Abstract: Objective To analyze the correlation between the thromboelastography (TEG) indexes and the indexes related to liver injury in patients with heat stroke, and explore the diagnostic value of TEG indexes for liver injury in patients with heat stroke. Methods A total of 95 patients with exertional heat stroke (EHS) admitted to 924 Hospital of the Joint Service Support Force of the People's Liberation Army of China from August 2020 to July 22 were selected, and divided into a non-liver injury group (55 cases) and a liver injury group (40 cases) according to whether there was liver injury. TEG instrument was used for the detection of thromboelastography to record the TEG parameters, including reaction time (R), agglutination time (K), α angle, maximum amplitude (MA value), and coagulation complex index (CI). The levels of glutamic transaminase (AST), alanine aminotransferase (ALT), total bilirubin (TBil), albumin (ALB) were detected by automatic biochemical analyzer. Pearson's method was applied to analyze the correlation between thromboelastography indexes R, K, α angle, CI and liver function indexes AST, ALT, TBil, ALB in patients with heat stroke after liver injury. Receiver operating characteristic curve (ROC) was applied to analyze the predictive value of thromboelastography indexes R, K, α angle, CI and combined detection for liver injury in patients with heat stroke. Results Compared with the non-liver injury group, the AST, ALT and TBil levels in patients with heat stroke in the liver injury group were higher (t=26.174, 16.923, 18.414, P<0.05), while the ALB level was lower (t=24.596, P<0.05); compared with the non-liver injury group, the R and K of patients with heat stroke in the liver injury group were higher (t=58.014, 52.862, P<0.05), and the α angle and CI were lower (t=46.853, 60.717, P<0.05); R was positively correlated with AST and ALT (r=0.532, 0.610, P<0.001), and negatively correlated with ALB (r=-0.551, P<0.001) in patients with heat stroke complicated with liver injury; K was positively correlated with AST, ALT and TBil (r=0.661, 0.531, 0.504, P<0.001); α angle was negatively correlated with AST and ALT (r=-0.473, -0.448, P<0.01), and positively correlated with ALB (r=0.539, P<0.001); CI was negatively correlated with AST, ALT and TBil (r=-0.458, -0.505, -0.549, P<0.001); the area under the curve (AUC) of thromboelastography indexes R, K, α angle and CI in predicting liver injury in patients with heat stroke was 0.807 (sensitivity of 70.0%, specificity of 81.6%), 0.831 (sensitivity of 77.5%, specificity of 85.5%), 0.747 (sensitivity of 67.5%, specificity of 74.5%), and 0.788 (sensitivity of 77.5%, specificity of 83.6%), respectively. The AUC of combined detection to predict liver injury in patients with heat stroke was 0.967 (sensitivity of 92.5%, specificity of 91.9%). Conclusions The thromboelastography indexes are correlated with the indexes related to liver injury in patients with heat stroke, and the thromboelastography indexes are helpful to diagnose liver injury in patients with heat stroke.

Objective:To confirm the relative humidity conditions of preparation of Hericium Erinaceus and Lumbricus ( HD) bio-transformation and the longest operation time under the conditions through the study on hygroscopicity of dry extract powder. Methods:The solution was prepared respectively in the environment with various humidity, and the moisture absorption rate and critical relative humidity ( CRH) of dry extract powder of HD biotransformation were measured, and then the mathematical model was built and the curves of moisture percentage vs relative humidity was drawn. The parameters of moisture absorption rate, and the relative humidity and operation time with the moisture percentage up to 5% were calculated. The analytic geometry and linear regression method were used to calculate CRH, so as to determine the air humidity control range and the conditions for the production and storage process. Results:The moisture absorption of HD dry extract powder could reach 5% when exposed to the air respectively with the relative humidity of 66. 0%, 80. 8%, 88. 2% and 99. 0% for 7. 088, 3. 953, 2. 892 and 0. 661 h. When the moisture percentage reached 5%, the rela-tive humidity and the shortest time was 50. 84% and 9. 937 h, respectively. The CRH of the dry extract powder of HD biotransforma-tion was 68. 12%. Conclusion:When the dry extract powder of HD biotransformation is under preparation, the relative humidity of en-vironment should be controlled below 50. 84% with the operation time shorter than 9. 937 h, or the relative humidity is controlled below 68. 12% with the operation time shorter than 6. 810 h. Under the above conditions, the moisture absorption rate of HD dry extract pow-der can be controlled below 5%, which does not affect the preparation.

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SDROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P＜0.05),with a decrease of ED for approximately 82.66％.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42％level but raised to 95.76％level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SDROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P＜0.05),whereas no significant difference was found between the former two(both adjusted P＞0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.