ObjectiveTo quantitatively analyze and evaluate the content of China's assistive technology industry policies. MethodsBased on the three-dimensional framework of policy "objectives-instruments-intensity", this study examined 101 national-level assistive technology industry policies issued between 2011 and 2024. Policy text analysis was conducted to assess the matching and coordination among policy objectives, policy instruments and policy intensity. Furthermore, the policy modeling consistency (PMC) index model was applied to establish an evaluation system consisting of nine primary indicators and 35 secondary indicators, and six key policies were quantitatively evaluated. ResultsThe policy objectives mainly focused on optimizing the industrial development structure, also emphasized supportive environments for industrial development and the enhancement of innovation capacity. Policy instruments were dominated by supply-oriented and environment-oriented instruments, supplemented by demand-oriented instruments, aiming to strengthen technological foundations and competitiveness, promote standardized development, and facilitate industrial development and upgrading. Regarding the matching between policy objectives and instruments, multiple instruments were adopted to support different objectives, with different priorities. In terms of the matching between policy objectives and policy intensity, policies mainly emphasized improving the accessibility of assistive technology products and meeting the rehabilitation needs of different populations. Regarding the matching between policy instruments and policy intensity, supply-oriented instruments were more frequently adopted and showed higher policy intensity, indicating strong governmental efforts to optimize production factor allocation and enhance the capacity of market entities. The average PMC index score was 6.45, indicating relatively high policy quality, strong internal consistency, stable policy orientation, and clear objectives. The PMC scores for policy evaluation and policy instrument dimensions were relatively high, while some policies showed relatively narrow objectives. ConclusionChina's assistive technology industry policies demonstrate relatively high quality. Future policy development should further strengthen industrial innovation capacity, optimize the structure of policy instruments, and improve the alignment between policy objectives and instruments.

Objective:To investigate the relationship between the expression levels of chemokines in serum of patients with differentiated thyroid carcinoma (DTC) and the progression of DTC.Methods:From January to April in 2017, blood samples of 76 patients (25 males, 51 females, median age: 39 years) with DTC after surgery in Nuclear Medicine Department of Tenth People′s Hospital Affiliated to Tongji University were collected retrospectively for detecting the expression levels of 40 chemokines. Patients were divided into different groups according to (1) with or without metastasis: the non-metastasis group ( n=13) and the metastasis group ( n=63); (2) degree of gradual dedifferentiation: without metastasis group ( n=13), lymph node metastasis group ( n=48), highly malignant group ( n=11) and radioactive iodine refractory (RAIR) with distant metastasis group ( n=4); (3) frequency of 131I treatment in follow-up for nearly 2 years: single treatment group ( n=51) and multiple treatment group ( n=25). Differences in chemokine levels among groups were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of differential chemokines′ levels on DTC metastasis and multiple 131I treatment. Independent-sample t test, Mann-Whitney U test and one-way analysis of variance were used to analyze the data. Results:Compared with the non-metastatic group, the expression levels of Eotaxin-3 ((25.94±6.05) vs (21.76±5.71) ng/L), interferon-γ (IFN-γ; (116.04±28.98) vs (98.71±26.18) ng/L), macrophage-derived chemokine (MDC; (1 468.08±401.74) vs (1 082.94±423.30) ng/L) and thymus expressd chemokine (TECK; (505.22(419.80, 563.36) vs 402.89(347.43, 442.97) ng/L) in metastatic group were decreased, and the differences were statistically significant ( t values: 2.376, 2.131, 3.007, U=215.000, all P<0.05). The area under the ROC curve of IFN-γ+ MDC+ TECK for predicting DTC metastasis was 0.844(95% CI: 0.755-0.932, P<0.001), and the sensitivity was 79.37%(50/63). Only the differences of MDC among without metastasis group, lymph node metastasis group, highly malignant group and RAIR with distant metastasis group were significant ((1 468.08±401.74), (1 121.59±454.20), (976.07±281.04), (922.68±342.41) ng/L; F=3.564, P<0.05), and the expression was gradually decreased with the degree of dedifferentiation. Only IL-8 was significantly increased in the multiple treatment group compared with the single treatment group (28.20(23.22, 32.51) vs 30.51(26.98, 35.57) ng/L; U=801.000, P<0.05). The area under the ROC curve of IL-8 for predicting multiple 131I treatment was 0.648(95% CI: 0.523-0.773, P<0.05), and the sensitivity was 100%(25/25). Conclusions:Decreased expression of IFN-γ, MDC and TECK may be potential markers for predicting metastasis in DTC. MDC is likely to be a potential molecular target for detecting the dedifferentiation degree of DTC, decreased expression of which may indicate the increased malignancy of tumor. IL-8 may be used to predict whether patients need multiple 131I treatments.

