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.

BACKGROUND: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic stroke. METHODS: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 days. RESULTS: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL, P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively ( median difference: 3.0 mL, IQR: -1.3 to 9.9 mL, P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups ( P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL, P = 0.03). CONCLUSIONS: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing cerebral infarcts and improving clinical prognosis. TRAIL REGISTRATION Clinicaltrials.gov , No. NCT04475328.
Aged ; Female ; Humans ; Male ; Middle Aged ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Ischemic Stroke/drug therapy* ; Pilot Projects ; Stroke/drug therapy* ; Treatment Outcome

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

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

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.

The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell-cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.