Interactions between myoblasts and macrophages under high glucose milieus result in inflammatory response and impaired insulin sensitivity.

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.

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

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.

Real-world use of first-line pembrolizumab + platinum + taxane combination regimens in recurrent / metastatic head and neck squamous cell carcinoma.

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.

Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation.

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.

Effects of Mesenchymal Stem Cells-Derived Extracellular Vesicles on Inhibition of Hepatic Fibrosis by Delivering miR-200a.

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.

Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.

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.

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing.

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.