Recent Submissions

  • Synthesis and Organization of Gold-Peptide Nanoparticles for Catalytic Activities

    Abbas, Manzar; Susapto, Hepi Hari; Hauser, Charlotte (ACS Omega, American Chemical Society (ACS), 2022-01-06) [Article]
    A significant development in the synthesis strategies of metal-peptide composites and their applications in biomedical and bio-catalysis has been reported. However, the random aggregation of gold nanoparticles provides the opportunity to find alternative fabrication strategies of gold-peptide composite nanomaterials. In this study, we used a facile strategy to synthesize the gold nanoparticles via a green and simple approach where they show self-alignment on the assembled nanofibers of ultrashort oligopeptides as a composite material. A photochemical reduction method is used, which does not require any external chemical reagents for the reduction of gold ions, and resultantly makes the gold nanoparticles of size ca. 5 nm under mild UV light exposure. The specific arrangement of gold nanoparticles on the peptide nanofibers may indicate the electrostatic interactions of two components and the interactions with the amino group of the peptide building block. Furthermore, the gold-peptide nanoparticle composites show the ability as a catalyst to degradation of environmental pollutant p-nitrophenol to p-aminophenol, and the reaction rate constant for catalysis is calculated as 0.057 min–1 at a 50-fold dilute sample of 2 mg/mL and 0.72 mM gold concentration in the composites. This colloidal strategy would help researchers to fabricate the metalized bioorganic composites for various biomedical and bio-catalysis applications.
  • A Novel GEMIN4 Variant in a Consanguineous Family Leads to Neurodevelopmental Impairment with Severe Microcephaly, Spastic Quadriplegia, Epilepsy, and Cataracts

    Aldhalaan, Hesham; AlBakheet, Albandary; Alruways, Sarah; Almutairi, Nouf; Alnakiyah, Maha; Alghofaili, Reema; Cardona-Londoño, Kelly J.; Alahmadi, Khalid Omar; Alqudairy, Hanan; Alrasheed, Maha M.; Colak, Dilek; Arold, Stefan T.; Kaya, Namik (Genes, MDPI AG, 2021-12-30) [Article]
    Pathogenic variants in GEMIN4 contribute to a hereditary disorder characterized by neu-rodevelopmental features, microcephaly, cataracts, and renal abnormalities (known as NEDMCR). To date, only two homoallelic variations have been linked to the disease. Moreover, clinical features associated with the variants have not been fully elucidated yet. Here, we identified a novel variant in GEMIN4 (NM_015721:exon2:c.440A>G:p.His147Arg) in two siblings from a consanguineous Saudi family by using whole exome sequencing followed by Sanger sequence verification. We comprehen-sively investigated the patients’ clinical features, including brain imaging and electroencephalogram findings, and compared their phenotypic characteristics with those of previously reported cases. In silico prediction and structural modeling support that the p.His147Arg variant is pathogenic.
  • Accelerating bioactive peptide discovery via mutual information-based meta-learning.

    He, Wenjia; Jiang, Yi; Jin, Junru; Li, Zhongshen; Zhao, Jiaojiao; Manavalan, Balachandran; Su, Ran; Gao, Xin; Wei, Leyi (Briefings in bioinformatics, Oxford University Press (OUP), 2021-12-09) [Article]
    Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on
  • msRepDB: a comprehensive repetitive sequence database of over 80 000 species.

