Transplantation of autologous peripheral blood mononuclear cells in the subarachnoid space for amyotrophic lateral sclerosis:a safety analysis of 14 patients

There is a small amount of clinical data regarding the safety and feasibility of autologous peripheral blood mononuclear cell transplantation into the subarachnoid space for the treatment of amyotrophic lateral sclerosis.The objectives of this retrospective study were to assess the safety and efficacy of peripheral blood mononuclear cell transplantation in 14 amyotrophic lateral sclerosis patients to provide more objective data for future clinical trials.After stem cell mobilization and collection,autologous peripheral blood mononuclear cells（1 × 109） were isolated and directly transplanted into the subarachnoid space of amyotrophic lateral sclerosis patients.The primary outcome measure was incidence of adverse events.Secondary outcome measures were electromyography 1 week before operation and 4 weeks after operation,Functional Independence Measurement,Berg Balance Scale,and Dysarthria Assessment Scale 1 week preoperatively and 1,2,4 and 12 weeks postoperatively.There was no immediate or delayed transplant-related cytotoxicity.The number of leukocytes,serum alanine aminotransferase and creatinine levels,and body temperature were within the normal ranges.Radiographic evaluation showed no serious transplant-related adverse events.Muscle strength grade,results of Functional Independence Measurement,Berg Balance Scale,and Dysarthria Assessment Scale were not significantly different before and after treatment.These findings suggest that peripheral blood mononuclear cell transplantation into the subarachnoid space for the treatment of amyotrophic lateral sclerosis is safe,but its therapeutic effect is not remarkable.Thus,a large-sample investigation is needed to assess its efficacy further.

Spontaneous conus infarction with"snake-eye appearance"on magnetic resonance imaging:A case report and literature review

BACKGROUND Infarction of the conus medullaris is a rare form of spinal cord infarction.The first symptom is usually acute non-characteristic lumbar pain,followed by lower limb pain,saddle numbness,fecal incontinence,and sexual dysfunction.Spontaneous conus infarction with"snake-eye appearance"on magnetic resonance imaging has rarely been reported.CASE SUMMARY We report a 79-year-old male patient with spontaneous conus infarction who had acute lower extremity pain and dysuria as the first symptoms.He did not have any recent history of aortic surgery and trauma.Magnetic resonance imaging revealed a rare"snake-eye appearance."In addition,we reviewed the literature on 23 similar cases and summarized the clinical features and magnetic resonance manifestations of common diseases related to the"snake-eye sign"to explore the etiology,imaging findings,and prognosis of spontaneous conus infarction.CONCLUSION We conclude that acute onset of conus medullaris syndrome combined with"snake-eye appearance"should be strongly suspected as conus medullaris infarction caused by anterior spinal artery ischemia.This special imaging manifestation is helpful in the early diagnosis and treatment of conus infarction.

Study of Human Action Recognition Based on Improved Spatio-temporal Features

Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.

Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3

Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.