Mixed Lineage Kinase Domain-like Protein MLKL Breaks Down Myelin following Nerve Injury.

Successful regeneration of severed peripheral nerves requires the breakdown and subsequent clearance of myelin, tightly packed membrane sheaths of Schwann cells that protect nerve fibers and harbor nerve growth-inhibitory proteins. How Schwann cells initiate myelin breakdown in response to injury is still largely unknown. Here we report that, following sciatic nerve injury, MLKL, a pseudokinase known to rupture cell membranes during necroptotic cell death, is induced and targets the myelin sheath membrane of Schwann cells to promote myelin breakdown. The function of MLKL in disrupting myelin sheaths requires injury-induced phosphorylation of serine 441, an activation signal distinct from the necroptosis-inducing phosphorylation by RIP3 kinase. Mice with Mlkl specifically knocked out in Schwann cells showed delayed myelin sheath breakdown. Lack of MLKL reduced nerve regeneration following injury, whereas overexpression of MLKL accelerated myelin breakdown and promoted the regeneration of axons.

Altered resting-state brain function in endurance athletes.

Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population.

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Spatial Localization of a Transformer Robot Based on Ultrasonic Signal Wavelet Decomposition and PHAT-ß-γ Generalized Cross Correlation.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-ß-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-ß-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

Transformer Discharge Carbon-Trace Detection Based on Improved MSRCR Image-Enhancement Algorithm and YOLOv8 Model.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

HSP-UNet: An Accuracy and Efficient Segmentation Method for Carbon Traces of Surface Discharge in the Oil-Immersed Transformer.

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-ß Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent ß (SCOT-ß) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-ß, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

The association between physical activity and positive affect in adolescents: the chain mediating role of psychological resilience and regulatory emotional self-efficacy.

Previous studies have indicated the positive impact of physical activity on positive affect in adolescents. However, the psychological mechanism is still under-explored. The current study aims to investigate the mediating role of psychological resilience and regulatory emotional self-efficacy on the relationship between physical activity and positive affect. This cross-sectional study recruited 580 adolescents (280 females, 12 to 16 years old). All participants completed the measures of the Physical Activity Questionnaire for Adolescents (PAQ-A), the Positive and Negative Affect Scale (PANAS), the Connor-Davidson Resilience Scale, and the Regulatory Emotional Self-Efficacy Questionnaire. Results showed that the association between physical activity and positive affect was significant in adolescents. Psychological resilience played a mediating role between physical activity and positive affect. The chain mediation effect of psychological resilience and regulatory emotional self-efficacy on the relationship between physical activity and positive affect was significant. These results indicated that physical activity could positively impact adolescents' positive affect through psychological resilience and regulatory emotional self-efficacy. Our findings highlighted the importance of physical activity on positive affect on adolescents, which would benefit the mental health of this population.

Distributed attribute representation in the superior parietal lobe during probabilistic decision-making.

Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1 = 34, nStudy2 = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

Reactivity of the ventromedial prefrontal cortex, but not the amygdala, to negative emotion faces predicts greed personality trait.

This study explored whether amygdala reactivity predicted the greed personality trait (GPT) using both task-based and resting-state functional connectivity analyses (ntotal = 452). In Cohort 1 (n = 83), task-based functional magnetic resonance imaging (t-fMRI) results from a region-of-interest (ROI) analysis revealed no direct correlation between amygdala reactivity to fearful and angry faces and GPT. Instead, whole-brain analyses revealed GPT to robustly negatively vary with activations in the right ventromedial prefrontal cortex (vmPFC), supramarginal gyrus, and angular gyrus in the contrast of fearful + angry faces > shapes. Moreover, task-based psychophysiological interaction (PPI) analyses showed that the high GPT group showed weaker functional connectivity of the vmPFC seed with a top-down control network and visual pathways when processing fearful or angry faces compared to their lower GPT counterparts. In Cohort 2, resting-state functional connectivity (rs-FC) analyses indicated stronger connectivity between the vmPFC seed and the top-down control network and visual pathways in individuals with higher GPT. Comparing the two cohorts, bilateral amygdala seeds showed weaker associations with the top-down control network in the high group via PPI analyses in Cohort 1. Yet, they exhibited distinct rs-FC patterns in Cohort 2 (e.g., positive associations of GPT with the left amygdala-top-down network FC but negative associations with the right amygdala-visual pathway FC). The study underscores the role of the vmPFC and its functional connectivity in understanding GPT, rather than amygdala reactivity.

