PURPOSE: To judge the injury mode and injury severity of the real human body through the measured values of anthropomorphic test devices (ATD) injury indices, the mapping relationship of lumbar injury between ATD and human body model (HBM) was explored. METHODS: Through the ATD model and HBM simulation, the mapping relationship of lumbar injury between the 2 subjects was explored. The sled environment consisted of a semi-rigid seat with an adjustable seatback angle and a 3-point seat belt system with a seatback-mounted D-ring. Three seatback recline states of 25°, 45°, and 65° were designed, and the seat pan angle was maintained at 15°. A 23 g, 47 km/h pulse was used. The validity of the finite element model of the sled was verified by the comparison of ATD simulation and test results. ATD model was the test device for human occupant restraint for autonomous vehicles (THOR-AV) dummy model and HBM was the total human model for safety (THUMS) v6.1. The posture of the 2 models was adjusted to adapt to the 3 seat states. The lumbar response of THOR-AV and the mechanical and biomechanical data on L1 - L5 vertebrae of THUMS were output, and the response relationship between THOR-AV and THUMS was descriptive statistically analyzed. RESULTS: Both THOR-AV and THUMS were submarined in the 65° seatback angle case. With the change of seatback angle, the lumbar spine axial compression force (F_z) of THOR-AV and THUMS changed in the similar trend. The maximum F_z ratio of THOR-AV to THUMS at 25° and 45° seatback angle cases were 1.6 and 1.7. The flexion moment (M_y) and the time when the maximum M_y occurred in the 2 subjects were very different. In particular, the form of moment experienced by the L1 - L5 vertebrae of THUMS also changed. The changing trend of M_y measured by THOR-AV over time can reflect the changing trend of maximum stress of L1 and L2 of THUMS. CONCLUSION The F_z of ATD and HBM presents a certain proportional relationship, and there is a mapping relationship between the 2 subjects on F_z. The mapping function can be further clarified by applying more pulses and adopting more seatback angles. It is difficult to map M_y directly because they are very different in ATD and HBM. The M_y of ATD and stress of HBM lumbar showed a similar change trend over time, and there may be a hidden mapping relationship.
Humans ; Lumbar Vertebrae/injuries* ; Finite Element Analysis ; Biomechanical Phenomena ; Manikins ; Spinal Injuries/physiopathology*

Eighteen compounds were isolated from the methanol extract of the fruits of Litsea cubeba by silica gel, ODS, Sephadex LH-20 column chromatography, semi-preparative RP-HPLC, chiral HPLC and recrystallization. Their structures were elucidated by spectroscopic analyses and by comparison with reported spectroscopic data and physicochemical properties, and the absolute configurations of the enantiomers were established by experimental and calculated electronic circular dichroism spectra. These compounds were identified as (+)-(R)-4-hydroxypiperitenone (1a), (-)-(S)-4-hydroxypiperitenone (1b), (3S,4S,6R)-3,6-dihydroxy-1-menthene (2), (4S,5R)-4-hydroxy-5-isopropyl-2-methylcyclohex-2-en-1-one (3), (R)-6-hydroxy-3-(2-hydroxypropan-2-yl)-6-methylcyclohex-2-enone (4), (4S,6R)-4-hydroxy-6-isopropyl-3-methylcyclohex-2-enone (5), (1R,3S,4R)-3-hydroxy isopulegol (6), subamone (7), (6S)-3,7-dimethyl-7-hydroxy-2(Z)-octen-6-olide (8), (6S)-6,7-dihydroxy-3,7-dimethyloct-2(Z)-enoate (9), holostylactone (10), sesamin (11), dimethylmatairesinol (12), p-hydroxybenzoylcarbinol (13), syringaldehyde (14), p-hydroxybenzaldehyde (15), 4-hydroxy-3-methoxybenzaldehyde (16), 5,4ʹ-dihydroxy-7-methoxyflavone (17). Among them, compounds 1a and 1b were a pair of new monoterpenoid enantiomers, 8 and 9 were new natural products, 2-7, 10, 11 and 17 were isolated from Litsea genus for the first time. The in vitro anti-inflammatory effects of compounds 1-17 were evaluated using lipopolysaccharide-stimulated RAW264.7 cells, the results showed that compound 14 exhibited significant anti-inflammatory activity with NO inhibitory rate of 66.27% at a concentration of 40 μmol·L^-1.

