BACKGROUND Cervical cancer is the fourth commonest malignancy in women around the world. It represents the second most commonly diagnosed cancer in South East Asian women, and an important cancer death cause in women of developing nations. Data collected in 2018 revealed 5690000 cervical cancer cases worldwide, 85% of which occurred in developing countries. AIM To assess self-perceived burden (SPB) and related influencing factors in cervical cancer patients undergoing radiotherapy. METHODS Patients were prospectively included by convenient sampling at The Fifth Affiliated Hospital of Sun Yat-Sen University, China between March 2018 and March 2019. The survey was completed using a self-designed general information questionnaire, the SPB scale for cancer patients, and the self-care self-efficacy scale, Strategies Used by People to Promote Health, which were delivered to patients with cervical cancer undergoing radiotherapy. Measurement data are expressed as the mean ± SD. Enumeration data are expressed as frequencies or percentages. Caregivers were the spouse, offspring, and other in 46.4, 40.9, and 12.7%, respectively, and the majority were male (59.1%). As for pathological type, 90 and 20 cases had squamous and adenocarcinoma/adenosquamous carcinomas, respectively. Stage IV disease was found in 12 (10.9%) patients. RESULTS A total of 115 questionnaires were released, and five patients were excluded for too long evaluation time ( n = 2) and the inability to confirm the questionnaire contents ( n = 3). Finally, a total of 110 questionnaires were collected. They were aged 31-79 years, with the 40-59 age group being most represented (65.4% of all cases). Most patients were married (91.8%) and an overwhelming number had no religion (92.7%). Total SPB score was 43.13 ± 16.65. SPB was associated with the place of residence, monthly family income, payment method, transfer status, the presence of radiotherapy complications, and the presence of pain ( P < 0.05). The SPB and self-care self-efficacy were negatively correlated ( P < 0.01). In multivariate analysis, self-care self-efficacy, place of residence, monthly family income, payment method, degree of radiation dermatitis, and radiation proctitis were influencing factors of SPB ( P < 0.05). CONCLUSION Patients with cervical cancer undergoing radiotherapy often have SPB. Self-care self-efficacy scale, place of residence, monthly family income, payment method, and radiation dermatitis and proctitis are factors independently influencing SPB.

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then, a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

Objective. Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wideranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition.&#xD;Approach. In this regard, we propose AutoEER (Automatic EEG-based Emotion Recognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG.&#xD;Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space. Main results. Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art (SOTA) manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods. Significance.&#xD;AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.

The causes for falls in the elderly are varied, and visual spatial neglect could be 1 contributing factor. Further, the presence of a carotid artery plaque, especially on the right side, might influence the visual spatial attention of the elderly. Our aim was to identify the intrinsic association between carotid plaques and lateralization of spatial attention in the elderly. Further, we sought to understand and potentially prevent the consequences of unilateral spatial neglect such as injury from falls. Participants aged 64 to 93 years were divided into a group with carotid artery plaque(s) of the right side or both sides (BOTH, n = 38; and 9/ 38 were right side only) and a group without right-side carotid artery plaque(s) (LEFT, n = 53). Participants were asked to perform a line bisection task and undergo doppler ultrasonography examinations. Contrary to expectations, compared to LEFT, the mean index and net scores of the line bisection errors in BOTH were significantly less leftward, but the mean diameter of the right-side common carotid artery in BOTH was significantly larger. Our results indicate that the presence of carotid plaque(s) might be linked to increased risk of falls in the elderly. The attenuated spatial neglect in participants with right-side carotid artery plaque(s) might be due to compensatory carotid artery dilatation.