Deficits in ascending pain modulation pathways in breast cancer survivors with chronic neuropathic pain: A resting-state fMRI study.

Purpose: Breast cancer (BC) is the highest frequent malignancy in women globally. Approximately 25-60% of BC patients with chronic neuropathic pain (CNP) result from advances in treating BC. Since the CNP mechanism is unclear, the various treatment methods for CNP are limited. We aimed to explore the brain alternations in BC patients with CNP and the relationship between depression and CNP utilizing resting-state functional magnetic resonance imaging (rs-fMRI). Methods: To collect the data, the female BC survivors with CNP (n = 20) and healthy controls (n = 20) underwent rs-fMRI. We calculated and compared the functional connectivity (FC) between the two groups using the thalamus and periaqueductal gray (PAG) as seed regions. Results: Patients with BC showed increased depression and FC between the thalamus and primary somatosensory cortices (SI). Moreover, the Hospital Anxiety and Depression Scale-Depression (HADS-D) and pain duration were linked positively to the strength of FC from the thalamus to the SI. Furthermore, the thalamus-SI FC mediated the impact of pain duration on HADS-D. Conclusion: In BC patients with CNP, the ascending pain regulation mechanism is impaired and strongly associated with chronic pain and accompanying depression. This research increased our knowledge of the pathophysiology of CNP in patients with BC, which will aid in determining the optimal therapeutic strategy for those patients.

Do Surrounding People's Emotions Affect Judgment of the Central Person's Emotion? Comparing Within Cultural Variation in Holistic Patterns of Emotion Perception in the Multicultural Canadian Society.

Previous studies in cultural psychology have suggested that when assessing a target person's emotion, East Asians are more likely to incorporate the background figure's emotion into the judgment of the target's emotion compared to North Americans. The objective of this study was to further examine cultural variation in emotion perception within a culturally diverse population that is representative of Canada's multicultural society. We aimed to see whether East-Asian Canadians tended to keep holistic tendencies of their heritage culture regarding emotion perception. Participants were presented with 60 cartoon images consisting of a central figure and four surrounding figures and were then asked to rate the central figure's emotion; out of the four cartoon figures, two were female and two were male. Each character was prepared with 5 different emotional settings with corresponding facial expressions including: extremely sad, moderately sad, neutral, moderately happy, and extremely happy. Each central figure was surrounded by a group of 4 background figures. As a group, the background figures either displayed a sad, happy, or neutral expression. The participant's task was to judge the intensity of the central figures' happiness or sadness on a 10-point Likert scale ranging from 0 (not at all) to 9 (extremely). For analysis, we divided the participants into three groups: European Canadians (N = 105), East Asian Canadians' (N = 104) and Non-East Asian/Non-European Canadians (N = 161). The breakdown for the Non-East Asian/Non-European Canadian group is as follows: 94 South Asian Canadians, 25 Middle Eastern Canadians, 23 African Canadians, 9 Indigenous Canadians, and 10 Latin/Central/South American Canadians. Results comparing European Canadians and East Asian Canadians demonstrated cultural variation in emotion judgment, indicating that East Asian Canadians were in general more likely than their European Canadian counterparts to be affected by the background figures' emotion. The study highlights important cultural variations in holistic and analytic patterns of emotional attention in the ethnically diverse Canadian society. We discussed future studies which broaden the scope of research to incorporate a variety of diverse cultural backgrounds outside of the Western educational context to fully comprehend cultural variations in context related attentional patterns.

Nomogram Incorporating the WNT/ß-Catenin Signaling Pathway for Predicting the Survival of Cutaneous Melanoma.

BACKGROUND: Accurate prediction of the survival of cutaneous melanoma (CM) permits the selection of the optimal treatment. Currently, the TNM stage has limitations in predicting the survival of CM. There is evidence that the WNT/ß-catenin signaling pathway has the potential to predict the CM prognosis. However, it still needs further investigation. OBJECTIVE: This study aims to establish a nomogram incorporating the WNT/ß-catenin signaling pathway to improve the predicted accuracy of the overall survival (OS) of CM. METHODS: Two hundred and eighty CM patients were recruited and followed up. The clinicopathological characteristics and the key genes of the WNT/ß-catenin signaling pathway (VEGF, ß-catenin, and DKK1) were chosen as potential variables associated with the OS. In the training cohort (n = 190), a nomogram was built to estimate the 1-, 3-, and 5-year OS, and its discriminations and calibrations were valid by the verification cohort (n = 90). The predicted accuracies of the nomogram with or without the Wnt/ß-catenin pathway and TNM stage were compared. RESULTS: A nomogram integrating independent risk factors (ulceration, lymph node metastasis, distant metastasis, Breslow thickness, dermal mitoses, ß-catenin, VEGF, and DKK1), which were evaluated by a multivariate analysis, was constructed to predict the 1-, 3-, and 5-year OS of CM patients. Good discrimination and calibration were obtained regardless of the training or validation datasets. The nomogram incorporating the Wnt/ß-catenin signaling pathway showed the highest accuracy [area under the curve (AUC)=0.914, 0.852, 0.785] compared with the nomogram without the Wnt/ß-catenin signaling pathway (AUC=0.693, 0.640, 0.615) and the TNM stage (AUC=0.726, 0.693, 0.673). CONCLUSION: The prognostic value of the established nomogram incorporating the WNT/ß-catenin signaling pathway was better than it without WNT/ß-catenin signaling pathway and TNM stage, which might be beneficial in the development of optimal treatment options.

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine.

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.