Objective To develop a diagnostic model based on deep neural network for intelligent discrimination of thyroid function. Methods A total of 1616 patients ( 283 males, 1333 females, average age:52 years) who underwent thyroid imaging between May 2016 and June 2018 were selected. According to the clinical diagnosis, the 1616 cases included 299 normal thyroid cases, 876 hyperthyroidism cases and 441 hypothyroidism cases. Feature extraction and learning training were performed on 1000 training set sam-ples by two deep neural network models ( AlexNet;deep convolution generative adversarial networks ( DCGAN) ) using deep learning algorithm. Performance verifications were implemented on 616 test set samples. The con-sistency between the verification results of the two models and the clinical diagnosis was analyzed by Kappa test. Meanwhile, the time advantage of the intelligent diagnosis models was analyzed. Results The average diagnostic time of AlexNet model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 82.29％(79/96), 94.62％(369/390), 100％(130/130), respectively. The Kappa value between results of AlexNet model and clinical diagnosis was 0.886 ( P<0.05) . The average di-agnostic time of DCGAN model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 85.42％(82/96), 95.64％(373/390), 99.23％(129/130), respectively. The Kappa value between results of DCGAN model and clinical diagnosis was 0.904 ( P<0.05) . Conclusion The deep neural network intelligent diagnosis model can quickly determine the functional status of thyroid gland in thyroid imaging, and it has a high recognition accuracy, thus providing a new method for thyroid image review.

OBJECTIVETo investigate general and clinicopathological characteristics of male breast cancer and analyzed the factors affecting the outcomes of the patients based on the data from a single institution.

METHODSTwenty-five male breast cancer patients treated at Sun Yet-sen University Cancer Center between January 1, 2000 and April 30, 2011 were included into the study. The patients were followed up for 1 to 90 months with a median follow-up of 51 months. The general and clinicopathological characteristics including family history, age, smoking, alcohol drinking, site of tumor, location of tumor, histological type, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), Ki-67, vascular endothelial growth factor (VEGF), P53 expression, neoadjuvant chemotherapy, surgery, adjuvant chemotherapy, adjuvant radiotherapy, adjuvant endocrine therapy, tumor size, lymph node status, distant metastasis and TNM stage were investigated by univariate analysis to evaluate the impact of these factors on patient survival.

RESULTSThe 5-year survival rate was 66.5% in these patients. Neoadjuvant chemotherapy, tumor size, lymph node status, distant metastasis and TNM stage were significant predictors for the overall survival. Patients receiving adjuvant endocrine therapy tended to have a better overall survival, though this was not supported statistically (P=0.086). However, patients with neoadjuvant chemotherapy had a poorer overall survival than those without it (P=0.000). Patients in stages I and II had better overall survival than those in stages III and IV (P=0.000).

CONCLUSIONThe 5-year survival rate was 66.5% in these male breast cancer patients. Neoadjuvant chemotherapy, tumor size, lymph node status, distant metastasis and TNM stage are significant predictors of the overall patient survival.

Breast Neoplasms, Male ; diagnosis ; drug therapy ; pathology ; Follow-Up Studies ; Humans ; Male ; Neoplasm Metastasis ; Neoplasm Staging ; Prognosis ; Survival Rate