A review of the endocrine disrupting effects of micro and nano plastic and their associated chemicals in mammals

Over the years, the vaste expansion of plastic manufacturing has dramatically increased the environmental impact of microplastics [MPs] and nanoplastics [NPs], making them a threat to marine and terrestrial biota because they contain endocrine disrupting chemicals [EDCs] and other harmful compounds. MPs and NPs have deleteriouse impacts on mammalian endocrine components such as hypothalamus, pituitary, thyroid, adrenal, testes, and ovaries. MPs and NPs absorb and act as a transport medium for harmful chemicals such as bisphenols, phthalates, polybrominated diphenyl ether, polychlorinated biphenyl ether, organotin, perfluorinated compounds, dioxins, polycyclic aromatic hydrocarbons, organic contaminants, and heavy metals, which are commonly used as additives in plastic production. As the EDCs are not covalently bonded to plastics, they can easily leach into milk, water, and other liquids affecting the endocrine system of mammals upon exposure. The toxicity induced by MPs and NPs is size-dependent, as smaller particles have better absorption capacity and larger surface area, releasing more EDC and toxic chemicals. Various EDCs contained or carried by MPs and NPs share structural similarities with specific hormone receptors; hence they interfere with normal hormone receptors, altering the hormonal action of the endocrine glands. This review demonstrates size-dependent MPs’ bioaccumulation, distribution, and translocation with potential hazards to the endocrine gland. We reviewed that MPs and NPs disrupt hypothalamic-pituitary axes, including the hypothalamic-pituitary-thyroid/adrenal/testicular/ovarian axis leading to oxidative stress, reproductive toxicity, neurotoxicity, cytotoxicity, developmental abnormalities, decreased sperm quality, and immunotoxicity. The direct consequences of MPs and NPs on the thyroid, testis, and ovaries are documented. Still, studies need to be carried out to identify the direct effects of MPs and NPs on the hypothalamus, pituitary, and adrenal glands

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

Wild Type and Mutant 2009 Pandemic Influenza A (H1N1) Viruses Cause More Severe Disease and Higher Mortality in Pregnant BALB/c Mice

BACKGROUND: Pregnant women infected by the pandemic influenza A (H1N1) 2009 virus had more severe disease and higher mortality but its pathogenesis is still unclear. PRINCIPAL FINDINGS: We showed that higher mortality, more severe pneumonitis, higher pulmonary viral load, lower peripheral blood T lymphocytes and antibody responses, higher levels of proinflammatory cytokines and chemokines, and worse fetal development occurred in pregnant mice than non-pregnant controls infected by either wild type (clinical isolate) or mouse-adapted mutant virus with D222G substitution in hemagglutinin. These disease-associated changes and the lower respiratory tract involvement were worse in pregnant mice challenged by mutant virus. Though human placental origin JEG-3 cell line could be infected and proinflammatory cytokines or chemokines were elevated in amniotic fluid of some mice, no placental or fetal involvement by virus were detected by culture, real-time reverse transcription polymerase chain reaction or histopathological changes. Dual immunofluorescent staining of viral nucleoprotein and type II alveolar cell marker SP-C protein suggested that the majority of infected alveolar epithelial cells were type II pneumocytes. CONCLUSION: The adverse effect of this pandemic virus on maternal and fetal outcome is largely related to the severe pulmonary disease and the indirect effect of inflammatory cytokine spillover into the systemic circulation

Full-Length Transcriptome Sequencing Provides Insights into Flavonoid Biosynthesis in Fritillaria hupehensis

One of the most commonly utilized medicinal plants in China is Fritillaria hupehensis (Hsiao et K.C. Hsia). However, due to a lack of genomic resources, little is known about the biosynthesis of relevant compounds, particularly the flavonoid biosynthesis pathway. A PacBio RS II sequencing generated a total of 342,044 reads from the bulb, leaf, root, and stem, of which 316,438 were full-length (FL) non-redundant reads with an average length of 1365 bp and a N50 of 1888 bp. There were also 38,607 long non-coding RNAs and 7914 simple sequence repeats detected. To improve our understanding of processes implicated in regulating secondary metabolite biosynthesis in F. hupehensis tissues, we evaluated potential metabolic pathways. Overall, this study provides a repertoire of FL transcripts in F. hupehensis for the first time, and it will be a valuable resource for marker-assisted breeding and research into bioactive compounds for medicinal and pharmacological applications

Curcumin Enhanced Busulfan-Induced Apoptosis through Downregulating the Expression of Survivin in Leukemia Stem-Like KG1a Cells

Leukemia relapse and nonrecurrence mortality (NRM) due to leukemia stem cells (LSCs) represent major problems following hematopoietic stem cell transplantation (HSCT). To eliminate LSCs, the sensitivity of LSCs to chemotherapeutic agents used in conditioning regimens should be enhanced. Curcumin (CUR) has received considerable attention as a result of its anticancer activity in leukemia and solid tumors. In this study, we investigated the cytotoxic effects and underlying mechanisms in leukemia stem-like KG1a cells exposed to busulfan (BUS) and CUR, either alone or in combination. KG1a cells exhibiting BUS-resistance demonstrated by MTT and annexin V/propidium iodide (PI) assays, compared with HL-60 cells. CUR induced cell growth inhibition and apoptosis in KG1a cells. Apoptosis of KG1a cells was significantly enhanced by treatment with CUR+BUS, compared with either agent alone. CUR synergistically enhanced the cytotoxic effect of BUS. Seven apoptosis-related proteins were modulated in CUR- and CUR+BUS-treated cells analyzed by proteins array analysis. Importantly, the antiapoptosis protein survivin was significantly downregulated, especially in combination group. Suppression of survivin with specific inhibitor YM155 significantly increased the susceptibility of KG1a cells to BUS. These results demonstrated that CUR could increase the sensitivity of leukemia stem-like KG1a cells to BUS by downregulating the expression of survivin

Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance

Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance without or with few real data. Following the physical law of magnetic resonance, IPADS generates signals from differential equations or analytical solution models, making the learning more scalable, explainable, and better protecting privacy. Key components of IPADS learning, including signal generation models, basic deep learning network structures, enhanced data generation, and learning methods are discussed. Great potentials of IPADS have been demonstrated by representative applications in fast imaging, ultrafast signal reconstruction and accurate parameter quantification. Finally, open questions and future work have been discussed

Synthesis and characterization of novel anion exchange membranes based on imidazolium-type ionic liquid for alkaline fuel cells

Novel anion exchange membranes based on the copolymers of 1-allyl-3-methylimidazolium chloride (AmimCl) ionic liquid either with methyl methacrylate (MMA) or butyl methacrylate (BMA) have been prepared via free radical polymerization The structures and characteristic properties of the membranes are studied It is found that the hydroxyl ionic conductivity of the synthesized membrane can reach 3 33 x 10(-2) S cm(-1) in deionized water at 30 degrees C. The methanol permeability is less than 10(-9) mol cm(2) s(-1) even at 60 degrees C. These membranes with imidazolium salt functional groups exhibit superior stability both chemically and thermally as well compared to the alkyl quaternary ammonium functionalized polymers Therefore, the membranes have good perspectives and great potential for alkaline fuel cell applications. (C) 2010 Elsevier B V All rights reservedHigh-Tech Research and Development Program of China [2008AA05Z107]; National Nature Science Foundation of China [20876129