Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq ...protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.
Objective
Illness uncertainty is a significant source of psychological distress that affects cancer patients' quality of life (QOL). Mishel's uncertainty in illness theory (UIT) proposes that illness ...uncertainty influences an individual's use of coping strategies, and directly and indirectly influences their QOL. This study tested the relationships depicted in the adapted UIT in cancer patients.
Methods
This cross‐sectional study is a secondary analysis of the baseline data from a randomized clinical trial (N = 263 prostate cancer patients). Patients were diagnosed with localized (64.6%), biochemical recurrent (12.6%), or advanced (22.8%) prostate cancer. Uncertainty, coping (avoidant and active coping strategies), and QOL (physical and mental well‐being) were measured using the Mishel's uncertainty of illness scale, Brief COPE, and the Medical Outcomes Study 12‐item short form (SF‐12), respectively. We used path analysis to achieve the research aim.
Results
Patients' illness uncertainty directly, negatively influenced their physical well‐being (P < .001) and mental well‐being (P < .05). Patients' illness uncertainty was positively related to their avoidant coping strategies (P < .001). Patients' active and avoidant coping strategies influenced their mental well‐being (P < .001). Uncertainty also negatively influenced mental well‐being through avoidant coping strategies. The model had excellent fit to the data.
Conclusions
Our findings have indicated the potential of improving QOL by decreasing illness uncertainty and reducing avoidant coping strategies. Future research is needed to better understand the complex relationships between illness uncertainty, coping strategies, and domains of QOL among patients with different types of cancer using longitudinal research.
Summary Background Current staging methods do not accurately predict the risk of disease recurrence and benefit of adjuvant chemotherapy for patients who have had surgery for stage II colon cancer. ...We postulated that expression patterns of multiple microRNAs (miRNAs) could, if combined into a single model, improve postoperative risk stratification and prediction of chemotherapy benefit for these patients. Method Using miRNA microarrays, we analysed 40 paired stage II colon cancer tumours and adjacent normal mucosa tissues, and identified 35 miRNAs that were differentially expressed between tumours and normal tissue. Using paraffin-embedded specimens from a further 138 patients with stage II colon cancer, we confirmed differential expression of these miRNAs using qRT-PCR. We then built a six-miRNA-based classifier using the LASSO Cox regression model, based on the association between the expression of every miRNA and the duration of individual patients' disease-free survival. We validated the prognostic and predictive accuracy of this classifier in both the internal testing group of 138 patients, and an external independent group of 460 patients. Findings Using the LASSO model, we built a classifier based on the six miRNAs: miR-21-5p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. Using this tool, we were able to classify patients between those at high risk of disease progression (high-risk group), and those at low risk of disease progression (low-risk group). Disease-free survival was significantly different between these groups in every set of patients. In the initial training group of patients, 5-year disease-free survival was 89% (95% CI 77·3–94·4) for the low-risk group, and 60% (46·3–71·0) for the high-risk group (hazard ratio HR 4·24, 95% CI 2·13–8·47; p<0·0001). In the internal testing set of patients, 5-year disease-free survival was 85% (95% CI 74·3–91·8) for the low-risk group, and 57% (42·8–68·5) for the high-risk group (HR 3·63, 1·86–7·01; p<0·0001), and in the independent validation set of patients, was 85% (79·6–89·0) for the low-risk group and 54% (46·4–61·1) for the high-risk group (HR 3·70, 2·56–5·35; p<0·0001). The six-miRNA-based classifier was an independent prognostic factor for, and had better prognostic value than, clinicopathological risk factors and mismatch repair status. In an ad-hoc analysis, the patients in the high-risk group were found to have a favourable response to adjuvant chemotherapy (HR 1·69, 1·17–2·45; p=0·0054). We developed two nomograms for clinical use that integrated the six-miRNA-based classifier and four clinicopathological risk factors to predict which patients might benefit from adjuvant chemotherapy after surgery for stage II colon cancer. Conclusion Our six-miRNA-based classifier is a reliable prognostic and predictive tool for disease recurrence in patients with stage II colon cancer, and might be able to predict which patients benefit from adjuvant chemotherapy. It might facilitate patient counselling and individualise management of patients with this disease. Funding Natural Science Foundation of China.
•The feasibility of using anhydrous sodium metasilicate as a geopolymer activator for soil stabilization had been verified.•The strengths of stabilized clay samples activated by a conventional ...activator and the ASM activator had been compared.•The effects of modulus ratio, content of activators, GGBS on the strengths of stabilized clay samples were evaluated.•The two-stage process of dissolution ofASM powder in geopolymer-stabilized clay was verified by the 29Si MAS-NMR results.
