bluuebunny/crossref_metadata_2025_split huggingface dataset:
Daily phytate intake increases adiponectin levels among patients with diabetes type 2: a randomized crossover trial
Authors: Sanchis Pilar , Calvo Paula , Pujol Antelm , Rivera Rosmeri , Berga Francisco , Fortuny Regina , Costa-Bauza Antonia , Grases Felix , Masmiquel Luis 
Published: March 2023

AbstractAimAdiponectin, a major adipokine secreted by adipose tissue, has been shown to improve insulin sensitivity. Myo-inositol hexaphosphate (phytate; InsP6) is a natural compound that is abundant in cereals, legumes, and nuts that has demonstrated to have different beneficial properties in patients with diabetes type 2.MethodsWe performed a randomized crossover trial to investigate the impact of daily consumption of InsP6 on serum levels of adiponectin, TNF-alpha, IL-6, and IL-1beta in patients with type 2 diabetes mellitus (T2DM;n = 39). Thus, we measure serum levels of these inflammatory markers, classic vascular risk factors, and urinary InsP6 at baseline and at the end of the intervention period.ResultsPatients who consumed InsP6 supplements for 3 months had higher levels of adiponectin and lower HbA1c than those who did not consume InsP6. No differences were found in TNF-alpha, IL-6, and IL-1beta.ConclusionThis is the first report to show that consumption of InsP6 increases plasma adiponectin concentration in patients with T2DM. Consequently, our findings indicate that following a phytate-rich diet has beneficial effects on adiponectin and HbA1c concentrations and it could help to prevent or minimize diabetic-related complications.
Urinary epidermal growth factor predicts complete remission of proteinuria in Chinese children with IgA nephropathy
Authors: Zhou Jianmei , Sun Shuzhen , Zhang Dongfeng , Mao Jianhua , Xiao Huijie , Yao Yong , Wang Fang , Yu Lichun , Liu Ling , Feng Chunyue , Li Chenglong , Su Baige , Zhang Hongwen , Liu Xiaoyu , Xu Ke , Ju Wenjun , Zhong Xuhui , Ding Jie 
Published: March 2023

AbstractBackgroundThis study investigated the association between urinary epidermal growth factor (EGF) and complete remission (CR) of proteinuria in children with IgA nephropathy (IgAN).MethodsWe included 108 patients from the Registry of IgA Nephropathy in Chinese Children. The urinary EGF at the baseline and follow-up were measured and normalized by urine creatinine (expressed as uEGF/Cr). The person-specific uEGF/Cr slopes were estimated using linear mixed-effects models for the subset of patients with longitudinal data of uEGF/Cr. Cox models were used to analyze the associations of baseline uEGF/Cr and uEGF/Cr slope with CR of proteinuria.ResultsPatients with high baseline uEGF/Cr were more likely to achieve CR of proteinuria (adjusted HR 2.24, 95% CI: 1.05–4.79). The addition of high baseline uEGF/Cr on the traditional parameters significantly improved the model fit for predicting CR of proteinuria. In the subset of patients with longitudinal data of uEGF/Cr, high uEGF/Cr slope was associated with a higher likelihood of CR of proteinuria (adjusted HR 4.03, 95% CI: 1.02–15.88).ConclusionsUrinary EGF may be a useful noninvasive biomarker for predicting and monitoring CR of proteinuria in children with IgAN.ImpactHigh levels of baseline uEGF/Cr (>21.45 ng/mg) could serve as an independent predictor for CR of proteinuria.The addition of baseline uEGF/Cr on the traditional clinical pathological parameters significantly improved the fitting ability for the prediction of CR of proteinuria.Longitudinal data of uEGF/Cr were also independently associated with CR of proteinuria.Our study provides evidence that urinary EGF may be a useful noninvasive biomarker in the prediction of CR of proteinuria as well as monitoring therapeutic response, thus guiding treatment strategies in clinical practice for children with IgAN.
Machine learning methods to predict outcomes of pharmacological treatment in psychosis
Authors: Del Fabro Lorenzo , Bondi Elena , Serio Francesca , Maggioni Eleonora , D’Agostino Armando , Brambilla Paolo 
Published: March 2023

