OBJECTIVE: To investigate the genetic etiology of a fetus diagnosed with Mandibulofacial dysostosis with microcephaly (MFDM). METHODS: A fetus that underwent prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, on May 19, 2025 was selected for analysis. Results of fetal ultrasound findings, chromosomal karyotyping, copy number variation sequencing (CNV-seq), and whole-exome sequencing (WES) were collected. Sanger sequencing was performed for familial validation of the pathogenic variant. The Human Protein Atlas (HPA), STRING, and Simple ClinVar databases were queried to characterize the biological features of the candidate gene. Three-dimensional structures of the wild-type and variant proteins were modeled and analyzed, and the evolutionary conservation of the affected amino acid was assessed using UGENE. Prenatal phenotypes associated with EFTUD2 variants were summarized through a review of the literature. This study was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Ethics No.: 2025-KY-029-01). RESULTS: At 23⁺² weeks of gestation, ultrasound examination revealed bilateral microtia with low-set ears, mild micrognathia with a reduced mandibular-facial angle, a single umbilical artery, a slightly narrow aortic diameter, and trivial mitral regurgitation. Amniotic fluid karyotyping and CNV-seq showed no abnormalities. WES identified a de novo, previously unreported EFTUD2 variant, c.698dupA (p.V235Gfs*27), in the fetus. This frameshift variant is predicted to alter the structural integrity of the EFTUD2 protein. Literature review indicated that micrognathia and microtia or low-set ears are the most common sonographic features in fetuses with EFTUD2 variants, while secondary findings may include abnormal stomach bubble, cleft palate, single umbilical artery, gastrointestinal atresia, polyhydramnios, and reduced aortic diameter. CONCLUSION The EFTUD2: c.698dupA (p.V235Gfs*27) variant is likely the genetic cause underlying MFDM in this fetus.
Humans ; Mandibulofacial Dysostosis/diagnostic imaging* ; Microcephaly/diagnostic imaging* ; Female ; Pregnancy ; Ribonucleoprotein, U5 Small Nuclear/chemistry* ; Peptide Elongation Factors/chemistry* ; Fetus ; DNA Copy Number Variations/genetics* ; Adult ; Ultrasonography, Prenatal

OBJECTIVES: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the EGFR and KRAS genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict EGFR and KRAS gene mutation status in NSCLC patients, thereby providing a reference for precision oncology. METHODS: Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting EGFR and KRAS gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation. RESULTS: The models effectively predicted EGFR and KRAS mutations in NSCLC patients, achieving an AUC of 0.95 for EGFR mutations and 0.90 for KRAS. The models also demonstrated high accuracy (EGFR 89.66%; KRAS 87.10%), sensitivity (EGFR 93.33%; KRAS 87.50%), and specificity (EGFR 85.71%; KRAS 86.67%). CONCLUSIONS A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating EGFR and KRAS gene mutation status in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Mutation ; Proto-Oncogene Proteins p21(ras)/genetics* ; ErbB Receptors/genetics* ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Female ; Male ; Neural Networks, Computer ; Middle Aged ; Aged

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

OBJECTIVES: To investigate the effect of high glucose on macrophage polarization and the role of immune-responsive gene 1 (IRG1) in mediating its effect. METHODS: RAW264.7 cells were transfected with IRG1-overexpressing plasmid or IRG1 siRNA via electroporation and cultured in either normal or high glucose for 72 h to observe the changes in cell viability and morphology using CCK-8 assay and phase contrast microscopy. The protein levels of IRG1, iNOS, Arg-1, IL-1β and IL-10 in the treated cells were detected with Western blotting, and the fluorescence intensities of iNOS and Arg-1 were detected using immunofluorescence assay. The protein levels of IL-1β and IL-10 in the culture medium were determined with ELISA. RESULTS: High glucose exposure significantly reduced IRG1 and Arg-1 expressions, increased iNOS and IL-1β expressions and IL-1β secretion, and decreased IL-10 level in RAW264.7 cells. Transfection with the IRG1-overexpressing plasmid provided the cells with obvious resistance to high glucose-induced changes in iNOS, Arg-1, IL-1β and IL-10, whereas IRG1 knockdown further enhanced the effects of high glucose exposure on Arg-1 expression and the expression and secretion of IL-10. CONCLUSIONS High glucose promotes M1 polarization of the macrophages possibly through a mechanism to inhibit the expression of IRG1 protein, thus leading to chronic inflammatory response.
Animals ; Mice ; Macrophages/drug effects* ; Glucose/pharmacology* ; Interleukin-10/metabolism* ; Nitric Oxide Synthase Type II/metabolism* ; RAW 264.7 Cells ; Interleukin-1beta/metabolism* ; Arginase/metabolism* ; RNA, Small Interfering/genetics* ; Transfection ; Inflammation

