Gene function and cell surface protein association analysis based on single-cell multiomics data.

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Expectation and Complaint: Online Consumer Complaint Behavior in COVID-19 Isolation

Purpose This study aims to articulate the nature of consumer complaining behavior (CCB) by analyzing the mechanism and characteristics of online CCB in COVID-19 isolated environment. Patients and Methods For the purpose, this study collected data via a web-based questionnaire survey from 408 consumers in Shanghai, China during COVID-19 isolation. Through building and analyzing a structural equation model that consists of six latent variables such as perceived service quality, perceived product quality, customer satisfaction, negative emotion, customer complaint;the study analyzed the basic characteristics of CCB, and focused on the moderation test of consumer expectation to validate its important role in consumer decision-making behavior. Results First, compared to perceived service quality, perceived product quality has a stronger influence on customer satisfaction and has a weaker influence on negative emotions in the COVID-19 isolated environment. Second, the total influence of perceived product quality on customer complaints is stronger than that of perceived service quality. Third, the direct impact of negative emotions on customer complaints was much stronger than the effect of customer satisfaction on customer complaints. Meanwhile, it can also act as a mediating variable to make customer satisfaction have an additional indirect effect on complaints. Finally, the study also found that consumer expectation can reinforce the influences of customer satisfaction on negative emotions and customer complaints, while it weakens the effect of negative emotions on customer complaints. Conclusion This study suggests that the classic CCB factors still exert a stable influence on customer complaints through cognitive and emotional response pathways, but the influence difference is obvious in the COVID-19 isolated environment. And the influence processes are significantly moderated by consumer expectation level. Enterprises should conduct more targeted marketing interactions, according to these CCB characteristics.

Follow-up study on COVID-19 survivors one year after discharge from hospital.

OBJECTIVE: To evaluate the long-term consequences of COVID-19 survivors one year after recovery, and to identify the risk factors associated with abnormal patterns in chest imaging manifestations or impaired lung function. METHODS: COVID-19 patients were recruited and prospectively followed up with symptoms, health-related quality of life, psychological questionnaires, 6-minute walking test, chest computed tomography (CT), pulmonary function tests, and blood tests. Multivariable logistic regression models were used to evaluate the association between the clinical characteristics and chest CT abnormalities or pulmonary function. RESULTS: Ninety-four patients with COVID-19 were recruited between January 16 and February 6, 2021. Muscle fatigue and insomnia were the most common symptoms. Chest CT scans were abnormal in 71.28% of participants. The results of multivariable regression showed an increased odds in age. Ten patients had diffusing capacity of the lung for carbon monoxide (DLCO) impairment. Urea nitrogen concentration on admission was significantly associated with impaired DLCO. IgG levels and neutralizing activity were significantly lower compared with those in the early phase. CONCLUSIONS: One year after hospitalization for COVID-19, a cohort of survivors were mainly troubled with muscle fatigue and insomnia. Pulmonary structural abnormalities and pulmonary diffusion capacities were highly prevalent in surviving COVID-19 patients. It is necessary to intervene in the main target population for long-term recovery.

Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study.

BACKGROUND: Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people's preferences for AI clinicians and traditional clinicians are worth exploring. OBJECTIVE: We aimed to quantify and compare people's preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people's preferences were affected by the pressure of pandemic. METHODS: We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people's preferences for different diagnosis methods. RESULTS: In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. CONCLUSIONS: Individuals' preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.