AbstractIn recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
Therapeutic utility of engineered myeloid cells in the tumor microenvironment
Authors: Canella Alessandro , Rajappa Prajwal 
Published: March 2023

AbstractDespite promising results shown in hematologic tumors, immunotherapies for the treatment of solid tumors have mostly failed so far. The immunosuppressive tumor microenvironment and phenotype of tumor infiltrating macrophages are among the more prevalent reasons for this failure. Tumor associated macrophages (TAMs, M2-macrophages) are circulating myeloid cells recruited to the local tumor microenvironment, and together with regulatory T cells (T-regs), are reprogrammed to become immune suppressive. This results in the inactivation or hampered recruitment of cytotoxic CD8 + T and Natural Killer (NK) cells. Recently, attempts have been made to try to leverage specific myeloid functions and properties, including their ability to reach the TME and to mediate the phagocytosis of cancer cells. Additionally, myeloid cells have been used for drug delivery and reprogramming the tumor microenvironment in cancer patients. This approach, together with the advancements in genome editing, paved the way for the development of novel cell-mediated immunotherapies. This article focuses on the latest studies that detail the therapeutic properties of genetically engineered or pharmacologically modulated myeloid cells in cancer preclinical models, limitations, pitfalls, and evaluations of these approaches in patients with cancer.
Population-level impacts of antibiotic usage on the human gut microbiome
Authors: Lee Kihyun , Raguideau Sebastien , Sirén Kimmo , Asnicar Francesco , Cumbo Fabio , Hildebrand Falk , Segata Nicola , Cha Chang-Jun , Quince Christopher 
Published: March 2023

AbstractThe widespread usage of antimicrobials has driven the evolution of resistance in pathogenic microbes, both increased prevalence of antimicrobial resistance genes (ARGs) and their spread across species by horizontal gene transfer (HGT). However, the impact on the wider community of commensal microbes associated with the human body, the microbiome, is less well understood. Small-scale studies have determined the transient impacts of antibiotic consumption but we conduct an extensive survey of ARGs in 8972 metagenomes to determine the population-level impacts. Focusing on 3096 gut microbiomes from healthy individuals not taking antibiotics we demonstrate highly significant correlations between both the total ARG abundance and diversity and per capita antibiotic usage rates across ten countries spanning three continents. Samples from China were notable outliers. We use a collection of 154,723 human-associated metagenome assembled genomes (MAGs) to link these ARGs to taxa and detect HGT. This reveals that the correlations in ARG abundance are driven by multi-species mobile ARGs shared between pathogens and commensals, within a highly connected central component of the network of MAGs and ARGs. We also observe that individual human gut ARG profiles cluster into two types or resistotypes. The less frequent resistotype has higher overall ARG abundance, is associated with certain classes of resistance, and is linked to species-specific genes in the Proteobacteria on the periphery of the ARG network.
Chemogenetic attenuation of cortical seizures in nonhuman primates
Authors: Miyakawa Naohisa , Nagai Yuji , Hori Yukiko , Mimura Koki , Orihara Asumi , Oyama Kei , Matsuo Takeshi , Inoue Ken-ichi , Suzuki Takafumi , Hirabayashi Toshiyuki , Suhara Tetsuya , Takada Masahiko , Higuchi Makoto , Kawasaki Keisuke , Minamimoto Takafumi 
Published: March 2023

