Prototypical Patterns Research Articles

Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph

This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical dependency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous lexemes with respect to a specific corpus. The proposed approach has the potential to be used as a complementary method to other NLP resources and tasks, including word disambiguation, domain relatedness, sense structure, metaphoricity, as well as a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations.

Efficient representations of tumor diversity with paired DNA-RNA aberrations.

Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of “paired DNA-RNA aberrations” that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene “Source-Target Pair” (STP) consists of a “source” regulatory gene and a “target” gene whose expression is plausibly “controlled” by the source gene. The STP is then “aberrant” in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is “RNA-aberrant”, meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects.

Personality Prototypes in People with Type 1 Diabetes and Their Relationship with Adherence.

Type 1 diabetes (Dm1) is a chronic endocrine and metabolic disease that affects the whole person and requires active, decisive treatment. However, personality traits may influence a patient’s adherence to treatment guidelines. The objective of this work is firstly to identify the 3 Asendorpf personality prototypes (resilient, undercontrolled and overcontrolled) in a sample of Dm1 individuals and determine whether there are any differences in comparison with a control sample; and, secondly, to study their association with adherence to self-care guidelines using both physiological indicators (HbA1C) and self-report measures. To achieve these objectives, a descriptive cross-sectional study was carried out. The sample comprised 294 participants, of whom 104 were people with Dm1 and 190 were controls. The participants, aged between 14 and 34 years, were classified by their scores in NEO-FFI-R, according to the personality characteristics inherent to Asendorpf’s prototypes. Asendorpf’s 3 prototypical personality patterns were found both in the group of people with Dm1 and in the control sample. These patterns showed different degrees of association with adherence to self-care guidelines for this disease and with psychological health factors. Importance should therefore be attached to the personality traits and Asendorpf prototypes of people with Dm1 when proposing interventions to address medical, psychological, and behavioral aspects.

Contour clustering: A field-data-driven approach for documenting and analysing prototypical f0 contours

ABSTRACTThis paper reports an automatic data-driven analysis for describing prototypical intonation patterns, particularly suitable for initial stages of prosodic research and language description. The approach has several advantages over traditional ways to investigate intonation, such as the applicability to spontaneous speech, language- and domain-independency, and the potential of revealing meaningful functions of intonation. These features make the approach particularly useful for language documentation, where the description of prosody is often lacking. The core of this approach is a cluster analysis on a time-series of f0 measurements and consists of two scripts (Praat and R, available from https://constantijnkaland.github.io/contourclustering/). Graphical user interfaces can be used to perform the analyses on collected data ranging from spontaneous to highly controlled speech. There is limited need for manual annotation prior to analysis and speaker variability can be accounted for. After cluster analysis, Praat textgrids can be generated with the cluster number annotated for each individual contour. Although further confirmatory analysis is still required, the outcomes provide useful and unbiased directions for any investigation of prototypical f0 contours based on their acoustic form.

On shear resistance of almond skin reinforced PLA composite matrix-based scaffold using cancellous screw

ABSTRACT The polylactic acid (PLA) matrix is being widely used as a scaffold for biomedical applications. But hitherto little has been reported on the use of 3D printed almond skin (AS) powder-reinforced matrix of PLA as a scaffold for ensuring shear resistance properties at different insertion angle, depth and infill pattern for holding a cancellous screw. This paper reports the shear fracture and morphological features of the 3D printed AS powder-reinforced matrix of PLA, prepared by fused deposition modelling for biomedical scaffolds using a cancellous pedicle screw. The study was performed using the angle of insertion 10°, 20° and 30°, depth of insertion of screw 40%, 70% and 100% in prototype and infill patterns (honeycomb, rectilinear and triangular) of 3D printed functional prototype. The study suggested that the maximum shear strength at peak (23.02 MPa) and maximum shear strength at break (22.90 MPa) were observed for honeycomb printing infill pattern, 100% insertion of screw and at 30° angle of insertion to prototype, whereas maximum modulus of toughness was observed for rectilinear pattern with 100% insertion of screw and 20° angle of insertion. Further morphological study was performed on the fractured surface to support the mechanical results obtained by pull-out test for ensuring shear resistance. Overall the study will be useful for an orthopaedic surgeon for ensuring angle, depth of insertion and infill pattern selection for minimising the shear fracture of the scaffold.

