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 evaluate the impact of assisted reproductive technology (ART) on postpartum breastfeeding outcomes.Methods:This paper electronically retrieved the China Biology Medicine disc, China National Knowledge Infrastructure, VIP, WanFang Data, PubMed, Embase, Web of Science, and Cochrane Library. The search period was from database establishment to March 15, 2023. After independent literature search, screening, data extraction, and quality evaluation by two researchers, Meta-analysis was conducted using R 4.2.2 software.Results:A total of 11 articles were included. Meta-analysis showed that compared with naturally conceived mothers, the rates of exclusive breastfeeding at 1th week postpartum ( RR=0.84, 95% CI: 0.73-0.97), exclusive breastfeeding at 6th months postpartum ( RR=0.77, 95% CI: 0.61-0.98), and the incidence of breastfeeding for >6 months postpartum ( RR=0.71, 95% CI: 0.53-0.96) were decreased, and the rate of artificial feeding at 12th months postpartum ( RR=1.09, 95% CI: 1.02-1.17) was increased. However, there were no statistically significant differences in the rate of artificial feeding at 8th months postpartum, the incidence of breastfeeding duration >12 months, and the incidence of breastfeeding difficulties ( P>0.05) . Conclusions:ART reduces the rate of exclusive breastfeeding in postpartum 1th week and 6th months, and the incidence of postpartum breastfeeding duration>6 months, and increases the artificial feeding rate in postpartum 12th months. However, the impact of ART on the incidence of breastfeeding difficulties is not yet clear and still needs to be further demonstrated by high-quality studies.

[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.

The aim of this study was to investigate the impact of overexpressing 70-kD heat shock pro-teins(Hsp70)on glycolysis in C2C12 cells during myogenesis and adipogenesis.Using C2C12 cells as the research material,adenovirus was used to overexpress the Hsp70 gene,and changes in the expression of glycolytic genes were detected using fluorescence quantitative PCR and Western blotting techniques.The study indicated that during C2C12 cell myogenic differentiation,the expression trend of the Hsp70 gene was consistent with that of Gsk3β,Pkm,Prkag3,Pfkm,and Hk-2 genes,suggesting a relationship between Hsp70 and the glycolytic pathway during myogenic differentiation.Overexpression of Hsp70 in the later stages of myogenic differentiation significantly upregulated the expression of Gsk3β,Pkm,Prk-ag3,and Pfkm genes(P＜0.05),with no significant impact on Hk-2 gene expression(P＞0.05).Dur-ing C2C12 cell adipogenic induction,the expression trend of the Hsp70 gene was similar to that of Gsk3β,Pkm,Prkag3,Pfkm,and Hk-2 genes,indicating a relationship between Hsp70 and the glycolytic path-way during adipogenic induction.Following Hsp70 overexpression,in the later stages of adipogenic in-duction,the number of lipid droplets was significantly higher compared to the control group,with a sig-nificant upregulation of Gsk3β,Pkm,Prkag3,and Pfkm gene expression(P＜0.05),while Hk-2 gene expression was not significantly affected(P＞0.05).In conclusion,Hsp70 in C2C12 cells in myogenic and adipogenic states promoted the breakdown of glycogen into 6-phospho-glucose,thereby enhancing the glycolytic pathway,providing insights into the functional role of the Hsp70 gene in glycolysis in C2C12 cells.