Background The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. Objective We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. Methods We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. Results In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. Conclusions The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.
Based on the importance and sensitivity of microbial communities to changes in the forest ecosystem, soil microorganisms can be used to indicate the health of the forest system. The metagenome sequencing was used to analyze the changes of microbial communities between natural and plantation Castanea henryi forests for understanding the effect of forest types on soil microbial communities. Our result showed the soil microbial diversity and richness were higher in the natural forests than in the plantation. Proteobacteria, Actinobacteria, and Acidobacteria are the dominant categories in the C. henryi rhizosphere, and Proteobacteria and Actinobacteria were significantly enriched in the natural forest while Acidobacteria was significantly enriched in the plantation. Meanwhile, the functional gene diversity and the abundance of functions in the natural forest were higher than that of the plantation. Furthermore, we found that the microbial network in the natural forests had more complex than in the plantation. We also emphasized the low-abundance taxa may play an important role in the network structure. These results clearly showed that microbial communities, in response to different forest types, provide valuable information to manipulate microbiomes to improve soil conditions of plantation.
Objective To describe COVID-19 subphenotypes regarding severity patterns including prognostic, ICU and morbimortality outcomes, through stratification based on gender and age groups, as described by inter-patient variability patterns in clinical phenotypes and demographic features. Materials and methods We used the COVID-19 open data from the Mexican Government including patient-level epidemiological and clinical data from 778 692 SARS-CoV-2 patients from January 13, 2020 to September 30, 2020. Inter-patient variability was analyzed by combining dimensionality reduction and hierarchical clustering methods. We produced cluster analyses for all combinations of gender and age groups (<18, 18-49, 50-64, and >64). For each group, the optimum number of clusters was selected combining a quantitative approach using the Silhouette coefficient, and a qualitative approach through a subgroup expert inspection via visual analytics. Using the features of the resultant age-gender clusters, we performed a meta-clustering analysis to provide an overall description of the population. Results We observed a total of 56 age-gender clusters, grouped in 11 clinically distinguishable meta-clusters with different outcomes. Meta-clusters 1 to 3 showed the highest recovery rates (90.27-95.22%). These clusters include: (1) healthy patients of all ages, (2) children with comorbidities who had priority in medical resources, (3) young patients with obesity and smoking habit. Meta-clusters 4 and 5 showed moderate recovery rates (81.3-82.81%): (4) patients with hypertension or diabetes of all ages, (5) typical obese patients with three highly correlated conditions, namely, pneumonia, hypertension and diabetes. Meta-clusters 6 to 11 had very low recovery rates (53.96-66.94%) which include: (6) immunosuppressed patients with the highest comorbidity rate in many diseases, (7) CKD patients with the worse survival length and recovery, (8) elderly smoker with mild COPD, (9) severe diabetic elderly with hypertension, (10, 11) oldest obese smokers with severe COPD and mild cardiovascular disease with the latter (11) showing a relatively higher age and smoke rate, severe COPD and shorter survival length, reinforcing a high correlation between smoking habit and COPD among elderly. Additionally, the source Mexican state and type of clinical institution proved to be an important factor for heterogeneity in severity. Discussion The proposed unsupervised learning approach successfully uncovered discriminative COVID-19 severity patterns for both genders and all age groups from clinical phenotypes and demographic features. A careful read of group outcomes showed consistent results regarding recent literature. Regarding the Mexican population, our results suggest that habits and comorbidities may play a key role in predicting mortality in older patients. Centenarians tended to fall in the groups with better outcomes repeatedly. Additionally, immunosuppression was not found as a relevant factor for severity alone but did when present along with chronic kidney disease. Further useful correlations could be found by evaluating the duration of unhealthy habits, demographic features, comorbidities, the time since diagnosis, recovery progress, readmission record, and the effect of source variability. Conclusion The resultant eleven meta-clusters provide bases to comprehend the classification of patients with COVID-19 based on comorbidities, habits, demographic characteristics, geographic data and type of clinical institutions, as well as revealing the correlations between the above characteristics thereby help to anticipate the possible clinical outcomes for every specifically characterized patient. These subphenotypes can establish target groups for automated stratification or triage systems to provide personalized therapies or treatments. Code available at: https://github.com/bdslab-upv/covid19-metaclustering Dynamic results visualization at: http://covid19sdetool.upv.es/?tab=mexicoGov
Aspect-based sentiment analysis (ABSA) contains three subtasks, namely aspect term extraction, opinion term extraction and aspect-level sentiment classification. In order to make full use of the relationship between the three subtasks, some recent studies have successfully tried to use a unified framework to solve the problem of aspect-based sentiment analysis. However, these studies have not yet integrated domain knowledge into the model. Inspired by the post-training task, we propose a joint model (RACL-BERT-PT). This model combines the pre-training model BERT-PT with domain knowledge and the unified joint training framework RACL. The experimental results show that our model has achieved better results than previous experiments on three public data.
Background Elderly patients with COVID-19 are among the most numerous populations being admitted in the ICU due to its high mortality rate and high comorbidity incidence. An early severity risk stratification at hospital admission could help optimize ICU usage towards those more vulnerable and critically ill patients. Methods Of 503 Spanish patients aged>64 years admitted in the ICU between 26 Feb and 02 Nov 2020 in two Spanish hospitals, we included 193 quality-controlled patients. The subphenotyping combined PCA and t-SNE dimensionality reduction methods to maximize non-linear correlation and reduce noise among age and full blood count tests (FBC) at hospital admission, followed by hierarchical clustering. Findings We identified five subphenotypes (Eld-ICU-COV19 clusters) with heterogeneous FBC patterns associated to significantly disparate 30-day ICU mortality rates ranging from 2% in a healthy cluster to 44% in a severe cluster, along three moderate clusters. Interpretations To our knowledge, this is the first study using age and FBC at hospital admission to early stratify the risk of death in ICU at 30 days in elderly patients. Our results provide guidance to comprehend the phenotypic classification and disparate severity patterns among elderly ICU patients with COVID-19, based only on age and FBC, that have the potential to establish target groups for early risk stratification or early triage systems to provide personalized treatments or aid the decision-making during resource allocation process for each target Eld-ICU-COV19 cluster, especially in those circumstances with resource scarcity problem. Funding FONDO SUPERA COVID-19 by CRUE-Santander Bank grant SUBCOVERWD-19. Research in context Evidence before this study We searched on PubMed and Google Scholar using the search terms “COVID-19”, “SARS-CoV2”, “phenotypes” for research published between 2020 to 2022, with no language restriction, to detect any published study identifying and characterizing phenotypes among ICU COVID-19 patients. A previous COVID-19 phenotyping study found three phenotypes from hospitalized patients associated with significantly disparate 30-day mortality rates (ranging from 2·5 to 60·7%). However, it seems to become harder to find phenotypes with discriminative mortality rates among ICU COVID-19 patients. For example, we found one study that uncovered two phenotypes from 39 ICU COVID-19 patients based on biomarkers with 39% and 63% mortality rates, but such difference was not statistically significant. We also found another study with more success that uncovered two ICU COVID-19 phenotypes using two different trajectories with somehow disparate 28-day mortality rates of 27% versus 37% (Ventilatory ratio trajectories) and of 25% versus 39% (mechanical power trajectories). Added value of this study To our knowledge, this is the first study that uses age and laboratory results at hospital admission (i.e., before ICU admission) in elderly patients to early stratify, prior ICU admission, the risk of death in ICU at 30 days. We classified 193 patients with COVID-19, based on age and ten Full Blood Count (FBC) tests, into five subphenotypes (one healthy, three moderate, and one severe) that showed significantly disparate 30-day ICU mortality rates from 2% to 44%. Implications of all the available evidence Identifying, from elderly ICU patients with COVID-19 (Eld-ICU-COV19), subphenotypes could spur further investigation to analyze the potential differences in their underlying disease mechanisms, acquire better phenotypical understanding among Eld-ICU-COV19 toward better decision-making in distributing the limited resources (including both logistic and medical) as well as shedding light on tailoring personalized treatment for each specific target subgroup in future medical research and clinical trial.
BACKGROUND The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. OBJECTIVE We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. METHODS We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. RESULTS In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. CONCLUSIONS The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.
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