In recent years, the computer science represented by artificial intelligence and high-throughput sequencing technology represented by omics play a significant role in the medical field. This paper reviews the research progress of the application of artificial intelligence combined with omics data analysis in the diagnosis and treatment of non-small cell lung cancer (NSCLC), aiming to provide ideas for the development of a more effective artificial intelligence algorithm, and improve the diagnosis rate and prognosis of patients with early NSCLC through a non-invasive way.
ObjectiveTo analyze the protein expression changes in the retina of non-arteritic anterior ischemic optic neuropathy (NAION) in rats.MethodsThe rat NAION (rNAION) model was established by Rose Bengal and laser. Twenty Sprague-Dawley rats were randomly divided into 4 groups, the normal control group, the laser control group, the RB injection control group, and the rNAION model group, with 5 rats in each group. The right eye was used as the experimental eye. The retina was dissected at the third day after modeling. Enzyme digestion method was used for sample preparation and data collection was performed in a non-dependent collection mode. The data were quantitatively analyzed by SWATH quantitative mass spectrometry, searching for differential proteins and performing function and pathway analysis.ResultsCompared with the other three control groups, a total of 184 differential proteins were detected in the rNAION group (expression fold greater than 1.5 times and P＜0.05), including 99 up-regulated proteins and 85 down-regulated proteins. The expressions of glial fibrillary acidic protein, guanine nucleotide binding protein 4, laminin 1, 14-3-3γ protein YWHAG were increased. Whereas the expressions of Leucine-rich glioma-inactivated protein 1, secretory carrier-associated membrane protein 5, and Clathrin coat assembly protein AP180 were decreased. The differential proteins are mainly involved in biological processes such as nerve growth, energy metabolism, vesicle-mediated transport, the regulation of synaptic plasticity, apoptosis and inflammation. Pathway enrichment analysis showed that PI3K-Akt signaling pathway and complement and thrombin reaction pathway was related to the disease.ConclusionThe protein expressions of energy metabolism, nerve growth, synaptic vesicle transport and PI3K-Akt signaling pathway can regulate the neuronal regeneration and apoptosis in NAION.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
Objective Toa nalyzed ifferentialp roteine xpressiono fc holangiocarcinomai np eripheralb loodb yproteomics technology, and to investigate the significance of proteomics technology in early diagnosis of bile ductmalignancy.M ethods Serum proteinf rom 58p atientsw ithc holangiocarcinomaa nd5 8c ontrols( 20p atientsw ithcholecystolithiasis and 38 healthy people) were detected by surface enhanced laser desorption/ionization-time offlight-mass spectrometry (SELDI-TOF-MS). Ciphergen protein chip software was used to identify proteinic spectra.R esults Comparedw itht hes pectrao fs erum proteini nc ontrolg roup,t herew ere1 0d ifferentiallye xpressedproteins in bile duct carcinoma group, among which three proteins with relative molecular masses of 5. 900 X 10’，9.08 0X 1 0’a nd1 1.86 3X 1 0’w ereu p-regulated( Plt;1 0-’)ands evenp roteinsw ithr elativem olecularm asseso f6.9 59X 1 0’，14.0 00X 1 0’，14.1 29X 1 0’，14.3 02X 1 0’，17.5 57X 10’，17.6 90X 1 0’a nd2 8.5 52X 1 0’w ered ownregulated(Plt; 10-’)。The average concentration of protein with the relative molecular mass of 11. 863 X 10’ incholangiocarcinoma group was eight times more than that in controls group. At the stage I of cholangiocarcinoma,thee xpressiono fp roteinp ointw itha r elativem olecularm asso f5 .90 0X1 0’w ass ignificantlyh ighert hant hosep atientsat the stage III and stage fV (Plt;10-’)，while there were no statistical difference of expression between diffeent clinical stages for the other 9 proteins points. And there were no significant expression differences of the above10 proteins between the patients with and without jaundice following cholangiocarcinoma. Instead, another threeproteinsw ithr elativem olecularm asseso f7 .25 5X 1 03，12.36 4X 1 0’a nd1 5.8 73X 1 03w ered etectedt oh aved ifferentproteine xpressions.A nda llo fth em showedh ighe xpressionsin j aundiceg roup( Plt;10-5).C onclusion Thereare remarkable differences of the expressions of serum proteins in peripheral blood in patients with cholangiocarcinoma.T hep roteinp ointw itha r elativem olecularm asso f1 1.86 3X 1 0’m ayb ea p otentialb iomarkerfo re arlyd iagnosisof cholangiocarcinoma
摘要:目的: 金黄色葡萄球菌(金葡菌)的感染近年来已成为医院内的主要致病菌,而其耐药性也呈逐渐升高的趋势,为了解该菌在我院的感染和耐药情况,为临床合理使用抗生素提供科学依据。 方法 : 用经典生理生化鉴定方法,对各种临床标本主要来源于痰液和各种伤口脓液标本分离到的102株金葡菌进行生物学特性及药敏试验。 结果 : 从我们医院2007年5月至2009年8月所分离出来的102株金葡菌中青霉素耐药性8923%,氨苄青霉素耐药率为9385%,没有发现万古霉素耐药菌。 结论 : 除万古霉素外,耐药率较低的依次是利福平、苯唑青霉素、环丙沙星、呋喃妥因、阿米卡星、磺胺甲基异恶唑、红霉素,而青霉素G、氨苄青霉素、四环素耐药性情况非常严重,并且多重耐药,耐药性强,应引起临床的高度重视。Abstract: Objective: To analyze the bionomics and antimicrobial susceptibility of staphylococcus aureus, which was the main pathogenic bacterium with high drug tolerance in our hospital, in order to provide the rational use of antibiotics. Methods : Samples of one hundred and two staphylococcus aureus cases from sputamentum and pus were evaluated by classic physiology and biochemistry methods to test the bionomics and antimicrobial susceptibility. Results : The drug resistance rate to penicillin, penbritin and vancomycin was 8923%, 9385% and 0, separately. Conclusion : Besides vancomycin, the drug resistance rate of rifampicin, oxazocilline, ciprofloxacin, furadantin, amikacin, sulfamethoxazole and sulfamethoxazole increased one by one. The resistance to penicillin G, penbritin and tetracycline was serious, including multidrug resistant, which should be paid highly attention.
