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.

Neutralizing antibodies have been designed to specifically target and bind to the receptor binding domain (RBD) of spike (S) protein to block severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus from attaching to angiotensin converting enzyme 2 (ACE2). This study reports a distinctive nanobody, designated as VHH21, that directly catalyzes the S-trimer into an irreversible transition state through postfusion conformational changes. Derived from camels immunized with multiple antigens, a set of nanobodies with high affinity for the S1 protein displays abilities to neutralize pseudovirion infections with a broad resistance to variants of concern of SARS-CoV-2, including SARS-CoV and BatRaTG13. Importantly, a super-resolution screening and analysis platform based on visual fluorescence probes was designed and applied to monitor single proteins and protein subunits. A spontaneously occurring dimeric form of VHH21 was obtained to rapidly destroy the S-trimer. Structural analysis via cryogenic electron microscopy revealed that VHH21 targets specific conserved epitopes on the S protein, distinct from the ACE2 binding site on the RBD, which destabilizes the fusion process. This research highlights the potential of VHH21 as an abzyme-like nanobody (nanoabzyme) possessing broad-spectrum binding capabilities and highly effective anti-viral properties and offers a promising strategy for combating coronavirus outbreaks.
Single-Domain Antibodies/immunology* ; Spike Glycoprotein, Coronavirus/metabolism* ; SARS-CoV-2/immunology* ; Animals ; Humans ; Antibodies, Neutralizing/immunology* ; Camelus ; COVID-19/immunology* ; Antibodies, Viral/immunology* ; Angiotensin-Converting Enzyme 2

The tumor microenvironment is a crucial factor in tumor occurrence and progression. The hypoxic microenvironment is widely present in tumor tissue and is a key endogenous factor accelerating tumor deterioration. The "blood stasis toxin" theory, as an emerging perspective in tumor research, is regarded as the unique "soil" in tumor progression from the perspective of traditional Chinese medicine(TCM) due to its dynamic evolution mechanism, which closely resembles the formation of the hypoxic microenvironment. Scientifically integrating TCM theories with the biological characteristics of tumors and exploring precise syndrome differentiation and treatment strategies are key to achieving comprehensive tumor prevention and control. This article focused on the hypoxic microenvironment of the tumor, elucidating its formation mechanisms and evolutionary processes and carefully analyzing the internal relationship between the "blood stasis toxin" theory and the hypoxic microenvironment. Additionally, it explored the interaction among blood stasis, toxic pathogens, and hypoxic environment and proposed micro-level prevention and treatment strategies targeting the hypoxic microenvironment based on the "blood stasis toxin" theory, aiming to provide TCM-based theoretical support and therapeutic approaches for precise regulation of the hypoxic microenvironment.
Humans ; Tumor Microenvironment/drug effects* ; Neoplasms/therapy* ; Animals ; Medicine, Chinese Traditional ; Disease Progression ; Drugs, Chinese Herbal

Objective To explore the molecular mechanism by which SOS Ras/Rac guanine nucleotide exchange factor 1-intronic transcript 1(SOS1-IT1)affects enhancer of zeste homolog 2(EZH2)protein expression in endometrial cancer cells Ishikawa and RL95-2.Methods Lentiviral transfection of short hairpin RNA(shRNA)and overexpression plasmid were used in Ishikawa and RL95-2 cell lines to knock down and overexpress SOS1-IT1.The mechanism of EZH2 expression regulation was studied using Real-time PCR,Western blotting,and chromatin immunoprecipitation.Results The expression of SOS1-IT1 and EZH2 genes was positively correlated in endometrial cancer tissues.Knocking down SOS1-IT1 significantly reduces the expression of EZH2,inhibited the proliferation and migration of Ishikawa and RL95-2 cells,and could reduced the acetylation of histone H3 at position 27(H3K27)and the enrichment of CREB binding protein(CBP)in the EZH2 gene promoter region.Overexpression of SOS1-IT1 could increased the expression of EZH2 and enhance the acetylation of H3K27 and the enrichment of CBP.CBP could bind to SOS1-IT1 RNA,and this binding ability was weakened when CBP was knocked down.Conclusion SOS1-IT1 can promote the expression level of EZH2 in endometrial cancer cells Ishikawa and RL95-2 by regulating the acetylation modification level of the EZH2 gene promoter region,thereby affecting the proliferation and migration ability of endometrial cancer cells.

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.

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

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.

AIM To investigate the effects of Wenyang Jiedu Tongluo Recipe(WYJDTLR)on macrophage recruitment and polarization function in a mouse model of diabetic kidney disease(DKD).METHODS 50 db/db mice were randomly divided into the model group,the valsartan group(10.29 mg/kg)and the high-dose,medium-dose and low-dose WYJDTLR groups(26.52,13.26 and 6.63 g/kg),with 10 mice in each group,in contrast to another 10 db/m mice of the blank group.After 8 weeks of administration,the mice had their levels of fasting blood glucose,24-hour urinary protein quantity(24h-UTP),serum creatinine(Scr)and blood urea nitrogen(BUN)observed;their morphological changes of renal tissues observed by HE staining;their degree of renal glycogen deposition observed by PAS staining;their degree of renal fibrosis observed by Masson staining;their levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1 β in renal tissue detected by ELISA;their renal protein expressions of VCAM-1 and ICAM-1 detected by IHC and Western blot;and their renal expressions of CD86 and CD206 detected by IF.RESULTS Compared with the model group,the WYJDTLR groups displayed decreased levels of fasting blood glucose,24h-UTP,Scr and BUN(P＜0.05,P＜0.01);improved degree of glomerular hypertrophy,mild proliferation of mesangial cells,dilatation of renal tubular,vacuolar degeneration of renal tubular epithelial cells,deposition of glomerular glycogen,and fibrosis of renal tissues(P＜0.01);decreased levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1β in renal tissue(P＜0.05,P＜0.01);decreased renal protein expressions of VCAM-1 and ICAM-1(P＜0.05,P＜0.01),thus reduced the recruitment of macrophages to the kidney;decreased renal CD86 protein expression(P＜0.01);and increased CD206 protein expression(P＜0.01),thus inhibited M1-type polarization of macrophages and promoted M2-type polarization of macrophages.CONCLUSION WYJDTLR can delay the DKD progression in mice by reducing the occurrence of inflammatory reactions through reducing the level of macrophage recruitment factor,inhibiting the M1-type polarization,and promoting the M2-type polarization.