Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.

Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.

Excess mortality in Ukraine during the course of COVID-19 pandemic in 2020-2021.

In this work, the COVID-19 pandemic burden in Ukraine is investigated retrospectively using the excess mortality measures during 2020-2021. In particular, the epidemic impact on the Ukrainian population is studied via the standardized both all-cause and cause-specific mortality scores before and during the epidemic. The excess mortality counts during the pandemic were predicted based on historic data using parametric and nonparametric modeling and then compared with the actual reported counts to quantify the excess. The corresponding standardized mortality P-score metrics were also compared with the neighboring countries. In summary, there were three "waves" of excess all-cause mortality in Ukraine in December 2020, April 2021 and November 2021 with excess of 32%, 43% and 83% above the expected mortality. Each new "wave" of the all-cause mortality was higher than the previous one and the mortality "peaks" corresponded in time to three "waves" of lab-confirmed COVID-19 mortality. The lab-confirmed COVID-19 mortality constituted 9% to 24% of the all-cause mortality during those three peak months. Overall, the mortality trends in Ukraine over time were similar to neighboring countries where vaccination coverage was similar to that in Ukraine. For cause-specific mortality, the excess observed was due to pneumonia as well as circulatory system disease categories that peaked at the same times as the all-cause and lab-confirmed COVID-19 mortality, which was expected. The pneumonias as well as circulatory system disease categories constituted the majority of all cases during those peak times. The seasonality in mortality due to the infectious and parasitic disease category became less pronounced during the pandemic. While the reported numbers were always relatively low, alcohol-related mortality also declined during the pandemic.

A multi-reference poly-conformational method for in silico design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARS-CoV-2 ligands.

Background: This work presents a novel computational multi-reference poly-conformational algorithm for design, optimization, and repositioning of pharmaceutical compounds. Methods: The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds, whose conformers are collectively close to the conformers of each compound in the reference set. Reference compounds may possess highly variable MoAs, which directly, and simultaneously, shape the properties of target candidate compounds. Results: The algorithm functionality has been case study validated in silico, by scoring ChEMBL drugs against FDA-approved reference compounds that either have the highest predicted binding affinity to our chosen SARS-CoV-2 targets or are confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed in separate studies) or show significant efficacy, in-vivo, against those selected targets. In addition to method case study validation, in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library's virtual compounds have been compared to the same set of reference drugs that we used for case study validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds has been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one, particular MoA. The goal here was to introduce a new methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.

Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors.

In addition to general challenges in drug discovery such as the identification of lead compounds in time- and cost-effective ways, specific challenges also exist. Particularly, it is necessary to develop pharmacological inhibitors that effectively discriminate between closely related molecular targets. DYRK1B kinase is considered a valuable target for cancer-specific mono- or combination chemotherapy; however, the inhibition of its closely related DYRK1A kinase is not beneficial. Existing inhibitors target both kinases with essentially the same efficiency, and the unavailability of the DYRK1B crystal structure makes the discovery of DYRK1B-specific inhibitors even more challenging. Here, we propose a novel multi-stage compound discovery pipeline aimed at in silico identification of both potent and selective small molecules from a large set of initial candidates. The method uses structure-based docking and ligand-based quantitative structure-activity relationship modeling. This approach allowed us to identify lead and runner-up small-molecule compounds targeting DYRK1B with high efficiency and specificity.

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. It requires the fulfillment of two core stages devoted to lung and disease segmentation as well as an additional post-processing stage devoted to scoring. The latter integrated block is utilized, mainly, for the estimation of segment scores and computes the overall severity score of a patient. The models of the proposed workflow were trained and tested on four publicly available X-ray datasets of COVID-19 patients and two X-ray datasets of patients with no pulmonary pathology. Based on a combined dataset consisting of 580 COVID-19 patients and 784 patients with no disorders, our best-performing algorithm is based on a combination of DeepLabV3 + , for lung segmentation, and MA-Net, for disease segmentation. The proposed algorithms' mean absolute error (MAE) of 0.30 is significantly reduced in comparison to established COVID-19 algorithms; BS-net and COVID-Net-S, possessing MAEs of 2.52 and 1.83 respectively. Moreover, the proposed two-stage workflow was not only more accurate but also computationally efficient, it was approximately 11 times faster than the mentioned methods. In summary, we proposed an accurate, time-efficient, and versatile approach for segmentation and scoring of lung diseases illustrated for COVID-19 and with broader future applications for pneumonia, tuberculosis, pneumothorax, amongst others.

Retraction notice to "Analysis of 329,942 SARS-CoV-2 records retrieved from GISAID database" [Comput. Biol. Med. 139 (2021) 104981].

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Authors. The Authors state that they have unknowingly and unintentionally violated GISAID data use rule. The publication uses data in violation of the terms and conditions that contributors and users agree to through the GISAID Database Access Agreement ("DAA") and therefore needs to be retracted. The Authors apologize for any inconvenience caused.

