Objective The Kellinghaus grading method was used to manually read and grade the thin-layer CT of sternal end of clavicle,and a variety of traditional statistical methods as well as machine learning methods were used to construct age estimation models for adolescents and adults in early adulthood,to explore the value of the application of machine learning technology in the study of age estimation of the Han Chinese population in Sichuan.Methods Thin-section CT images of the chest were retrospectively collected from 491 individuals aged 10～30 years,and the collected samples were assigned a reading grade with reference to the Kellinghaus grading method.10%of the xases were randomly selected as the test set,and the remaining data were used as the training set to construct a variety of traditional statistical regression models and machine learning models for estimating the age of adolescents and adults in early adulthood,and the performance of the models was evaluated by using the mean absolute error(MAE).Results The statistical regression model with the best efficacy was the cubic regression model,with an MAE value of 1.34 for males and 1.57 for females;of the three machine learning models,the Random Forest model had the best predictive efficacy for males,with an MAE value of 1.39,and the Support Vector model had the best predictive efficacy for females,with an MAE value of 1.51.Conclusion In the construction of age estimation models for sternal end of clavicle,the machine learning model has a certain improvement in the accuracy of age prediction,but there is no obvious advantage compared with the traditional statistical regression model,and the use of the machine learning method in age estimation based on sternal end of clavicle still needs further exploration.

The network of laughter/smile production and propagation in the brain is not yet fully understood.In this paper,we report two cases of medically refractory epilepsy patients with stereotactic EEG implantation,in which smiles(without pleasurable emotions and motor awareness)and laughter(with situationally incompatible pleasurable emotions)were repeatedly induced by electrical stimulation in the left precentral gyrus,and the right insular short gyrus,respectively.This phenomenon reflects the existence of distinct and linked emotional and behavioral networks for laughter.

Humans ; Male ; Middle Aged ; Multiple Myeloma ; complications ; diagnosis ; Spondylitis, Ankylosing ; complications

Objective To develop a deep learning method for volumetric assessment of traumatic intracerebral hemorrhage(TICH)using the Trans-UNet model and to compare its performance with traditional formula-based methods.Methods CT data from 141 TICH patients admitted to Army Medical Center of PLA between May 2018 and May 2023 were collected.A deep learning method based on the Trans-UNet model was established.Manual delineation via picture archiving and communication system(PACS)was served as the gold standard for comparing the accuracy,consistency,and time efficiency of our method against 10 different formula-based methods for measuring the amount of TICH.Results The median volume of TICH,as manual delineation via PACS,was 1.167 mL,with a median measurement time of 135 s per patient.The median percentage error in volume between the deep learning method and manual delineation via PACS was 3.59％.Spearman correlation coefficient was 0.999(P＜0.001),and a median measurement time was only 4.38 s per patient.In contrast,in the formula-based methods,the lowest median percentage error in volume was 16.451％,the highest Spearman correlation coefficient was 0.986(P＜0.001),and the lowest median measurement time was 20 s for a single patient.The statistical differences were observed in percentage error in volume and measurement time between the 2 types of methods(all P＜0.001).Conclusion Our developed deep learning method for volumetric assessment of TICH is superior to the formula-based methods in terms of measurement accuracy and time efficiency.

OBJECTIVE To explore the potential mechanism of ketamine-induced cognitive impairment in mice based on metabolomics. METHODS Male C57BL/6 mice were randomly divided into control group and ketamine group （25 mg/kg）， with 12 mice in each group. Each group of mice was intraperitoneally injected with normal saline or corresponding drugs， 4 times a day， for 10 consecutive days. On the last 2 days of drug administration， the cognitive behavior was evaluated by Y maze and novel object recognition test， and the histopathological changes in the prefrontal cortex （PFC） were observed. Ultra-high performance liquid chromatography-tandem mass spectrometry technology was used to analyze the changes of metabolites in PFC， screen for differential metabolites， and perform pathway enrichment analysis. RESULTS Compared with the control group， the morphology of PFC neurons in the ketamine group of mice was inconsistent. There were cavities around the nucleus， and the number of deeply stained cells increased. The mean optical density value of the Nissl staining positive area was significantly reduced， and the alternation rate and discrimination index were significantly reduced （P＜0.05 or P＜0.01）. In the PFC tissue samples of mice of the two groups， there were a total of 114 differential metabolites， including 73 up-regulated and 41 down-regulated metabolites， including glutamine， succinic acid， ketoglutarate， and choline， etc. The differential metabolites mentioned above were mainly enriched in metabolism of alanine， aspartate and glutamate， metabolism of arginine and proline， γ aminobutyric acid synapses， pyrimidine metabolism， cholinergic synapses pathways， etc. CONCLUSIONS Ketamine can induce cognitive impairment in mice. Its neurotoxicity is related to abnormal synaptic transmission and energy metabolism， and neuroimmune regulation disorders.