    Liao, Xingyu; Hu, Kang; Salhi, Adil; Zou, You; Wang, Jianxin; Gao, Xin (Nucleic acids research, Oxford University Press (OUP), 2021-12-01) [Article]
    Repeats are prevalent in the genomes of all bacteria, plants and animals, and they cover nearly half of the Human genome, which play indispensable roles in the evolution, inheritance, variation and genomic instability, and serve as substrates for chromosomal rearrangements that include disease-causing deletions, inversions, and translocations. Comprehensive identification, classification and annotation of repeats in genomes can provide accurate and targeted solutions towards understanding and diagnosis of complex diseases, optimization of plant properties and development of new drugs. RepBase and Dfam are two most frequently used repeat databases, but they are not sufficiently complete. Due to the lack of a comprehensive repeat database of multiple species, the current research in this field is far from being satisfactory. LongRepMarker is a new framework developed recently by our group for comprehensive identification of genomic repeats. We here propose msRepDB based on LongRepMarker, which is currently the most comprehensive multi-species repeat database, covering >80 000 species. Comprehensive evaluations show that msRepDB contains more species, and more complete repeats and families than RepBase and Dfam databases. (
  • Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor–Positive Breast Cancer: A Multicohort Study

    Fan, Ming; Cui, Yajing; You, Chao; Liu, Li; Gu, Yajia; Peng, Weijun; Bai, Qianming; Gao, Xin; Li, Lihua (Radiology, Radiological Society of North America (RSNA), 2021-11-30) [Article]
    Radiogenomic signatures associated with genomic assays (Oncotype DX) were identified as independent predictors after adjusting for clinical factors for survival and neoadjuvant chemotherapy response in estrogen receptor–positive breast cancer.
  • Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex

    Zhu, Lizhe; Jiang, Hanlun; Cao, Siqin; Unarta, Ilona Christy; Gao, Xin; Huang, Xuhui (Communications Biology, Springer Science and Business Media LLC, 2021-11-30) [Article]
    AbstractDespite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems.
  • Rational design of Striga hermonthica-specific seed germination inhibitors

    Zarban, Randa Alhassan Yahya; Hameed, Umar Farook Shahul; Jamil, Muhammad; Ota, Tsuyoshi; Wang, Jian You; Arold, Stefan T.; Asami, Tadao; Al-Babili, Salim (Plant Physiology, Oxford University Press (OUP), 2021-11-27) [Article]
    The obligate hemiparasitic weed Striga hermonthica grows on cereal roots and presents a severe threat to global food security by causing enormous yield losses, particularly in Sub-Saharan Africa. The rapidly increasing Striga seed bank in infested soils provides a major obstacle in controlling this weed. Striga seeds require host derived strigolactones (SLs) for germination, and corresponding antagonists could be used as germination inhibitors. Recently, we demonstrated that the common detergent Triton X-100 is a specific inhibitor of Striga seed germination by binding non-covalently to its receptor, Striga hermonthica HYPO-SENSITIVE TO LIGHT 7 (ShHTL7), without blocking the rice (Oryza sativa) SL receptor DWARF14 (OsD14). Moreover, triazole ureas, the potent covalently binding antagonists of rice SL perception with much higher activity towards OsD14, showed inhibition of Striga but were less specific. Considering that Triton X-100 is not suitable for field application and by combining structural elements of Triton and triazole urea, we developed two hybrid compounds, KK023-N1 and KK023-N2, as potential Striga-specific germination inhibitors. Both compounds blocked the hydrolysis activity of ShHTL7 but did not affect that of OsD14. Binding of KK023-N1 diminished ShHTL7 interaction with Striga hermonthica MORE AXILLARY BRANCHING 2 (ShMAX2), a major component in SL signal transduction, and increased ShHTL7 thermal specificity. Docking studies indicate that KK023-N1 binding is not covalent but is caused by hydrophobic interactions. Finally, in vitro and greenhouse tests revealed specific inhibition of Striga seed germination, which led to a 38% reduction in Striga infestation in pot experiments. These findings reveal that KK023-N1 is a potential candidate for combating Striga and a promising basis for rational design and development of further Striga-specific herbicides.
  • Prescribed-Time High-Gain Nonlinear Observer Design for Triangular Systems

