Rapid Diagnosis by Microfluidic Techniques

Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields

Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions

PURPOSEWe aimed to evaluate digital breast tomosynthesis (DBT)-based radiomics in the differentiation of benign and malignant breast lesions in women.METHODSA total of 185 patients who underwent DBT scans were enrolled between December 2017 and June 2019. The features of handcrafted and deep learning-based radiomics were extracted from the tumoral and peritumoral regions with different radial dilation distances outside the tumor. A 3-step method was used to select discriminative features and develop the radiomics signature. Discriminative clinical factors were identified by univariate logistic regression. The clinical fac- tors with P < .05 were used to build a clinical model with multivariate logistic regression. The radiomics nomogram was developed by integrating the radiomics signature and discriminative clinical factors. Discriminative performance of the radiomics signature, clinical model, nomo- gram, and breast imaging reporting and data system assessment were evaluated and compared with the receiver operating characteristic and decision curves analysis (DCA).RESULTSA total of 2 handcrafted and 2 deep features were identified as the most discriminative features from the peritumoral regions with 2 mm dilation distances and used to develop the radiomics signature. The nomogram incorporating the radiomics signature, age, and menstruation status showed the best discriminative performance with area under the curve (AUC) values of 0.980 (95% CI, 0.960 to 1.000; sensitivity =0.970, specificity =0.946) in the training cohort and 0.985 (95% CI, 0.960 to 1.000; sensitivity = 0.909, specificity = 0.966) in the validation cohort. DCA con- firmed the potential clinical usefulness of our nomogram.CONCLUSIONOur results illustrate that the radiomics nomogram integrating the DBT imaging features and clinical factors (age and menstruation status) can be considered as a useful tool in aiding the clinical diagnosis of breast cancer

Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer

PURPOSEWhether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC).METHODSThis retrospective study enrolled patients with LARC (06/2014-08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance.RESULTSThe machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892–0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model.CONCLUSIONThe radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment

Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer

PURPOSEThis retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods.METHODSA total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann–Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA).RESULTSA total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE)=0.831 and sensitivity (SEN)=0.581) in the training cohort and 0.788 (SPE=0.727, SEN=0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model.CONCLUSIONOur results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Rapid Capture and Analysis of Airborne Staphylococcus aureus in the Hospital Using a Microfluidic Chip

In this study we developed a microfluidic chip for the rapid capture, enrichment and detection of airborne Staphylococcus (S.) aureus. The whole analysis took about 4 h and 40 min from airborne sample collection to loop-mediated isothermal amplification (LAMP), with a detection limit down to about 27 cells. The process did not require DNA purification. The chip was validated using standard bacteria bioaerosol and was directly used for clinical airborne pathogen sampling in hospital settings. This is the first report on the capture and analysis of airborne S. aureus using a novel microfluidic technique, a process that could have a very promising platform for hospital airborne infection prevention (HAIP)

Expression of Acidothermus cellulolyticus thermostable cellulases in tobacco and rice plants

The production of cellulases in plants is an economical method for the conversion of lignocellulosic biomass into fuels. Herein we report the expressions of two thermostable Acidothermus cellulolyticus cellulases, endo-1,4-β-D-glucanase (E1) and exoglucanase (Gux1), in tobacco and rice. To evaluate the expression of these recombinant cellulases, we expressed the full-length E1, the catalytic domains of E1 (E1cd) and Gux1 (Gux1cd), as well as an E1–Gux1cd fusion enzyme in various subcellular compartments. In the case of tobacco, transgenic plants that expressed apoplast-localized E1 showed the highest level of activity, about three times higher than those that expressed the cytosolic E1. In the case of rice, the level of cellulase-specific activity in the transgenic plants ranged from 11 to 20 nmol 4-methylumbelliferone min−1 mg−1 total soluble protein. The recombinant cellulases exhibited good thermostability below 70 °C. Furthermore, transgenic rice leaves that were stored at room temperature for a month lost about 20% of the initial cellulase activity. Taken together, the results suggested that heterologous expression of thermostable cellulases in plants may be a viable option for biomass conversion

Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement

Single-cell analysis with childhood and adult systemic lupus erythematosus

AbstractPatients with systemic lupus erythematosus (SLE), a heterogeneous and chronic autoimmune disease, exhibit unique changes in the complex composition and transcriptional signatures of peripheral blood mononuclear cells (PBMCs). While the mechanism of pathogenesis for both childhood-onset SLE (cSLE) and adult-onset SLE (aSLE) remains unclear, cSLE patients are considered more unpredictable and dangerous than aSLE patients. In this study, we analysed single-cell RNA sequencing data (scRNA-seq) to profile the PBMC clusters of cSLE/aSLE patients and matched healthy donors and compared the PBMC composition and transcriptional variations between the two groups. Our analysis revealed that the PBMC composition and transcriptional variations in cSLE patients were similar to those in aSLE patients. Comparative single-cell transcriptome analysis between healthy donors and SLE patients revealed IFITM3, ISG15, IFI16 and LY6E as potential therapeutic targets for both aSLE and cSLE patients. Additionally, we observed that the percentage of pre-B cells (CD34-) was increased in cSLE patients, while the percentage of neutrophil cells was upregulated in aSLE patients. Notably, we found decreased expression of TPM2 in cSLE patients, and similarly, TMEM150B, IQSEC2, CHN2, LRP8 and USP46 were significantly downregulated in neutrophil cells from aSLE patients. Overall, our study highlights the differences in complex PBMC composition and transcriptional profiles between cSLE and aSLE patients, providing potential biomarkers that could aid in diagnosing SLE

GC-MS Analysis of the Volatile Constituents in the Leaves of 14 Compositae Plants

The green organs, especially the leaves, of many Compositae plants possess characteristic aromas. To exploit the utility value of these germplasm resources, the constituents, mainly volatile compounds, in the leaves of 14 scented plant materials were qualitatively and quantitatively compared via gas chromatography-mass spectrometry (GC-MS). A total of 213 constituents were detected and tentatively identified in the leaf extracts, and terpenoids (especially monoterpene and sesquiterpene derivatives), accounting for 40.45–90.38% of the total compounds, were the main components. The quantitative results revealed diverse concentrations and compositions of the chemical constituents between species. Principal component analysis (PCA) showed that different groups of these Compositae plants were characterized by main components of α-thujone, germacrene D, eucalyptol, β-caryophyllene, and camphor, for example. On the other hand, cluster memberships corresponding to the molecular phylogenetic framework, were found by hierarchical cluster analysis (HCA) based on the terpenoid composition of the tested species. These results provide a phytochemical foundation for the use of these scented Compositae plants, and for the further study of the chemotaxonomy and differential metabolism of Compositae species