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Multilocus sequence typing of Treponema pallidum in male patients with genital ulcers in a public sexually transmitted infections clinic: a new allele and almost complete macrolide resistance

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Anais Brasileiros de Dermatologia 2025年第3期100卷 569-573页

作者： Esteves, Leonardo Souza Grassi, Vera Mileide Trivellato Grassi, Liliane Trivellato Nicola, Maria Rita Castilhos Silva, Marcia Susana Nunes Rossetti, Maria Lucia Rosa Ramos, Mauro Cunha Laboratory of Molecular Biology Applied to Microbacteria Fundação Oswaldo Cruz RJ Rio de Janeiro Brazil Program in Cellular and Molecular Biology Applied to Health Sciences Universidade Luterana do Brasil RS Canoas Brazil Biomedical Engineering Universidade Luterana do Brasil RS Canoas Brazil Sanitary Dermatology Outpatient Unit Secretaria Estadual de Saúde RS Porto Alegre Brazil DNTech - Development of New Technologies LTDA Agência Estadual de Pesquisa Desenvolvimento e Inovação do Rio Grande do Sul / Financiadora de Estudos e Projetos RS Porto Alegre Brazil Postgraduate Program Faculdade de Biologia Celular e Molecular Aplicada à Saúde Universidade Luterana do Brasil RS Canoas Brazil

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Dysregulated microRNAs in type 2 diabetes and breast cancer:Potential associated molecular mechanisms

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World Journal of Diabetes 2024年第6期15卷 1187-1198页

作者： Alex Cleber Improta-Caria Filipe Ferrari João Lucas Penteado Gomes Paloma Brasilio Villalta Úrsula Paula RenóSoci Ricardo Stein Edilamar M Oliveira Laboratory of Biochemistry and Molecular Biology of the Exercise Physical Education and Sport SchoolUniversity of São PauloSão Paulo 05508-030Brazil Graduate Program in Cardiology and Cardiovascular Sciences Federal University of Rio Grande do SulHospital de Clínicas de Porto AlegrePorto Alegre 90035003Brazil Laboratory of Metabolic Disorders(Labdime) School of Applied SciencesUniversity of Campinas-UNICAMPCampinas 13484-350Brazil Departments of Internal Medicine Molecular Pharmacology and PhysiologyCenter for Regenerative MedicineUSF Health Heart InstituteMorsani College of MedicineUniversity of South FloridaTampaFL 33602United States

Type 2 diabetes(T2D)is a multifaceted and heterogeneous syndrome associated with complications such as hypertension,coronary artery disease,and notably,breast cancer(BC).The connection between T2D and BC is established through processes that involve insulin resistance,inflammation and other *** this comprehension the specific cellular and molecular mechanisms linking T2D to BC,especially through microRNAs(miRNAs),remain *** are regulators of gene expression at the post-transcriptional level and have the function of regulating target genes by modulating various signaling pathways and biological ***,the signaling pathways and biological processes regulated by miRNAs that are associated with T2D and BC have not yet been *** review aims to identify dysregulated miRNAs in both T2D and BC,exploring potential signaling pathways and biological processes that collectively contribute to the development of BC.

关键词： Type 2 diabetes Breast cancer MicroRNAs molecular mechanisms

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N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification

arXiv

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arXiv 2022年

作者： Ali Heydari, A. Davalos, Oscar A. Hoyer, Katrina K. Sindi, Suzanne S. Department of Applied Mathematics University of California United States Health Sciences Research Institute Quantitative and Systems Biology Graduate Program Department of Molecular and Cell Biology University of California Merced United States

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods–especially deep learning models–have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural attention to detect salient genes for use in cell-types identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI. © 2022, CC BY.