    Adil, Ania; Ndoye, Ibrahima; Hamaz, Abdelghani; Zemouche, Ali; Laleg-Kirati, Taous-Meriem (IEEE, 2021-11-24) [Conference Paper]
    This paper proposes a prescribed-time varying high gain observer for a class of nonlinear systems. The fixed time convergence of the observer within a predefined time is shown through a Lyapunov differential inequality and a state transformation that involves a scaling function. The effectiveness of the proposed observer is illustrated through two numerical examples. Furthermore, a comparison with the standard high-gain observer and the tuning gain parameter of the scaling function is provided to demonstrate the superiority of the proposed prescribed-time observer of reducing the peaking phenomena and enhancing the estimation convergence error, respectively.
  • A Homozygous Missense Variant in PPP1R1B/DARPP-32 Is Associated With Generalized Complex Dystonia

    Khan, Amjad; Molitor, Anne; Mayeur, Sylvain; Zhang, Gaoqun; Rinaldi, Bruno; Lannes, Béatrice; Lhermitte, Benoît; Umair, Muhammad; Arold, Stefan T.; Friant, Sylvie; Rastegar, Sepand; Anheim, Mathieu; Bahram, Seiamak; Carapito, Raphael (Movement Disorders, Wiley, 2021-11-24) [Article]
    Background The dystonias are a heterogeneous group of hyperkinetic disorders characterized by sustained or intermittent muscle contractions that cause abnormal movements and/or postures. Although more than 200 causal genes are known, many cases of primary dystonia have no clear genetic cause. Objectives To identify the causal gene in a consanguineous family with three siblings affected by a complex persistent generalized dystonia, generalized epilepsy, and mild intellectual disability. Methods We performed exome sequencing in the parents and two affected siblings and characterized the expression of the identified gene by immunohistochemistry in control human and zebrafish brains. Results We identified a novel missense variant (c.142G>A (NM_032192); p.Glu48Lys) in the protein phosphatase 1 regulatory inhibitor subunit 1B gene (PPP1R1B) that was homozygous in all three siblings and heterozygous in the parents. This gene is also known as dopamine and cAMP-regulated neuronal phosphoprotein 32 (DARPP-32) and has been involved in the pathophysiology of abnormal movements. The uncovered variant is absent in public databases and modifies the conserved glutamate 48 localized close to the serine 45 phosphorylation site. The PPP1R1B protein was shown to be expressed in cells and regions involved in movement control, including projection neurons of the caudate-putamen, substantia nigra neuropil, and cerebellar Purkinje cells. The latter cells were also confirmed to be positive for PPP1R1B expression in the zebrafish brain. Conclusions We report the association of a PPP1R1B/DARPP-32 variant with generalized dystonia in man. It might be relevant to include the sequencing of this new gene in the diagnosis of patients with otherwise unexplained movement disorders. © 2021 International Parkinson and Movement Disorder Society
  • Editorial: AI in Biological and Biomedical Imaging

    Gao, Xin; Li, Lihua; Xu, Min (Frontiers in Molecular Biosciences, Frontiers Media SA, 2021-11-24) [Editorial]
  • Green Synthesis of Silver-Peptide Nanoparticles Generated by the Photoionization Process for Anti-Biofilm Application