The regulating effect of boron doping and its concentration on the photocatalytic overall water splitting of a polarized g-C3N5 material.

Photocatalytic overall water splitting with two-dimensional materials is a promising strategy to solve the problems of environmental pollution and energy shortage. However, conventional photocatalysts are often limited to a narrow visible photo-absorption range, low catalytic activity, and poor charge separation. Herein, given the intrinsic polarization facilitating the improvement of photogenerated carrier separation, we adopt a polarized g-C3N5 material combining the doping strategy to alleviate the abovementioned problems. Boron (B), as a Lewis acid, has a great chance to improve the capture and catalytic activity of water. By doping B into g-C3N5, the overpotential for the complicated four-electron process of the oxygen reduction reaction is only 0.50 V. Simultaneously, the B doping-induced impurity state effectively reduces the band gap and broadens the photo-absorption range. Moreover, with the increase of B doping concentration, the photo-absorption range and catalytic activity can be gradually improved. Whereas when the concentration exceeds 33.3%, the reduction potential of the conduction band edge will not meet the demand for hydrogen evolution. Therefore, excessive doping is not recommended in experiments. Our work affords not only a promising photocatalyst but also a practical design scheme by combining polarizing materials and the doping strategy for overall water splitting.

Stimulus transformation into motor action: Dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects.

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging-associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase-locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually-cued finger-tapping task. The network analysis yielded specific age-related network structures varying in time in the low frequencies (2-7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were-temporally arranged in this order-heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization in older subjects.

The Effects of Inter-Set Recovery Time on Explosive Power, Electromyography Activity, and Tissue Oxygenation during Plyometric Training.

Performing continuous sets to failure is fatiguing during the plyometric training. Cluster sets have been used to redistribute total rest time to create short frequent sets so that muscle fatigue can be avoided. The purpose of the study was to investigate the effects of inter-set recovery time on lower extremity explosive power, neuromuscular activity, and tissue oxygenation during plyometric exercise and recovery. An integrated assessment of explosive power, muscle electrical activity, and tissue oxygenation was adopted in the present study to help understand local muscle metabolism and fatigue during plyometric exercise and recovery. Ten university male basketball players participated in this study. Subjects performed 4 groups of exercise, each group comprised of 3 sets of jumps: 1, 2, 3, or 5 min. Surface electromyography (sEMG) signals were collected from 9 lower extremity muscles; near-infrared spectroscopy (NIRS) was recorded on vastus lateralis; mechanical data during plyometric exercise were collected from a force plate. No significant differences among sets and among groups were found regarding explosive power, jump height, EMG intensity, mean power frequency, the rate of tissue saturation index, and HbO2 changes between baseline and recovery. The current study has shown no muscular fatigue induced during the 4 groups of exercise. The results of this study may help inform recommendations concerning the recovery time during plyometric exercises at low loads (30% 1 RM).

Exchange-biased hybrid γ-Fe2O3/NiO core-shell nanostructures: three-step synthesis, microstructure, and magnetic properties.

A two-step solvothermal method combining a calcination process was conducted to synthesize γ-Fe2O3/NiO core-shell nanostructures with controlled microstructure. The formation mechanism of this binary system has been discussed, and the influence of microstructures on magnetic properties has been analyzed in detail. Microstructural characterizations reveal that the NiO shells consisted of many irregular nanosheets with disordered orientations and monocrystalline structures, packed on the surface of the γ-Fe2O3 microspheres. Both the grain size and NiO content of nanostructures increase with the increasing calcination temperature from 300 °C to 400 °C, accompanied by an enhancement of the compactness of NiO shells. Magnetic studies indicate that their magnetic properties are determined by four factors: the size effect, NiO phase content, interface microstructure, i.e. contact mode, area, roughness and compactness, and FM-AFM (where FM and AFM denote the ferromagnetic γ-Fe2O3 and the antiferromagnetic NiO components, respectively) coupling effect. At 5 K, the γ-Fe2O3/NiO core-shell nanostructures display certain exchange bias (HE = 60 Oe) and enhanced coercivity (HC = 213 Oe).

Fast growth of well-aligned ZnO nanowire arrays by a microwave heating method and their photocatalytic properties.

The fast growth of aligned ZnO nanowire arrays with optimized structure is attractive for electrical and optical devices. In this paper, we report a controllable and rapid growth of ZnO nanowire arrays by a microwave-assisted hydrothermal method. When using different zinc salts as the precursors, the morphology of the samples changes a lot and the length growth rate is several times different. The growth mechanism is also investigated. It is found that the solution near neutral pH value is ideal for fast nanowire growth, in which the length of the nanowires increases linearly with growth time and the growth rate is over ten times faster than that in the traditional hydrothermal method. Therefore, aligned ZnO nanowire arrays can grow up to tens of microns in a few hours, while the density and sizes of these nanowires can be well controlled. The ZnO nanowire arrays used as photocatalysts present good photocatalytic performance to the degradation of methyl orange (MO) due to the large surface area. So this paper provides an effective method to obtain vertically aligned ZnO nanowire arrays for practical applications.

Identification and characterization of a novel NAC-like gene in chrysanthemum (Dendranthema lavandulifolium).

KEY MESSAGE: A NAC -like gene named DlNAC1 was identified in chrysanthemum and characterized; it may be involved in regulation of response to abiotic stressors, especially in tolerance to drought and salinity. NAC transcription factors in plants play crucial roles in tolerance to abiotic stressors, and overexpression of the NAC gene in Arabidopsis has been demonstrated to lead to improved drought tolerance. Functions of the NAC genes in chrysanthemum, however, remain poorly understood. In this study, a NAC-like gene named DlNAC1 was identified in chrysanthemum (Dendranthema lavandulifolium) and characterized. Phylogenetic analysis indicated that DlNAC1 contains a typical NAC domain and belongs to the ONAC022 subgroup. According to the subcellular localization and yeast one-hybrid assay, the DlNAC1 protein is localized to nuclei and has a transcription activation ability. Moreover, quantitative real-time PCR analyses showed that DlNAC1 was induced by low-temperature, high-salinity, and drought conditions (separately), but not by abscisic acid (ABA) and heat shock. In these experiments, the downstream genes of NAC transcription factors were found to be up-regulated, including stress-responsive genes KIN1 and AMY1. To further explore the effects of DlNAC1 in response to abiotic stressors, DlNAC1 was overexpressed in tobacco, and these transgenic plants showed significantly enhanced tolerance to drought and salinity. This study suggests that in chrysanthemum, the DlNAC1 gene is involved in regulation of the response to abiotic stressors, especially in tolerance to drought and salinity.

[Expression of osteopontin splice variant and its clinical significance in gastric cancer].

OBJECTIVE: To investigate the expression of osteopontin (OPN) splice variant mRNA, including the three isoforms OPN-A, OPN-B, and OPN-C, to explore its correlation with clinicopathologic features in gastric cancer, and to elucidate their role in tumor invasion and distant metastasis of gastric cancer. METHODS: The expression of OPN-A, OPN-B and OPN-C mRNA were detected by real-time reverse transcriptase-polymerase chain reaction in 66 gastric cancer tissues. The relationship between the expression of OPN-A, OPN-B and OPN-C mRNA and clinicopathologic features of gastric cancer was analyzed. RESULTS: The expression of OPN-C mRNA in the gastric cancer tissue was 3.21-fold higher than that in peritumoral mucosal tissue, showing a significant difference between them (P < 0.001). OPN-C mRNA expression was correlated with the depth of tumor invasion, tumor diameter, lymph node meatastasis, distant meatastasis and had no correlation with differentiation grades. The low expression of OPN-C mRNA was correlated with long survival benefit (P = 0.03). The expression of OPN-A and OPN-B mRNA had no significant relationship with clinicopathologic features of gastric cancer. CONCLUSIONS: One of the isoform of osteopontin (OPN) OPN-C mRNA is overexpressed in gastric cancer. The overexpression of OPN-C mRNA may reflect the progression and is associated with the prognosis of gastric cancer. OPN-C mRNA may have value as a prognostic biomarker in gastric cancer. However, the expression of OPN-A and OPN-B are not correlated with the progression and metastasis of gastric cancer.

Circulating tumor cells correlate with recurrence in stage III small-cell lung cancer after systemic chemoradiotherapy and prophylactic cranial irradiation.

