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.

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

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

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.

【Objective】 To establish an effective quality monitoring indicator system for blood quality control in blood banks, in order to analyze the quality control indicators for blood collection and supply, and evaluate blood quality control process, thus promoting continuous improvement and standardizing management of blood quality control in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation services, component preparation, blood testing, blood supply and quality control was established. The Questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process was distributed to 17 blood banks in Shandong, which clarified the definition and calculation formula of indicators. The quality monitoring indicator data from January to December 2022 in each blood bank were collected, and 20 quality control indicators data were analyzed by SPSS25.0 software. 【Results】 The average pass rate of key equipment monitoring, environment monitoring, key material monitoring, and blood testing item monitoring of 17 blood banks were 99.47%, 99.51%, 99.95% and 98.99%, respectively. Significant difference was noticed in the pass rate of environment monitoring among blood banks of varied scales(P<0.05), and the Pearson correlation coefficient (r) between the total number of blood quality testing items and the total amount of blood component preparation was 0.645 (P<0.05). The average discarding rates of blood testing or non-blood testing were 1.14% and 3.36% respectively, showing significant difference among blood banks of varied scales (P<0.05). The average discarding rate of lipemic blood was 3.07%, which had a positive correlation with the discarding rate of non testing (r=0.981 3, P<0.05). There was a statistically significant difference in the discarding rate of lipemic blood between blood banks with lipemic blood control measures and those without (P<0.05). The average discarding rate of abnormal color, non-standard volume, blood bag damage, hemolysis, blood protein precipitation and blood clotting were 0.20%, 0.14%, 0.06%, 0.06%, 0.02% and 0.02% respectively, showing statistically significant differences among large, medium and small blood banks(P<0.05).The average discarding rates of expired blood, other factors, confidential unit exclusion and unqualified samples were 0.02%, 0.05%, 0.003% and 0.004%, respectively. The discarding rate of blood with air bubbles was 0.015%, while that of blood with foreign body and unqualified label were 0. 【Conclusion】 The quality control indicator system of blood banks in Shandong can monitor weak points in process management, with good applicability, feasibility, and effectiveness. It is conducive to evaluate different blood banks, continuously improve the quality control level of blood collection and supply, promote the homogenization and standardization of blood quality management, and lay the foundation for comprehensive evaluation of blood banks in Shandong.

Objective To quantitatively analyze the impact of environmental hygiene on the occurrence of health-care-associated infections(HAI).Methods Monitoring data of HAI and environmental hygiene from a tertiary first-class hospital from January 2018 to December 2022 were collected,and the impact of environmental bacterial colony forming unit(CFU)on the occurrence of HAI was analyzed by a time-series generalized additive model.Results The single-contamination model showed a significant positive correlation between HAI and staff's hand bacterial CFU(β1=0.009,P=0.012).For an increase of 1 interquartile range(IQR)in the monthly mean CFU per dish(MCFU/Dish)of staffs'hand,the incidence of HAI increased by 13.28％(95％CI:2.82％-24.81％).Subgroup and lag effect analysis showed that when the monthly MCFU/Dish(after hand disinfection)of staffs'hand in-creased by one IQR,the excess risk(ER)of HAI for the month(lag0)was 16.26％(95％CI:15.45％-17.09％).In the multi-contamination model,the correlation between surface contamination and HAI was also statistically sig-nificant.Conclusion There is a significant correlation between hospital environmental hygiene and the occurrence of HAI.