    Seferji, Kholoud; Susapto, Hepi Hari; Khan, Babar Khalid; Rehman, Zahid Ur; Abbas, Manzar; Emwas, Abdul-Hamid M.; Hauser, Charlotte (ACS Applied Bio Materials, American Chemical Society (ACS), 2021-11-23) [Article]
    An alarming increase in antibiotic-resistant bacterial strains is driving clinical demand for new antibacterial agents. One of the oldest antimicrobial agents is elementary silver (Ag), which has been used for thousands of years. Even today, elementary Ag is used for medical purposes such as treating burns, wounds, and microbial infections. In consideration of the effectiveness of elementary Ag, the present researchers generated effective antibacterial/antibiofilm agents by combining elementary Ag with biocompatible ultrashort peptide compounds. The innovative antibacterial agents comprised a hybrid peptide bound to Ag nanoparticles (IVFK/Ag NPs). These were generated by photoionizing a biocompatible ultrashort peptide, thus reducing Ag ions to form Ag NPs with a diameter of 6 nm. The IVFK/Ag NPs demonstrated promising antibacterial/antibiofilm activity against reference Gram-positive and Gram-negative bacteria compared with commercial Ag NPs. Through morphological changes in Escherichia coli and Staphylococcus aureus, we proposed that the mechanism of action for IVFK/Ag NPs derives from their ability to disrupt bacterial membranes. In terms of safety, the IVFK/Ag NPs demonstrated biocompatibility in the presence of human dermal fibroblast cells, and concentrations within the minimal inhibitory concentration had no significant effect on cell viability. These results demonstrated that hybrid peptide/Ag NPs hold promise as a biocompatible material with strong antibacterial/antibiofilm properties, allowing them to be applied across a wide range of applications in tissue engineering and regenerative medicine.
  • Unraveling the differential impact of PAHs and dioxin-like compounds on AKR1C3 reveals the EGFR extracellular domain as a critical determinant of the AHR response

    Vogeley, Christian; Sondermann, Natalie C.; Woeste, Selina; Momin, Afaque Ahmad Imtiyaz; Gilardino, Viola; Hartung, Frederick; Heinen, Markus; Maaß, Sophia K.; Mescher, Melina; Pollet, Marius; Rolfes, Katharina M.; Vogel, Christoph F.A.; Rossi, Andrea; Lang, Dieter; Arold, Stefan T.; Nakamura, Motoki; Haarmann-Stemmann, Thomas (Environment International, Elsevier BV, 2021-11-20) [Article]
    Polycyclic aromatic hydrocarbons (PAHs), dioxin-like compounds (DLCs) and structurally-related environmental pollutants may contribute to the pathogenesis of various diseases and disorders, primarily by activating the aryl hydrocarbon receptor (AHR) and modulating downstream cellular responses. Accordingly, AHR is considered an attractive molecular target for preventive and therapeutic measures. However, toxicological risk assessment of AHR-modulating compounds as well as drug development is complicated by the fact that different ligands elicit remarkably different AHR responses. By elucidating the differential effects of PAHs and DLCs on aldo–keto reductase 1C3 expression and associated prostaglandin D2 metabolism, we here provide evidence that the epidermal growth factor receptor (EGFR) substantially shapes AHR ligand-induced responses in human epithelial cells, i.e. primary and immortalized keratinocytes and breast cancer cells. Exposure to benzo[a]pyrene (B[a]P) and dioxin-like polychlorinated biphenyl (PCB) 126 resulted in a rapid c-Src-mediated phosphorylation of EGFR. Moreover, both AHR agonists stimulated protein kinase C activity and enhanced the ectodomain shedding of cell surface-bound EGFR ligands. However, only upon B[a]P treatment, this process resulted in an auto-/paracrine activation of EGFR and a subsequent induction of aldo–keto reductase 1C3 and 11-ketoreduction of prostaglandin D2. Receptor binding and internalization assays, docking analyses and mutational amino acid exchange confirmed that DLCs, but not B[a]P, bind to the EGFR extracellular domain, thereby blocking EGFR activation by growth factors. Finally, nanopore long-read RNA-seq revealed hundreds of genes, whose expression is regulated by B[a]P, but not by PCB126, and sensitive towards pharmacological EGFR inhibition. Our data provide novel mechanistic insights into the ligand response of AHR signaling and identify EGFR as an effector of environmental chemicals.
  • A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

    Fan, Ming; Yuan, Wei; Liu, Weifen; Gao, Xin; Xu, Maosheng; Wang, Shiwei; Li, Lihua (Physics in Medicine & Biology, IOP Publishing, 2021-11-17) [Article]
    Objective Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of DCE-MRI can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors. Approach To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype. Main results The decomposition performance of DMFDE was evaluated by the root mean square error (RMSE) and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model with K=3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC=0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC= 0.813), which is significantly higher than that based on CAM. Conclusion This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.
  • A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics

