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 establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Exosomal circular RNA (circRNAs) are pivotal in cancer biology, and tumor pathophysiology. These stable, non-coding RNAs encapsulated in exosomes participated in cancer progression, tumor growth, metastasis, drug sensitivity and the tumor microenvironment (TME). Their presence in bodily fluids positions them as potential non-invasive biomarkers, revealing the molecular dynamics of cancers. Research in exosomal circRNAs is reshaping our understanding of neoplastic intercellular communication. Exploiting the natural properties of exosomes for targeted drug delivery and disrupting circRNA-mediated pro-tumorigenic signaling can develop new treatment modalities. Therefore, ongoing exploration of exosomal circRNAs in cancer research is poised to revolutionize clinical management of cancer. This emerging field offers hope for significant breakthroughs in cancer care. This review underscores the critical role of exosomal circRNAs in cancer biology and drug resistance, highlighting their potential as non-invasive biomarkers and therapeutic targets that could transform the clinical management of cancer.

This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*

This paper interprets the best practice guidelines for Practice Education in Nursing published by the Registered Nurses' Association of Ontario (RNAO), providing a scientific basis and insights for the development and progress of nursing students' practical education in China. The goal is to improve the quality of nursing students' clinical practice and enhance their clinical service capabilities.

Purpose To investigate the predictive value of the ratio of the square of the cross-sectional perimeter(C2)to the area(A)of the venous lumen(C2/A)in relation to lower extremity deep venous thrombosis(DVT)among orthopaedic inpatients.Materials and Methods A total of 150 inpatients without DVT from the Orthopedics Department of Xijing Hospital Affiliated to the Air Force Military Medical University were prospectively chosen from June 2020 to June 2021.Before the operation,the parameters including A,C,C2/A,inner diameter values,venous flow velocity and blood flow volume of the common femoral vein(CFV),superficial femoral vein(SFV)and popliteal vein(POV)were examined by ultrasound.After the operation,the formation of thrombus was monitored using ultrasound.Subsequently,the clinical data and ultrasound parameters of the thrombus group and the non-thrombus group were compared.Moreover,the receiver operating characteristic(ROC)curve was plotted to assess the efficacy and cut-off values of each parameter in predicting DVT.Results There were statistically significant differences between the two groups in CFV inner diameter,CFV blood flow,CFV-C,CFV-A,CFV-C2/A,SFV blood flow,SFV-C,SFV-C2/A,POV blood flow,POV-C,POV-A and POV-C2/A(t=2.64-10.41,all P＜0.05).When the cut-off values of the ultrasonic parameters CFV-C2/A,SFV-C2/A,and POV-C2/A were＞16.01,＞16.53,and＞16.54 respectively,the area under the curve for predicting DVT was the largest,which were 0.906,0.920,and 0.870 respectively.The sensitivities were 83.8%,78.4%,and 81.1%respectively,and the specificities were 82.3%,89.4%,and 85.8%respectively.Conclusion The novel ultrasonic parameter C2/A exhibits a relatively high predictive efficacy for DVT,and it is capable of furnishing novel reference information for the implementation of early DVT prevention in the clinical setting.

