Since the sudden outbreak of coronavirus disease 2019 (COVID-19), it has rapidly evolved into a momentous global health concern. Due to the lack of constructive information on the pathogenesis of COVID-19 and specific treatment, it highlights the importance of early diagnosis and timely treatment. In this study, 11 key blood indices were extracted through random forest algorithm to build the final assistant discrimination tool from 49 clinical available blood test data which were derived by commercial blood test equipments. The method presented robust outcome to accurately identify COVID-19 from a variety of suspected patients with similar CT information or similar symptoms, with accuracy of 0.9795 and 0.9697 for the cross-validation set and test set, respectively. The tool also demonstrated its outstanding performance on an external validation set that was completely independent of the modeling process, with sensitivity, specificity, and overall accuracy of 0.9512, 0.9697, and 0.9595, respectively. Besides, 24 samples from overseas infected patients with COVID-19 were used to make an in-depth clinical assessment with accuracy of 0.9167. After multiple verification, the reliability and repeatability of the tool has been fully evaluated, and it has the potential to develop into an emerging technology to identify COVID-19 and lower the burden of global public health. The proposed tool is well-suited to carry out preliminary assessment of suspected patients and help them to get timely treatment and quarantine suggestion. The assistant tool is now available online at http://lishuyan.lzu.edu.cn/COVID2019_2/ . Funding: This work was supported by the Fundamental Research Funds for the Central Universities (lzujbky-2020-sp11) and the Gansu Provincial COVID-19 Science and Technology Major Project, China.

Background: Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. Objective: The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. Methods: The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. Results: In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. Conclusions: Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.

Endoscopic retrograde cholangiopancreatography (ERCP) is an established treatment for common bile duct (CBD) stones. Post- ERCP cholecystitis (PEC) is a known complication of such procedure and there are no effective models and clinical applicable tools for PEC prediction. A random forest (RF) machine learning model was developed to predict PEC. Eligible patients at The First Hospital of Lanzhou University in China with common bile duct (CBD) stones and gallbladders in-situ were enrolled from 2010 to 2019. Logistic regression analysis was used to compare the predictive discrimination and accuracy values based on receiver operation characteristics (ROC) curve and decision and clinical impact curve. The RF model was further validated by another 117 patients. This study was registered with ClinicalTrials.gov, NCT04234126. A total of 1117 patients were enrolled (90 PEC, 8.06%) to build the predictive model for PEC. The RF method identified white blood cell (WBC) count, endoscopic papillary balloon dilatation (EPBD), increase in WBC, residual CBD stones after ERCP, serum amylase levels, and mechanical lithotripsy as the top six predictive factors and has a sensitivity of 0.822, specificity of 0.853 and accuracy of 0.855, with the area under curve (AUC) value of 0.890. A separate logistic regression prediction model was built with sensitivity, specificity, and AUC of 0.811, 0.791, and 0.864, respectively. An additional 117 patients (11 PEC, 9.40%) were used to validate the RF model, with an AUC of 0.889 compared to an AUC of 0.884 with the logistic regression model. The results suggest that the proposed RF model based on the top six PEC risk factors could be a promising tool to predict the occurrence of PEC.

EEG is widely applied in emotion recognition, brain disease detection, and other fields due to its high temporal resolution and non-invasiveness. However, artifact removal remains a crucial issue in EEG signal processing. Recently, with the rapid development of deep learning, there has been a significant transformation in the methods of EEG artifact removal. Nonetheless, existing research still exhibits some limitations: (1) insufficient capability to remove unknown artifacts; (2) inability to adapt to tasks where artifact removal needs to be applied to the overall input of multi-channel EEG data. Therefore, this study proposes CLEnet by integrating dual-scale CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), and incorporating an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention Mechanism). CLEnet can extract the morphological features and temporal features of EEG, thereby separating EEG from artifacts. We conducted experiments on three datasets, and the results showed that CLEnet performed best. Specifically, in the task of removing artifacts from multi-channel EEG data containing unknown artifacts, CLEnet shows improvements of 2.45% and 2.65% in SNR(signal-to-noise ratio) and CC(average correlation coefficient). Moreover, RRMSE_t(relative root mean square error in the temporal domain) and RRMSE_f (relative root mean square error in the frequency domain) decrease by 6.94% and 3.30%.

Background: There was no study investigating real-world utilization and outcome of LCT in Osimertinib-treated NSCLC with oligo-residual disease. This study was to analyze the clinical value of local consolidative therapy (LCT) in Osimertinib-treated non-small cell lung cancer (NSCLC) patients with oligo-residual disease.Methods: Patients receiving standard Osimertinib treatment and developing oligo-residual disease (five or fewer residual metastatic lesions) were retrospectively reviewed. Local therapies performed to the oligo-residual tumor lesions or primary lung site before Osimertinib treatment failure were considered as LCT.Results: Of 108 patients recruited, first-line and second-line Osimertinib were administered in 25 and 83 patients, respectively, while LCT was performed in 14 patients. With a median follow-up of 43.6 months, 69 patients developed progressive disease. LCT significantly improved progression-free survival (PFS) (NR vs 12.8 months,p = 0.01) and was independently associated with prolonged PFS (HR = 0.29, 95%CI 0.12 to 0.68,p = 0.004). Patients receiving LCT had a numerically longer overall survival (OS) (85.8 vs 77.1 months,p = 0.58) and after adjusting for potentially confounding factors, LCT was associated with a non-significantly prolonged OS (HR = 0.37, 95%CI 0.12–1.16,p = 0.089). Pattern of failure analyses indicated that progressive disease developed at the originally existed oligo-residual lesions in 76.2% of the 63 patients who didn’t receive LCT and had Osimertinib treatment failure. Of note, 7 (70%) of the 10 patients who had oligo-residual cranial disease but didn’t receive LCT, developed more than five progressive lesions in the brain, which were no longer suitable for stereotactic radiosurgery.Conclusion: Among Osimertinib-treated NSCLC patients having oligo-residual lesions, LCT could improve local control and significantly increase PFS, which need to be verified by further investigations.

Aging is regarded as a dominant risk factor for cancer. Additionally, inflammation and asthenic immune surveillance with aging may facilitate tumor formation and development. However, few studies have comprehensively analyzed the relationship between aging-related genes (AGs) and the prognosis, inflammation and tumor immunity of head and neck squamous cell carcinoma (HNSCC). Here, we initially screened 41 differentially expressed AGs from The Cancer Genome Atlas (TCGA) database. In the training set, a prognosis risk model with seven AGs (APP, CDKN2A, EGFR, HSPD1, IL2RG, PLAU and VEGFA) was constructed and validated in the TCGA test set and the GEO set (P < 0.05). Using univariate and multivariate Cox regression analyses, we confirmed that risk score was an independent prognostic factor of HNSCC patients. In addition, a high risk score was significantly correlated with immunosuppression, and high expression of PLAU, APP and EGFR was the main factor. Furthermore, we confirmed that a high risk score was significantly associated with levels of proinflammatory factors (IL-1α, IL-1β, IL-6 and IL-8) in HNSCC samples. Thus, this risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.