such as β-lactamase like blaTEM, blaSHV, and blaCTX-M. The isolates were highly pathogenic to mice, as evidenced by the diseased organs of the deceased mice observed on autopsy. [Conclusion] The three strains of hvKp isolated in this study carried multiple drug-resistant genes and were highly pathogenic, which suggests that in laboratories and clinical practices it is imperative to promptly identify and adopt necessary disposal strategies to prevent further spread of hvKp.

Objective To investigate whether microRNA(miR)-199a-5p regulates blood-brain barrier(BBB)integrity through PI3K/Akt pathway after cerebral ischemia.Methods A permanent middle cerebral artery occlusion(MCAO)model was established in SPF adult male SD rats.Totally 48 rats were randomly divided into sham group(n=12),model group(n=12),MCAO+miR-199a-5p group(n=12),and MCAO+miR-199a-5p negative control group(n=12).The Ludmila Bellayev 12 point score was used to evaluate the neurobehavioral performance of rats;The integrity of the BBB after ischemia stroke was detected through Evans blue staining;Immunofluorescent staining was used to determine apoptosis after cerebral ischemia;Western blotting technology was used to detect the protein expression of claudin-5,phosphatidylinositol-3 kinase regulatory subunit 2(PIK3R2),p-Akt,Akt,and vascular endothelial growth factor(VEGF)-A;Real-time PCR was used to investigate the expression levels of miR-199a-5p,claudin-5,and VEGF-A in the ischemic penumbra and infarcted area of the brain.Results The result showed that miR-199a-5p mimic intervention improved proprioception and motor ability in MCAO rats.MiR-199a-5p mimic reduced the expression of PIK3R2 following ischemia stroke,activated the Akt signaling pathway,and increased the expression of claudin-5 and VEGF-A in the ischemic penumbra.In addition,miR-199a-5p alleviated inflammation after cerebral ischemia.MiR-199a-5p mimic reduced BBB permeability and reduced neuronal apoptosis after cerebral ischemia.Conclusion MiR-199a-5p can reduce the expression of PIK3R2 following ischemic stroke,activate the Akt signaling pathway,reduce the expression of inflammatory cytokines,and alleviate the damage to the blood-brain barrier.

Aim To investigate the effect of Dahuang Tangluo pills(DHTL)on NOD-like receptor protein 3(NLRP3)/cysteine aspartate proteolytic enzyme-1(caspase-1)/apodermic D(GSDMD)pathway-media-ted pyroptosis in db/db mice with diabetic kidney dis-ease(DKD)and the underlying mechanism.Methods Eight db/m mice were selected as control group,and forty db/db mice were randomly divided into mod-el group,low dose group,medium dose group,high dose group and dapagliflozin group,with eight mice in each group.The control group and model group were given equal volume normal saline intragastric adminis-tration,the low,medium and high dose groups were given DHTL solution of 0.9,1.8 and 3.6 mg·kg-1,respectively,and the dapagliflozin group was given dapagliflozin tablet solution of 1.5 mg·kg-1,and the six groups were given intragastric administration once a day for 10 weeks.The body weight of mice was meas-ured daily and the dose was adjusted during adminis-tration.Fasting blood glucose(FBG)and body weight were measured after administration.The levels of 24-hour urinary total protein(24h-UTP),blood creatinine(Scr)and urea nitrogen(BUN)were measured by au-tomatic biochemical analyzer.The levels of interleukin-1 β(IL-1β),interleukin-6(IL-6),interleukin-18(IL-18)and tumor necrosis factor-α(TNF-α)in re-nal tissue of mice were determined by enzyme-linked immunosorbent assay(ELISA).The pathological changes of renal tissue were observed by hematoxylin-eosin(HE)staining.The DNA damage in mouse kid-ney tissue was observed using in situ end labeling(TUNEL)staining.The mRNA and protein expres-sions of NLRP3,caspase-1 and GSDMD in mouse kid-ney tissues were detected by Real-time quantitative PCR and Western blot.Results Compared with the control group,FBG,body weight,IL-1β,IL-6,IL-18 and TNF-α in the model group significantly increased(P＜0.01).The mRNA and protein expressions of NLRP3,caspase-1 and GSDMD in mouse kidney tis-sues significantly increased(P＜0.01).Compared with the model group,the levels of FBG,body weight,IL-1β,IL-6,IL-18 and TNF-α in each administration group significantly decreased(P＜0.05).The patho-logical morphology of renal tissue was improved in dif-ferent degrees,and the number of positive cells in re-nal tissue was significantly reduced(P＜0.05).The mRNA and protein expressions of NLRP3,caspase-1 and GSDMD in renal tissue of mice in high and medi-um dose of DHTL and dapagliflozin group significantly decreased(P＜0.05).Conclusions DHTL can im-prove the renal injury of DKD,and its mechanism may be through the regulation of NLRP3/caspase-1/GSD-MD pathway to inhibit pyroptosis and relieve the in-flammatory response of DKD mice.