Malignant tumors are major diseases with a high incidence worldwide and seriously threaten human health， and their occurrence and development are closely associated with the abnormal activation of multiple signaling pathways. Among them， the vascular endothelial growth factor （VEGF）/VEGF receptor （VEGFR） signaling pathway plays a key regulatory role in tumor angiogenesis， which can be widely implicated in tumor proliferation， invasion， and metastasis through regulating the expression of downstream signaling proteins and genes， serving as a key target for cancer targeted therapy. Traditional Chinese medicine （TCM） possesses unique advantages including multiple components， multiple targets， holistic regulation， and mild adverse reactions in cancer prevention and treatment. This paper systematically sorts out existing research findings and analyzes the anti-tumor mechanisms of TCM in modulating this signaling pathway. Furthermore， this paper summarizes that TCM active components （such as oxymatrine， quercetin， and celastrol）， extracts （such as Prunellae Spica extract and Cinnamomi Cortex extract）， and compound prescriptions （such as Yupingfeng San and modified Wenyang Sanjie Formula） can inhibit tumor angiogenesis， reverse epithelial-mesenchymal transition， improve the tumor microenvironment， promote cancer cell apoptosis， and suppress cancer cell proliferation， migration， and invasion by regulating the VEGF/VEGFR signaling pathway and its key molecules， thereby exerting anti-tumor effects. This review provides new ideas for the research and development of cancer drugs and the optimization of treatment regimens.

The phase changes and quantity-quality transfer of 17 batches of Ostreae Concha(Ostrea rivularis) during the raw material-calcined decoction pieces-standard decoction process were analyzed. The content of calcium carbonate(CaCO_3), the main component, was determined by chemical titration, and the extract yield and transfer rate were calculated. The CaCO_3 content in the raw material, calcined decoction pieces, and standard decoction was 94.39%-98.80%, 95.03%-99.22%, and 84.58%-90.47%, respectively. The process of raw material to calcined decoction pieces showed the yield range of 96.85% to 98.55% and the CaCO_3 transfer rate range of 96.92% to 99.27%. The process of calcined decoction pieces to standard decoction showed the extract yield range of 2.86% to 5.48% and the CaCO_3 transfer rate range of 2.59% to 5.13%. The results of X-ray fluorescence(XRF) assay showed that the raw material, calcined decoction pieces, and standard decoction mainly contained Ca, Na, Mg, Si, Br, Cl, Al, Fe, Cr, Mn, and K. The chemometric results showed an increase in the relative content of Cr, Fe, and Si from raw material to calcined decoction pieces and an increase in the relative content of Mg, Al, Br, K, Cl, and Na from calcined decoction pieces to standard decoction. X-ray diffraction(XRD) was employed to establish XRD characteristic patterns of the raw material, calcined decoction pieces, and standard decoction. The XRD results showed that the main phase of all three was calcite, and no transformation of crystalline form or generation of new phase was observed. Fourier transform infrared spectroscopy(FTIR) was employed to establish the FTIR characteristic spectra of the raw material, calcined decoction pieces, and standard decoction. The FTIR results showed that the raw material had internal vibrations of O-H, C-H, C=O, C-O, and CO■ groups. Due to the loss of organic matter components after calcination, no information about the vibrations of C-H, C=O, and C-O groups was observed in the spectra of calcined decoction pieces and standard decoction. In summary, this study elucidated the quantity-quality transfer and phase changes in the raw material-calcined decoction pieces-standard decoction process by determining the CaCO_3 content, calculating the extract yield and transfer rate, and comparing the element changes, FTIR characteristic spectra, and XRD characteristic pattern. The results were reasonable and reliable, laying a foundation for the subsequent process research and quality control of the formula granules of calcined Ostreae Concha(O. rivularis Gould), and providing ideas and methods for the quality control of the whole process of raw material-decoction pieces-standard decoction-formula granules of Ostreae Concha and other testacean traditional Chinese medicine.
Drugs, Chinese Herbal/isolation & purification* ; Calcium Carbonate/analysis* ; Quality Control

