The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis.

Background: Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. Objective: This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. Results: A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations). Conclusions: Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.

Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis.

Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response.

[Effect of electroacupuncture stimulation of "Baihui" (GV 20) and " Yongquan" (KI 1) on expression of hippocampal amyloid-ß and low density lipoprotein receptor-related protein-1 in APP/PS 1 transgenic mice].

OBJECTIVE: To observe the effect of electroacupuncture (EA) treatment on the level of hippocampal amyloid-beta peptide (Aß) and its key transport receptor low density lipoprotein receptor-related protein-1 (LRP 1) in APP/PS 1 transgenic mice so as to explore its mechanism underlying improvement of Alzheimer's disease (AD). METHODS: Twenty-four male APP/PS 1 transgenic mice were equally and randomly divided into model group and EA treatment group, and 12 C 57 BL/6 mice were used as the normal control group. EA (1 Hz/50 Hz, 0.3 mA) was applied to "Baihui" (GV 20) and "Yongquan" (KI 1) for 15 min, once every other day for 6 weeks. The learning-memory ability was detected by using Morris water maze testing, left hippocampal Aß 1-40 and Aß 1-42 contents were assayed by ELISA, and right hippocampal LRP 1 expression was detected using Western blot (WB). RESULTS: Results of Morris water maze test showed no significant differences among the three groups in the escape latency, the times of the platform-site crossovers, the time spent in the target platform quadrant (P>0.05). Compared with the model group, the moderately increased escape latency had a decreasing tendency in the EA treatment group. ELISA assaying showed that hippocampal Aß 1-42, Aß 1-40, and ratio of Aß 1-42/Aß 1-40 of the model group were significantly higher than those of the normal control group (P<0.01). After EA intervention, the increased Aß 1-42 , Aß 1-40, and ratio of Aß 1-42/Aß 1-40 were remarkably down-regulated in the EA treatment group (P<0.01). WB detection displayed that the right hippocampal LRP 1 expression level of the model group was markedly lower than that of the normal control group (P<0.05). After EA treatment, LRP 1 expression level was moderately up-regulated but without significant difference between the model and EA treatment groups (P>0.05). CONCLUSION: EA intervention can lower the level of hippocampal Aß in APP/PS 1 transgenic mice, but its effects on Aß transport receptor LRP 1 expression and learning-memory ability need being confirmed further.