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged

OBJECTIVES: To investigate the effect of curcumin on lipid metabolism in non-small cell lung cancer (NSCLC) and its molecular mechanism. METHODS: The inhibitory effect of curcumin (0-70 μmol/L) on proliferation of A549 and H1299 cells was assessed using MTT assay, and 20 and 40 μmol/L curcumin was used in the subsequent experiments. The effect of curcumin on lipid metabolism was evaluated using cellular uptake assay, wound healing assay, triglyceride (TG)/free fatty acid (NEFA) measurements, and Oil Red O staining. Western blotting was performed to detect the expressions of PGC-1α, PPAR-α, and HIF-1α in curcumin-treated cells. Network pharmacology was used to predict the metabolic pathways, and the results were validated by Western blotting. In a nude mouse model bearing A549 cell xenograft, the effects of curcumin (20 mg/kg) on tumor growth and lipid metabolism were assessed by measuring tumor weight and observing the changes in intracellular lipid droplets. RESULTS: Curcumin concentration-dependently inhibited the proliferation of A549 and H1299 cells and significantly reduced TG and NEFA levels and intracellular lipid droplets. Western blotting revealed that curcumin significantly upregulated PGC-1α and PPAR‑α expressions in the cells. KEGG pathway enrichment analysis predicted significant involvement of the HIF-1 signaling pathway in curcumin-treated NSCLC, suggesting a potential interaction between HIF-1α and PPAR‑α. Western blotting confirmed that curcumin downregulated the expression of HIF-1α. In the tumor-bearing mice, curcumin treatment caused significant reduction of the tumor weight and the number of lipid droplets in the tumor cells. CONCLUSIONS Curcumin inhibits NSCLC cell proliferation and lipid metabolism by downregulating the HIF-1α pathway.
Curcumin/pharmacology* ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Animals ; Lipid Metabolism/drug effects* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Mice, Nude ; Down-Regulation ; Mice ; Cell Proliferation/drug effects* ; Cell Line, Tumor ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha ; PPAR alpha/metabolism* ; Signal Transduction/drug effects* ; A549 Cells

OBJECTIVES: To investigate the inhibitory effect of Fuzheng Huaji Decoction against non-small cell lung cancer (NSCLC) cells in vitro and explore the underlying mechanism. METHODS: The active ingredients and targets of Fuzheng Huaji Decoction were identified using TCMSP and SwissTargetPrediction databases. NSCLC-related targets from GeneCards and PharmGKB were intersected with the targets of the Decoction, and a protein-protein interaction (PPI) network was constructed to identify the core targets, which were analyzed with GO and KEGG pathway enrichment analysis. Cultured A549 cells were treated with different concentrations of Fuzheng Huaji Decoction-medicated serum, and the changes in cell proliferation, apoptosis, and protein expressions were examined using CCK-8 assay, annexin V-FITC/PI staining and Western blotting. RESULTS: Fuzheng Huaji Decoction contained 140 active ingredients, and 707 drug-disease intersecting targets were identified. Among these targets, TP53, AKT1, HIF1A, GAPDH, ALB, EGFR, CTNNB1, and TNF were identified as the core targets which were involved in the biological processes related to kinases and receptors and the PI3K-AKT, Ras, calcium, and MAPK pathways. Molecular docking studies indicated strong binding affinity of the active ingredients with TP53, AKT1, and HIF1A. In cultured A549 cells, treatment with 2.5%, 5%, and 10% Fuzheng Huaji Decoction-medicated serum significantly inhibited cell proliferation, promoted cell apoptosis, and downregulated the expression levels of HIF1A, p-AKT (Thr308), and TP53 proteins. CONCLUSIONS Fuzheng Huaji Decoction inhibits proliferation of NSCLC cells possibly by downregulating the expressions of HIF1A, p-AKT (Thr308), and TP53.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Lung Neoplasms/metabolism* ; A549 Cells ; Proto-Oncogene Proteins c-akt/metabolism* ; Protein Interaction Maps ; Signal Transduction/drug effects* ; Cell Line, Tumor