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Protein tyrosine phosphatase nonreceptor type 2 (PTPN2) is a promising target for sensitizing solid tumors to immune checkpoint blockades. However, the highly polar active sites of PTPN2 hinder drug discovery efforts. Leveraging small interfering RNA (siRNA) technology, we developed a novel glutathione-responsive nano-platform HPssPT (HA/PEIss@siPtpn2) to silence PTPN2 and enhance immunotherapy efficacy in hepatocellular carcinoma (HCC). HPssPT showed potent transfection and favorable safety profiles. PTPN2 deficiency induced by HPssPT amplified the interferon γ signaling in HCC cells by increasing the phosphorylation of Janus-activated kinase 1 and signal transducer and activator of transcription 1, resulting in enhanced antigen presentation and T cell activation. The nano-platform was also able to promote the M1-like polarization of macrophages in vitro. The unique tropism of HPssPT towards tumor-associated macrophages, facilitated by hyaluronic acid coating and CD44 receptor targeting, allowed for simultaneous reprogramming of both tumor cells and tumor-associated macrophages, thereby synergistically reshaping tumor microenvironment to an immunostimulatory state. In HCC, colorectal cancer, and melanoma animal models, HPssPT monotherapy provoked robust antitumor immunity, thereby sensitizing tumors to PD-1 blockade, which provided new inspiration for siRNA-based drug discovery and tumor immunotherapy.

Objective:To compare the diagnostic efficacy of the added 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT/CT imaging (tomography imaging) after early or delayed planar imaging in different types of hyperparathyroidism, and to seek for the optimal timing of preoperative imaging. Methods:A total of 339 patients (148 males, 191 females, age (52±13) years) with primary or secondary hyperparathyroidism and pathological results from January 2016 to June 2024 in the Second Affiliated Hospital of Soochow University were retrospectively analyzed. The patients were divided into primary early tomography imaging group ( n=63) and delayed tomography imaging group ( n=47), secondary early tomography imaging group ( n=89) and delayed tomography imaging group ( n=140). χ2 test was used to compare the diagnostic efficacies of early and delayed tomography imaging. Results:The difference of accuracy between primary delayed (98.40%(185/188)) and early tomography imaging (94.84%(239/252)) was statistically significant ( χ2=3.90, P=0.048). There were significant differences in sensitivity (77.29%(405/524) and 85.40%(275/322)), accuracy (75.89%(425/560) and 83.99%(299/356)) and negative predictive value (14.39%(20/139) and 33.80%(24/71)) between secondary delayed and early tomography imaging ( χ2 values: 8.33, 8.61, 10.70, all P<0.01). Conclusion:The optimal timing of preoperative 99Tc m-MIBI SPECT/CT imaging for primary and secondary hyperparathyroidism is after delayed planar imaging and after early planar imaging respectively.

Objective:To investigate the feasibility of the bortezomib, lenalidomide, and dexamethasone (VRD) regimen combined with autologous hematopoietic stem cell transplantation (auto-HSCT) in patients with multiple myeloma (MM) and renal impairment, analyze treatment efficacy and renal responses stratified based on renal dysfunction severity, and explore the prognostic significance of early renal response and its affecting factors.Methods:This retrospective study, conducted at the First Affiliated Hospital of Soochow University, categorized 316 patients with newly diagnosed MM (NDMM) from August 2018 to October 2022 based on renal function for analysis of clinical characteristics, treatment response, and prognosis. Continuous variables were compared using t-tests or Mann-Whitney U tests, categorical variables utilizing Chi-square tests, survival outcomes employing Kaplan-Meier and Log-rank tests, and renal response predictors with logistic regression.Results:Patients were stratified based on baseline estimated glomerular filtration rate (eGFR) : normal [≥90 ml·min -1· (1.73 m 2) -1, n=160], mild [≥60 ml·min -1· (1.73 m 2) -1 to <90 ml·min -1· (1.73 m 2) -1, n=55], moderate [≥30 ml·min -1· (1.73 m 2) -1 to <60 ml·min -1· (1.73 m 2) -1, n=39], and severe impairment [<30 ml·min -1· (1.73 m 2) -1, n=62]. Moderate and severe renal impairment correlated with advanced International Staging System/Revised International Staging System classification, lower hemoglobin levels, frailty, and higher light-chain/IgD subtype prevalence ( P<0.05). Despite younger age ( P=0.001) and higher transplant rates ( P=0.041) in severe cases, overall response rates ( ORR: 93.7% ; ≥VGPR: 82.9% ) were comparable across groups ( P>0.05). Among 24 dialysis-dependent patients at diagnosis, 11 (45.8% ) achieved dialysis independence after induction [median: 3.0 (0.5–4.0) months], including 10 undergoing auto-HSCT. In 89 evaluable patients [baseline eGFR <50 ml·min -1· (1.73 m 2) -1], renal ORR (RORR) was 70.8% [rapid complete response: 31.5% ; rapid partial response: 11.2% ; rapid minimal response (RMR) : 28.1% ]. Renal response predicted better survival (overall survival: HR=0.36, 95% CI: 0.13–0.99, P=0.049). Moderate-to-severe renal impairment was associated with increased transplant-related adverse events and delayed engraftment ( P<0.05) ; however, auto-HSCT significantly improved outcomes after 33.5-month median follow-up (range: 2–65 months). Multivariate analysis identified 1q21+ ( OR=3.58, 95% CI: 1.17–11.02, P=0.026) and light-chain subtype ( OR=2.86, 95% CI: 1.08–7.69, P=0.036) as independent predictors of poor renal response. Conclusion:VRD regimen plus auto-HSCT demonstrates robust efficacy in NDMM, including patients with renal impairment, with a 70.8% RORR and manageable toxicity. Achieving ≥RMR correlates with superior prognosis, whereas 1q21+ and light-chain subtype independently predict inferior renal response.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

