Retinal microcirculatory disorder is a key factor in the occurrence and development of diabetic retinopathy （DR）， and also an important link in the prevention and treatment of DR. The theory of "Gaozhuo" holds that the microcirculatory disorder in DR is based on the deficiency of spleen Qi and is characterized by the obstruction caused by "Gaozhuo" and blood stasis. The deficiency of spleen Qi is an essential precondition for the endogenous formation and accumulation of Gaozhuo， while Gaozhuo invasion is the direct cause of microcirculatory disorders in DR. The deficiency of spleen Qi and the endogenous formation of Gaozhuo mean the process in which glucose metabolism dysfunction induces an excessive production of inflammatory factors and lipid metabolites. The obstruction caused by "Gaozhuo" and blood stasis is the direct pathogenesis of microcirculatory disorders in DR， encompassing two stages： Gaozhuo obstruction and turbidity and stasis stagnation. Gaozhuo obstruction and turbidity and stasis stagnation represent the process in which inflammatory factors and lipid metabolites damage the retinal microcirculation and induce thrombosis， thus mediating microcirculatory disorders. Turbidity and stasis stagnation and blood extravasation outside the vessels reveal the progression to microvascular rupture and hemorrhage resulting from the microcirculatory disorders. According to the pathogenesis evolution of the theory of "Gaozhuo"， microcirculatory disorders in DR can be divided into deficiency of spleen Qi with Gaozhuo obstruction， deficiency of spleen Qi with turbidity and stasis stagnation， and turbidity and stasis stagnation with blood extravasation outside the vessels. Clinically， treatment principles should focus on strengthening the spleen and benefiting Qi， resolving turbidity， and dispersing stasis. Different syndrome patterns should be addressed with tailored therapies， such as enhancing the spleen and benefiting Qi while regulating Qi and reducing turbidity， strengthening the spleen and benefiting Qi while resolving turbidity and dispelling stasis， and strengthening the spleen and resolving turbidity while removing stasis and stopping bleeding. Representative prescriptions include modified Wendantang， modified Buyang Huanwutang， modified Danggui Buxuetang， Zhuixue Mingmu decoction， Tangmuqing， Shengqing Jiangzhuo Tongluo Mingmu prescription， Danhong Huayu decoction， and Yiqi Yangyin Huoxue Lishui formula.

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.

ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

OBJECTIVE To investigate the induction of apoptosis in hepatocellular carcinoma cells by polyphyllin 9 （PP9） through the regulation of the Fas/Fas ligand （FasL） signaling pathway， and its inhibitory effect on the growth of transplanted tumor in nude mice. METHODS Based on the screening of cell lines and intervention conditions， HepG2 cells were selected as the experimental subject to investigate the effects of 2 μmol/L and 4 μmol/L PP9 treatment on cell colony formation activity， apoptosis rate， as well as the protein expressions of Fas， FasL， cleaved caspase-8 and cleaved caspase-3. Additionally， Fas inhibitor KR- 33493 was introduced to investigate the underlying mechanism of PP9’s anti-hepatocellular carcinoma activity. Using HepG2 cell tumor-bearing nude mice model as the object， and 5-fluorouracil （20 mg/kg） as the positive control， the effects of 10 mg/kg PP9 on tumor volume， tumor mass， and the protein expressions of the nuclear proliferation-associated antigen Ki-67 and cleaved caspase-3 in tumor-bearing nude mice were investigated. RESULTS Compared with the control group， 2， 4 μmol/L PP9 significantly decreased the number of clones and the clone formation rate of cells， but significantly increased the apoptosis rate， the protein expressions of Fas， FasL， cleaved caspase-8 and cleaved caspase-3 （P＜0.05 or P＜0.01）. However， the combination of Fas inhibitor KR-33493 could significantly reverse the effect of PP9 on the up-regulation of proteins related to the Fas/FasL signaling pathway （P＜0.01）. Compared with the control group， the tumor volume （on day 27）， mass and protein expression of Ki- 67 in nude mice of the PP9 group were significantly decreased， while the protein expression of cleaved caspase-3 was significantly increased （P＜0.01）. CONCLUSIONS PP9 can induce apoptosis of HepG2 cells by activating the Fas/FasL signaling pathway. Meanwhile， PP9 can also effectively inhibit the growth of transplanted tumors in nude mice.

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.

Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.