Single photon emission computed tomography (SPECT) has been employed to detect Parkinson’s disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models’ performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

This study aimed to develop a novel magnetic resonance imaging (MRI)-detectable boron (B)-containing nanoassemblies and evaluate their potential for boron neutron capture therapy (BNCT). Starting from the citrate-coated gold nanoparticles (AuNPs) (23.9 ± 10.2 nm), the diameter of poly (D, L-lactide-co-glycolide) AuNPs (PLGA-AuNPs) increased approximately 110 nm after the encapsulation of the PLGA polymer. Among various B drugs, the self-produced B cages had the highest loading efficiency. The average diameter of gadolinium (Gd)- and B-loaded NPs (PLGA-Gd/B-AuNPs) was 160.6 ± 50.6 nm with a B encapsulation efficiency of 28.7 ± 2.3%. In vitro MR images showed that the signal intensity of PLGA-Gd/B-AuNPs in T1-weighted images was proportional to its Gd concentration, and there exists a significantly positive relationship between Gd and B concentrations (R² = 0.74, p < 0.005). The hyperintensity of either 250 ± 50 mm³ (larger) or 100 ± 50 mm³ (smaller) N87 xenograft was clearly visualized at 1 h after intravenous injection of PLGA-Gd/B-AuNPs. However, PLGA-Gd/B-AuNPs stayed at the periphery of the larger xenograft while located near the center of the smaller one. The tumor-to-muscle ratios of B content, determined by inductively coupled plasma mass spectrometry, in smaller- and larger-sized tumors were 4.17 ± 1.42 and 1.99 ± 0.55, respectively. In summary, we successfully developed theranostic B- and Gd-containing AuNPs for BNCT in this study.

Background: Fibromyalgia (FM) is characterized by complex pain symptoms lacking impersonal considerations in diagnosis and treatment evaluation, which often happens in women. Chronic and persistent widespread pain is the key symptom disturbing patients with FM, leading to depression, obesity, and sleep disturbances. Toll-like receptor 4 (TLR4) activation produces a harmful sensory input involved in central pain; this is the focus of this study. Electroacupuncture (EA) has beneficial effects in reducing FM pain, but its connection with TLR4 signaling is still unknown. Methods: Intermittent cold stress significantly induced mechanical and thermal pain. EA, but not sham EA, reliably attenuated mechanical and thermal hyperalgesia. The increased inflammatory mediators in FM mice were reduced in the EA group, but not in the sham group. Results: All TLR4 and related molecule levels increased in the FM mice’s hypothalamus, periaqueductal gray (PAG), and cerebellum. These increases could be attenuated by EA but not sham stimulation. Activation of TLR4 by lipopolysaccharide (LPS) significantly induced FM and can be further reversed by a TLR4 antagonist. Conclusions: These mechanisms provide evidence that the analgesic effect of EA is related to the TLR4 pathway. In addition, we showed that inflammation can activate the TLR4 pathway and provided new possible therapeutic targets for FM pain.

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model’s accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.

Fibromyalgia is a chronic illness usually accompanied by long-lasting, general pain throughout the body, often accompanied by anxiety, depression, fatigue, and sleep disruption. Meanwhile, doctors and scientists have not entirely discovered detailed mechanisms; patients always have an exaggerated sensation to pervasive pain without satisfied medical service. Given the lack of knowledge on its underlying mechanism, current treatments aim to provide pain and/or symptom relief. The present study aimed to clarify the role of cannabinoid receptor 1 (CB1) signaling in a mouse fibromyalgia pain model. To develop the mouse fibromyalgia model, mice were subjected to intermittent cold stress (ICS). Our results indicated that mechanical (2.09 ± 0.09 g) and thermal hyperalgesia (4.77 ± 0.29 s), which were evaluated by von Frey and Hargraves’ tests, were induced by ICS, suggesting successful modeling. The hurting replies were then provoked by electroacupuncture (EA) but not for sham EA mice. Further, in a Western blot analysis, we found significantly decreased CB1 protein levels in the thalamus, somatosensory cortex, and anterior cingulate cortex. In addition, the levels of pain-related protein kinases and transcription factor were increased. Treatment with EA reliably increased CB1 expression in various brain regions sequentially alleviated by nociceptive mediators. Furthermore, the administration of a CB1 agonist significantly attenuated fibromyalgia pain, reversed EA analgesia by the CB1 antagonist, and further reversed the chemogenetic inhibition of SSC. Our innovative findings evidence the role of CB1 signaling in the interaction of EA and fibromyalgia, suggesting its potential for clinical trials and as a treatment target.

Fibromyalgia (FM) is characterized by widespread musculoskeletal pain and tenderness, cognitive dysfunction, fatigue, and insomnia. Electroacupuncture (EA) has documented efficacy against FM-associated pain, while cannabinoid receptor 1 (CB1) plays a critical role in endogenous analgesia. Herein, we examined whether pain relief initiated by EA was linked with differing cerebellar CB1 levels and signaling in an intermittent cold stress (ICS) mouse model of FM. FM-like hyperalgesia and recovery were assessed by measuring mechanical and thermal nociceptive thresholds. Compared to control mice, ICS-induced FM-model mice exhibited a significantly reduced mechanical withdrawal threshold (2.3 ± 0.1 g) and shorter thermal withdrawal latency (4.0 ± 0.5 s), indicative of mechanical and thermal hyperalgesia. Both conditions were reversed by 2 Hz EA but not sham EA. Hyperalgesia was associated with reduced CB1 receptor expression and the enhanced activity of multiple nociceptive signaling pathways (PKA, PI3K, Akt, mTOR, ERK, and NF-kB) in the mouse cerebellum. The 2 Hz EA treatment reliably reversed these abnormalities, while the sham EA treatment did not. Intracerebroventricular injection of the CB1 agonist anandamide (AEA) recapitulated the effects of EA on pain thresholds, while the analgesic effects of EA were blocked by the CB1 antagonist AM251. Precise chemogenetic stimulation at the paraventricular nucleus (PVN) of the hypothalamus reliably induced FM pain. Chemogenetic inhibition at the PVN diminished FM through the CB1 pathway in the cerebellum. Our findings suggest that dysregulation of CB1 expression and aberrant hyperactivity of nociceptive signaling pathways in the cerebellum contribute to the etiology of FM and that the upregulation of CB1 signaling mediates the analgesic efficacy of EA.