关键词： Genes

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Benefits of Stryphnodendron adstringens when associated with hydrogel on wound healing in diabetic rats

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Clinical Phytoscience 2021年第1期7卷 1-12页

作者： de Souza de Aguiar, Patricia Correa, Áurea Pandolfo Antunes, Flavia Tasmin Techera de Barros Ferraz, Alexandre Falcão Vencato, Suele Biehals Amado, Gabriela Jouglard Vasques Wiiland, Elenir Corrêa, Dione Silva Grivicich, Ivana de Souza, Alessandra Hubner Postgraduate Program in Cellular and Molecular Biology Applied to Health Lutheran University of Brazil Canoas Brazil Department of Pharmacy Lutheran University of Brazil Canoas Brazil

The plant Stryphnodendron adstringens is a species of legume in the genus Stryphnodendron that is found in Brazil. It is also known as “barbatimão-verdadeiro” and it is popularly used in folk medicine due to its healing properties. Purpose: The aim of this study was to evaluate the healing activity of hydrogel, together with an S. adstringens hydroalcoholic extract, in diabetic and non-diabetic rats. Methods: The phytochemical analysis of the S. adstringens hydroalcoholic extract was evaluated through HPLC and its antioxidant activity was measured by the DPPH assay. The cytotoxicity, the scratch assay, and the fibroblast cell proliferation were also evaluated. Forty (40) Wistar rats were submitted to an excision for a full-thickness wound of 1.5 cm × 1.5 cm in their dorsum. The animals were treated twice a day over 16 days, with a rich layer of the corresponding treatment: Hydrogel; Hydrogel associated with 5% S. adstringens hydroalcoholic extract (H + SAHE); S. adstringens hydroalcoholic extract (SAHE); Collagenase with Chloramphenicol; or Carbopol. Morphological and histological analyses of the injury were performed. Results: The phytochemical analysis of SAHE indicated the presence of phenolic compounds, tannins, and flavonoids. The hydroalcoholic extract showed strong antioxidant activities (IC50 = 25.56 ± 1.04 μg/mL). H + SAHE induced the fibroblast proliferation (148 ± 6.9%) and it was not cytotoxic. The association with H + SAHE showed a more pronounced healing activity than did the other treatments in the non-diabetic animals and in the diabetic animals, hence, promoting angiogenesis and reepithelialization. Conclusion: Under these scenarios, this study has demonstrated effectiveness in the recovering wounds of diabetic rats.

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Baccharis trimera aqueous extract modulates inflammation and nociception in mice

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Clinical Phytoscience 2021年第1期7卷 1-10页

作者： de Almeida, Natiely Silveira Ferraz, Alexandre de Barros Falcão Pedron, Claudia Correa, Dione Silva Vieira, Luciene Bruno Antunes, Flavia Tasmim Techera de Souza, Alessandra Hubner Graduate Program in Genetics and Toxicology (PPGGTA) Lutheran University of Brazil Canoas Brazil Graduate Program in Cellular and Molecular Biology Applied to Health Lutheran University of Brazil Canoas Brazil Departamento de Farmacologia ICB Universidade Federal de Minas Gerais Belo Horizonte Brazil

The aerial parts of Baccharis trimera (Less.) are frequently used as a tea to treat several diseases. Therefore, the aim of this study was to identify the constituents of an aqueous extract of B. trimera, focusing on their antioxidant, anti-inflammation, and antinociception activities and properties. For that, the researchers performed in vivo assays using the formalin test and Freund’s Complete Adjuvant (FCA) to measure the acute and chronic inflammatory pain in mice. Moreover, the myeloperoxidase enzyme (MPO) was analyzed in the subcutaneous tissue after the FCA injection, together with the counting of lymphocytes in the peripheral blood of the mice. The qualitative phytochemical analysis indicated the presence of flavonoids and saponins in the B. trimera aqueous extract. The high-performance liquid chromatography (HPLC) analyses showed the presence of phenolic compounds, such as chlorogenic acid, ellagic acid, rosmarinic acid, as well as flavonoids, such as rutin, quercetin, and luteolin. The DPPH assay was used in order to measure the antioxidant activity of the aqueous extract of B. trimera and this showed an IC50 of 118.18 ± 1.02 μg/mg. The data from the formalin test demonstrated that a single dose of the aqueous extract of B. trimera was not able to decrease the nociceptive behavior during the neurogenic phase, at any of the tested doses (20, 40, or 80 mg/kg p.o.). However, during the inflammatory phase of this test, the aqueous extract of B. trimera at 80 mg/kg (p.o.) significantly decreased the nociceptive behavior, showing more effectiveness when compared to the other tested doses (p < 0.05). Importantly, in the chronic inflammatory model on the 5th day of treatment, the aqueous extract of B. trimera (80 mg/kg p.o.) significantly reduced mechanical allodynia (p < 0.01), heat thermal hyperalgesia (p < 0.001), and paw edema (p < 0.05). There were no changes in the MPO activity, but the data exhibited an equivalent decrease in the number of lymph

