With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

ObjectiveOxygen partial pressure (pO₂) is a critical indicator of the tumor microenvironment and plays an essential role in regulating tumor progression and therapeutic response, particularly for oxygen-dependent treatments such as photodynamic therapy (PDT). Although photoacoustic imaging (PAI) has shown great potential for functional and molecular imaging, conventional oxygenation imaging approaches based on hemoglobin absorption mainly provide relative oxygen saturation information and cannot directly quantify tissue oxygen partial pressure. Therefore, this study aimed to develop a fast laser sequence-based pump-probe photoacoustic tomography (PP-PAT) system for quantitative, non-invasive, and dynamic monitoring of oxygen partial pressure in tumor tissues. MethodsThe proposed PP-PAT system was developed based on the oxygen-sensitive triplet-state lifetime characteristics of methylene blue (MB). Upon optical excitation, MB molecules undergo transitions from the ground state to an excited state and subsequently form metastable triplet states through intersystem crossing. The lifetime of the triplet state is strongly affected by surrounding oxygen concentration due to dynamic oxygen quenching, providing a direct correlation between triplet-state kinetics and local oxygen partial pressure. A dual-wavelength pump-probe excitation scheme was implemented to selectively interrogate MB triplet-state dynamics. The pump pulse was used to initiate the triplet-state population, while the delayed probe pulse was applied to detect the transient triplet-state response. By optimizing the temporal interval between the pump and probe pulses, transient triplet-state differential (TTD) signals were extracted to enhance the specificity of oxygen-dependent molecular information and suppress background interference from conventional photoacoustic signals. The performance of the PP-PAT system was systematically evaluated through phantom and in vivo experiments. First, a single-tube phantom containing MB solution was used to investigate signal stability, reproducibility, and the influence of signal averaging on imaging quality. Subsequently, a dual-tube phantom model was established to simulate oxygen consumption during PDT, where one MB-containing tube was exposed to laser irradiation and the other served as a control. Finally, in vivo experiments were performed using a subcutaneous tumor model in nude mice. MB was locally injected into the tumor region before imaging to evaluate the feasibility of tumor oxygen partial pressure mapping using the proposed PP-PAT system. ResultsPhantom experiments demonstrated that the extracted TTD signals exhibited high stability and effectively reflected MB triplet-state dynamics while reducing non-specific background contributions. Signal averaging significantly improved the signal-to-noise ratio, and 50 repeated acquisitions provided an optimal compromise between image quality and acquisition efficiency. In the dual-tube phantom experiments, continuous PDT irradiation induced progressive oxygen depletion in the treated sample. The corresponding TTD decay rate gradually decreased, accompanied by an increase in the calculated triplet-state lifetime, indicating reduced oxygen quenching and decreased oxygen partial pressure. In contrast, the control sample showed relatively stable TTD kinetics and oxygenation levels during the same period. These results demonstrated that PP-PAT could quantitatively characterize oxygen consumption dynamics associated with photochemical reactions. In vivo experiments further demonstrated the capability of PP-PAT for spatially resolved oxygen partial pressure imaging in MB-injected tumor tissues. The reconstructed pO₂ maps revealed heterogeneous oxygen distributions within tumors, showing relatively hypoxic regions in the tumor core and higher oxygenation levels near the tumor boundary. This spatial oxygen distribution pattern was consistent with the typical physiological characteristics of solid tumors, where abnormal vascular structures and limited oxygen diffusion result in intratumoral oxygen gradients. ConclusionThis study presents a fast laser sequence-based PP-PAT system capable of quantitative and non-invasive imaging of tissue oxygen partial pressure. By utilizing the oxygen-dependent triplet-state lifetime of MB and extracting TTD signals, the proposed method provides molecularly specific information related to oxygen dynamics beyond conventional photoacoustic oxygenation imaging. The system enables dynamic monitoring of oxygen consumption during PDT and reveals spatial oxygen heterogeneity within tumors. This technique provides a promising imaging approach for tumor hypoxia characterization, evaluation of oxygen-dependent therapeutic responses, and precision-guided cancer treatment.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling

