Dynamic 3D imaging of cerebral blood flow in awake mice using self-supervised-learning-enhanced optical coherence Doppler tomography.

Cerebral blood flow (CBF) is widely used to assess brain function. However, most preclinical CBF studies have been performed under anesthesia, which confounds findings. High spatiotemporal-resolution CBF imaging of awake animals is challenging due to motion artifacts and background noise, particularly for Doppler-based flow imaging. Here, we report ultrahigh-resolution optical coherence Doppler tomography (µODT) for 3D imaging of CBF velocity (CBFv) dynamics in awake mice by developing self-supervised deep-learning for effective image denoising and motion-artifact removal. We compare cortical CBFv in awake vs. anesthetized mice and their dynamic responses in arteriolar, venular and capillary networks to acute cocaine (1 mg/kg, i.v.), a highly addictive drug associated with neurovascular toxicity. Compared with awake, isoflurane (2-2.5%) induces vasodilation and increases CBFv within 2-4 min, whereas dexmedetomidine (0.025 mg/kg, i.p.) does not change vessel diameters nor flow. Acute cocaine decreases CBFv to the same extent in dexmedetomidine and awake states, whereas decreases are larger under isoflurane, suggesting that isoflurane-induced vasodilation might have facilitated detection of cocaine-induced vasoconstriction. Awake mice after chronic cocaine show severe vasoconstriction, CBFv decreases and vascular adaptations with extended diving arteriolar/venular vessels that prioritize blood supply to deeper cortical capillaries. The 3D imaging platform we present provides a powerful tool to study dynamic changes in vessel diameters and morphology alongside CBFv networks in the brain of awake animals that can advance our understanding of the effects of drugs and disease conditions (ischemia, tumors, wound healing).

Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs.

Recent studies have shown that Dental Panoramic Radiograph (DPR) images have great potential for prescreening of osteoporosis given the high degree of correlation between the bone density and trabecular bone structure. Most of the research works in these area had used pretrained models for feature extraction and classification with good success. However, when the size of the data set is limited it becomes difficult to use these pretrained networks and gain high confidence scores. In this paper, we evaluated the diagnostic performance of deep convolutional neural networks (DCNN)based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists. With the available labelled dataset of 70 images, results were reproduced for the preliminary study model. Furthermore, the model performance was enhanced using different computer vision techniques. Specifically, the age meta data available for each patient was leveraged to obtain more accurate predictions. Lastly, we tried to leverage these images, ages and osteoporotic labels to create a neural network based regression model and predict the Bone Mineral Density (BMD) value for each patient. Experimental results showed that the proposed CAD system was in high accord with experienced oral and maxillofacial radiologists in detecting osteoporosis and achieved 87.86% accuracy.Clinical relevance- This paper presents a method to detect osteoporosis using DPR images and age data with multi-column DCNN and then leverage this data to predict Bone Mineral Density for each patient.

A classification algorithm to predict chronic pain using both regression and machine learning - A stepwise approach.

This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community. In Stage 1, regression models were employed to determine unique sensor-based behavioral predictors of pain. These sensor-based parameters were used to create a classification model to predict the weekly recalled pain intensity and interference level using a deep neural network model, a machine learning approach, in Stage 2. Daily step count was a unique predictor for both pain intensity (75% Accuracy, F1 = 0.58) and pain interference (82% Accuracy, F1 = 0.59). The developed classification model performed well in this dataset with acceptable accuracy scores. This study demonstrated that machine learning technique can be used to identify the relationship between patients' pain and the risk factors.

Detection of Trabecular Landmarks for Osteoporosis Prescreening in Dental Panoramic Radiographs.

Dental panoramic radiography (DPR) images have recently attracted increasing attention in osteoporosis analysis because of their inner correlation. Many approaches leverage machine learning techniques (e.g., deep convolutional neural networks (CNNs)) to study DPR images of a patient to provide initial analysis of osteoporosis, which demonstrates promising results and significantly reduces financial cost. However, these methods heavily rely on the trabecula landmarks of DPR images that requires a large amount of manual annotations by dentist, and thus are limited in practical application. Addressing this issue, we propose to automatically detect trabecular landmarks in DPR images. In specific, we first apply CNNs-based detector for trabecular landmark detection and analyze its limitations. Using CNNs-based detection as a baseline, we then introduce a statistic shape model (SSM) for trabecular landmark detection by taking advantage of spatial distribution prior of trabecular landmarks in DPR images and their structural relations. In experiment on 108 images, our solution outperforms CNNs-based detector. Moreover, compared to CNN-based detectors, our method avoids the needs of vast training samples, which is more practical in application.

Structure and melt rheology of polystyrene-based layered silicate nanocomposites.

The melt-state viscoelastic properties of exfoliated in situ polymerized and intercalated solution-blended polystyrene (PS) and organically modified montmorillonite nanocomposites were investigated and compared. The PS nanocomposites prepared by nitroxide-mediated polymerization (NMP) exhibit a stable exfoliated structure whereas the PS nanocomposites prepared by solution mixing exhibit an intercalated structure. The linear viscoelastic properties were strongly correlated with the dispersion state of the nanocomposites. On the other hand, the non-linear oscillatory shear properties exhibited shear thinning character and were consistent with the weak interactions between the polymer and the layered silicate.