AbstractEpilepsy is a disorder in which abnormal neuronal hyperexcitation causes several types of seizures. Because pharmacological and surgical treatments occasionally interfere with normal brain function, a more focused and on-demand approach is desirable. Here we examined the efficacy of a chemogenetic tool—designer receptors exclusively activated by designer drugs (DREADDs)—for treating focal seizure in a nonhuman primate model. Acute infusion of the GABAA receptor antagonist bicuculline into the forelimb region of unilateral primary motor cortex caused paroxysmal discharges with twitching and stiffening of the contralateral arm, followed by recurrent cortical discharges with hemi- and whole-body clonic seizures in two male macaque monkeys. Expression of an inhibitory DREADD (hM4Di) throughout the seizure focus, and subsequent on-demand administration of a DREADD-selective agonist, rapidly suppressed the wide-spread seizures. These results demonstrate the efficacy of DREADDs for attenuating cortical seizure in a nonhuman primate model.
MYTHO is a novel regulator of skeletal muscle autophagy and integrity
Authors: Leduc-Gaudet Jean-Philippe , Franco-Romero Anais , Cefis Marina , Moamer Alaa , Broering Felipe E. , Milan Giulia , Sartori Roberta , Chaffer Tomer Jordi , Dulac Maude , Marcangeli Vincent , Mayaki Dominique , Huck Laurent , Shams Anwar , Morais José A. , Duchesne Elise , Lochmuller Hanns , Sandri Marco , Hussain Sabah N. A. , Gouspillou Gilles 
Published: March 2023

AbstractAutophagy is a critical process in the regulation of muscle mass, function and integrity. The molecular mechanisms regulating autophagy are complex and still partly understood. Here, we identify and characterize a novel FoxO-dependent gene,d230025d16rikwhich we namedMytho(Macroautophagy and YouTH Optimizer), as a regulator of autophagy and skeletal muscle integrity in vivo.Mythois significantly up-regulated in various mouse models of skeletal muscle atrophy. Short term depletion of MYTHO in mice attenuates muscle atrophy caused by fasting, denervation, cancer cachexia and sepsis. While MYTHO overexpression is sufficient to trigger muscle atrophy, MYTHO knockdown results in a progressive increase in muscle mass associated with a sustained activation of the mTORC1 signaling pathway. Prolonged MYTHO knockdown is associated with severe myopathic features, including impaired autophagy, muscle weakness, myofiber degeneration, and extensive ultrastructural defects, such as accumulation of autophagic vacuoles and tubular aggregates. Inhibition of the mTORC1 signaling pathway in mice using rapamycin treatment attenuates the myopathic phenotype triggered by MYTHO knockdown. Skeletal muscles from human patients diagnosed with myotonic dystrophy type 1 (DM1) display reducedMythoexpression, activation of the mTORC1 signaling pathway and impaired autophagy, raising the possibility that lowMythoexpression might contribute to the progression of the disease. We conclude that MYTHO is a key regulator of muscle autophagy and integrity.
Macrophage internalization creates a multidrug-tolerant fungal persister reservoir and facilitates the emergence of drug resistance
Authors: Arastehfar Amir , Daneshnia Farnaz , Cabrera Nathaly , Penalva-Lopez Suyapa , Sarathy Jansy , Zimmerman Matthew , Shor Erika , Perlin David S. 
Published: March 2023

AbstractCandida glabrata is a major fungal pathogen notable for causing recalcitrant infections, rapid emergence of drug-resistant strains, and its ability to survive and proliferate within macrophages. Resembling bacterial persisters, a subset of genetically drug-susceptible C. glabrata cells can survive lethal exposure to the fungicidal echinocandin drugs. Herein, we show that macrophage internalization induces cidal drug tolerance in C. glabrata, expanding the persister reservoir from which echinocandin-resistant mutants emerge. We show that this drug tolerance is associated with non-proliferation and is triggered by macrophage-induced oxidative stress, and that deletion of genes involved in reactive oxygen species detoxification significantly increases the emergence of echinocandin-resistant mutants. Finally, we show that the fungicidal drug amphotericin B can kill intracellular C. glabrata echinocandin persisters, reducing emergence of resistance. Our study supports the hypothesis that intra-macrophage C. glabrata is a reservoir of recalcitrant/drug-resistant infections, and that drug alternating strategies can be developed to eliminate this reservoir.
Probing sub-5 Ångstrom micropores in carbon for precise light olefin/paraffin separation
Authors: Du Shengjun , Huang Jiawu , Ryder Matthew R. , Daemen Luke L. , Yang Cuiting , Zhang Hongjun , Yin Panchao , Lai Yuyan , Xiao Jing , Dai Sheng , Chen Banglin 
Published: March 2023