OBJECTIVE: We investigated the correlation between circulating tumor cells and the incidence of brain metastases as a first site of recurrence among patients with small-cell lung cancer after systemic chemoradiotherapy and prophylactic cranial irradiation. In addition, we assessed the contribution of circulating tumor cells for planning the appropriate total dose of prophylactic cranial irradiation for small-cell lung cancer. METHODS: Patients (n = 112) with diagnosed Stage III small-cell lung cancer were treated with four cycles of platinum-based regimen and concurrent chest irradiation, and then prophylactic cranial irradiation. Blood samples for circulating tumor cell analysis were obtained before the initiation of chemotherapy and after the first and fourth cycle of chemotherapy. RESULTS: Circulating tumor cells after the first cycle of chemotherapy correlated with tumor response after completion of chemotherapy (P = 0.012). Patients with brain as the first site suffered a higher rate of further metastases to other organs, and local recurrence, compared with those whose first site was the other organs (P < 0.001), and their survival rates were worse. Circulating tumor cells at baseline were the sole independent prognostic factor for specific progression-free survival. Receiver operating characteristic curves based on median specific progression-free survival revealed a circulating tumor cell cutoff at baseline of 218, and circulating tumor cells ≤218 at baseline correlated with significantly higher progression-free survival (P = 0.007), specific progression-free survival (P = 0.001) and overall survival (P = 0.001). CONCLUSIONS: Circulating tumor cells prior to the initiation of chemotherapy are a valuable predictor of specific progression-free survival in Stage III small-cell lung cancer. For patients with circulating tumor cells >218, prophylactic cranial irradiation at a total dose of 30 Gy in 15 fractions is insufficient.

Genetic instability in patients with pancreatic cancer analyzed by SCARs and electrochemical sensors.

BACKGROUND: Pancreatic cancer is an aggressive disease and the fourth most common cause of cancer death across the globe. It is often not diagnosed until it is advanced. It is necessary to establish a new technology to detect DNA instabilities during the progression of pancreatic cancer and to screen for new molecular markers coupled to putative unknown oncogenes. METHODS: A total of 25 pancreatic cancer tissue specimens were analyzed by sequence-characterized amplified regions (SCARs), including two pathological types (pancreatic ductal adenocarcinoma and neuroendocrine carcinoma). There were 41 random primers and eight long fragment primers used for PCR amplification, and the difference of dNTPs consumptions were detected by nano-electrochemical sensors. Once both dATP and dGTP are significantly different in oxidation current (reduce or increase simultaneously), separate the different genes by electrophoresis, then clone and sequence the genes, and carry out homology analysis. RESULTS: Both dGTP and dATP showed good oxidation behavior on the carbon nanotube modified glassy carbon electrode. There were 32 different fragments in malignant tissues compared with normal control, among them a SNP located in 5'UTR of the leucine zipper protein 4 gene which is significantly correlated with pancreatic cancer (OR = 9.50) and it was confirmed by direct sequencing. CONCLUSIONS: SCARs combined with the nanoelectrochemical sensor can be used for screening genetic instabilities in pancreatic cancer, and leucine zipper protein 4 was a novel pancreatic cancer-related gene.

Positive effects of brisk walking and Tai Chi on cognitive function in older adults: An fNIRS study.

Exercise has shown to have beneficial effects on cognition in older adults. The purpose of this study was to investigate the cortical hemodynamic responses during the word-color Stroop test (WCST) prior and after acute walking and Tai Chi exercise by functional near-infrared spectroscopy (fNIRS). Twenty participants (9 males, mean age 62.8 ± 5.2), first underwent a baseline WCST test, after which they took three WCST tests in a randomized order, (a) after sitting rest (control), (b) after 6 minutes performing Tai Chi Quan, and (c) after a bout of 6 minutes brisk walking. During these four WCST tests cortical hemodynamic changes in the prefrontal area were monitored with fNIRS. Both brisk walking and Tai Chi enhanced hemodynamic activity during the Stroop incongruent tasks, leading to improved cognitive performance (quicker reaction time). Brisk walking induced a greater hemodynamic activity in the right dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) area, whereas Tai Chi induced a greater bilateral hemodynamic activity in the DLPFC and VLPFC areas. The present study provided empirical evidence of enhanced hemodynamic response in task- specific regions of the brain that can be achieved by a mere six minutes of brisk walking or Tai Chi in older adults.