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BACKGROUND: Skeletal muscle handles about 80% of insulin-stimulated glucose uptake and become the major organ occurring insulin resistance (IR). Many studies have confirmed the interactions between macrophages and skeletal muscle regulated the inflammation and regeneration of skeletal muscle. However, despite of the decades of research, whether macrophages infiltration and polarization in skeletal muscle under high glucose (HG) milieus results in the development of IR is yet to be elucidated. C2C12 myoblasts are well-established and excellent model to study myogenic regulation and its responses to stimulation. Further exploration of macrophages' role in myoblasts IR and the dynamics of their infiltration and polarization is warranted. AIM: To evaluate interactions between myoblasts and macrophages under HG, and its effects on inflammation and IR in skeletal muscle. METHODS: We detected the polarization status of macrophages infiltrated to skeletal muscles of IR mice by hematoxylin and eosin and immunohistochemical staining. Then, we developed an in vitro co-culture system to study the interactions between myoblasts and macrophages under HG milieus. The effects of myoblasts on macrophages were explored through morphological observation, CCK-8 assay, Flow Cytometry, and enzyme-linked immunosorbent assay. The mediation of macrophages to myogenesis and insulin sensitivity were detected by morphological observation, CCK-8 assay, Immunofluorescence, and 2-NBDG assay. RESULTS: The F4/80 and co-localization of F4/80 and CD86 increased, and the myofiber size decreased in IR group (P < 0.01, g = 6.26). Compared to Mc group, F4/80+CD86+CD206- cells, tumor necrosis factor-α (TNFα), inerleukin-1ß (IL-1ß) and IL-6 decreased, and IL-10 increased in McM group (P < 0.01, g > 0.8). In McM + HG group, F4/80+CD86+CD206- cells, monocyte chemoattractant protein 1, TNFα, IL-1ß and IL-6 were increased, and F4/80+CD206+CD86- cells and IL-10 were decreased compared with Mc + HG group and McM group (P < 0.01, g > 0.8). Compered to M group, myotube area, myotube number and E-MHC were increased in MMc group (P < 0.01, g > 0.8). In MMc + HG group, myotube area, myotube number, E-MHC, GLUT4 and glucose uptake were decreased compared with M + HG group and MMc group (P < 0.01, g > 0.8). CONCLUSION: Interactions between myoblasts and macrophages under HG milieus results in inflammation and IR, which support that the macrophage may serve as a promising therapeutic target for skeletal muscle atrophy and IR.

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When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

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BACKGROUND: Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives. OBJECTIVE: This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health records using deep learning-based NLP techniques. METHODS: We obtained data of 3281 industry-sponsored phase 2 or 3 interventional clinical trials recruiting patients with non-small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, and Crohn disease from ClinicalTrials.gov, spanning the period between 2013 and 2020. A customized bidirectional long short-term memory- and conditional random field-based NLP pipeline was used to extract all eligibility criteria attributes and convert hypernym concepts into computable hyponyms along with their corresponding values. To illustrate the simulation of clinical trial design for optimization purposes, we selected a subset of patients with non-small cell lung cancer (n=2775), curated from the Mount Sinai Health System, as a pilot study. RESULTS: We manually annotated the clinical trial eligibility corpus (485/3281, 14.78% trials) and constructed an eligibility criteria-specific ontology. Our customized NLP pipeline, developed based on the eligibility criteria-specific ontology that we created through manual annotation, achieved high precision (0.91, range 0.67-1.00) and recall (0.79, range 0.50-1) scores, as well as a high F1-score (0.83, range 0.67-1), enabling the efficient extraction of granular criteria entities and relevant attributes from 3281 clinical trials. A standardized eligibility criteria knowledge base, compatible with electronic health records, was developed by transforming hypernym concepts into machine-interpretable hyponyms along with their corresponding values. In addition, an interface prototype demonstrated the practicality of leveraging real-world data for optimizing clinical trial protocols and identifying eligible patients. CONCLUSIONS: Our customized NLP pipeline successfully generated a standardized eligibility criteria knowledge base by transforming hypernym criteria into machine-readable hyponyms along with their corresponding values. A prototype interface integrating real-world patient information allows us to assess the impact of each eligibility criterion on the number of patients eligible for the trial. Leveraging NLP and real-world data in a data-driven approach holds promise for streamlining the overall clinical trial process, optimizing processes, and improving efficiency in patient identification.