    Fan, Ming; Zhang, You; Fu, Zhenyu; Xu, Maosheng; Wang, Shiwei; Xie, Sangma; Gao, Xin; Wang, Yue; Li, Lihua (Medical Physics, Wiley, 2021-11-13) [Article]
    Purpose :Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki-67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. Methods : To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical and texture features were extracted from dynamic enhanced magnetic imaging (DCE-MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)-based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. Results : By integrating radiomics from DCE-MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. Conclusions : DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging-scale patterns for interactions with molecular-scale histological information and is promising in the tumor characterization and management of patients.
  • Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies

    Napolitano, Francesco; Xu, Xiaopeng; Gao, Xin (Briefings in Bioinformatics, Oxford University Press (OUP), 2021-11-11) [Article]
    SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.
  • Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

    Albaradei, Somayah; Uludag, Mahmut; Thafar, Maha A.; Gojobori, Takashi; Essack, Magbubah; Gao, Xin (Frontiers in Genetics, Frontiers Media SA, 2021-11-10) [Article]
    Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset’s molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, “external” TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models’ way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.
  • Fingerprinting Arctic and North Atlantic Macroalgae with eDNA – Application and perspectives

    Ørberg, Sarah B.; Krause-Jensen, Dorte; Geraldi, Nathan; Ortega, Alejandra; Diaz Rua, Ruben; Duarte, Carlos M. (Environmental DNA, Wiley, 2021-11-05) [Article]
    Macroalgae are key primary producers in North Atlantic and Arctic coastal ecosystems, and tracing their fate and distribution is vital to improve our understanding of their ecological role and provision of ecosystem services. Recent advances from environmental DNA (eDNA) have added a new capacity to fingerprint and trace macroalgae. However, further development of resources for amplifying and identifying macroalgal eDNA are much needed. Here, we examined the performance in terms of resolution and specificity of two 18S primers (18S-V7 and 18S-V9) recently applied in identifying macroalgae from eDNA. We also built a local barcode database for primer 18S-V7 with 31 widespread Arctic and North Atlantic macroalgal species to complement the existing DNA databases. Furthermore, we applied metabarcoding of eDNA to identify macroalgae in Arctic marine sediments (Disko Bay, W. Greenland) and evaluated the contributions from our local barcode database. We identified macroalgal DNA from 19 families across 11 orders in surface (0–1 cm, with both primers) and sub-surface (5–10 cm, with 18S-V7 primer) sediments. The barcode database developed here with the 18S-V7 primer improved the identification of unique families, from 16 to 19 families, thereby strengthening the taxonomic assignment possible relative to pre-existing barcode reference sequences. Overall, this study demonstrates the feasibility of eDNA to resolve contributions of macroalgae in Arctic marine sediments, and enhances the fingerprinting resolution. We thereby document a novel pathway to answer key questions on the ecological role and fate of macroalgae in the Arctic.
  • Levothyroxine Treatment and the Risk of Cardiac Arrhythmias – Focus on the Patient Submitted to Thyroid Surgery