This paper presents recent research on the use of anhydrous sodium metasilicate (ASM) powder as the alkaline activator of a one-part geopolymer binder for use in soil stabilization, to enhance the strength properties of soft clay. Geotechnical, mineralogical and microstructural tests were used to determine the performance of the geopolymer binder when mixed into an artificial soft clay. Comparison with the strength development of geopolymer-stabilized clay samples activated by a conventional sodium hydroxide activator and the ASM activator had been made. The effects of modulus ratio (SiO2/Na2O), content of the activators, as well as the GGBS content, water content, and curing time on the strength development of the geopolymer-stabilized clay samples were evaluated. The results indicate that the ASM powder alone is suitable as an alkaline activator to generate a one-part geopolymer binder for soil stabilization based on its high 28-d strength (4.2MPa) and its modulus ratio, which was in the optimal range (0.9–1.2). The microstructural analyses confirm the influence of the two-stage dissolution process of the hydrated amorphous alkali metal silicate powders on the strength development of the ASM-powder clay samples and the formation of the geopolymer gels. This study demonstrates that the ASM powder can be used to substitute for sodium hydroxide in generating a geopolymer binder, which can lead to more practical applications for geopolymer in soil stabilization.
Cyclization of propargylamines with CO2 to obtain 2‐oxazolidone heterocyclic compounds is an essential reaction in industry but it is usually catalyzed by noble‐metal catalysts with organic bases as ...co‐catalysts under harsh conditions. We have synthesized a unique CuI/CuII mixed valence copper‐based framework {(CuI6I5)Cu3IIL6(DMA)3(NO3)⋅9DMA}n (1) with good solvent and thermal stability, as well as a high density of uncoordinated amino groups evenly distributed in the large nanoscopic channels. Catalytic experiments show that 1 can effectively catalyze the reaction of propargylamines with CO2, and the yield can reach 99 %. The turnover frequency (TOF) reaches a record value of 230 h−1, which is much higher than that of reported noble‐metal catalysts. Importantly, this is the first report of heterogeneously catalyzed green conversion of propargylamines with CO2 without solvents and co‐catalysts under low temperature and atmospheric pressure. A mechanistic study reveals that a triply synergistic catalytic effect between CuI/CuII and uncoordinated amino groups promotes highly efficient and green conversion of CO2. Furthermore, 1 directly catalyzes this reaction with high efficiency when using simulated flue gas as a CO2 source.
A mixed valence copper‐based cationic framework {(CuI6I5)Cu3IIL6(DMA)3(NO3)⋅9DMA}n was synthesized. The material realized the efficient and green conversion of propargylamine with CO2 in flue gas under solvent‐free and co‐catalyst free conditions.
Underwater vital signs monitoring of respiratory rate, blood pressure, and the heart's status is essential for healthcare and sports management. Real‐time electrocardiography (ECG) monitoring ...underwater can be one solution for this. However, the current electrodes used for ECGs are not suitable for aquatic applications since they may lose their adhesiveness to skin, stable conductivity, or/and structural stability when immersed into water. Here, the design and fabrication of water‐resistant electrodes to repurpose stretchable electrodes for applications in an aquatic environment are reported. The electrodes are composed of stretchable metal–polymer composite film as the substrate and dopamine‐containing polymer as a coating. The polymer is designed to possess underwater adhesiveness from the dopamine motif, water stability from the main scaffold, and ionic conductivity from the carboxyl groups for signal transmission. Stable underwater conductivity and firm adhesion to skin allow the electrodes to collect reliable ECG signals under various conditions in water. It is shown that wearable devices incorporated with the water‐resistant electrodes can acquire real‐time ECG signals during swimming, which can be used for revealing the heart condition. These water‐resistant electrodes realize underwater detection of ECG signals and can be used for health monitoring and sports management during aquatic activities.
Water‐resistant stretchable electrodes are fabricated with a specially designed polymer. The polymer is adhesive underwater to bridge the electrode and skin, and ionic‐conductive to transmit electrophysiological signals. The conformal electrodes realize reliable electrocardiography (ECG) detection when moving the body or being impacted with water flow, which enables stable wireless real‐time ECG collection during swimming with a wearable device.
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images ...offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net ) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net , a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.
Although base editors are useful tools for precise genome editing, current base editors can only convert either adenines or cytosines. We developed a dual adenine and cytosine base editor (A&C-BEmax) ...by fusing both deaminases with a Cas9 nickase to achieve C-to-T and A-to-G conversions at the same target site. Compared to single base editors, A&C-BEmax's activity on adenines is slightly reduced, whereas activity on cytosines is higher and RNA off-target activity is substantially decreased.
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