The chemical constituents from leaves of Cyclocarya paliurus were isolated and purified by chromatography on silica gel, C_(18) reverse-phase silica gel, and Sephadex LH-20 gel, as well as semi-preparative high-performance liquid chromatography. Six compounds were identified by UV, IR, NMR, MS, calculated ECD, and comparison with literature data as cyclopaloside D(1), boscialin(2),(5R,6S)-6-hydroxy-6-[(E)-3-hydroxybut-1-enyl]-1,1,5-trimethylcyclohexanone(3), 3S,5R-dihydroxy-6R,7-megastigmadien-9-one(4), 3S,5R-dihydroxy-6S,7-megastigmadien-9-one(5), and gingerglycolipid A(6), respectively. Among them, compound 1 was identified as a new tetralone glycoside, and compounds 2-6 were isolated from leaves of C. paliurus for the first time. Furthermore, compound 1 exhibited strong antioxidant activity, with the IC_(50) of(454.20±31.81)μmol·L~(-1) and(881.82±42.31)μmol·L~(-1) in scavenging DPPH and ABTS free radicals, respectively.
Plant Leaves/chemistry* ; Glycosides/isolation & purification* ; Juglandaceae/chemistry* ; Tetralones/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification*

This study aims to reveal the effect and mechanism of Gentianella turkestanorum total extract(GTI) in ameliorating non-alcoholic steatohepatitis(NASH). UPLC-Q-TOF-MS was employed to identify the chemical components in GTI. SwissTarget-Prediction, GeneCards, OMIM, and TTD were utilized to screen the targets of GTI components and NASH. The common targets shared by GTI components and NASH were filtered through the STRING database and Cytoscape 3.9.0 to identify core targets, followed by GO and KEGG enrichment analysis. AutoDock was used for molecular docking of key components with core targets. A mouse model of NASH was established with a methionine-choline-deficient high-fat diet. A 4-week drug intervention was conducted, during which mouse weight was monitored, and the liver-to-brain ratio was measured at the end. Hematoxylin-eosin staining, Sirius red staining, and oil red O staining were employed to observe the pathological changes in the liver tissue. The levels of various biomarkers, including aspartate aminotransferase(AST), alanine aminotransferase(ALT), hydroxyproline(HYP), total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol(HDL-C), malondialdehyde(MDA), superoxide dismutase(SOD), and glutathione(GSH), in the serum and liver tissue were determined. RT-qPCR was conducted to measure the mRNA levels of interleukin 1β(IL-1β), interleukin 6(IL-6), tumor necrosis factor α(TNF-α), collagen type I α1 chain(COL1A1), and α-smooth muscle actin(α-SMA). Western blotting was conducted to determine the protein levels of IL-1β, IL-6, TNF-α, and potential drug targets identified through network pharmacology. UPLC-Q-TOF/MS identified 581 chemical components of GTI, and 534 targets of GTI and 1 157 targets of NASH were screened out. The topological analysis of the common targets shared by GTI and NASH identified core targets such as IL-1β, IL-6, protein kinase B(AKT), TNF, and peroxisome proliferator activated receptor gamma(PPARG). GO and KEGG analyses indicated that the ameliorating effect of GTI on NASH was related to inflammatory responses and the phosphoinositide 3-kinase(PI3K)/AKT pathway. The staining results demonstrated that GTI ameliorated hepatocyte vacuolation, swelling, ballooning, and lipid accumulation in NASH mice. Compared with the model group, high doses of GTI reduced the AST, ALT, HYP, TC, and TG levels(P<0.01) while increasing the HDL-C, SOD, and GSH levels(P<0.01). RT-qPCR results showed that GTI down-regulated the mRNA levels of IL-1β, IL-6, TNF-α, COL1A1, and α-SMA(P<0.01). Western blot results indicated that GTI down-regulated the protein levels of IL-1β, IL-6, TNF-α, phosphorylated PI3K(p-PI3K), phosphorylated AKT(p-AKT), phosphorylated inhibitor of nuclear factor kappa B alpha(p-IκBα), and nuclear factor kappa B(NF-κB)(P<0.01). In summary, GTI ameliorates inflammation, dyslipidemia, and oxidative stress associated with NASH by regulating the PI3K/AKT/NF-κB signaling pathway.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Mice ; Network Pharmacology ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Chromatography, High Pressure Liquid ; Liver/metabolism* ; Mice, Inbred C57BL ; Humans ; Mass Spectrometry ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation

This study investigated the effects of total flavonoids of Dracocephalum moldavica(TFDM) on apoptosis in rat H9c2 cells induced by endoplasmic reticulum stress(ERS) established by oxygen-glucose deprivation and reoxygenation(OGD/R) injury and tunicamycin(TM), and explored the potential mechanisms. After successful modeling, the following groups were set in this experiment: control group, model(OGD/R or TM) group, and TFDM low-, medium-, and high-dose groups(12.5, 25, and 50 μg·mL~(-1)). The OGD/R injury model was constructed in vitro. Cell proliferation was assessed using the cell counting kit-8(CCK-8) method. The levels of lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CKMB) in the cell supernatant were detected. Western blot was used to assess the expression of ERS-related proteins, including glucose regulatory protein 78(GRP78), C/EBP homologous protein(CHOP), activating transcription factor 6(ATF6), and apoptotic proteins B-cell lymphoma 2(Bcl-2) and Bcl-2-associated X protein(Bax). Apoptosis was detected using the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL) method. In the TM-induced ERS model, Western blot was used to measure the expression of ERS pathway-related proteins GRP78, CHOP, inositol-requiring enzyme 1(IRE1), X-box binding protein 1(XBP1), protein kinase RNA-like endoplasmic reticulum kinase(PERK), eukaryotic initiation factor 2α(eIF2α), ATF6, p-ATF6, and apoptotic proteins Bcl-2, Bax, cysteinyl aspartate specific proteinase-12(caspase-12), and cleaved caspase-12. Gene expression of GRP78, CHOP, PERK, and ATF6 was detected by real-time fluorescence quantitative PCR(RT-qPCR). Apoptosis was again detected using the TUNEL method. The results showed that in the OGD/R model, compared with the control group, the levels of LDH and CKMB in the cell supernatant were significantly increased in the OGD/R group. Compared with the OGD/R group, the levels of LDH and CKMB in the TFDM group were significantly reduced. Western blot results revealed that compared with the control group, the expression of ERS-related proteins and Bax in the OGD/R group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the OGD/R group, the expression of ERS-related proteins and Bax in the TFDM groups was significantly reduced, and the expression of Bcl-2 was significantly increased. TUNEL assay showed that apoptosis was significantly decreased after TFDM treatment. In the TM-induced ERS experiment, compared with the control group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TM group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the TM group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TFDM group was significantly reduced, and the expression of Bcl-2 was significantly increased. These results suggest that ERS exists in the OGD/R-injured H9c2 cell model, and TFDM can effectively inhibit ERS-induced apoptosis. The mechanism may be related to the downregulation of ERS pathway-related proteins and apoptotic proteins.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Apoptosis/drug effects* ; Rats ; Flavonoids/pharmacology* ; Glucose/metabolism* ; Cell Line ; Lamiaceae/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Oxygen/metabolism* ; Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology*

Astragalus membranaceus(A. membranaceus), a traditional tonic, contains flavonoids as one of its main bioactive components and key indicators for quality standard detection. These compounds predominantly exist in glycosylated forms after glycosylation modification within the plant. The catalytic products of flavonoid glycosyltransferases in A. membranaceus have been reported to be mostly monoglycosides, and only AmUGT28 catalyzes luteolin to form diglycosides. In this study, we cloned a glycosyltransferase gene, AmGT90, from A. membranaceus, with an ORF length of 1 335 bp, encoding 444 amino acids, and the protein had a relative molecular mass of 50.5 kDa. Phylogenetic tree analysis indicated that AmGT90 belongs to the UGT74 family. In vitro enzymatic reaction showed that AmGT90 had broad substrate specificity and could catalyze the glycosylation of various flavonoids, including isoflavones, flavones, flavanones, and chalcones. AmGT90 not only catalyzed the formation of monoglycosides but also diglycosides. In addition, the mechanism of AmGT90 catalyzing the formation of diglycosides from luteolin was preliminarily explored. The experimental results showed that AmGT90 may preferentially recognize C4'-OH of luteolin and then recognize C7-OH to form diglycosides. This study reported a glycosyltransferase from A. membranaceus capable of converting flavonoids into monoglycosides and diglycosides. This finding not only enhances our understanding of the biosynthetic pathways of flavonoid glycosides in A. membranaceus but also introduces a new component for glycoside production through synthetic biology.
Glycosyltransferases/chemistry* ; Flavonoids/chemistry* ; Astragalus propinquus/classification* ; Phylogeny ; Glycosylation ; Plant Proteins/chemistry* ; Substrate Specificity ; Cloning, Molecular ; Amino Acid Sequence

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer