Purpose Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). Results The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Conclusion The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

Background Pruritus is identified as an adverse drug reaction to arsenic trioxide, but the association of arsenic exposure with pruritus has not been investigated. Methods A cross‐sectional study was conducted in Shimen, China. A Mendelian randomization analysis was conducted to confirm the causal relationship between genetically predicted percentages of monomethylated arsenic (MMA%) and dimethylated arsenic (DMA%) in urine with chronic pruritus in UK Biobank. A case–control study was then conducted to determine the biomarker for pruritus. Arsenite‐treated mice were used to confirm the biomarker, and von Frey test was used to induce scratching bouts. Last, a randomized, double‐blind, placebo‐controlled trial was conducted to test the efficacy of naloxone in arsenic‐exposed patients with pruritus in Shimen. Results Hair arsenic (μg/g) showed a dose–response relationship with the intensity of itch in 1079 participants, with odds ratios (OR) of 1.11 for moderate‐to‐severe itch (p = 0.012). The Mendelian randomization analysis confirmed the causal relationship, with ORs of 1.043 for MMA% (p = 0.029) and 0.904 for DMA% (p = 0.077) above versus under median. Serum β‐endorphin was identified as a significant biomarker for the intensity of itch (p < 0.001). Consistently, treatment with arsenite upregulated the level of β‐endorphin (p = 0.002) and induced scratching bouts (p < 0.001) in mice. The randomized controlled trial in 126 participants showed that treatment with sublingual naloxone significantly relieved the intensity of itch in arsenic‐exposed participants in 2 weeks (β = −0.98, p = 0.04). Conclusion Arsenic exposure is associated with pruritus, and β‐endorphin serves as a biomarker of pruritus. Naloxone relieves pruritus in patients with arseniasis.

Background: Pruritus has been reported as an adverse drug reaction to arsenic trioxide, but the association of arsenic exposure with pruritus has not been systematically investigated. To investigate the association of arsenic exposure with pruritus, we performed observational, interventional, and Mendelian randomization studies. Methods: A cross-sectional study was conducted in Shimen, China. A Mendelian randomization study was conducted to confirm the causal relationship between susceptibility to arsenic toxicity, in terms of genetically predicted percentages of monomethylated arsenic (MMA%) and dimethylated arsenic (DMA%) in urine, and chronic pruritus in the UK Biobank participants. Then, a case-control study in Shimen participants was conducted to determine the biomarker for pruritus, and arsenite-treated mice were used to confirm the biomarker. Last, a randomized, double-blind, placebo-controlled trial was conducted to test the efficacy of naloxone, a μ-opioid receptor antagonist, in arsenic-exposed patients with pruritus in Shimen. Results: Hair arsenic showed a dose-response relationship with the intensity of itch in 1092 participants. The Mendelian randomization analysis confirmed the causal relationship in the UK Biobank participants, with odds ratios of 1.043 for MMA% and 0.904 for DMA% above versus under median. Serum β-endorphin was identified as a significant biomarker associated with the intensity of itch. Consistently, treatment with arsenite in mice upregulated the level of β-endorphin. The randomized controlled trial showed that treatment with sublingual naloxone significantly relieved the intensity of itch in arsenic-exposed participants. Conclusion: Arsenic exposure is associated with pruritus, and β-endorphin serves as a biomarker of pruritus. Naloxone relieves pruritus in patients with [arseniasis](javascript:;).

Purpose Orbital 99mTc-DTPA SPECT/CT is an important new method for the assessment of inflammatory activity in patients with Graves' Orbitopathy (GO), but it consumes a heavy workload for physicians for interpretation. We aim to propose an automated method, called GO-Net, to detect the activity of GO to assist physicians for diagnosis. Materials and methods GO-Net had two stages: a semantic V-Net segmentation network (SV-Net) to extract extraocular muscles (EOMs) on orbital CT images; a three-channel convolutional neural network (CNN), including SPECT/CT images and segmentation results, to classify inflammatory activity. Manual corrections were applied when the segmentation results were not accurate. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) from Xiangya Hospital of Central South University were enrolled. For the segmentation, five-fold cross-validation with 194 eyes were used for training and internal validation. For the classification, 80% of eyes were trained and internally validated by five-fold cross-validation, and 20% of eyes were used for testing. The contours of the EOMs were drawn manually by an experienced physicians and used as the ground truth. The criteria for the diagnosis of GO activity were determined by the physician through the clinical activity score(CAS) and 99mTc-DTPA uptake. Results Our GO-Net method achieved an accuracy of 84.25%, a precision of 83.35%, a sensitivity of 84.63%, a specificity of 83.87%, an F1 score of 0.83, and an area under the receiver (AUC) of 0.89. For EOMs segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Contours of EOMs in 47 eyes (4.91%) were manually corrected and the average correction time was 5 mins for each eye. Conclusion Our proposed Go-Net model could accurately detect GO activity, which has great potential for the diagnosis of GO.