OBJECTIVES: To investigate the structural changes of rat thoracic aorta and changes in expression levels of Bmal1 and cyclins in thoracic aorta endothelial cells following heat stress. METHODS: Twenty male SD rats were randomized equally into control group and heat stress group. After exposure to 32 ℃ for 2 weeks in the latter group, the rats were examined for histopathological changes and Bmal1 expression in the thoracic aorta using HE staining and immunohistochemistry. In the cell experiments, cultured rat thoracic aortic endothelial cells (RTAECs) were incubated at 40 ℃ for 12 h with or without prior transfection with a Bmal1-specific small interfering RNA (si-Bmal1) or a negative sequence. In both rat thoracic aorta and RTAECs, the expressions of Bmal1, the cell cycle proteins CDK1, CDK4, CDK6, and cyclin B1, and apoptosis-related proteins Bax and Bcl-2 were detected using Western blotting. TUNEL staining was used to detect cell apoptosis in rat thoracic aorta, and the changes in cell cycle distribution and apoptosis in RTAECs were analyzed with flow cytometry. RESULTS: Compared with the control rats, the rats exposed to heat stress showed significantly increased blood pressures and lowered heart rate with elastic fiber disruption and increased expressions of Bmal1, cyclin B1 and CDK1 in the thoracic aorta (P<0.05). In cultured RTAECs, heat stress caused significant increase of Bmal1, cyclin B1 and CDK1 protein expression levels, which were obviously lowered in cells with prior si-Bmal1 transfection. Bmal1 knockdown also inhibited heat stress-induced increase of apoptosis in RTAECs as evidenced by decreased expression of Bax and increased expression of Bcl-2. CONCLUSIONS Heat stress upregulates Bmal1 expression and causes alterations in expressions of cyclins to trigger apoptosis of rat thoracic aorta endothelial cells, which can be partly alleviated by suppressing Bmal1 expression.
Animals ; ARNTL Transcription Factors/genetics* ; Male ; Aorta, Thoracic/metabolism* ; Rats ; Rats, Sprague-Dawley ; Endothelial Cells/metabolism* ; Apoptosis ; Cells, Cultured ; Heat-Shock Response ; Cyclin B1/metabolism* ; CDC2 Protein Kinase/metabolism* ; Cyclins/metabolism* ; RNA, Small Interfering ; bcl-2-Associated X Protein/metabolism*

OBJECTIVES: To construct a nomogram for predicting the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (aNSCLC) by integrating chest CT radiomics signature that reflects the tumor microenvironment (TME) and clinical parameters of the patients. METHODS: Transcriptomic and CT imaging data from TCGA, GEO and TCIA databases were integrated for weighted gene co-expression network analysis (WGCNA) of the GEO cohort to identify the immunotherapy-related genes (IRGs) associated with ICIs response. A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups. Radiomics features were extracted from TCIA lung_3 cohort using PyRadiomics, and 94 features showing strong association with IRGs (|r|>0.4) were selected. A retrospective cohort consisting of 210 aNSCLC patients receiving first-line ICIs at Guangdong Provincial People's Hospital was analyzed and divided into training (n=147) and validation (n=63) groups. Least absolute shrinkage and selection operator was used for radiomic features selection, and logistic regression was applied to construct a combined clinical-radiomic model and nomogram for predicting ICIs therapy response. The performance of the model was evaluated using ROC curve, calibration curve, and decision curve analysis. RESULTS: WGCNA identified 84 IRGs enriched in immune activation pathways. The combined model outperformed individual models in both the training (AUC=0.725, 95% CI: 0.644-0.807) and validation cohorts (AUC=0.706, 95% CI: 0.577-0.836). Calibration curve and decision curve analyses confirmed the clinical efficacy of the nomogram for predicting ICIs therapy response in aNSCLC patients. CONCLUSIONS The genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC, potentially facilitating personalized immunotherapy decision-making.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; Tumor Microenvironment ; Lung Neoplasms/therapy* ; Immunotherapy ; Tomography, X-Ray Computed ; Nomograms ; Retrospective Studies ; Immune Checkpoint Inhibitors/therapeutic use* ; Prognosis ; Male ; Female ; Radiomics

Mitochondria are the convergence point of multiple pathways that trigger programmed cell death (PCD). Mitochondrial-associated PCD (mtPCD) is involved in the pathogenesis of several diseases. However, the role of mtPCD in the prognostic prediction of cancers including non-small-cell lung cancer (NSCLC) remains to be investigated. Here, 12 mtPCD patterns were analyzed in transcriptomics, genomics, and clinical data collected from 4 datasets containing 977 patients. A risk-score assessment system containing 18 genes was established. We found that NSCLC patients with a high-risk score had a poorer prognosis. A nomogram was constructed by incorporating the risk score with clinical features. The risk score was further associated with clinicopathological information, tumor-mutation frequency, and immunotherapy responses. NSCLC patients with a high risk score had more Treg cells infiltration. However, these patients had higher tumor-mutation burden scores and may be more sensitive to immunotherapy. Moreover, receptor-interacting serine/threonine protein kinase 2 (RIPK2) was selected from mtPCD gene model for validation. We found that RIPK2 exhibited oncogenic function, and its expression level was inversely associated with the overall survival of NSCLC. Taken together, our results indicated the accuracy and practicability of the mtPCD gene model and RIPK2 in predicting the prognosis of NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Prognosis ; Male ; Female ; Nomograms ; Middle Aged ; Mitochondria/metabolism* ; Apoptosis/genetics* ; Mutation ; Biomarkers, Tumor/genetics* ; Aged