AbstractOlefin/paraffin separation is an important but challenging and energy-intensive process in petrochemical industry. The realization of carbons with size-exclusion capability is highly desirable but rarely reported. Herein, we report polydopamine-derived carbons (PDA-Cx, where x refers to the pyrolysis temperature) with tailorable sub-5 Å micropore orifices together with larger microvoids by one-step pyrolysis. The sub-5 Å micropore orifices centered at 4.1–4.3 Å in PDA-C800 and 3.7–4.0 Å in PDA-C900 allow the entry of olefins while entirely excluding their paraffin counterparts, performing a precise cut-off to discriminate olefin/paraffin with sub-angstrom discrepancy. The larger voids enable high C2H4 and C3H6 capacities of 2.25 and 1.98 mmol g−1 under ambient conditions, respectively. Breakthrough experiments confirm that a one-step adsorption-desorption process can obtain high-purity olefins. Inelastic neutron scattering further reveals the host–guest interaction of adsorbed C2H4 and C3H6 molecules in PDA-Cx. This study opens an avenue to exploit the sub-5 Å micropores in carbon and their desirable size-exclusion effect.
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
Authors: Szymanski Nathan J. , Bartel Christopher J. , Zeng Yan , Diallo Mouhamad , Kim Haegyeom , Ceder Gerbrand 
Published: March 2023

AbstractMachine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.
Evidence of a predictive coding hierarchy in the human brain listening to speech
Authors: Caucheteux Charlotte , Gramfort Alexandre , King Jean-Rémi 
Published: March 2023

AbstractConsiderable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.
P-selectin-targeted nanocarriers induce active crossing of the blood–brain barrier via caveolin-1-dependent transcytosis
Authors: Tylawsky Daniel E. , Kiguchi Hiroto , Vaynshteyn Jake , Gerwin Jeffrey , Shah Janki , Islam Taseen , Boyer Jacob A. , Boué Daniel R. , Snuderl Matija , Greenblatt Matthew B. , Shamay Yosi , Raju G. Praveen , Heller Daniel A. 
Published: March 2023

AbstractMedulloblastoma is the most common malignant paediatric brain tumour, with ~30% mediated by Sonic hedgehog signalling. Vismodegib-mediated inhibition of the Sonic hedgehog effector Smoothened inhibits tumour growth but causes growth plate fusion at effective doses. Here, we report a nanotherapeutic approach targeting endothelial tumour vasculature to enhance blood–brain barrier crossing. We use fucoidan-based nanocarriers targeting endothelial P-selectin to induce caveolin-1-dependent transcytosis and thus nanocarrier transport into the brain tumour microenvironment in a selective and active manner, the efficiency of which is increased by radiation treatment. In a Sonic hedgehog medulloblastoma animal model, fucoidan-based nanoparticles encapsulating vismodegib exhibit a striking efficacy and marked reduced bone toxicity and drug exposure to healthy brain tissue. Overall, these findings demonstrate a potent strategy for targeted intracranial pharmacodelivery that overcomes the restrictive blood–brain barrier to achieve enhanced tumour-selective penetration and has therapeutic implications for diseases within the central nervous system.
A deep-learning algorithm to classify skin lesions from mpox virus infection
Authors: Thieme Alexander H. , Zheng Yuanning , Machiraju Gautam , Sadee Chris , Mittermaier Mirja , Gertler Maximilian , Salinas Jorge L. , Srinivasan Krithika , Gyawali Prashnna , Carrillo-Perez Francisco , Capodici Angelo , Uhlig Maximilian , Habenicht Daniel , Löser Anastassia , Kohler Maja , Schuessler Maximilian , Kaul David , Gollrad Johannes , Ma Jackie , Lippert Christoph , Billick Kendall , Bogoch Isaac , Hernandez-Boussard Tina , Geldsetzer Pascal , Gevaert Olivier 
Published: March 2023

AbstractUndetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.
Multi-order graph attention network for water solubility prediction and interpretation
Authors: Lee Sangho , Park Hyunwoo , Choi Chihyeon , Kim Wonjoon , Kim Ki Kang , Han Young-Kyu , Kang Joohoon , Kang Chang-Jong , Son Youngdoo 
Published: March 2023

AbstractThe water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.