    Gluvic, Zoran; Obradovic, Milan; Stewart, Alan J.; Essack, Magbubah; Pitt, Samantha J.; Samardzic, Vladimir; Soskic, Sanja; Gojobori, Takashi; Isenovic, Esma R. (Frontiers in Endocrinology, Frontiers Media SA, 2021-11-04) [Article]
    Levothyroxine (LT4) is used to treat frequently encountered endocrinopathies such as thyroid diseases. It is regularly used in clinical (overt) hypothyroidism cases and subclinical (latent) hypothyroidism cases in the last decade. Suppressive LT4 therapy is also part of the medical regimen used to manage thyroid malignancies after a thyroidectomy. LT4 treatment possesses dual effects: substituting new-onset thyroid hormone deficiency and suppressing the local and distant malignancy spreading in cancer. It is the practice to administer LT4 in less-than-high suppressive doses for growth control of thyroid nodules and goiter, even in patients with preserved thyroid function. Despite its approved safety for clinical use, LT4 can sometimes induce side-effects, more often recorded with patients under treatment with LT4 suppressive doses than in unintentionally LT4-overdosed patients. Cardiac arrhythmias and the deterioration of osteoporosis are the most frequently documented side-effects of LT4 therapy. It also lowers the threshold for the onset or aggravation of cardiac arrhythmias for patients with pre-existing heart diseases. To improve the quality of life in LT4-substituted patients, clinicians often prescribe higher doses of LT4 to reach low normal TSH levels to achieve cellular euthyroidism. In such circumstances, the risk of cardiac arrhythmias, particularly atrial fibrillation, increases, and the combined use of LT4 and triiodothyronine further complicates such risk. This review summarizes the relevant available data related to LT4 suppressive treatment and the associated risk of cardiac arrhythmia.
  • Towards Characterization of the Complex and Frequency-dependent Arterial Compliance based on Fractional-order Capacitor

    Bahloul, Mohamed; Aboelkassem, Yasser; Laleg-Kirati, Taous-Meriem (IEEE, 2021-11-01) [Conference Paper]
    Arterial compliance is a vital determinant of the ventriculo-arterial coupling dynamic. Its variation is detrimental to cardiovascular functions and associated with heart diseases. Accordingly, assessment and measurement of arterial compliance are essential in the diagnosis and treatment of chronic arterial insufficiency. Recently, experimental and theoretical studies have recognized the power of fractional calculus to perceive viscoelastic blood vessel structure and biomechanical properties. This paper presents five fractional-order model representations to describe the dynamic relationship between the aortic blood pressure input and blood volume. Each configuration incorporates a fractional-order capacitor element (FOC) to lump the apparent arterial compliance’s complex and frequency dependence properties. FOC combines both resistive and capacitive attributes within a unified component, which can be controlled through the fractional differentiation order factor, α. Besides, the equivalent capacitance of FOC is by its very nature frequency-dependent, compassing the complex properties using only a few numbers of parameters. The proposed representations have been compared with generalized integer-order models of arterial compliance. Both models have been applied and validated using different aortic pressure and flow rate data acquired from various species such as humans, pigs, and dogs. The results have shown that the fractional-order framework is able to accurately reconstruct the dynamic of the complex and frequency-dependent apparent compliance dynamic and reduce the complexity. It seems that this new paradigm confers a prominent potential to be adopted in clinical practice and basic cardiovascular mechanics research.
  • Combining Machine Learning and Blind Estimation for Central Aortic Blood Pressure Reconstruction

    Magbool, Ahmed; Bahloul, Mohamed; Ballal, Tarig; Al-Naffouri, Tareq Y.; Laleg-Kirati, Taous-Meriem (IEEE, 2021-11-01) [Conference Paper]
    Central blood pressure is a vital signal that provides relevant physiological information about cardiovascular diseases risk factors. The standard clinical protocols for measuring these signals are challenging due to their invasive nature. This makes the estimation-based methods more convenient, however, they are usually not accurate as they fail to capture some important features of the central pressure waveforms. In this paper, we propose a novel data-driven approach that combines machine learning tools and cross-relation-based blind estimation methods to reconstruct the aortic blood pressure waves from the distorted peripheral pressure signals. Due to the lack of large real datasets, in this study, we utilize virtual pulse waves in-silico databases to train the machine learning models. The performance of the proposed approach is compared with the pure machine learning-based model and the cross-relation-based blind estimation approach. In both cases, the hybrid approach shows promising results as the root-mean-squared error has been reduced by 25% with regards to the pure machine learning method and by 40% compared to the cross-relation approach.

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