Cytokine concentration in peripheral blood of patients with colorectal cancer

IntroductionThe role of tumour secretory cytokines and peripheral circulatory cytokines in tumour progression has received increasing attention; however, the role of tumour-related inflammatory cytokines in colorectal cancer (CRC) remains unclear. In this study, the concentrations of various cytokines in the peripheral blood of healthy controls and patients with CRC at different stages were compared.MethodsPeripheral blood samples from 4 healthy participants and 22 colorectal cancer patients were examined. Luminex beads were used to evaluate concentration levels of 40 inflammatory cytokines in peripheral blood samples.ResultsIn peripheral blood, compared with healthy controls and early stage (I + II) CRC patients, advanced CRC (III + IV) patients had increased concentrations of mononuclear/macrophage chemotactic-related proteins (CCL7, CCL8, CCL15, CCL2, and MIF), M2 polarization-related factors (IL-1β, IL-4), neutrophil chemotactic and N2 polarization-related cytokines (CXCL2, CXCL5, CXCL6, IL-8), dendritic cells (DCs) chemotactic-related proteins (CCL19, CCL20, and CCL21), Natural killer (NK) cell related cytokines (CXCL9, CXCL10), Th2 cell-related cytokines (CCL1, CCL11, CCL26), CXCL12, IL-2, CCL25, and CCL27, and decreased IFN-γ and CX3CL1 concentrations. The differential upregulation of cytokines in peripheral blood was mainly concentrated in CRC patients with distant metastasis and was related to the size of the primary tumour; however, there was no significant correlation between cytokine levels in peripheral blood and the propensity and mechanism of lymph node metastasis.DiscussionDifferent types of immune cells may share the same chemokine receptors and can co-localise in response to the same chemokines and exert synergistic pro-tumour or anti-tumour functions in the tumour microenvironment. Chemokines and cytokines affect tumour metastasis and prognosis and may be potential targets for treatment

Correlation Between Gait and Near-Infrared Brain Functional Connectivity Under Cognitive Tasks in Elderly Subjects With Mild Cognitive Impairment

Older adults with mild cognitive impairment (MCI) have a high risk of developing Alzheimer’s disease. Gait performance is a potential clinical marker for the progression of MCI into dementia. However, the relationship between gait and brain functional connectivity (FC) in older adults with MCI remains unclear. Forty-five subjects [MCI group, n = 23; healthy control (HC) group, n = 22] were recruited. Each subject performed a walking task (Task 01), counting backward–walking task (Task 02), naming animals–walking task (Task 03), and calculating–walking task (Task 04). The gait parameters and cerebral oxygenation signals from the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left motor cortex (LMC), right motor cortex (RMC), left occipital leaf cortex (LOL), and right occipital leaf cortex (ROL) were obtained simultaneously. Wavelet phase coherence was calculated in two frequency intervals: low frequency (interval I, 0.052–0.145 Hz) and very low frequency (interval II, 0.021–0.052 Hz). Results showed that the FC of RPFC–RMC is significantly lower in interval I in Task 03 compared with that in Task 02 in the MCI group (p = 0.001). Also, the right relative symmetry index (IDpsR) is significantly lower in Task 03 compared with that in Task 02 (p = 0.000). The IDpsR is positively correlated with the FC of RPFC–RMC in interval I in the MCI group (R = 0.205, p = 0.041). The gait symmetry such as left relative symmetry index (IDpsL) and IDpsR is significantly lower in the dual-task (DT) situation compared with the single task in the two groups (p < 0.05). The results suggested that the IDpsR might reflect abnormal change in FC of RPFC–RMC in interval I in the MCI population during Task 03. The gait symmetry is affected by DTs in both groups. The findings of this study may have a pivotal role in the early monitoring and intervention of brain dysfunction among older adults with MCI

Functional MRI Reveals Locomotion-Control Neural Circuits in Human Brainstem

The cuneiform nucleus (CN) and the pedunculopontine nucleus (PPN) in the midbrain control coordinated locomotion in vertebrates, but whether similar mechanisms exist in humans remain to be elucidated. Using functional magnetic resonance imaging, we found that simulated gait evoked activations in the CN, PPN, and other brainstem regions in humans. Brain networks were constructed for each condition using functional connectivity. Bilateral CN–PPN and the four pons–medulla regions constituted two separate modules under all motor conditions, presenting two brainstem functional units for locomotion control. Outside- and inside-brainstem nodes were connected more densely although the links between the two groups were sparse. Functional connectivity and network analysis revealed the role of brainstem circuits in dual-task walking and walking automaticity. Together, our findings indicate that the CN, PPN, and other brainstem regions participate in locomotion control in humans

Strong Functional Connectivity among Homotopic Brain Areas Is Vital for Motor Control in Unilateral Limb Movement