BACKGROUND:The role of microRNA-214 in osteoporosis has been reported both at home and abroad,whereas the interrelationship between microRNA-214 and osteoarthritic articular cartilage and subchondral bone degeneration is unclear. OBJECTIVE:To investigate the relationship between microRNA-214 and cartilage and subchondral bone degeneration in mice with knee osteoarthritis. METHODS:Thirty C57BL/6J mice were randomly grouped:Experiment 1:sham operation group and medial meniscus destabilization group (n=3 per group) underwent hematoxylin-eosin staining and qPCR to detect changes in microRNA-214 gene expression;Experiment 2:sham operation group,medial meniscus destabilization group,medial meniscus destabilization+null-loaded adenovirus group (null-loaded group),and medial meniscus destabilization+microRNA-214 antagonist overexpression adenovirus group (antagonist group;n=6 per group). Cartilage tissues were taken from each group 4 weeks after surgery,and stained with hematoxylin-eosin,safranin O-fast green,and toluidine blue. qPCR and western blot were used to detect the expression of related factors in articular cartilage. RESULTS AND CONCLUSION:(1) In Experiment 1,hematoxylin-eosin staining results showed that cartilage degeneration was visible in the medial meniscus destabilization group compared with the sham operation group. qPCR assay results showed that microRNA-214 was expressed in all the samples,and the expression level of microRNA-214 in cartilage samples of the medial meniscus destabilization group was significantly higher than that of the sham operation group (P<0.05). (2) In Experiment 2,the results of hematoxylin-eosin staining,safranin O-fast green staining,and toluidine blue staining showed that the degree of cartilage degeneration in the antagonist group was significantly reduced compared with the medial meniscus destabilization group. Adenovirus-validated PCR assay showed that the microRNA-214 expression level in cartilage tissue was higher in the null-loaded group than in the antagonist group (P<0.05). (3) In Experiment 2,X-ray results showed typical osteoarthritis imaging changes in the medial meniscus destabilization group and null-loaded group,while the degree of degenerative joint lesions was relatively mild in the antagonist group. The results of microcomputed tomography showed that after injection of microRNA-214 antagonist,trabecular structure model index became smaller in the antagonist group,and the data were better than those of the medial meniscus destabilization group and null-loaded group. (4) In Experiment 2,western blot results showed that The relative expression levels of cartilage-associated factor type Ⅱ collagen α1,sex-determining region Y-box 9,Runt-associated transcription factor 2,and osteopontin in cartilage specimens of the medial meniscus destabilization group and the null-loaded group were lower than that in the sham operation group and the antagonist group (P<0.05),whereas the relative expression level of matrix metalloproteinase 13 was higher in the medial meniscus destabilization group and the null-loaded group than the sham operation group and the antagonist group (P<0.05). (5) In Experiment 2,PCR results indicated that the relative mRNA expression of tumor necrosis factor-α and interleukin-6 was relatively higher in the medial meniscus destabilization group and null-loaded group,but relatively lower in the antagonist group,as compared with the sham operation group (P<0.05). The relative mRNA expression of tumor necrosis factor-α and interleukin-6 was also higher in the medial meniscus destabilization group and the null-loaded group compared with the antagonist group (P<0.05). To conclude,the expression level of microRNA-214 in articular cartilage was elevated in the mouse osteoarthritis model,suggesting that the elevated expression level of microRNA-214 is closely related to osteoarthritis;and injection of microRNA-214 antagonist into the knee joint cavity of the mouse osteoarthritis model could delay articular cartilage degradation,promote subchondral bone remodeling,and ameliorate the progression of osteoarthritis.

In recent years, there has been a growing interest in the research on NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome inhibitors in the treatment of inflammatory diseases. The NLRP3 inflammasome is integral to the innate immune response, and its abnormal activation can lead to the release of pro-inflammatory cytokine, consequently facilitating the progression of various pathological conditions. Therefore, investigating the pharmacological inhibition pathway of the NLRP3 inflammasome represents a promising strategy for the treatment of inflammation-related diseases. Currently, the Food and Drug Administration(FDA) has not approved drugs targeting the NLRP3 inflammasome for clinical use due to concerns regarding liver toxicity and gastrointestinal side effects associated with chemical small molecule inhibitors in clinical trials. Natural small molecule compounds such as polyphenols, flavonoids, and alkaloids are ubiquitously found in animals, plants, and other natural substances exhibiting pharmacological activities. Their abundant sources, intricate and diverse structures, high biocompatibility, minimal adverse reactions, and superior biochemical potency in comparison to synthetic compounds have attracted the attention of extensive scholars. Currently, certain natural small molecule compounds have been demonstrated to impede the activation of the NLRP3 inflammasome via various action mechanisms, so they are viewed as the innovative, feasible, and minimally toxic therapeutic agents for inhibiting NLRP3 inflammasome activation in the treatment of both acute and chronic inflammatory diseases. Hence, this study systematically examined the effects and potential mechanisms of natural small molecule compounds derived from traditional Chinese medicine on the activation of NLRP3 inflammasomes at their initiation, assembly, and activation stages. The objection is to furnish theoretical support and practical guidance for the effective clinical application of these natural small molecule inhibitors.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/metabolism* ; Inflammation/drug therapy* ; Anti-Inflammatory Agents/therapeutic use* ; Humans ; Animals ; Disease Models, Animal ; Biological Products/therapeutic use* ; Drug Discovery ; Medicine, Chinese Traditional/methods*