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Polyvalvular Dysplasia and Vascular Abnormalities in a Neonate With an FLNA Variant

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JACC: Case Reports 2024年第18期29卷 102556-102556页

作者： Frenkel, Amy Frenkel, Max Schulte, Jefree J. Srinivasan, Shardha Lamers, Luke School of Medicine and Public Health University of Wisconsin Madison WI United States Cellular and Molecular Biology Graduate Program University of Wisconsin Madison WI United States Medical Scientist Training Program University of Wisconsin Madison WI United States Department of Pathology and Laboratory Medicine School of Medicine and Public Health University of Wisconsin Madison WI United States Division of Cardiology Department of Pediatrics School of Medicine and Public Health University of Wisconsin Madison WI United States

There is growing appreciation for inherited structural heart diseases and their genetic causes. One causal gene for congenital cardiac and vascular lesions is FLNA which encodes a critical protein for cytoskeletal and extracellular matrix development. A newborn infant male, with prenatally diagnosed polyvalvular dysfunction, presented with low cardiac output and postnatally detected aortic arch hypoplasia and coarctation. Attempted palliative coarctation intervention resulted in vascular complications that ultimately contributed to his demise. This case report highlights polyvalvular dysplasia, vascular abnormalities, and a likely causal de novo missense variant in the FLNA gene (c.5180 C>T p.P1727L) not previously described. © 2024 The Authors

关键词： cardiac catheterization filamin A variant effect prediction vasculopathy

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Multi-Task Curriculum Learning for Partially Labeled Data

Multi-Task Curriculum Learning for Partially Labeled Data

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IEEE International Symposium on Biomedical Imaging

作者： Won-Dong Jang Stanislav Lukyanenko Donglai Wei Jiancheng Yang Brian Leahy Helen Yang Dalit Ben-Yosef Daniel Needleman Hanspeter Pfister School of Engineering and Applied Sciences Harvard University MA USA Technical University of Munich Germany Boston College MA USA EPFL Switzerland Department of Molecular and Cellular Biology Harvard University MA USA Harvard Graduate Program in Biophysics Harvard University MA USA Tel Aviv Sourasky Medical Center Fertility & IVF Institute Israel Dept. of Cell and Developmental Biology Sagol School of Neuroscience Tel Aviv University Israel

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint learning for partially labeled data is adding self-supervised learning for tasks without ground truths by augmenting an input image and forcing the multi-task model to return the same outputs for both the input and augmented images. However, the partially labeled setting can result in imbalanced learning of tasks since not all tasks are trainable with ground truth supervisions for each data sample. In this work, we propose a multi-task curriculum learning method tailored for partially labeled data. For balanced learning of tasks, our multitask curriculum prioritizes less performing tasks during training by setting different supervised learning frequencies for each task. We demonstrate that our method outperforms standard approaches on one biomedical and two natural image datasets. Furthermore, our learning method with partially labeled data performs better than the standard multi-task learning methods with fully labeled data for the same number of annotations.