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Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

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BACKGROUND: Hepatic fibrosis (HF) is a common pathological feature of chronic hepatic diseases. We aimed to illuminate the significance of amniotic mesenchymal stem cells (AMSCs)-derived extracellular vesicles (AMSCs-EVs) in HF. METHODS: Human AMSCs-EVs were isolated and identified. HF mice were constructed and treated with EVs. The fibrosis was observed by staining experiments and Western blot (WB) assay. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and hepatic hydroxyproline (Hyp) were detected to confirm liver function. For the in vitro experiments, human hepatic stellate cells were induced with transforming growth factor-ß and treated with EVs. To measure the degree of HF, the expression of alpha-smooth muscle actin (α-SMA) and Collagen I was detected by WB assay, and cell proliferation was detected by cell counting kit 8 assay. The levels of miR-200a, Zinc finger E-box binding homeobox 1 (ZEB1), and phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) were detected by WB and real-time quantitative polymerase chain reaction. The binding of ZEB1 to PIK3R3 and miR-200a to ZEB1 was analyzed by chromatin immunoprecipitation and dual luciferase assays to validate their relationships. RESULTS: Human AMSCs and AMSCs-EVs were obtained. Serum ALT, AST, TBIL, and hepatic Hyp were increased, implying the fibrosis degree was aggravated in HF mice, which was decreased again after EV treatment. EVs inhibited HF degree by reducing α-SMA and Collagen I and promoting cell proliferation. AMSCs-EVs delivered miR-200a into hepatocytes, which up-regulated miR-200a expression, inhibited ZEB1 expression, and reduced its enrichment on the PIK3R3 promoter, therefore inhibiting PIK3R3 expression and alleviating HF. Overexpression of ZEB1 or PIK3R3 attenuated the anti-fibrotic effect of AMSCs-EVs. CONCLUSION: Human AMSCs-derived EVs mediated miR-200a delivery and inhibition of intracellular ZEB1/PIK3R3 axis to exert anti-fibrosis effects.

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Introduction: The programmed death-1 (PD-1) immune checkpoint inhibitor pembrolizumab is currently approved in the US for the first-line (1L) treatment of recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC), either alone or in combination with platinum and 5-fluorouracil (5-FU). However, the toxicity of 5-FU has motivated the study of alternate combinations that replace 5-FU with a taxane. The objective of the current study was to describe the baseline characteristics, treatment patterns and sequences, and real-world outcomes of individuals receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC in the US. Methods: This was a retrospective study of US adults ≥18 years of age receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC, using electronic health record data from a nationwide de-identified database. Real-world overall survival (rwOS), time on treatment (rwToT), and time to next treatment (rwTTNT) outcomes were assessed using Kaplan-Meier analysis. Results: The study population comprised 83 individuals (80.7% male) with a median age of 64 years. The most common tumor site was the oropharynx (48.2%); 70.0% of these tumors were HPV-positive. A total of 71.1% of the study population had an Eastern Cooperative Oncology Group performance status of 0-1 at index date, 71.8% had a combined positive score for programmed death ligand-1 (PD-L1) expression of ≥1, and 30.8% had a score of ≥20. The median (95% CI) rwOS was 14.9 (8.8-23.3) months, rwToT was 5.3 (4.0-8.2) months, and rwTTNT was 8.7 (6.8-12.3) months. Among the 24 individuals who received a subsequent therapy, the most common second-line therapies were cetuximab-based (n = 9) or pembrolizumab-containing (n = 8) regimens. Conclusions: The rwOS and other real-world outcomes observed for this study population further support pembrolizumab + platinum + taxane combination therapy as a potential 1L treatment option for R/M HNSCC.

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BACKGROUD: Downhill running has recently become a promising exercise modality for metabolic syndrome, but the effect and precise mechanism of downhill running training on insulin resistance (IR) induced skeletal muscle atrophy remains unclear. The current study aimed to explore the benefits of downhill running training accompanied by a low-fat diet on skeletal muscle atrophy in IR mice and its possible mechanisms. METHODS: For in vivo study, high fat diet (HFD) -induced IR mice were submitted to the downhill running training or/and caloric restriction for 8 weeks. In vitro study was performed using co-cultured RAW264.7 macrophages and C2C12 myoblasts model. Glucose tolerance test (GTT), insulin tolerance test (ITT), immunofluorescence staining, Western blot analysis, hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), Cell counting kit-8 (CCK-8) assays and glucose uptake assays were employed to explore the benefits and possible mechanisms of downhill running training accompanied by a low-fat diet on IR mice. RESULTS: Our data revealed that HFD induces IR, which leading to skeletal muscle atrophy. Downhill running accompanied by caloric restriction mitigated HFD-induced IR and improve skeletal muscle atrophy. Further study suggested that descended TRIB3 mediated the favorable impact of downhill running on IR induced skeletal muscle atrophy by suppressing M1-like macrophages and promoting M2-like macrophages. Macrophages-specific knockdown of TRIB3 exerted similar effects on the macrophage polarization and IR related myogenesis to downhill running training accompanied by caloric restriction. In contrast, macrophages-specific overexpression of TRIB3 descended phosphorylation of AKT, further activated M1-like macrophages and aggravated IR related inhibition of myogenesis. CONCLUSIONS: This finding demonstrated the beneficial effects of downhill running training and caloric restriction on IR related skeletal muscle atrophy by promoting M2-like macrophages through TRIB3-AKT pathway.

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BACKGROUND@#Hepatic fibrosis (HF) is a common pathological feature of chronic hepatic diseases. We aimed to illuminate the significance of amniotic mesenchymal stem cells (AMSCs)-derived extracellular vesicles (AMSCs-EVs) in HF. @*METHODS@#Human AMSCs-EVs were isolated and identified. HF mice were constructed and treated with EVs. The fibrosis was observed by staining experiments and Western blot (WB) assay. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and hepatic hydroxyproline (Hyp) were detected to confirm liver function.For the in vitro experiments, human hepatic stellate cells were induced with transforming growth factor-b and treated with EVs. To measure the degree of HF, the expression of alpha-smooth muscle actin (a-SMA) and Collagen I was detected by WB assay, and cell proliferation was detected by cell counting kit 8 assay. The levels of miR-200a, Zinc finger E-box binding homeobox 1 (ZEB1), and phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) were detected by WB and realtime quantitative polymerase chain reaction. The binding of ZEB1 to PIK3R3 and miR-200a to ZEB1 was analyzed by chromatin immunoprecipitation and dual luciferase assays to validate their relationships. @*RESULTS@#Human AMSCs and AMSCs-EVs were obtained. Serum ALT, AST, TBIL, and hepatic Hyp were increased, implying the fibrosis degree was aggravated in HF mice, which was decreased again after EV treatment. EVs inhibited HF degree by reducing a-SMA and Collagen I and promoting cell proliferation. AMSCs-EVs delivered miR-200a into hepatocytes, which up-regulated miR-200a expression, inhibited ZEB1 expression, and reduced its enrichment on the PIK3R3 promoter, therefore inhibiting PIK3R3 expression and alleviating HF. Overexpression of ZEB1 or PIK3R3 attenuated the anti-fibrotic effect of AMSCs-EVs. @*CONCLUSION@#Human AMSCs-derived EVs mediated miR-200a delivery and inhibition of intracellular ZEB1/PIK3R3 axis to exert anti-fibrosis effects.

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Mitochondrial dysfunction is an important pathogenic factor in non-alcoholic fatty liver disease (NAFLD). Methyltransferase-like 14 (METTL14) has been implicated in mitochondrial fission processes. This research aimed to investigate the mechanism of METTL14 in the mitochondrial function of NAFLD. We first established NAFLD mouse models and cell models, recording body and liver weights and examining pathological changes in liver tissues. Subsequently, serum levels of liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], total cholesterol [TC], and triglycerides [TG]), inflammatory markers (tumor necrosis factor-alpha [TNF-α], interleukin [IL]-6, and IL-1ß), and mitochondrial dysfunction indicators (fission 1 protein [Fis1], dynamin-related protein 1 [Drp1], mitofusin 2 [Mfn2], SID1 transmembrane family member 2 [SIDT2], and mitochondrial membrane potential [MMP]) in the liver and cells were evaluated. The N6-methyladenosine (m6A) modification level of primary microRNA (pri-miRNA) and m6A enrichment on pri-miR-34a were quantified. Co-immunoprecipitation and dual-luciferase reporter gene assays were utilized to validate gene interactions. Our findings revealed highly elevated METTL14 expression in NAFLD mouse and cell models. Silencing METTL14 reduced weight gain and mitigated adverse liver function indices, inflammation, hepatic steatosis, and structural damage in NAFLD mice. It also led to a decrease in Fis1/Drp1 levels and an increase in MMP/Mfn2 in the liver and cells. Moreover, METTL14 increased the m6A level, promoting the binding of DiGeorge syndrome critical region 8 (DGCR8) to pri-miR-34a, which enhanced miR-34a-5p expression. Databases and dual-luciferase reporter gene assays indicated that miR-34a-5p could suppress SIDT2 expression. The overexpression of miR-34a-5p or inhibition of SIDT2 expression negated the alleviative effects of METTL14 silencing on mitochondrial homeostasis imbalance. In conclusion, METTL14, through m6A modification, modulates the miR-34a-5p/SIDT2 axis, impairing mitochondrial homeostasis in NAFLD.

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We construct an islamic lattice by considering the nearest-neighbor (NN) hoppings with staggered magnetic fluxes and the next-NN hoppings. This model supports abundant quantum phases for various values of filling fractions. At1/4filling, Chern insulator (CI) phases with Chern numbersC=±1, -2and a zero-Chern-number topological insulator (ZCNTI) phase exist. At3/8filling, several CI phases with Chern numbersC=±1, 3and the ZCNTI phase are obtained. For the filling fraction 3/4, CI phases with Chern numbersC=±1, 2and two ZCNTI phase areas appear. Interestingly, these ZCNTI phases host both robust corner states and gapless edge states which can be characterized by the quantized polarization and quadrupole moment. We further find that staggered magnetic fluxes can give rise to the ZCNTI state at1/4and3/4fillings. Phase diagrams for filling fractions1/8,1/2,5/8and7/8are presented as well. In addition, flat bands are obtained for various filling fractions by tuning the hopping parameters. At 1/8 filling, a best topological flat band (TFB) with flatness ratio about 12 appears. Several trivial flat bands but with total Chern number|C|=1emerge in this model and exactly flat band is found at 3/8 filling. We further investigateν=1/2fractional Chern insulate state when hard-core bosons fill into this TFB model.

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Immunogenicity is critical for biologics. However, reference biologics labeling documents do not necessarily mention immunogenicity impact, rendering the development of biosimilars more challenging. We aimed to investigate the comparative assessment of immunogenicity profiles between biosimilars and their respective reference biologics in the review reports of the biosimilar monoclonal antibody applications approved by the Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA) as of March 13, 2022, covering 22 applications approved between April 5, 2016, and December 17, 2021. The maximum differences in anti-drug antibody (ADA) and neutralizing antibody (NAb) incidences between biosimilars and reference products mostly fell within ± 15% (-13.6% to 12%) and ± 20% (-17.4% to 17.1%, except extreme values of -23.4% and 66.7%), respectively. In comparison with antineoplastic agents, more immunosuppressants had ADA-positive (11/11, 100.0% vs. 8/10, 80.0%)/NAb-positive (11/11, 100.0% vs. 3/10, 30.0%) subjects, and the distribution of the aforementioned incidence differences was wider. The investigated biosimilars with available data for analysis demonstrated a high degree of consistency with their reference products in terms of the impact on pharmacokinetic parameters. No increase in immunogenicity was found in available switching studies. Most (16/22, 72.7%) biosimilars were issued post-marketing requirements that were not directly related to immunogenicity concerns. The FDA considered the totality of evidence assessing clinical consequences of immunogenicity differences, if any. Additional information on titers and subgroup analysis may be warranted to elucidate the critical attributes of immunogenicity impact and to aid in forming cost-effective strategies for biosimilar development.

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Background: Although optimal sequencing of systemic therapy in cancer care is critical to achieving maximal clinical benefit, there is a lack of analysis of treatment sequencing in advanced non-small cell lung cancer (aNSCLC) in real-world settings. Methods: A retrospective cohort study of 13,340 lung cancer patients within the Mount Sinai Health System (MSHS) was performed. Systemic therapy data of aNSCLC in 2,106 patients was the starting point in our analysis to investigate how treatment sequencing has evolved, the impact of sequencing patterns on clinical outcomes, and the effectiveness of 2nd line chemotherapy after patients progressed on immune checkpoint inhibitor (ICI)-based therapy as the 1st line of therapy (LOT). Results: There is a significant shift to more ICI-based therapy and multiple lines of targeted therapy after 2015. We compared clinical outcomes of two patient populations with different treatment sequencing patterns, with the 1st group receiving chemotherapy as the 1st LOT followed by ICI-based treatment, and the 2nd group treated in the opposite order receiving a 1st line ICI-containing regimen followed by a 2nd line chemotherapy. No statistically significant difference in overall survival (OS) was observed between the two groups [group 2 vs. group 1, adjusted hazard ratio (aHR) =1.36, P=0.39]. We assessed the efficacy of the 2nd line chemotherapy in three patient populations given either 1st line ICI single agent, 1st line ICI-chemotherapy combination, or 1st line chemotherapy alone, there was no statistically significant difference in time-to-next treatment (TTNT) and in OS among the three patient groups. Conclusions: Analysis of real-world data has shown two treatment sequencing patterns in aNSCLC, ICI followed by chemotherapy or chemotherapy followed by ICI, achieved similar clinical benefit. The chemotherapies routinely used following platinum doublet 1st LOT, is effective as the 2nd line option after ICI-chemotherapy combination in the 1st line setting.

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Background: Guidelines widely recommend thyrotropin suppression to reduce the risk of recurrence in intermediate- and high-risk papillary thyroid cancer (PTC) after total thyroidectomy. However, an insufficient or excessive dosage may result in a number of symptoms/complications especially in older patients. Patients and methods: We constructed a retrospective cohort including 551 PTC patient encounters. Using propensity score matching and logistic regression models, we determined the independent risk factors affecting levothyroxine therapy at different ages. Our outcomes included: expected TSH level and an unexpected TSH level, which was based on the initial thyroid-stimulating hormone (TSH) goal< 0.1 mIU/L with usual dosage of L-T4 (1.6 µg/kg/day). Results: From our analysis, more than 70% of patients undergoing total thyroidectomy did not achieve the expected TSH level using an empirical medication regimen, and the effect of the drug was affected by age (odds ratio [OR], 1.063; 95% CI, 1.032-1.094), preoperative TSH level (OR, 0.554; 95% CI, 0.436-0.704) and preoperative fT3 level (OR, 0.820; 95% CI, 0.727-0.925). In patients with age < 55 years old, preoperative TSH level (OR, 0.588; 95% CI, 0.459-0.753), and preoperative fT3 level (OR, 0.859; 95% CI, 0.746-0.990) were two independent protective factors, while, in patients with age ≥ 55 years old, only preoperative TSH level (OR, 0.490; 95% CI, 0.278-0.861) was the independent protective factors to achieve expected TSH level. Conclusion: Our retrospective analysis suggested the following significant risk factors of getting TSH suppression in PTC patients: age (≥55 years), lower preoperative TSH and fT3 levels.

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BACKGROUND: Solvent tolerance is a desired feature of microorganisms for their application in biotechnology. Organic solvent-tolerant microorganisms are able to thrive in the presence of organic solvents. Several mechanisms have been proposed to elucidate their intrinsic tolerance to organic solvents. OBJECTIVE: The present review aims to summarize the state of the art of the roles of membrane proteins in microbial organic solvent tolerance. Strategies and challenges for improving the protective function of membrane proteins in organic solvent stress are also proposed. RESULTS: Membrane proteins related to transporter, signal transduction, and material and energy metabolism are involved in solvent tolerance. Optimization of the expression level of membrane proteins and engineering of membrane proteins are utilized to tackle the toxicity caused by organic solvents. CONCLUSIONS: Membrane proteins occupy a strikingly important position in microbial solvent tolerance. Further research on novel methods in membrane proteins, trade-offs among overexpression and toxicity of membrane proteins and solvent yield, and a direct relationship between signaling pathways and solvent tolerance will advance the utilization of organic solvent-tolerant microorganisms in biotechnology.

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BACKGROUND: Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE: We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS: We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS: Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS: Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.

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We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

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C-di-GMP is a ubiquitous second messenger in bacterium, which regulates cellular functions such as the formation of biofilm membrane, cell mobility, virulence, cell adhesion, cell cycle et al. These functions are associated with an increasing number of c-di-GMP effector proteins and/or riboswitchs. In the study, CEP1 (c-di-GMP effector protein 1), a novel c-di-GMP binding protein, was screened with a combination of affinity pull-down and LC/MS/MS methods. The binding of CEP1 and c-di-GMP was demonstrated by surface plasmon resonance, with the dissociation constants of 127 ± 1.03 µM. Quantitative real time PCR assay showed the mRNA levels of cep1 gene in Rhodococcus ruber SD3 increased to 63.29 times and 71.18 times after toluene and phenol stress, respectively. Furthermore, cep1 gene enhanced strain was constructed using shuttle plasmid pNV18, which showed improved growth compared to the wild-type strain in the presence of different organic solvents. The study provided an insight into a mechanism, by which c-di-GMP was connected with organic solvent tolerance of Rhodococcus ruber SD3.

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