Excess mortality in Belarus during the COVID-19 pandemic as the case study of a country with limited non-pharmaceutical interventions and limited reporting.

Public health intervention to contain the ongoing COVID-19 pandemic significantly differed by country since the SARS-CoV-2 spread varied regionally in time and in scale. Since vaccinations were not available until the end of 2020 non-pharmaceutical interventions (NPIs) remained the only strategies to mitigate the pandemic spread at that time. Belarus in Europe is one of a few countries with a high Human Development Index where no lockdowns have ever been implemented and only limited NPIs have taken place for a period of time. Therefore, the Belarusian case was evaluated and compared in terms of the mortality burden. Since the COVID-19 mortality was low, the excess overall mortality was studied for Belarus. Since no overall mortality data have been reported past June 2020 the analysis was complemented by the study of Google Trends funeral-related search queries up until August 2021. Depending on the model, the Belarusian mortality for June of 2020 was 29 to 39% higher than otherwise expected with the corresponding estimated excess death was from 2953 to 3690 while the reported COVID-19 mortality for June 2020 was only 157 cases. The Belarusian excess mortality for June 2020 was higher than for all neighboring countries with an excess of 5% for Poland, 5% for Ukraine, 8% for Russia, 11% for Lithuania and 11% for Latvia. The relationship between Google Trends and mortality time series was studied using Granger's test and the results were statistically significant. The results for Google Trends searches did vary by key phrase with the largest excess of 138% for April 2020 and 148% for September 2020 was observed for a key phrase "coffin", while the largest excess of 218% for January 2021 was observed for "funeral services". In summary, there are indications of the excess overall mortality in Belarus, which is larger than the reported COVID-19-related mortality.

Indirect supervision applied to COVID-19 and pneumonia classification.

The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

Analysis of 329,942 SARS-CoV-2 records retrieved from GISAID database.

BACKGROUND: The SARS-CoV-2 virus caused a worldwide pandemic - although none of its predecessors from the coronavirus family ever achieved such a scale. The key to understanding the global success of SARS-CoV-2 is hidden in its genome. MATERIALS AND METHODS: We retrieved data for 329,942 SARS-CoV-2 records uploaded to the GISAID database from the beginning of the pandemic until the January 8, 2021. A Python variant detection script was developed to process the data using pairwise2 from the BioPython library. Sequence alignments were performed for every gene separately (except ORF1ab, which was not studied). Genomes less than 26,000 nucleotides long were excluded from the research. Clustering was performed using HDBScan. RESULTS: Here, we addressed the genetic variability of SARS-CoV-2 using 329,942 samples. The analysis yielded 155 SNPs and deletions in more than 0.3% of the sequences. Clustering results suggested that a proportion of people (2.46%) was infected with a distinct subtype of the B.1.1.7 variant, which contained four to six additional mutations (G28881A, G28882A, G28883С, A23403G, A28095T, G25437T). Two clusters were formed by mutations in the samples uploaded predominantly by Denmark and Australia (1.48% and 2.51%, respectively). A correlation coefficient matrix detected 160 pairs of mutations (correlation coefficient greater than 0.7). We also addressed the completeness of the GISAID database, patient gender, and age. Finally, we found ORF6 and E to be the most conserved genes (96.15% and 94.66% of the sequences totally match the reference, respectively). Our results indicate multiple areas for further research in both SARS-CoV-2 studies and health science.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data.

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this epidemiological study is to fill this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data.

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this study is to contribute to filling this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.

Development of an interactive, agent-based local stochastic model of COVID-19 transmission and evaluation of mitigation strategies illustrated for the state of Massachusetts, USA.

Since its discovery in the Hubei province of China, the global spread of the novel coronavirus SARS-CoV-2 has resulted in millions of COVID-19 cases and hundreds of thousands of deaths. The spread throughout Asia, Europe, and the Americas has presented one of the greatest infectious disease threats in recent history and has tested the capacity of global health infrastructures. Since no effective vaccine is available, isolation techniques to prevent infection such as home quarantine and social distancing while in public have remained the cornerstone of public health interventions. While government and health officials were charged with implementing stay-at-home strategies, many of which had little guidance as to the consequences of how quickly to begin them. Moreover, as the local epidemic curves have been flattened, the same officials must wrestle with when to ease or cease such restrictions as to not impose economic turmoil. To evaluate the effects of quarantine strategies during the initial epidemic, an agent based modeling framework was created to take into account local spread based on geographic and population data with a corresponding interactive desktop and web-based application. Using the state of Massachusetts in the United States of America, we have illustrated the consequences of implementing quarantines at different time points after the initial seeding of the state with COVID-19 cases. Furthermore, we suggest that this application can be adapted to other states, small countries, or regions within a country to provide decision makers with critical information necessary to best protect human health.

Novel Prefrontal Synthesis Intervention Improves Language in Children with Autism.