Business model patterns in the sharing economy

Sharing platforms struggle to remain financially viable and preserve their prosocial and environmental aspirations; therefore, effort to empirically study successful sharing economy business models (SEBMs) is needed. The aim of this research is to identify business model patterns among existing SEBMs in order to suggest business model attributes that support successful implementation. Patterns describe one or several recurring business model attributes observed among existing business models. This study investigates 63 SEBMs across 93 different configuration options. The k-medoids clustering approach was used to identify configuration options recurring repeatedly across the data. The empirical results were triangulated with existing business model patterns from literature. The study presents a framework to describe and analyse SEBMs; eight prototypical patterns, with a corresponding list of relevant business model attributes; and six solution patterns unique to the sharing economy. The patterns – as well as insights across locations, shared practices, and platform types – advance knowledge on the sharing economy. Furthermore, these patterns support sharing platforms to communicate, learn, and experiment, ideally supporting successful implementation of SEBMs.

HMGB1/TLR4 Signaling Affects Regulatory T Cells in Acute Lung Injury

BackgroundHigh-mobility group box-1 protein (HMGB1) serves as the prototypic damage-associated molecular pattern molecule, and TLR4 is considered a receptor for HMGB1. Regulatory T cells (Tregs) play a crucial role in infectious diseases. The role of HMGB1 in the modulation of Tregs is of great interest.MethodsSerum HMGB1 and Treg proportions were detected in 58 patients with acute lung injury (ALI) and 36 healthy volunteers. The correlations of these parameters with disease severity were analyzed. The WT and TLR4-/- mice were administered HMGB1 by intratracheal injection. After 48 h, the mice were sacrificed. The morphological changes and wet/dry ratio of the lung were measured. Spleen CD4+CD25+ Tregs were sorted from spleen cells, the expression of FOXP3 and CTLA-4, and releasing of cytokines was detected. CD4+CD25+ Tregs were cocultured with effector T cells, the inhibitory effect, and release of cytokines was detected.ResultsSignificantly increased plasma levels of HMGB1 and reduced CD4+CD25+CD127low Tregs were detected in ALI patients. In the mouse model, lung injury was significantly increased after HMGB1 instillation in the WT and TLR4-/- groups compared with control group. The lung wet/dry ratio and the TNF-α and IL-1β contents in BALF were significantly increased, and the severity of WT mice was higher than that of TLR4-/- mice. The expression of FOXP3 and CTLA-4 in TLR4-/- mice was significantly increased compared with that in WT mice and was associated with a similar trend of IL-10 and TGF-β levels (p<0.05). In coculture with effector T cells, Tregs isolated from TLR4-/- mice exhibited decreased IL-2 and IFN-γ and increased IL-4 levels compared with Tregs from WT mice. Increased polarization of TLR4-/- CD4+CD25+ Treg cells to Th2 cells was observed.ConclusionIn HMGB1-induced lung injury, HMGB1 affects the expression of FOXP3 and CTLA-4 through TLR4, thus reducing the immunosuppressive function of Treg cells.

HMGB1 Translocation is Associated with Tumor-Associated Myeloid Cells and Involved in the Progression of Fibroblastic Sarcoma.

The morphological variability and genetic complexity of fibroblastic sarcoma makes its diagnosis and treatment a challenge. High-mobility group box 1 protein (HMGB1), which functions as a DNA chaperone and a prototypical damage-associated molecular pattern, plays a paradoxical role in cancer. However, the expression pattern and role of HMGB1 in fibroblastic sarcomas is ill defined. By immunostaining of 95 tissue microarray cores of fibroblastic sarcomas, HMGB1 was found to be expressed in most tumor tissues. Nuclear HMGB1 translocation to cytoplasm was observed both in tumor cells and vascular endothelial cells. A visible number of tumor-associated myeloid cells including CD68+ and CD163+ macrophages and CD33+ myeloid cells were also detected in most tumor tissues. HMGB1 translocation was not only associated with CD68, CD163, and CD33 density, but also with disease progression. These results imply that HMGB1, an important regulator of the tumor microenvironment, is associated with tumor-associated myeloid cells and involved in the progression of fibroblastic sarcomas; HMGB1 may serve as a promising prognostic biomarker and a potential therapeutic target for fibroblastic sarcoma.

A Transformative Metasurface Based on Zerogap Embedded Template

AbstractThe ideals of reconfigurable metasurfaces would be operation in a broad frequency range with a high extinction ratio and fatigue resistivity. In this paper, all the above is achieved in the microwave regime by transforming a bare metallic film into well‐controlled nanometer sized gaps in a fully reversible manner. It is shown that adjacent metallic patterns deposited at different times can form “zero‐nanometer gaps,” or “zerogaps,” while maintaining the optical and electrical connectivity. The zerogaps readily open and recover with gentle bending and relaxing of the flexible substrate, precisely along the rims of the pre‐patterns of centimeter lengths. In a prototypical pattern of densely packed slit arrays, these gaps when opened serve as antennas achieving transparency for polarizations perpendicular to the length of the gap and shut off all the incident lights when closed. In such transformation between a polarizer and a mirror, 75% of transmission is observed with polarization extinction ratio of 7500 coming back down to 5 orders of magnitude extinction repeatable over 10 000 times. This work has long‐standing implications to metamaterials and metasurfaces as well as the fundamental aspect of extending a picometer scale distance controllability toward the wafer scale.

New Syntheses, Analytic Spin Hamiltonians, Structural and Computational Characterization for a Series of Tri-, Hexa- and Hepta-Nuclear Copper (II) Complexes with Prototypic Patterns

We present a series of pyrazolato-bridged copper complexes with interesting structures that can be considered prototypic patterns for tri-, hexa- and hepta- nuclear systems. The trinuclear shows an almost regular triangle with a μ3-OH central group. The hexanuclear has identical monomer units, the Cu6 system forming a regular hexagon. The heptanuclear can be described as two trinuclear moieties sandwiching a central copper ion via carboxylate bridges. In the heptanuclear system, the pyrazolate bridges are consolidating the triangular faces, which are sketching an elongated trigonal antiprism. The magnetic properties of these systems, dominated by the strong antiferromagnetism along the pyrazolate bridges, were described transparently, outlining the energy levels formulas in terms of Heisenberg exchange parameters J, within the specific topologies. We succeeded in finding a simple Kambe-type resolution of the Heisenberg spin Hamiltonian for the rather complex case of the heptanuclear. In a similar manner, the weak intermolecular coupling of two trimer units (aside from the strong exchange inside triangles) was resolved by closed energy formulas. The hexanuclear can be legitimately proposed as a case of coordination-based aromaticity, since the phenomenology of the six-spins problem resembles the bonding in benzene. The Broken-Symmetry Density Functional Theory (BS-DFT) calculations are non-trivial results, being intrinsically difficult at high nuclearities.

Emotion recognition from posed and spontaneous dynamic expressions: Human observers versus machine analysis.

The majority of research on the judgment of emotion from facial expressions has focused on deliberately posed displays, often sampled from single stimulus sets. Herein, we investigate emotion recognition from posed and spontaneous expressions, comparing classification performance between humans and machine in a cross-corpora investigation. For this, dynamic facial stimuli portraying the six basic emotions were sampled from a broad range of different databases, and then presented to human observers and a machine classifier. Recognition performance by the machine was found to be superior for posed expressions containing prototypical facial patterns, and comparable to humans when classifying emotions from spontaneous displays. In both humans and machine, accuracy rates were generally higher for posed compared to spontaneous stimuli. The findings suggest that automated systems rely on expression prototypicality for emotion classification and may perform just as well as humans when tested in a cross-corpora context. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A neurophysiological approach to spatial filter selection for adaptive brain–computer interfaces

Objective. The common spatial patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain–computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI. Approach. A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated. Main results. Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relationship between these categories of CSP patterns and classification performance revealed discarding neurophysiologically improbable filters can decrease decoder performance. Further analysis revealed that the spatial orientation of EEG modulations can evolve over time, and that the features extracted from the original CSP filters can become inseparable. Importantly, it was shown through a novel adaptive CSP technique that adaptation in response to these emerging patterns can restore feature separability. Significance. These findings highlight the importance of considering and reporting on spatial filter activation patterns in both online and offline studies. They also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.

Racial identity, discrimination, and polysubstance use: Examining culturally relevant correlates of substance use profiles among Black young adults.

Among Black Americans, young adulthood marks an important developmental period for the initiation of problematic substance use and related health disparities. Unique cultural factors, such as discrimination and racial identity, influence substance use during this period, but it is unknown whether they influence more severe patterns such as polysubstance use. The current study identifies prototypical patterns of substance use among Black young adults and examines whether cultural factors are associated with those patterns. The current study used latent class analysis to characterize prototypical substance use among a community-based sample of Black young adults (N = 147). Culturally relevant correlates of substance use classes (i.e., perceived discrimination and racial identity) were examined in univariate and multivariate models. Five profiles of substance use emerged, including three characterized by polysubstance use. In univariate models only, racial identity was differentially associated with membership in classes characterized by co-use of alcohol and cannabis. In both models, discrimination was significantly associated with membership in a class characterized by polysubstance use. Findings suggest a need to integrate cultural resilience factors into substance use interventions for Black young adults. Helping Black youth build coping skills for discrimination may also confer resilience against polysubstance use and associated health disparities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Neural Signatures of Interface Errors in Remote Agent Manipulation

The manipulation of remote agents such as robotic arms in remote surgery or in BCI-wheelchair control are prone to errors. Some of these are related to user intent misclassification or other interface system errors, which lead to an incorrect movement. Here we focused on errors originating from unpredicted interface movements violating user intent and producing sensory conflicts. In addition, we examined effects of incongruent/congruent sensory stimuli induced by interface errors, focusing on haptic and visual cues in the system. The overarching goal was to identify the prototypical patterns of electroencephalogram (EEG) error signals associated with two types of interface errors rising when the visual and proprioceptive feedback are congruent or incongruent. For purposes of comparison validity, both types of errors were recorded in the same 3D virtual game environment.The comparison of congruent and incongruent interface errors revealed significant and marginally significant differences in EEG potentials with respect to profile, latencies, scalp distribution and sources. Different EEG time–frequency combinations had high power content. Incongruence between visual and proprioceptive feedback in interface errors not only elicited distinct EEG signal characteristics, but also produced a marginally significant Stroop effect. Incongruency in visuo-haptic feedback modalities cause a delayed user response. This effect is of major importance for the design of controlling interfaces and can provide designers with crucial information when aiming to control human response time.

3. Early Adolescents in Adversity: A Latent Class Approach to Understanding Patterns of Emotional and Behavioral Problems Across Low-Resource Urban Settings Worldwide

Early adolescence (ages 10-14) is a critical period for psychosocial development. With a substantial proportion of lifetime mental health problems manifesting by age 14, poor psychosocial adjustment in early adolescence can set the stage for impairment throughout the life course. Youth living in low- and middle-income countries (LMICs) are at particular risk of experiencing emotional and behavioral problems, but little research on adolescent psychosocial development has been conducted in these settings. The aim of this study was to characterize prototypical patterns of emotional and behavioral problems among early adolescents living in four LMICs, and to explore the extent to which these patterns varied by country and sex.

The Regulatory Role of High-Mobility Group Protein 1 in Sepsis-Related Immunity.

High-mobility group box 1 (HMGB1), a prototypical damage-associated molecular pattern (DAMP) molecule, participates in multiple processes of various inflammatory diseases through binding to its corresponding receptors. In the early phase, sepsis is mainly characterized as a multi-bacterial-induced complex, excessive inflammatory response accompanied by the release of pro-inflammatory mediators, which subsequently develops into immune paralysis. A growing number of in vivo and in vitro investigations reveal that HMGB1 plays a pivotal role in the processes of inflammatory response and immunosuppression of sepsis. Therefore, HMGB1 exerts an indispensable role in the immune disorder and life-threatening inflammatory syndrome of sepsis. HMGB1 mainly mediate the release of inflammatory factors via acting on immune cells, pyroptosis pathways and phosphorylating nuclear factor-κB. Moreover HMGB1 is also associated with the process of sepsis-related immunosuppression. Neutrophil dysfunction mediated by HMGB1 is also an aspect of the immunosuppressive mechanism of sepsis. Myeloid-derived suppressor cells (MDSCs), which are also one of the important cells that play an immunosuppressive effect in sepsis, may connect with HMGB1. Thence, further understanding of HMGB1-associated pathogenesis of sepsis may assist in development of promising treatment strategies. This review mainly discusses current perspectives on the roles of HMGB1 in sepsis-related inflammation and immunosuppressive process and its related internal regulatory mechanisms.

