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Photoactivatable Biomedical Materials According to Luminogens with Aggregation-Induced Release (AIE) Traits.
Antibacterial photodynamic therapy (aPDT) is of vital importance for the treatment of periodontal diseases due to its great potential on effective elimination of pathogenic bacteria via overwhelming reactive oxygen species (ROS) generation. However, the excessive ROS after the therapeutic process may impose an oxidative stress within periodontal pockets, consequently leading to an irreversible destroy in surrounding tissue and severely limit its biomedical applications. In this study, considering the contradiction between ROS in bacteriostasis and inflammation, the role of ROS in different temporal and spatial states has been fully studied. Accordingly, we have designed composite nanomaterials that can play ROS based aPDT and anti-inflammatory effect by eliminating ROS, taking account of different ratio of photosensitizer/ROS scavenger to realize a time-sequential manner. Herein, a simple multifunctional nanocomposite was fabricated by coating red light-excited photosensitizer chlorin e6 (Ce6) onto nanoceria, achieving simultaneous sterilization and inflammation elimination via a dual directional regulation effect. This nano-based platform could utilize the aPDT for antibacterial purpose in the first stage with red-light irradiation, and subsequently scavenge the residual ROS via nanoceria to modulate host immunity by down-regulating the M1 polarization (pro-inflammatory) of macrophages and up-regulating the M2 polarization (anti-inflammatory and regenerative) of macrophages. Moreover, the local ROS level induced by activated inflammation pathway can be adjusted in a very long time because of the charge conversion effect of CeO2. The regenerative potential of inflammatory surrounding tissues was improved in the animal model. Our strategy will open a new inspiration to fight against the defects of aPDT in the treatment of periodontal disease, even in the anti-infection therapy for the future clinical application.Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. selleck The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50-100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.During navigation, animals integrate sensory information with body movements to guide actions. The impact of both navigational and movement-related signals on cortical visual information processing remains largely unknown. We review recent studies in awake rodents that have revealed navigation-related signals in the primary visual cortex (V1), including speed, distance travelled and head-orienting movements. Both cortical and subcortical inputs convey self-motion related information to V1 neurons for example, top-down inputs from secondary motor and retrosplenial cortices convey information about head movements and spatial expectations. Within V1, subtypes of inhibitory neurons are critical for the integration of navigation-related and visual signals. We conclude with potential functional roles of navigation-related signals in V1 including gain control, motor error signals and predictive coding.Repetitive TMS (rTMS) allows for non-invasive and transient disruption of local neuronal functioning. We used machine learning approaches to assess whether brain tumor patients can be accurately classified into aphasic and non-aphasic groups using their rTMS language mapping results as input features. Given that each tumor affects the subject-specific language networks differently, resulting in heterogenous rTMS functional mappings, we propose the use of machine learning strategies to classify potential patterns of rTMS language mapping results. We retrospectively included 90 patients with left perisylvian world health organization (WHO) grade II-IV gliomas that underwent presurgical navigated rTMS language mapping. Within our cohort, 29 of 90 (32.2%) patients suffered from at least mild aphasia as shown in the Aachen Aphasia Test based Berlin Aphasia Score (BAS). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each stimulated cortical area (28 regions of interestionally, our results could point towards a decreasing potential for neuroplasticity with age.Hypertension is prevalent among postmenopausal women worldwide and is a risk factor for cognitive impairment. This study examined the cognitive differences between Taiwanese postmenopausal women with and without hypertension after natural menopause and identified factors associated with cognitive function in hypertensive postmenopausal women. A two-group comparative design study of 171 Taiwanese postmenopausal participants (mean age = 64.8±6.6) was conducted in southern Taiwan. Cognitive performance on neuropsychological tests was evaluated, and demographic, health, menopausal, and disease related variables were assessed. Compared to the non-hypertensive group, women with hypertension showed significantly worse performance and a higher percentage of impairment in global cognitive functioning and memory. Fewer years of education and longer duration of hypertension were associated with lower global cognitive function, and increased age was associated with lower scores in delayed memory. Cognitive screening as well as training on global cognitive functioning and memory are needed for postmenopausal hypertensive women.
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