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New candidate genes potentially involved in Zika virus teratogenesis

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Computers in biology and Medicine 2024年 173卷 108259-108259页

作者： Ferrão Maciel-Fiuza, Miriãn Rengel, Bruna Duarte Wachholz, Gabriela Elis do Amaral Gomes, Julia de Oliveira, Maikel Rosa Kowalski, Thayne Woycinck Roehe, Paulo Michel Luiz Vianna, Fernanda Sales Schüler-Faccini, Lavínia Mayer, Fabiana Quoos Varela, Ana Paula Muterle Fraga, Lucas Rosa Graduate Program in Genetics and Molecular Biology Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Instituto Nacional de Genética Médica Populacional Porto Alegre Brazil Genomics Medicine Laboratory Center of Experimental Research Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Department of Morphological Sciences Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine: Medical Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Teratogen Information System Medical Genetics Service Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Bioinformatics Core Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Centro Universitário CESUCA Cachoeirinha Brazil Department of Microbiology Immunology and Parasitology Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Molecular and Cellular Biology Biotechnology Center Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Biosciences Universidade Federal de Ciências da Saúde de Porto Alegre Porto Alegre Brazil

Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in databases for genes related to brain development and CZS were performed, and a protein interaction network was created. The expression of these genes was analyzed in a CZS animal model and secondary gene expression analysis (DGE) was performed in human cells exposed to ZIKV. A total of 2610 genes were identified in the databases, of which 1013 were connected. By applying centrality statistics of the global network, 36 candidate genes were identified, which, after selection resulted in nine genes. Gene expression analysis revealed distinctive expression patterns for PRKDC, PCNA, ATM, SMC3 as well as for FGF8 and SHH in the CZS model. Furthermore, DGE analysis altered expression of ATM, PRKDC, PCNA. In conclusion, systems biology are helpful tools to identify candidate genes to be validated in vitro and in vivo. PRKDC, PCNA, ATM, SMC3, FGF8 and SHH have altered expression in ZIKV-induced brain malformations. © Elsevier Ltd

关键词： Viruses

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Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation

Learning Vector Quantized Shape Code for Amodal Blastomere I...

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IEEE International Symposium on Biomedical Imaging

作者： Won-Dong Jang Donglai Wei Xingxuan Zhang Brian Leahy Helen Yang James Tompkin Dalit Ben-Yosef Daniel Needleman Hanspeter Pfister School of Engineering and Applied Sciences Harvard University MA USA Boston College MA USA Department of Molecular and Cellular Biology Harvard University MA USA Harvard Graduate Program in Biophysics Harvard University MA USA Department of Computer Science Brown University RI USA Tel Aviv Sourasky Medical Center Fertility & IVF Institute Israel Dept. of Cell and Developmental Biology Sagol School of Neuroscience Tel Aviv University Israel

Blastomere instance segmentation is important for analyzing embryos’ abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to recover an object’s complete silhouette even when the object is not fully visible. For each detected object, previous methods directly regress the target mask from input features. However, images of an object under different amounts of occlusion should have the same amodal mask output, making it harder to train the regression model. To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes. First, we pre-train the Vector Quantized Variational Autoencoder (VQ-VAE) model to learn these discrete shape codes from ground truth amodal masks. Then, we incorporate the VQ-VAE model into the amodal instance segmentation pipeline with an additional refinement module. We also detect an occlusion map to integrate occlusion information with a backbone feature. As such, our network faithfully detects bounding boxes of amodal objects. On an internal embryo cell image benchmark, the proposed method outperforms previous state-of-the-art methods. To show generalizability, we show segmentation results on the public KINS natural image benchmark. Our method would enable accurate measurement of blastomeres in In Vitro Fertilization (IVF) clinics, potentially increasing the IVF success rate.

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Predicting regional somatic mutation rates using DNA motifs

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PLoS Computational biology 2023年第10 October期19卷 e1011536-e1011536页

作者： Liu, Cong Wang, Zengmiao Wang, Jun Liu, Chengyu Wang, Mengchi Ngo, Vu Wang, Wei Department of Chemistry and Biochemistry University of California San Diego La Jolla CA United States State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health Faculty of Geographical Science Beijing Normal University Beijing China Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA United States

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature. © 2023 Liu et al.

关键词： Neural network models

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