A robustness measure for singular point and index estimation in discretized orientation and vector fields

AbstractThe identification of singular points or topological defects in discretized vector fields occurs in diverse areas ranging from the polarization of the cosmic microwave background to liquid crystals to fingerprint recognition and bio‐medical imaging. Due to their discrete nature, defects and their topological charge cannot depend continuously on each single vector, but they discontinuously change as soon as a vector changes by more than a threshold. Considering this threshold of admissible change at the level of vectors, we develop a robustness measure for discrete defect estimators. Here, we compare different template paths for defect estimation in discretized vector or orientation fields. Sampling prototypical vector field patterns around defects shows that the robustness increases with the length of template path, but less so in the presence of noise on the vectors. We therefore find an optimal trade‐off between resolution and robustness against noise for relatively small templates, except for the “single pixel” defect analysis, which cannot exclude zero robustness. The presented robustness measure paves the way for uncertainty quantification of defects in discretized vector fields.

Extracting Cell Patterns From High-Dimensional Radio Network Performance Datasets Using Self-Organizing Maps and K-Means Clustering

Mobile Radio Networks produces many of Operations, Administration, and Maintenance (OAM) data used by operators for network operational assurance. These data include multiple and diverse performance measurements and indicators that characterize the behavior of the radio cells. Being able to properly cluster the apparently dissimilar behaviors exhibited by a large number of individual cells into a reduced set of prototype patterns constitutes a valuable tool to support multiple processes such as cell configuration optimization or fault performance root cause analysis. While powerful clustering methods such as Self Organized Maps (SOM) exist, there is practically no literature showing the applicability of these methods of OAM datasets with a high number of attributes (>20) collected from live network deployments. Moreover, the applicability of the clustering methods does not come free of open questions since, for instance, when using SOM there is no explicitly obtained information about clusters after the SOM training in the underlying data, so the k-means technique for grouping SOM units has to be applied afterward. In this context, this paper describes a methodology to cluster radio cells based on a combination of SOM and K-means methods. The methodology is applied to extract cell patterns of the characterization of the long-term behavior (15 days' observation period) and short-term behavior (hourly observation periods) of mobile cells. OAM datasets collected from a live 4G/LTE network deployed in a major European city are used in the analysis.

Nearly symmetric orthogonal wavelets for time-frequency-shape joint analysis: Introducing the discrete shapelet transform’s third generation (DST-III) for nonlinear signal analysis

This article introduces the third generation of an interesting tool created for time-frequency-shape (TFS) joint analysis. Called Discrete Shapelet Transform (DST-III), it improves both its predecessors, i.e., DST-I and DST-II, in such a way that nearly symmetric major shapelet functions, and consequently almost linear-phase filterbanks, are obtained. Following a brief review on important concepts, the DST-III formulation is specified in detail and complemented with a numerical example. In addition, a prototype pattern matching strategy, where a comparison with ordinary wavelets takes place, and a spike sorting application are also presented and discussed. Notably, wavelet expansions use to provide very concise signal representations, thus simplifying subsequent nonlinear signal analysis in both time and frequency and, consequently, bringing advantages for non-stationary problem solving in science and engineering. This reassures the efficacy of this new tool.

HMGB1 as a potential biomarker and therapeutic target for severe COVID-19

COVID-19 has attracted global attention due to its rapid spread around the world with substantial morbidity and associated mortality. Severe COVID-19 can be complicated by the acute respiratory distress syndrome, sepsis and septic shock leading to death. These complications are thought to result from an overactivation of the immune system, leading to a cytokine storm syndrome associated with multiple organ failure. Here, we report that high mobility group box 1 (HMGB1), a prototypical damage-associated molecular pattern (DAMP) and a central mediator of lethal inflammation, could be a potential target for innovative therapeutic strategies for COVID-19. Serum HMGB1 in severe COVID-19 patients is elevated (189.40 ± 140.88 ng/ml). Exogenous HMGB1 induces the expression of SARS-CoV-2 entry receptor ACE2 in alveolar epithelial cells in an AGER-dependent manner. Importantly, genetic (using AGER siRNA) or pharmacological (using glycyrrhizin, chloroquine, hydroxychloroquine, and FPS-ZM1) inhibition of the HMGB1-AGER pathway blocks ACE2 expression. Thus, HMGB1 inhibitors are likewise promising drug candidates for the treatment of patients suffering from COVID-19.