Value of computed tomography radiomics combined with inflammation indices in predicting the efficacy of immunotherapy in patients with locally advanced and metastatic non-small cell lung cancer.

Background: Although immunotherapy has revolutionized the treatment landscape of lung cancer and improved the prognosis of this malignancy, many patients with lung cancer still are not able to benefit from it because of many different reasons. The expression of programmed death ligand-1 (PD-L1) in tumor cells has been approved for the prediction of immunotherapy efficacy; however, its clinical application has been limited by the invasiveness of PD-L1 determination and the heterogeneity of tumor cells. As a promising technology, radiomics has made significant progress in the diagnosis and treatment of lung cancer. Thus, we constructed a noninvasive predictive model which based on radiomics to predict the immunotherapy efficacy of lung caner patients. Methods: Data of 82 patients with stage IIIa/IVb NSCLC who received immunotherapy at the First Affiliated Hospital of Soochow University from December 2019 to January 2023 were retrospectively collected. These patients were followed up for durable clinical benefit (DCB), as defined by whether progression-free survival (PFS) reached 12 months. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen for the radiomic features in the training set, and a radiomics score (Rad-score) was calculated. The clinical baseline data were analyzed, and the peripheral blood inflammation indices were calculated. Univariate and multivariate analyses were performed to identify the applicable indices, which were combined with the Rad-score to create a comprehensive forecasting model (CFM) and nomograms. Internal validation was performed in the validation set. Results: Up to the last follow-up time, 48 of 82 patients had a PFS of more than 12 months. The area under the receiver operating characteristic (ROC) curve (AUC) of the Rad-score was 0.858 and 0.812, respectively, in the training set and validation set. A systemic immune-inflammation index (SII) score of <500.88 after two cycles of immunotherapy was a protective factor for PFS >12 months [odds ratio (OR) 0.054; P=0.003]. The CFM had an AUC of 0.930 and 0.922, respectively, in the training and validation sets. The calibration curves and decision curve analysis (DCA) demonstrated the reliability and clinical applicability of the model, respectively. Conclusions: The radiomics model performed well in predicting whether patients with locally advanced or metastatic NSCLC can achieve DCB after receiving immunotherapy. The CFM had good predictive performance and reliability.

Preparation and epitope mapping of monoclonal antibodies against African swine fever virus p22 protein.

African swine fever (ASF), caused by the African swine fever virus (ASFV), is now widespread in many countries and severely affects the commercial rearing of swine. Rapid and early diagnosis is crucial for the prevention of ASF. ASFV mature virions comprise the inner envelope protein, p22, making it an excellent candidate for the serological diagnosis and surveillance of ASF. In this study, the prokaryotic-expressed p22 recombinant protein was prepared and purified for immunization in mice. Four monoclonal antibodies (mAbs) were identified using hybridoma cell fusion, clone purification, and immunological assays. The epitopes of mAbs 14G1 and 22D8 were further defined by alanine-scanning mutagenesis. Our results showed that amino acids C39, K40, V41, D42, C45, G48, E49, and C51 directly bound to 14G1, while the key amino acid epitope for 22D8 included K161, Y162, G163, D165, H166, I167, and I168. Homologous and structural analysis revealed that these sites were highly conserved across Asian and European ASFV strains, and the amino acids identified were located on the surface of p22. Thus, our study contributes to a better understanding of the antigenicity of the ASFV p22 protein, and the results could facilitate the prevention and control of ASF.

High Accuracy Protein Identification: Fusion of Solid-State Nanopore Sensing and Machine Learning.

Proteins are arguably one of the most important class of biomarkers for health diagnostic purposes. Label-free solid-state nanopore sensing is a versatile technique for sensing and analyzing biomolecules such as proteins at single-molecule level. While molecular-level information on size, shape, and charge of proteins can be assessed by nanopores, the identification of proteins with comparable sizes remains a challenge. Here, solid-state nanopore sensing is combined with machine learning to address this challenge. The translocations of four similarly sized proteins is assessed using amplifiers with bandwidths (BWs) of 100 kHz and 10 MHz, the highest bandwidth reported for protein sensing, using nanopores fabricated in <10 nm thick silicon nitride membranes. F-values of up to 65.9% and 83.2% (without clustering of the protein signals) are achieved with 100 kHz and 10 MHz BW measurements, respectively, for identification of the four proteins. The accuracy of protein identification is further enhanced by classifying the signals into different clusters based on signal attributes, with F-value and specificity of up to 88.7% and 96.4%, respectively, for combinations of four proteins. The combined use of high bandwidth instruments, advanced clustering and machine learning methods allows label-free identification of proteins with high accuracy.

Value of contrast-enhanced magnetic resonance imaging-T2WI-based radiomic features in distinguishing lung adenocarcinoma from lung squamous cell carcinoma with solid components >8 mm.

