BIOMED Research Publications

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    DDR2 signaling and mechanosensing orchestrate neuroblastoma cell fate through different transcriptome mechanisms
    (John Wiley and Sons Inc, 2024) Vessella, Theadora; Xiang, Steven; Xiao, Cong; Stilwell, Madelyn; Fok, Jaidyn; Shohet, Jason; Rozen, Esteban; Zhou, H. Susan; Wen, Qi
    The extracellular matrix (ECM) regulates carcinogenesis by interacting with cancer cells via cell surface receptors. Discoidin Domain Receptor 2 (DDR2) is a collagen-activated receptor implicated in cell survival, growth, and differentiation. Dysregulated DDR2 expression has been identified in various cancer types, making it as a promising therapeutic target. Additionally, cancer cells exhibit mechanosensing abilities, detecting changes in ECM stiffness, which is particularly important for carcinogenesis given the observed ECM stiffening in numerous cancer types. Despite these, whether collagen-activated DDR2 signaling and ECM stiffness-induced mechanosensing exert similar effects on cancer cell behavior and whether they operate through analogous mechanisms remain elusive. To address these questions, we performed bulk RNA sequencing (RNA-seq) on human SH-SY5Y neuroblastoma cells cultured on collagen-coated substrates. Our results show that DDR2 downregulation induces significant changes in the cell transcriptome, with changes in expression of 15% of the genome, specifically affecting the genes associated with cell division and differentiation. We validated the RNA-seq results by showing that DDR2 knockdown redirects the cell fate from proliferation to senescence. Like DDR2 knockdown, increasing substrate stiffness diminishes cell proliferation. Surprisingly, RNA-seq indicates that substrate stiffness has no detectable effect on the transcriptome. Furthermore, DDR2 knockdown influences cellular responses to substrate stiffness changes, highlighting a crosstalk between these two ECM-induced signaling pathways. Based on our results, we propose that the ECM could activate DDR2 signaling and mechanosensing in cancer cells to orchestrate their cell fate through distinct mechanisms, with or without involving gene expression, thus providing novel mechanistic insights into cancer progression. © 2024 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
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    The Price of Pain
    (Lippincott Williams and Wilkins, 2024) Manning, Christin; Jorgensen, Michael J.
    Objective: The aim of the study is to describe cost and frequency of work-related musculoskeletal disorders in Kansas. Methods: Data were provided by the Kansas Department of Labor and included all closed workers' compensation claims entailing indemnity and medical costs from 2014 to 2022. Results: Work-related musculoskeletal disorder claims entailed a median total cost of $20, 097. Medical comprised 48.4% of costs, indemnity 46.4%, and legal 5.2%. The most frequently injured and costliest body part was the shoulder. Manufacturing comprised 28.4% of claims, followed by health care and office. Lifting was the most common cause, generating 32.0% of claims. Education, transportation, and mining were among industries with above average claim rates. Conclusions: Very few studies use workers' compensation data to assess work-related musculoskeletal disorder costs. This study introduces a state not yet analyzed and presents more recent years of data than available in the literature. Copyright © 2024 American College of Occupational and Environmental Medicine
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    PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2023-12) Aarotale, Parshuram N.; Hill, Twyla J.; Rattani, Ajita
    Due to an alarming trend related to obesity affecting 93.3 million adults in the United States alone, body mass index (BMI) and body weight have drawn significant interest in various health monitoring applications. Consequently, several studies have proposed self-diagnostic facial image-based BMI prediction methods for healthy weight monitoring. These methods have mostly used convolutional neural network (CNN) based regression baselines, such as VGG19, ResNet50, and EfficientNetB0, for BMI prediction from facial images. However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones. This paper aims to develop a lightweight facial patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the deployment and weight monitoring using smartphones. Extensive experiments on BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a size of about 3.3 million parameters. On cross-comparison with heavyweight models, such as ResNet-50 and Xception, trained for BMI prediction from facial images, our proposed PatchBMI-Net obtains equivalent MAE along with the model size reduction of about 5.4x and the average inference time reduction of about 3x when deployed on Apple-14 smartphone. Thus, demonstrating performance efficiency as well as low latency for on-device deployment and weight monitoring using smartphone applications.
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    Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
    (IEEE Computer Society, 2023-10) Aarotale, Parshuram N.; Rattani, Ajita
    Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI-based system for ECG-based arrhythmia classification. To this front, a deep-learning-based solution has been proposed for ECG-based arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10 sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from PhysioNet with four different types of arrhythmias were used. The sampling frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was estimated and stacked row-wise to obtain a single image from 12 channels, resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF scalograms are fed to the pretrained convolutional neural network (CNN), 1D CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia classification. The fine-tuned CNN models obtained the best test accuracy of about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia classification.
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    Influence of Metal Processing on Microstructure and Properties: Implications for Biodegradable Metals: A Mini Review
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09) Jaraba, Khulud; Mahapatro, Anil
    Biodegradable metallic alloys are currently being explored extensively for use in temporary implant applications, since the prolonged existence of implants within the body has been linked with health complications and metal toxicity. There are many metal alloy fabrication methods available in the industrial, aerospace, and biomedical fields; some of them have more advanced techniques and specialized equipment than others. Past studies have shown that the performances of materials is greatly affected by the concentration of alloying elements and the metal processing techniques used. However, the impact each fabrication method has on the chemical and mechanical properties of the material is not fully understood; this lack of knowledge limits the advancement of the field of biodegradable metals. This review provides a general introduction to biodegradable metals and their applications and then aims to give a broad overview of the influence of metal processing on the microstructure and properties of metal alloys. The possible implications of these fabrication methods for the biodegradable metals are discussed.