In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved "You Only Look Once" 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ‌were introduced‌ into the plasma datasets, and ‌the average accuracy of the final classification reached 98.7%‌. The results of this paper's experiment were obtained through the combination of several key algorithm modules including‌ omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ‌and‌ re-parameterization convolution. The method of this paper‌ obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
Algorithms ; Humans ; Hemolysis ; Plasma ; Deep Learning ; Image Processing, Computer-Assisted/methods*

Objective : To analyze interacting proteins of tropomodulin1 (TMOD1 ) in Raw264．7 mouse monocyte macrophage line by mass spectrometry and GeneCards database． Methods : Immunoprecipitation combined with mass spectrometry was used to find interacting proteins of TMOD1 after overexpress TMOD1 in Raw264．7 cells. GeneCards database was used to search for known genes for macrophage migration．Bioinformatics ＆ Systems Biolo- gy was used to analyze correlation between known targets and mass spectrometry proteins to find common differenti- ally expressed proteins( CO-DEPs) ．WoLF PSORT was used to predict subcellular localization of CO-DEPs．Egg- NOG databasewas used to analyze eukaryotic orthologous group(KOG) of CO-DEPs．DAVID database was used to analyze gene ontology( GO) enrichment kyoto encyclopedia of genes and genomes( KEGG) pathway of CO-DEPs. String database was used to analyze protein interaction network and CytoScape software drawing. Results : There were 41 CO-DEPs in mass spectrometry and GeneCards database．Subcellular localization of CO-DEPs was mainly distributed in cytoplasm，nucleus and mitochondria．KOG notes were mainly O : post-translational modification，Z : cytoskeleton and J : translation．GO enrichment found that CO-DEPs was mainly involved in poly (A) RNA bind- ing，protein folding and focal adhesion．KEGG was mainly enriched in arrhythmogenic right ventricular cardiomyop- athy (ARVC) and tight junction．ACTB was a protein with large protein interaction． Conclusion The proteins in- teracting with TMOD1 in macrophages mainly include myosin heavy chain-9 (MYH9) ，α-actinin 1 (ACTN1) and β-actin (ACTB) ，etc，suggesting that TMOD1 is related to macrophages migrate.

Objective To investigate the effects of remifentanil on sinoatrial (SA) node autorhythmicity in rabbits.Methods Twenty-four healthy rabbits of both sexes weighing 1.8-2.2 kg were sacrificed.Their hearts were removed and sinoatrial nodes were dissected and placed in Tyrode solution saturated with 95 ％ O2-5 ％ CO2 at 36 ℃.The action potentials of the sinus node pacemaker cells were recorded by intracellular glass microelectrode technique.The experiment was performed in 3 parts (n =8 each).Part Ⅰ:the sinoatrial node was exposed to remifentanil 2,4,8,16 and 32 ng/ml respectively.The action potentials were recorded after the sinoatrial nodes were exposed to each concentration of remifentanil for 15 min.Part Ⅱ and Ⅲ:the sinoatrial nodes were first exposed to Ca2+ channel agonist Bay K8644 0.5 μmol/L or K+ channel blocker TEA 20 nmol/L for 15 min.Then remifentanil was added until the concentration reached 16 ng/ml (final concentration) and 15 min later the action potentials were recorded.The action potential parameters included,amplitude of action potential (APA),rate of pacemaker firing (RPF),action potential duration at 90％ repolarization (APD90) and velocity of diastolic depolarization (VDD).Results Remifentanil significantly decreased,APA,RPF,VDD and prolonged APD90 in a concentration dependent manner as compared with the baseline values.Pretreatment with Bay K8644 could block the effects of remifentanil on SA node pacemaker cells,while TEA did not affect the electrophysiologic effects of remifentanil on SA node pacemaker cells.Conclusion Remifentanil exerts a negative chronotropic action on SA node pacemaker cells.These effects are likely produced by decrease in Ca2+ current,while opening of K + channels is not involved in these effects.