Objective:To study the correlation between the number of hemophagocytes and peripheral blood cells in bone marrow of patients with fever of unknown origin.Methods:A total of 465 patients with fever of unknown origin in our hospital from January 2019 to December 2021 were selected as the research objects,which was to reviewed retrospectively the correlation between the number of hemophagocytes and peripheral blood cells in bone marrow.Results:The positive rates of hemophagocytes detected in the three lines decreased group,the two lines decreased group,the one line decreased group,normal group of the three lines and at least one of the three lines increased group were 86.4％,62.1％,38.3％,34.6％and 33.3％,respectively.The number of hemophagocytes per unit area in the three lines decreased group was significantly higher than that in the other four groups(P＜0.001).The number of hemophagocytes per unit area in the two lines decreased group was higher than that in the one line decreased group,normal group of three lines and at least one of the three lines increased group(P＜0.01).There was no significant difference in the number of hemophagocytes per unit area between the group with a decreased number of one line and the other two groups with a normal number of three lines and the group with at least one increased number of three lines(P＞0.05).The missed rates of hemophagocytes in the five groups were 15.78％,22.03％,62.22％,77.78％and 53.84％,respectively.Conclusion:For patients with fever of unknown origin,especially those with obvious decrease in the number of three lines and two lines in peripheral blood cells,which should pay attention to the detection of hemophagocytes in bone marrow.Meanwhile,if the number of three lines was normal even at least one of the three lines increased,the presence of hemophagocytes in the bone marrow slice should be also carefully observed.

Objective To explore the improve mechanism of ultra-filtration extract from Angelica Sinensis Radix and Hedysari Radix on kidney injury in diabetic kidney disease(DKD)rats.Methods The diabetes model was prepared by intraperitoneal injection of streptozotocin,and then fed with high sugar and high fat diet.The rats with successful DKD were randomly divided into model group,positive control group and experimental-L,-M,-H groups with 8 cases per group.Additionally,selected 8 Wistar rats as the blank group.The experimental-L,-M,-H groups were given 1.5,3.0 and 6.0 g·kg-1 ultra-filtration extract from Angelica Sinensis Radix and Hedysari Radix by gavage,respectively.The positive control group was given 1.75 × 10-3 g·kg-1irbesartan suspension by gavage.The blank and model groups were given equal volume of pure water by gavage.Six groups were administrated once a day for 12 consecutive weeks.The 24 h-urinary total protein were detected by coomassie brilliant blue method.The protein expression levels of nuclear factor erythroid 2-related factor 2(Nrf2),heme oxygenase-1(HO-1)and andacyl-CoA synthetase long chain family member 4(ACSL4)in kidney tissue were detected by Western blot.Results The 24 h-UTP in the experimental-M,-H groups,positive control group,model group and blank group were(47.70±3.85),(43.57±6.38),(36.80±6.52),(64.34±13.38)and(7.58±3.71)mg;the relative expression levels of Nrf2 protein were 0.86±0.08,0.75±0.06,0.64±0.08,1.09±0.06 and 0.60±0.07;the relative expression levels of HO-1 protein were 0.77±0.04,0.63±0.07,0.47±0.05,1.04±0.06 and 0.34±0.07;the relative expression levels of ACSL4 protein were 0.62±0.07,0.55±0.07,0.46±0.06,1.08±0.07 and 0.30±0.01,respectively.Compared with the model group,the above indexes in the experimental-M,-H groups and positive control group were significantly different(P＜0.05,P＜0.01).Conclusion Ultra-filtration extract from Angelica Sinensis Radix and Hedysari Radix can improve the damage of kidney tissue in DKD rats,and the mechanism may be related to ferroptosis caused by excessive activation of Nrf2/HO-1 signaling pathway.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.