ObjectiveTo investigate the correlation between electro-acupunctured point Houxi(SI3)and activated brain function areas, contrast the images produced with electroacupuncture at left and right points Houxi and make a comparison with electroacupuncture at point Hegu(LI4)in patients with peripheral facial paralysis.MethodPatients with peripheral facial paralysis, six on the left side and six on the right side, were enrolled as subjects. A scan of the whole brain was taken using fMRI during electroacupuncture stimulation. The images were processed using SPM software. An analysis using at-test (P<0.01) showed differences in brain functional images produced with electroacupuncture at different points.ResultElectroacupuncture at left point Houxi increased the signals of brain regions: rightcaudate nucleus, right cingulate gyrus, right parahippocampal gyrus, right superior temporal gyrus, the brainstem and the cerebellar vermis. Electroacupuncture at right point Houxi increased the signals of brain regions: right medial frontal gyrus, left middle frontal gyrus, left anterior cingulate gyrus, rightcingulate gyrus and right superior temporal gyrus.ConclusionThere is a difference in the image between electroacupuncture at point Houxi and at point Hegu or Dicang(ST4). There is also a larger difference in the image between bilateral points Houxi. The brain regions with high-frequency or low-frequency signals are not consistent. The results further prove the scientificalness of “Point Hegu is indicated for diseasesin the face and mouth” and also show that the cognominal acupoints on the two sides ofthe human body may have some differences, e.g. the conduction pathways are not completely the same and the therapeutic effects are not completely consistent.

Objective To explore the brain changes of electroacupuncturing (EA) different acupoints of peripheral facial paralysis (PFP) with functional magnetic resonance imaging (fMRI). Methods Eighteen patients with left PFP were randomly divided into three groups. Six of them received electroacupuncturing left Dicang, 6 received electroacupuncturing left Hegu, and 6 received electroacupuncturing left Houxi. fMRI data were obtained from scanning of the whole brain. Functional data were processed by SPM99 software and functional responses were established with t-test analysis (P<0.05). Results Electroacupuncturing Dicang and Hegu on the left induced decreasing of signal in bilateral middle frontal gyrus, left cingulate gyrus, signal increased of right precentral gyrus, bilateral postcentral gyrus, left superior temporal gyrus and right insular, while electroacupuncturing Houxi on the left induced decrease of signal in bilateral inferior frontal gyrus, left lentiform nucleus, right middle temporal gyrus, right cerebellar tonsil, signal increased of right caudate head, right cingulate gyrus, brainstem, cerebellar vermis and right parahippocampal gyrus. Conclusion Electroacupunctuing Hegu and Dicang can cause corresponding functional activation in cerebrum, while electroacupuncturing Houxi can not, suggesting that there is association between cerebral and acupoint of owned meridian.

The high-content image-based assay is commonly leveraged for identifying the pheno-typic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,tempera-ture,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(DeepNoise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and DeepNoise achieved an extremely high classification score(accuracy:99.596％),ranking the 2nd place among 866 participating groups.This promising result indicates the success-ful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.