The mechanism underlying brain region organization for motor control in humans remains poorly understood. In this functional magnetic resonance imaging (fMRI) study, right-handed volunteers were tasked to maintain unilateral foot movements on the right and left sides as consistently as possible. We aimed to identify the similarities and differences between brain motor networks of the two conditions. We recruited 18 right-handed healthy volunteers aged 25 ± 2.3 years and used a whole-body 3T system for magnetic resonance (MR) scanning. Image analysis was performed using SPM8, Conn toolbox and Brain Connectivity Toolbox. We determined a craniocaudally distributed, mirror-symmetrical modular structure. The functional connectivity between homotopic brain areas was generally stronger than the intrahemispheric connections, and such strong connectivity led to the abovementioned modular structure. Our findings indicated that the interhemispheric functional interaction between homotopic brain areas is more intensive than the interaction along the conventional top–down and bottom–up pathways within the brain during unilateral limb movement. The detected strong interhemispheric horizontal functional interaction is an important aspect of motor control but often neglected or underestimated. The strong interhemispheric connectivity may explain the physiological phenomena and effects of promising therapeutic approaches. Further accurate and effective therapeutic methods may be developed on the basis of our findings

Weak but Critical Links between Primary Somatosensory Centers and Motor Cortex during Movement

Motor performance is improved by stimulation of the agonist muscle during movement. However, related brain mechanisms remain unknown. In this work, we perform a functional magnetic resonance imaging (fMRI) study in 21 healthy subjects under three different conditions: (1) movement of right ankle alone; (2) movement and simultaneous stimulation of the agonist muscle; or (3) movement and simultaneous stimulation of a control area. We constructed weighted brain networks for each condition by using functional connectivity. Network features were analyzed using graph theoretical approaches. We found that: (1) the second condition evokes the strongest and most widespread brain activations (5147 vs. 4419 and 2320 activated voxels); and (2) this condition also induces a unique network layout and changes hubs and the modular structure of the brain motor network by activating the most “silent” links between primary somatosensory centers and the motor cortex, particularly weak links from the thalamus to the left primary motor cortex (M1). Significant statistical differences were found when the strength values of the right cerebellum (P < 0.001) or the left thalamus (P = 0.006) were compared among the three conditions. Over the years, studies reported a small number of projections from the thalamus to the motor cortex. This is the first work to present functions of these pathways. These findings reveal mechanisms for enhancing motor function with somatosensory stimulation, and suggest that network function cannot be thoroughly understood when weak ties are disregarded

Hierarchical support vector machine for facial micro-expression recognition

The sample category distribution of spontaneous facial micro-expression datasets is unbalanced, due to the experimental environment, collection equipment, and individualization of subjects, which brings great challenges to micro-expression recognition. Therefore, this paper introduces a micro-expression recognition model based on the Hierarchical Support Vector Machine (H-SVM) to reduce the interference of sample category distribution imbalance. First, we calculated the position of the apex frame in the micro-expression image sequence. To keep micro-expression frames balanced, we sparsely sample the images sequence according to the apex frame. Then, the Low-level Descriptors of the region of interest of the micro-expression image sequence and the High-level Descriptors of apex frame are extracted. Finally, the H-SVM model is used to classify the fusion features of different levels. The experimental results on SMIC, CAMSE2, SAMM, and their composite datasets show that our method can achieve superior performance in micro-expression recognition.</p

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications

Micro-expression recognition by two-stream difference network

Facial micro-expression is a superposition of micro-expression features and identity information of a subject. For the problem of identity information interference in micro-expression recognition, this study proposes a new method for facial micro-expression recognition by de-identity information, called two-stream difference network (TSDN). First, a two-stream encoder-decoder network is trained by a convolutional neural network, where the input of the micro-expression stream is a micro-expression image, and the identity stream is a facial identity image. The micro-expression image is the apex image, and the identity image is the onset image in the micro-expression sequence. The identity information and micro-expression features are recorded in the intermediate layer of the micro-expression stream, while the intermediate layer of the identity stream contains only the identity information of a subject. Then, the identity information is removed by the difference network, but micro-expression features are stored in the intermediate layer of the micro-expression stream. Given the sequence of the micro-expressions, the TSDN model of de-identity information learns the difference that stores in the expression stream. Two public spontaneous facial micro-expression data sets (SMIC and CASME II) are employed in our experiments. The experiment results show that our model can achieve a superior performance in micro-expression recognition.</p

Intermittent Sequential Pneumatic Compression Improves Coupling between Cerebral Oxyhaemoglobin and Arterial Blood Pressure in Patients with Cerebral Infarction

This study aims to explore the effect of intermittent sequential pneumatic compression (ISPC) intervention on the coupling relationship between arterial blood pressure (ABP) and changes in oxyhaemoglobin (Δ [O2Hb]). The coupling strength between the two physiological systems was estimated using a coupling function based on dynamic Bayesian inference. The participants were 22 cerebral infarction patients and 20 age- and sex-matched healthy controls. Compared with resting state, the coupling strength from ABP to Δ [O2Hb] oscillations was significantly lower in the bilateral prefrontal cortex (PFC), sensorimotor cortex (SMC), and temporal lobe cortex (TLC) during the ISPC intervention in cerebral infarction patients in interval II. Additionally, the coupling strength was significantly lower in the bilateral SMC in both groups in interval III. These findings indicate that ISPC intervention may facilitate cerebral circulation in the bilateral PFC, SMC, and TLC in cerebral infarction patients. ISPC may promote motor function recovery through its positive influences on motor-related networks. Furthermore, the coupling between Δ [O2Hb] and ABP allows non-invasive assessments of autoregulatory function to quantitatively assess the effect of rehabilitation tasks and to guide therapy in clinical situations