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

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 investigate the clinical value of low molecular weight heparin bridging therapy for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents.Methods:The propensity score matching and retrospective cohort study was conducted. The clinical data of 126 patients undergoing tension-free inguinal hernia repair who with long-term oral antiplatelet agents and admitted to Sichuan Academy of Medical Sciences & Sichuan Provincial People′s Hospital (Affiliated Hospital of University of Electronic Science and Technology of China) from January 2017 to January 2025 were collected. There were 120 males and 6 females, aged (74±9)years. Of the 126 patients, 77 patients who discontinued antiplatelet agents alone before inguinal hernia repair were set as the drug withdrawal group, and 49 patients who discontinued antiplatelet agents with low molecular weight heparin bridging therapy before inguinal hernia repair were set as the bridging group. Observation indicators: (1) propensity score matching and comparison of general data of patients between the two groups after matching; (2) intraoperative and postopera-tive conditions; (3) follow-up. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching and comparison of general data of patients between the two groups after matching. Of the 126 patients, 90 patients were success-fully matched, with 45 cases in each of the drug withdrawal group and the bridging group. After propensity score matching, the elimination of hernia ring size, activated partial thromboplasmin time and surgical method factors confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, patients using plasma drainage tubes during the operation in the drug withdrawal group and the bridging group were 8 and 1, respec-tively, showing a significant difference between the two groups ( P<0.05). The visual analogue scale scores of patients in the drug withdrawal group and the bridging group at 48 hours after surgery were 2(range, 1-2) and 2(range, 2-3), respectively, showing a significant difference between the two groups ( Z=-2.57, P<0.05). (3) Follow-up. After propensity score matching, all 90 patients were followed up after surgery for 16.5(range, 9.0-30.0)days. During the follow-up period, there was no significant difference in pain, seroma, incisional infection, readmission within 30 days after surgery getween two groups (P>0.05). No serious thrombotic events occurred in either group of patients, and no patient died. Conclusion:Compared with patients who discontinued antiplatelet agents alone before surgery, preoperative low molecular weight heparin bridging therapy after discontinua-tion of medication is safe and feasible for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents, in additon to less plasma drainage tubes using during the operation and without more risk of bleeding, but more postoperative pain.

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.

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.