Prefrontal synthesis (PFS) is defined as the ability to juxtapose mental visuospatial objects at will. Paralysis of PFS may be responsible for the lack of comprehension of spatial prepositions, semantically-reversible sentences, and recursive sentences observed in 30 to 40% of individuals with autism spectrum disorder (ASD). In this report we present data from a three-year-long clinical trial of 6454 ASD children age 2 to 12 years, which were administered a PFS-targeting intervention. Tablet-based verbal and nonverbal exercises emphasizing mental-juxtaposition-of-objects were organized into an application called Mental Imagery Therapy for Autism (MITA). The test group included participants who completed more than one thousand exercises and made no more than one error per exercise. The control group was selected from the rest of participants by a matching procedure. Each test group participant was matched to the control group participant by age, gender, expressive language, receptive language, sociability, cognitive awareness, and health score at first evaluation using propensity score analysis. The test group showed a 2.2-fold improvement in receptive language score vs. control group (p < 0.0001) and a 1.4-fold improvement in expressive language (p = 0.0144). No statistically significant change was detected in other subscales not targeted by the exercises. These findings show that language acquisition improves after training PFS and that a further investigation of the PFS-targeting intervention in a randomized controlled study is warranted.

Differentiation of key biomarkers in tea infusions using a target/nontarget gas chromatography/mass spectrometry workflow.

Climatic conditions affect the chemical composition of edible crops, which can impact flavor, nutrition and overall consumer preferences. To understand these effects new data analysis software capable of tracking hundreds of compounds across years of samples under various environmental conditions is needed. Our recently developed mass spectral (MS) subtraction algorithms have been used with spectral deconvolution to efficiently analyze complex samples by 2-dimensional gas chromatography/mass spectrometry (GC-GC/MS). In this paper, we address the accuracy of identifying target and nontarget compounds by GC/MS. Findings indicate that Yunnan tea contains higher concentrations of floral compounds. In contrast, Fujian tea contains higher concentrations of compounds that exhibit fruity characteristics, but contains much less monoterpenes. Principal components analysis shows that seasonal changes in climate impact tea plants similarly despite location differences. For example, spring teas contained more of the sweet, floral and fruity compounds compared to summer teas, which had higher concentrations of green, woody, herbal compounds.

Optimizing targeted/untargeted metabolomics by automating gas chromatography/mass spectrometry workflows.

New database building and MS subtraction algorithms have been developed for automated, sequential two-dimensional gas chromatography/mass spectrometry (GC-GC/MS). This paper reports the first use of a database building tool, with full mass spectrum subtraction, that does not rely on high resolution MS data. The software was used to automatically inspect GC-GC/MS data of high elevation tea from Yunnan, China, to build a database of 350 target compounds. The database was then used with spectral deconvolution to identify 285 compounds by GC/MS of the same tea. Targeted analysis of low elevation tea by GC/MS resulted in the detection of 275 compounds. Non-targeted analysis, using MS subtraction, yielded an additional eight metabolites, unique to low elevation tea.

Toward high-resolution population genomics using archaeological samples.

The term 'ancient DNA' (aDNA) is coming of age, with over 1,200 hits in the PubMed database, beginning in the early 1980s with the studies of 'molecular paleontology'. Rooted in cloning and limited sequencing of DNA from ancient remains during the pre-PCR era, the field has made incredible progress since the introduction of PCR and next-generation sequencing. Over the last decade, aDNA analysis ushered in a new era in genomics and became the method of choice for reconstructing the history of organisms, their biogeography, and migration routes, with applications in evolutionary biology, population genetics, archaeogenetics, paleo-epidemiology, and many other areas. This change was brought by development of new strategies for coping with the challenges in studying aDNA due to damage and fragmentation, scarce samples, significant historical gaps, and limited applicability of population genetics methods. In this review, we describe the state-of-the-art achievements in aDNA studies, with particular focus on human evolution and demographic history. We present the current experimental and theoretical procedures for handling and analysing highly degraded aDNA. We also review the challenges in the rapidly growing field of ancient epigenomics. Advancement of aDNA tools and methods signifies a new era in population genetics and evolutionary medicine research.

New spectral deconvolution algorithms for the analysis of polycyclic aromatic hydrocarbons and sulfur heterocycles by comprehensive two-dimensional gas chromatography-quadrupole mass spectrometery.

New mass spectral deconvolution algorithms have been developed for comprehensive two-dimensional gas chromatography/quadrupole mass spectrometry (GC × GC/qMS). This paper reports the first use of spectral deconvolution of full scan quadrupole GC × GC/MS data for the quantitative analysis of polycyclic aromatic hydrocarbons (PAH) and polycyclic aromatic sulfur heterocycles (PASH) in coal tar-contaminated soil. A method employing four ions per isomer and multiple fragmentation patterns per alkylated homologue (MFPPH) is used to quantify target compounds. These results are in good agreement with GC/MS concentrations, and an examination of method precision, accuracy, selectivity, and sensitivity is discussed. MFPPH and SIM/1-ion concentration differences are also examined.