Electrical Impedance Tomography might be a Practical Tool to Provide Information about COVID-19 Pneumonia Progression

COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

Identification and Prediction of Novel Clinical Phenotypes for Intensive Care Patients With SARS-CoV-2 Pneumonia: An Observational Cohort Study

Background: Phenotypes have been identified within heterogeneous disease, such as acute respiratory distress syndrome and sepsis, which are associated with important prognostic and therapeutic implications. The present study sought to assess whether phenotypes can be derived from intensive care patients with coronavirus disease 2019 (COVID-19), to assess the correlation with prognosis, and to develop a parsimonious model for phenotype identification.Methods: Adult patients with COVID-19 from Tongji hospital between January 2020 and March 2020 were included. The consensus k means clustering and latent class analysis (LCA) were applied to identify phenotypes using 26 clinical variables. We then employed machine learning algorithms to select a maximum of five important classifier variables, which were further used to establish a nested logistic regression model for phenotype identification.Results: Both consensus k means clustering and LCA showed that a two-phenotype model was the best fit for the present cohort (N = 504). A total of 182 patients (36.1%) were classified as hyperactive phenotype, who exhibited a higher 28-day mortality and higher rates of organ dysfunction than did those in hypoactive phenotype. The top five variables used to assign phenotypes were neutrophil-to-lymphocyte ratio (NLR), ratio of pulse oxygen saturation to the fractional concentration of oxygen in inspired air (Spo2/Fio2) ratio, lactate dehydrogenase (LDH), tumor necrosis factor α (TNF-α), and urea nitrogen. From the nested logistic models, three-variable (NLR, Spo2/Fio2 ratio, and LDH) and four-variable (three-variable plus TNF-α) models were adjudicated to be the best performing, with the area under the curve of 0.95 [95% confidence interval (CI) = 0.94–0.97] and 0.97 (95% CI = 0.96–0.98), respectively.Conclusion: We identified two phenotypes within COVID-19, with different host responses and outcomes. The phenotypes can be accurately identified with parsimonious classifier models using three or four variables.

Nomenclature and clinical phenotypes of atopic dermatitis

Atopic dermatitis is a heterogeneous disease and resists classification. In this review, we discuss atopic dermatitis nomenclature and identify morphologic phenotypes, which will facilitate correct diagnoses and development of treatment strategies. We support using the term ‘atopic dermatitis’ rather than eczema, because it describes the allergic background and inflammation (‘itis’) as drivers of the disease. Atopic dermatitis has many morphologic manifestations that vary by topographic area affected, age, or race and require consideration in differential diagnosis. Different phenotypes based on morphology and topographic location, ethnicity, and age are discussed. A better-defined phenotype identification for atopic dermatitis will facilitate earlier and correct diagnosis of this complex condition and inform selection of the most appropriate treatment choice in an era in which targeted therapies may generate more individualized patient care.

An Automated Image Analysis and Cell Identification System Using Machine Learning Methods

In many biological laboratories, biologists analyze images and identify cell or organ states manually. There are some problems: lack of human resource and high experimental costs, among others. Identification results vary according to the person. To solve these problems, the process automation of biologists’ operations and quantitative identification are needed. Here, a cell-foci-phenotype identification system is developed by applying image processing and machine learning methods to fluorescent cell images. With this system, cell-foci-phenotype with high accuracy can be predicted and biologists’ efforts in doing image analysis can be reduced.

Modern approaches to the management of patients with bronchoectasia

The bronchoectasia (BE) is a chronic heterogeneous lung disease characterized by recurrent infection, inflammation, persistent cough and sputum discharge. The early BE diagnosis is one of the main recommendations of the European Respiratory Society (ERS) guidelines, which requires medical history collection and multispiral computed tomography (MSCT) of thoracic organs. Despite the complex examination, in most patients BE is classified as idiopathic. The minimum set of tests, including serum immunoglobulins, allergic bronchopulmonary aspergillosis tests and hematology is proposed in ERS guideline for detection of BE causes. Other examinations are recommended to perform based on disease history and radiological characteristics, indicating the importance of BE clinical phenotype identification by different healthcare specialists, for which special examinations are required. Initial examination algorithms and management of patients with BE, in particular, MSCT-semiotics and clinical features, which could help to identify specific reasons are presented in the article.

Genetic diversity analysis and the genetic fingerprinting of 33 DUS test standard tobacco varieties

BackgroundAt present, the distinctness, uniformity, and stability (DUS) test of tobacco varieties still depends on the field phenotype identification, lack of simple and reliable molecular marker technology. To improve the efficiency and reliability of the identification of tobacco varieties, a molecular marker-based method was developed for the DUS test of tobacco varieties.ResultsIn total, 91 simple sequence repeats (SSR) markers with clear, polymorphic amplification bands were obtained, with polymorphism information content, Nei’s index, and Shannon’s information index values of 0.3603, 0.4040, and 0.7228, respectively. Clustering analysis showed that the 33 study varieties could all be distinguished from each other. Further analysis showed that a minimum of 25 markers was required to reveal the genetic diversity of these varieties. Following the principle of two markers per linkage group, 48 pairs of SSR markers were selected. Correlation analysis showed that the genetic relationships revealed by the 48 SSR markers were consistent with those found using the 91 SSR markers.ConclusionsGenetic fingerprints of the 33 study varieties were constructed using 48 SSR markers, and an SSR marker-based identification technique for new tobacco varieties was developed. This study provides a reliable technological approach for determining the novelty of new tobacco varieties and offers a solid technical basis for the accreditation and protection of new tobacco varieties.