Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill

Non-monotonic changes in clonogenic cell survival induced by disulphonated aluminum phthalocyanine photodynamic treatment in a human glioma cell line

<p>Abstract</p> <p>Background</p> <p>Photodynamic therapy (PDT) involves excitation of sensitizer molecules by visible light in the presence of molecular oxygen, thereby generating reactive oxygen species (ROS) through electron/energy transfer processes. The ROS, thus produced can cause damage to both the structure and the function of the cellular constituents resulting in cell death. Our preliminary investigations of dose-response relationships in a human glioma cell line (BMG-1) showed that disulphonated aluminum phthalocyanine (AlPcS₂) photodynamically induced loss of cell survival in a concentration dependent manner up to 1 μM, further increases in AlPcS₂concentration (>1 μM) were, however, observed to decrease the photodynamic toxicity. Considering the fact that for most photosensitizers only monotonic dose-response (survival) relationships have been reported, this result was unexpected. The present studies were, therefore, undertaken to further investigate the concentration dependent photodynamic effects of AlPcS₂.</p> <p>Methods</p> <p>Concentration-dependent cellular uptake, sub-cellular localization, proliferation and photodynamic effects of AlPcS₂were investigated in BMG-1 cells by absorbance and fluorescence measurements, image analysis, cell counting and colony forming assays, flow cytometry and micronuclei formation respectively.</p> <p>Results</p> <p>The cellular uptake as a function of extra-cellular AlPcS₂concentrations was observed to be biphasic. AlPcS₂was distributed throughout the cytoplasm with intense fluorescence in the perinuclear regions at a concentration of 1 μM, while a weak diffuse fluorescence was observed at higher concentrations. A concentration-dependent decrease in cell proliferation with accumulation of cells in G₂+M phase was observed after PDT. The response of clonogenic survival after AlPcS₂-PDT was non-monotonic with respect to AlPcS₂concentration.</p> <p>Conclusions</p> <p>Based on the results we conclude that concentration-dependent changes in physico-chemical properties of sensitizer such as aggregation may influence intracellular transport and localization of photosensitizer. Consequent modifications in the photodynamic induction of lesions and their repair leading to different modes of cell death may contribute to the observed non-linear effects.</p

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

Activation of Epidermal Growth Factor Receptor Is Required for NTHi-Induced NF-κB-Dependent Inflammation

Inflammation is a hallmark of many serious human diseases. Nontypeable Haemophilus influenzae (NTHi) is an important human pathogen causing respiratory tract infections in both adults and children. NTHi infections are characterized by inflammation, which is mainly mediated by nuclear transcription factor-kappa B (NF-κB)-dependent production of proinflammatory mediators. Epidermal growth factor receptor (EGFR) has been shown to play important roles in regulating diverse biological processes, including cell growth, differentiation, apoptosis, adhesion, and migration. Its role in regulating NF-κB activation and inflammation, however, remains largely unknown.In the present study, we demonstrate that EGFR plays a vital role in NTHi-induced NF-κB activation and the subsequent induction of proinflammatory mediators in human middle ear epithelial cells and other cell types. Importantly, we found that AG1478, a specific tyrosine kinase inhibitor of EGFR potently inhibited NTHi-induced inflammatory responses in the middle ears and lungs of mice in vivo. Moreover, we found that MKK3/6-p38 and PI3K/Akt signaling pathways are required for mediating EGFR-dependent NF-κB activation and inflammatory responses by NTHi.Here, we provide direct evidence that EGFR plays a critical role in mediating NTHi-induced NF-κB activation and inflammation in vitro and in vivo. Given that EGFR inhibitors have been approved in clinical use for the treatment of cancers, current studies will not only provide novel insights into the molecular mechanisms underlying the regulation of inflammation, but may also lead to the development of novel therapeutic strategies for the treatment of respiratory inflammatory diseases and other inflammatory diseases

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p

Revisiting the mechanism of coagulation factor XIII activation and regulation from a structure/functional perspective

The activation and regulation of coagulation Factor XIII (FXIII) protein has been the subject of active research for the past three decades. Although discrete evidence exists on various aspects of FXIII activation and regulation a combinatorial structure/functional view in this regard is lacking. In this study, we present results of a structure/function study of the functional chain of events for FXIII. Our study shows how subtle chronological submolecular changes within calcium binding sites can bring about the detailed transformation of the zymogenic FXIII to its activated form especially in the context of FXIIIA and FXIIIB subunit interactions. We demonstrate what aspects of FXIII are important for the stabilization (first calcium binding site) of its zymogenic form and the possible modes of deactivation (thrombin mediated secondary cleavage) of the activated form. Our study for the first time provides a structural outlook of the FXIIIA 2 B 2 heterotetramer assembly, its association and dissociation. The FXIIIB subunits regulatory role in the overall process has also been elaborated upon. In summary, this study provides detailed structural insight into the mechanisms of FXIII activation and regulation that can be used as a template for the development of future highly specific therapeutic inhibitors targeting FXIII in pathological conditions like thrombosis

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Education in Twins and Their Parents Across Birth Cohorts Over 100 years: An Individual-Level Pooled Analysis of 42-Twin Cohorts.

Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990-1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected