Clostridium Infections ; complications ; Fatal Outcome ; Female ; Hemoglobinuria ; etiology ; Hemolysis ; Humans ; Middle Aged ; Sepsis ; complications ; microbiology

The neurochemical features of the nitrergic and peptidergic innervation of the porcine esophagus were investigated by means of immunohistochemical methods combined with vagotomy. Neuronal cell bodies in both the submucosal and the myenteric plexus (MP) were detected immunoreactivities for nNOS, VIP, GAL, NPY, PACAP, L-ENK, SP, 5-HT and CB, while CGRP- and SOM-immunoreactive (ir) somata were not encountered. In addition, nNOS- and CB-ir myenteric neurons constituted the separate enteric subpopulations.Double immunostainings with a general neuronal marker (PGP9.5 ) and the specific markers, such as nNOS, VIP and SP revealed (1)nNOS-ir myenteric neurons in the porcine esophagus accounted for a higher percentage (63 % ) of all esophageal intrinsic PGP9.5-ir neurons in comparison of VIP-ir (36%) and SP-ir populations (28%); (2) An increasing rostrocaudal gradient in the number of myenteric neurons per ganglion as well as a significantly higher number of enteric ganglia within both plexuses in the abdominal segment; ( 3 ) The densest nerve fibers within the esophageal musculature were VIP-/GAL-/NPY-ir, some of which also co-expressed nNOS and/or PACAP immunoreactivity. The number of L-ENK- and/or SP-ir fibers was significantly higher in lamina muscularis mucosae ( LMM ) than in tunica muscularis externa (TME). In contrast to reports in other species, CGRP-ir fibers within the porcine esophagus constituted a very limited population and were extrinsic; (4) Vagotomy experiments revealed an obvious decrease of PACAP-and 5-HT-ir nerve fibers within the MP,suggesting that these fibers originate from the vagal nerve, while these nNOS- and/or VIP-/GAL-/NPY-ir fibers innervating both the TME and the LMM did not appear to be significantly affected by the vagotomy procedure, possibly being the intrinsic origin.

Objective    To study the effect of Tangeretin on non-small cell lung cancer (NSCLC) and the tumor stemness, and to find the molecular mechanism of its effect. Methods    We used cell counting and cell cloning experiments to study the effect of Tangeretin on the proliferation of NSCLC cells in vitro. The effect of Tangeretin on the invasion of NSCLC cells was detected by transwell assay. We detected the effect of Tangeretin on the proliferation of NSCLC cells in vivo by nude mouse tumor-bearing experiment. The effect of Tangeretin on tumor stemness of NSCLC cells was detected by self-renew assay, and CD133 and Nanog protein expressions. The expressions of PI3K/AKT/mTOR signaling pathway-related proteins were detected by Western blotting (WB). Results    Tangeretin had a good inhibitory effect on the proliferation of NSCLC cells in vivo and in vitro. Cell counting experiment, clonal formation experiment and nude mouse tumor-bearing experiment showed that Tangeretin could inhibit the proliferation activity, clonal formation ability, and tumor size of NSCLC cells in vivo. Self-renew experiments showed that Tangeretin could inhibit the self-renew ability of NSCLC cells. WB experiments showed that Tangeretin inhibited the expressions of tumor stemness markers CD133 and Nanog in NSCLC cells. Tangeretin could inhibit the activation of PI3K/AKT/mTOR signaling pathway-related proteins in NSCLC cells, and the activation of PI3K/AKT/mTOR signaling pathway could partially remit the inhibitory  effect of Tangeretin on tumor stemness of NSCLC cells. Conclusion    Tangeretin can inhibit the tumor stemness of NSCLC cells, which may be related to the regulation of PI3K/AKT/mTOR signaling pathway.

Objective: To investigate the effect of protein ki nase C on signal transduction such as tyrosine phosphorylation, c-fos and c-ju n mRNA expression in antigen activated mast cells. Methods: RBL-2H3 cells either untreated or treated with phorbol 12-myristate 13 -acetate (PMA) were sensitized with anti-DNP IgE, and activated with DNP-BSA, histamine release and tyrosine phosphorylation were quantitatively measured by ELISA and flow cytometry, respectively. The effect of PKC on the ex pression of c-fos and c-jun in serum-deprived RBL-2H3 cells activated by DNP-BSA detected by ethidium staining of PCR-amplified cDNA, the amplified cDNA products were subjected to Southern blot hybridization using specific prob es to determine the veracity of amplification. Results: Tyr osine phosphorylation and histamine release were significantly reduced from (4.4 7±0.03)% to (2.79±0.07)% and (104.47±1.31) nmol/L to (60.75±1.38) nm ol/L, respectively, 45 min after DNP-BSA stimulation in sensitized cells pre treated with PMA for 48 h. Bands of the size predicted for the amplified cDNA we re obtained: 299 bp for c-fos, and 651 bp for c-jun, a decrease of 91% and 82% , respectively, for c-fos and c-jun mRNAs was observed in antigen stimulated c ells pretreated with PMA for 48 h. Conclusion: PKC plays an impo rtant role in modulating the tyrosine phosphorylation and histamine release resp onses and may upregulate the expression of c-fos and c-jun in antigen activate d mast cell.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.
Humans ; Autism Spectrum Disorder/diagnosis* ; Vision, Ocular ; Behavior

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagery, Psychotherapy ; Imagination

Interventional embolization therapy is widely used for procedures such as targeted tumour therapy, anti-organ hyperactivity and haemostasis. During embolic agent injection, doctors need to work under X-ray irradiation environment. Moreover, embolic agent injection is largely dependent on doctors' experience and feelings, and over-injection of embolic agent can lead to reflux, causing ectopic embolism and serious complications. As an effective way to reduce radiation exposure and improve the success rate of interventional embolization therapy, embolic agent injection robot is highly anticipated, but how to decide the injection flow velocity of embolic agent is a problem that remains to be solved. On the basis of fluid dynamics simulation and experiment, we established an arterial pressure-injection flow velocity boundary curve model that can avoid reflux, which provides a design basis for the control of embolic agent injection system. An in vitro experimental platform for injection system was built and validation experiments were conducted. The results showed that the embolic agent injection flow speed curve designed under the guidance of the critical flow speed curve model of reflux could effectively avoid the embolic agent reflux and shorten the embolic agent injection time. Exceeding the flow speed limit of the model would lead to the risk of embolization of normal blood vessels. This paper confirms the validity of designing the embolic agent injection flow speed based on the critical flow speed curve model of reflux, which can achieve rapid injection of embolic agent while avoiding reflux, and provide a basis for the design of the embolic agent injection robot.
Embolization, Therapeutic/methods*

For the detection and identification of abnormal nodular tissues on the body surface, a microwave sensor structure loaded with a spiral resonator is proposed in this paper, a sensor simulation model is established using HFSS software, the structural parameters are optimized, and the actual sensor is fabricated. The S21 parameters of the tissue were obtained when nodules appeared by simulation, and the characteristic relationship between the difference of S21 parameters with position was analyzed and tested experimentally. The results showed that when nodules were present in normal tissues, the curve of S21 parameter difference with position change had obvious inverted bimodal characteristics, and the extreme value of S21 parameter difference appeared when the sensor was directly above the nodules, which was easy to identify the position of nodules. It provides an objective detection tool for the identification of abnormal nodular tissues on the body surface.
Microwaves ; Recognition, Psychology ; Computer Simulation ; Software

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
Animals ; Support Vector Machine ; Whales ; Eye Movements ; Algorithms

There is a shared problem in current optical imaging technologies of how to obtain the optical parameters of biological tissues with complex profiles. In this work, an imaging system for obtaining the optical parameters of biological tissues with complex profile was presented. Firstly, Fourier transformation profilometry was used for obtaining the profile information of biological tissues, and then the difference of incident light intensity at different positions on biological tissue surface was corrected with the laws of illumination, and lastly the optical parameters of biological tissues were achieved with the spatial frequency domain imaging technique. Experimental results indicated the proposed imaging system could obtain the profile information and the optical parameters of biological tissues accurately and quickly. For the slab phantoms with height variation less than 30 mm and angle variation less than 40º, the maximum relative errors of the profile uncorrected optical parameters were 46.27% and 72.18%, while the maximum relative errors of the profile corrected optical parameters were 6.89% and 10.26%. Imaging experiments of a face-like phantom and a human's prefrontal lobe were performed respectively, which demonstrated the proposed imaging system possesses clinical application value for the achievement of the optical parameters of biological tissues with complex profiles. Besides, the proposed profile corrected method can be used to combine with the current optical imaging technologies to reduce the influence of the profile information of biological tissues on imaging quality.
Diagnostic Imaging ; Humans ; Light ; Optical Imaging ; Phantoms, Imaging