Novel Peptide-Based Magnetic Nanoparticle for Mesenchymal Circulating Tumor Cells Detection.

The monitoring of circulating tumor cells (CTCs) has recently served as a promising approach for assessing prognosis and evaluating cancer treatment. We have already developed a CTCs enrichment platform by EpCAM recognition peptide-functionalized magnetic nanoparticles (EP@MNPs). However, considering heterogeneous CTCs generated through epithelial-mesenchymal transition (EMT), mesenchymal CTCs would be missed with this method. Notably, N-cadherin, overexpressed on mesenchymal CTCs, can facilitate the migration of cancer cells. Hence, we screened a novel peptide targeting N-cadherin, NP, and developed a new CTCs isolation approach via NP@MNPs to complement EpCAM methods' deficiencies. NP@MNPs had a high capture efficiency (about 85%) of mesenchymal CTCs from spiked human blood. Subsequently, CTCs were captured and sequenced at the single-cell level via NP@MNPs and EP@MNPs, RNA profiles of which showed that epithelial and mesenchymal subgroups could be distinguished. Here, a novel CTCs isolation platform laid the foundation for mesenchymal CTCs isolation and subsequent molecular analysis.

The landscape of abnormal pathway activation confers COVID-19 patients' molecular sequelae earlier than clinical phenotype.

Rationale: The 2019 coronavirus disease (COVID-19) pandemic poses a significant threat to human health. After SARS-CoV-2 infection, major clinical concerns are organ damage and possible sequelae. Methods: In this study, we analyzed serum multi-omics data based on population-level, including healthy cohort, non-COVID-19 and COVID-19 covered different severity cohorts. We applied the pseudo-SpatioTemporal Consistency Alignment (pST-CA) strategy to correct for individualized disease course differences, and developed pseudo-deterioration timeline model and pseudo-recovery timeline model based on the "severe index" and "course index". Further, we comprehensively analyzed and discussed the dynamic damage signaling in COVID-19 deterioration and/or recovery, as well as the potential risk of sequelae. Results: The deterioration and course models based on the pST-CA strategy can effectively map the activation of blood molecular signals on cellular, pathway, functional and disease phenotypes in COVID-19 deterioration and throughout the disease course. The models revealed the neurological, cardiovascular, and hepatic toxicity present in SARS-CoV-2. The abundance of differentially expressed proteins and the activity of upstream regulators were comprehensively analyzed and evaluated to predict possible target drugs for SARS-CoV-2. On molecular docking simulation analysis, it was further demonstrated that blocking CEACAM1 is a potential therapeutic target for SARS-CoV-2. Conclusions: Clinically, the risk of organ failure and death in COVID-19 patients rises with increasing number of infections. Individualized sequelae prediction for patients and assessment of individualized intervenable targets and available drugs in combination with the upstream regulator analysis results are of great clinical value.

Active regression model for clinical grading of COVID-19.

Background: In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation. Methods: This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features. Results: Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume. Conclusion: In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.

CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seq.

Single-cell RNA sequencing (scRNA-seq) is one of the most efficient technologies for human tumor research. However, data analysis is still faced with technical challenges, especially the difficulty in efficiently and accurately discriminating cancer/normal cells in the scRNA-seq expression matrix. If we can address these challenges, we can have a deeper understanding of the intratumoral and intertumoral heterogeneity. In this study, we developed a cancer/normal cell discrimination pipeline called pan-Cancer Seeker (CaSee) devoted to scRNA-seq expression matrix, which is based on the traditional high-quality pan-cancer bulk sequencing data using transfer learning. CaSee is the first tool directly used to discriminate cancer/normal cells in the scRNA-seq expression matrix, with much wider application fields and higher efficiency than copy number variation (CNV) method which requires corresponding reference cells. CaSee is user-friendly and can adapt to a variety of data sources, including but not limited to scRNA tissue sequencing data, scRNA cell line sequencing data, scRNA xenograft cell sequencing data and scRNA circulating tumor cell sequencing data. It is compatible with mainstream sequencing technology platforms, 10× Genomics Chromium, Smart-seq2, and Microwell-seq. Here, CaSee pipeline exhibited excellent performance in the multicenter data evaluation of 11 retrospective cohorts and one independent dataset, with an average discrimination accuracy of 96.69%. In general, the development of a deep-learning based, pan-cancer cell discrimination model, CaSee, to distinguish cancer cells from normal cells will be compelling to researchers working in the genomics, cancer, and single-cell fields.

Simultaneous determination of vancomycin and ceftazidime in cerebrospinal fluid in craniotomy patients by high-performance liquid chromatography.

A simple, accurate and rapid method for simultaneous analysis of vancomycin and ceftazidime in cerebrospinal fluid (CSF), utilizing high-performance liquid chromatography (HPLC), has been developed and thoroughly validated to satisfy strict FDA guidelines for bioanalytical methods. Protein precipitation was used as the sample pretreatment method. In order to increase the accuracy, tinidazole was chosen as the internal standard. Separation was achieved on a Diamonsil C18 column (200 mm x 4.6mm I.D., 5 microm) using a mobile phase composed of acetonitrile and acetate buffer (pH 3.5) (8:92, v/v) at room temperature (25 degrees C), and the detection wavelength was 240 nm. All the validation data, such as accuracy, precision, and inter-day repeatability, were within the required limits. The method was applied to determine vancomycin and ceftazidime concentrations in CSF in five craniotomy patients.