Background: Radiomics is one of the research frontiers in the field of imaging and has excellent diagnostic performance. However, there is a lack of magnetic resonance imaging (MRI)-based omics studies on identifying pathological subtypes of lung cancer. Here we explored the value of the contrast-enhanced MRI-T2-weighted imaging (T2WI)-based radiomic analysis in distinguishing adenocarcinoma (Ade) from squamous cell carcinoma (Squ) with solid components >8 mm. Methods: A retrospective analysis was performed of a total of 71 lung cancer patients who undergoing contrast-enhanced MRI and computed tomography (CT) before treatment, and the nodules had solid components ≥8 mm in our center from January 2020 to September 2021. All enrolled patients were divided into Squ and Ade groups according to the pathological results. In addition, the two groups were randomly divided into training set and validation set in a ratio of about 7:3. Radiomics software was used to extract the relevant radiomic features. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomic features that were most relevant to lung cancer subtypes, thus calculating the radiomic scores (Rad-score) and constructing the radiomic models. Multivariate logistic regression was used to combine relevant clinical features with Rad-score to form combined model nomograms. The receiver operating characteristic (ROC) curves. the area under the ROC curve (AUC), the decision curve analysis (DCA) and the DeLong's test were used to evaluate the clinical application potentials. Results: The sensitivity and specificity of the clinical model based on smoking was 75.0% and 93.8%. The AUC of the constructed magnetic resonance (MR)-Rad model for differentiating the pathological subtypes of lung cancer was 0.8651 in the validation sets. The AUC of the CT-Rad model in the validation set were 0.9286. The combined model constructed by combining clinical features and Rad-score had AUC of 0.8016, for identifying the 2 pathological subtypes of lung cancer in the validation set. There was no significant difference in diagnostic performance between MR-Rad model and CT-Rad model (P>0.05). Conclusions: The MR-Rad model has a diagnostic performance similar to that of CT-Rad model, while the diagnostic performance of the combined mode was better than the single MR model.

Application of computed tomography-based radiomics combined with clinical factors in the diagnosis of malignant degree of lung adenocarcinoma.

Background: As an emerging technology, radiomics is being widely used in the diagnosis of early lung cancer due to its excellent diagnostic performance. However, there is a lack of studies that apply radiomics to the diagnosis of malignancy of lung adenocarcinoma. Thus, we used computed tomography (CT)-based radiomics to construct a model for the diagnosis of high-risk lung adenocarcinoma. Methods: Data of 170 patients who underwent surgical treatment at the First Affiliated Hospital of Soochow University and had a maximum nodule diameter ≤2 cm on preoperative CT images between January 2020 and December 2021 were retrospectively analyzed. All enrolled patients were randomly divided into experimental and validation groups according to the ratio of 7:3. The diagnosis of lung adenocarcinoma was based on postoperative pathological results. The region of interest was delineated on preoperative CT images, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to screen the radiomics features thus obtaining the radiomics score (Radscore), which was the basis of the radiomics model. Based on the multivariate regression analysis, independent predictors were screened from the clinical baseline data and imaging features thus constructing clinical model. Multivariate logistic regression was used to combine independent predictors and the Radscore to form a comprehensive nomogram. The diagnostic performance of constructed models was evaluated based on receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The sensitivity and specificity of the clinical model based on consolidation-to-tumor ratio (CTR), lobulated signs and vascular anomaly signs was 70.0% and 76.7% in the validation group. The radiomics model [area under the curve (AUC) 0.926; 95% confidence interval (CI): 0.857-0.995] and the comprehensive model (AUC 0.922; 95% CI: 0.851-0.992) performed better than clinical model (AUC 0.839; 95% CI: 0.720-0.958) in the validation group. The sensitivity and specificity of the comprehensive model was 85.0% and 80.0% in the validation group. DCA of radiomics model and comprehensive model suggested they have better net survival benefit than clinical model. Conclusions: Compared with clinical model, radiomics model and comprehensive model had better diagnostic performance in distinguishing malignant degree of lung adenocarcinoma.

Serum tissue inhibitor of metalloproteinase-1 and risk of cognitive impairment after acute ischaemic stroke.

The expression of tissue inhibitor metalloproteinase-1 (TIMP-1) significantly increased after acute cerebral ischaemia and involved in neurodegeneration. The purpose was to prospectively investigate the relationship between serum TIMP-1 with post-stroke cognitive impairment. Our participants were from an ancillary study of China Antihypertensive Trial in Acute Ischemic Stroke. 598 ischaemic stroke patients from seven participating hospitals were included. Cognitive impairment was evaluated using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at 3 months. 316 (52.84%) or 384 (64.21%) participants had cognitive impairment according to MMSE or MoCA, respectively. Compared with the first quartile of TIMP-1, the multivariate-adjusted odds ratios (95% confidence intervals) for the highest quartile were 1.80 (1.09-2.97) for cognitive impairment defined by MMSE and 2.55 (1.49-4.35) by MoCA. Multiple-adjusted spline regression models showed linear associations between TIMP-1 concentrations and cognitive impairment (P value for linearity < 0.01). The addition of TIMP-1 to models including conventional factors improved reclassification for cognitive impairment, as shown by net reclassification index or integrated discrimination improvement (P < 0.05). Participants with both higher TIMP-1 and matrix metalloproteinase-9 levels simultaneously had highest risk of cognitive impairment. Higher serum TIMP-1 levels were associated with increased risk of cognitive impairment after acute ischaemic stroke, independently of established risk factors.