The focus of green analytical chemistry (GAC) is to minimize the negative impacts of analytical procedures on human safety, human health, and the environment. Several factors, such as the reagents used, sample collection, sample processing, instruments, energy consumed, and the quantities of hazardous materials and waste generated during analytical procedures, need to be considered in the evaluation of the greenness of analytical assays. In this study, we propose a greenness evaluation metric for analytical methods (GEMAM). The new greenness metric is simple, flexible, and comprehensive. The evaluation criteria are based on both the 12 principles of GAC (SIGNIFICANCE) and the 10 factors of sample preparation, and the results are presented on a 0-10 scale. The GEMAM calculation process is easy to perform, and its results are easy to interpret. The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.

Phenylpyruvic acid (PPA) is used as a food and feed additive and has a wide range of applications in the pharmaceutical, chemical and other fields. At present, PPA is mainly produced by chemical synthesis. With the green transformation of the manufacturing industry, biotransformation will be a good alternative for PPA production. The L-amino acid deaminase (PmiLAAD) from Proteus mirabilis has been widely studied for the production of PPA. However, the low yield limits its industrial production. To further enhance the production of PPA and better meet industrial demands, a more efficient synthesis method for PPA was established. In this study, PmiLAAD was heterologously expressed in Escherichia coli. Subsequently, a colorimetric reaction method was established to screen the strains with high PPA production. The semi-rational design of PmiLAAD was carried out, and the obtained triple-site mutant V18 (V437I/S93C/E417A) showed a 35% increase in catalytic activity compared with the wild type. Meanwhile, the effect of N-terminal truncation on the catalytic activity of the V18 mutant was investigated. After the optimization of the whole-cell conditions for the obtained mutant V18-N7, fed-batch conversion was carried out in a 5-L fermenter, and 44.13 g/L of PPA was synthesized with a conversion rate of 88%, which showed certain potential for industrial application. This study lays foundation for the industrial production of phenylpyruvic acid and also offers insights into the biosynthesis of other chemicals.
Escherichia coli/metabolism* ; Proteus mirabilis/genetics* ; Phenylpyruvic Acids/metabolism* ; Protein Engineering/methods* ; Recombinant Proteins/biosynthesis* ; Bacterial Proteins/metabolism*

L-tyrosine is one of the 20 amino acids that make up proteins and is an essential amino acid for mammals, often used as a nutritional supplement. The conventional methods for synthesizing L-tyrosine have some problems such as the production of many by-products, high requirements for production conditions, and environmental pollution. In this study, we designed and constructed a multi-enzyme cascade for the synthesis of L-tyrosine with alanine, glutamate, ammonium chloride, and phenol as substrates. Initially, the sources of glutamate oxidase, alanine aminotransferase, and tyrosine phenol lyase were screened and analyzed, which was followed by the identification of the rate-limiting enzyme in the reaction process. A colorimetric screening method was established, and the rate-limiting enzyme DbAlaA was engineered to enhance its activity by 40.0%. Subsequently, the reaction conditions, including temperature, pH, cell concentration, and surfactant and coenzyme dosages, were optimized. After optimization, the yield of L-tyrosine reached 9.93 g/L, with a alanine conversion rate of 54.90%. Finally, a feed-batch fermentation strategy was adopted, and the yield of L-tyrosine reached 56.07 g/L after 24 h, with a alanine conversion rate of 65.22%. This study provides a reference for the whole-cell catalytic synthesis of L-tyrosine and its industrialization.
Tyrosine/biosynthesis* ; Escherichia coli/metabolism* ; Tyrosine Phenol-Lyase/genetics* ; Multienzyme Complexes/metabolism* ; Fermentation