ObjectiveTo explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). MethodsThe clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). ResultsFour radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. ConclusionThe machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.
ObjectiveTo introduce economic evaluation methods for anticancer-drugs with basket trial design, and to provide references for related research and decision-making. MethodsA case analysis was conducted on economic evaluation methods for anticancer-drugs with basket trial design, which was issued by Canadian Agency for Drugs and Technologies in Health (CADTH) in the Economic Guidance Report. Moreover, both the advantages and disadvantages of the methods were analyzed in accordance with the characteristics of basket trials. ResultsPooled analysis and tumor-specific analysis were two methods frequently employed in the case analysis. However, great uncertainties were available in both of them. The uncertainty of the former was mainly reflected in the heterogeneity of the targeted population, while the uncertainty of the latter was mainly shown in the insufficient sample size of the subgroup. ConclusionCurrently, economic evaluation methods for anticancer-drugs with basket trial design are immature. Thus, researchers are required to explore the methods of innovation evaluation with lower uncertainty; reimbursement decision-makers should fully consider the uncertainty of evaluation results and enterprises should collect the real-world data for the demands of evaluation to promote the reasonable allocation of healthcare resources in China.
Objective To explore the effects on quality of life (QOL), the targeted rates of metabolic parameters and cost-effectiveness in newly diagnosed type 2 diabetic patients who underwent multifactorial intensive intervention. Methods One hundred and twenty seven cases in an intensive intervention and 125 cases in a conventional intervention group were investigated by using the SF-36 questionnaire. The comparison of QOL and the targeted rates of metabolic parameters between the two groups were made. We assessed the influence factors of QOL by stepwise regression analysis and evaluated the efficiency by pharmacoeconomic cost-effectiveness analysis. Results The targeted rates of blood glucose, blood lipid and blood pressure with intensive policies were significantly higher than those with conventional policy (P＜0.05). The intensive group’s role limitations due to physical problems (RP), general health (GH), vitality (VT), role limitation due to emotional problems (RE) and total scores after 6 months intervention were significantly higher than those of baseline (P＜0.05). The vitality scores and health transition (HT) of the intensive group were better than those of the conventional group after 6 months intervention. But the QOL scores of the conventional group were not improved after intervention. The difference of QOL’s total scores after intervention was related to that of HbA1c. The total cost-effectiveness rate of blood glucose, blood lipid, blood pressure control and the total cost-effectiveness rate of QOL with intensive policy were higher than those with the conventional policy. Conclusions Quality of life and the targeted rates of blood glucose, blood lipid and blood pressure in newly diagnosed type 2 diabetic patients with multifactorial intensive intervention policy are better and more economic than those with conventional policy.
Non-small cell lung cancer is one of the primary types of cancer that leads to brain metastases. Approximately 10% of patients with non-small cell lung cancer have brain metastases at the time of diagnosis, and 26%-53% of patients develop brain metastases during the progression of their disease. However, the underlying mechanisms of lung cancer brain metastasis have not been fully elucidated. With the continuous development of single-cell and spatial transcriptomics, the genomic and transcriptomic characteristics of lung cancer brain metastasis are gradually being revealed. In February 2025, the journal Nature Medicine published an article titled "Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases". This article aims to provide a brief interpretation of the paper for colleagues in research and clinical practice.
Objective To review the progress of artificial intelligence (AI) and radiomics in the study of abdominal aortic aneurysm (AAA). Method The literatures related to AI, radiomics and AAA research in recent years were collected and summarized in detail. Results AI and radiomics influenced AAA research and clinical decisions in terms of feature extraction, risk prediction, patient management, simulation of stent-graft deployment, and data mining. Conclusion The application of AI and radiomics provides new ideas for AAA research and clinical decisions, and is expected to suggest personalized treatment and follow-up protocols to guide clinical practice, aiming to achieve precision medicine of AAA.