Objective To explore the efficacy of extracting biopsy needle pose in chest CT images based on point cloud processing.Methods Three-dimensional point clouds were generated through segmentation of chest CT images and surface reconstruction.Spatial point cloud clustering and geometric constraints were applied to filter regions contained the puncture needle in space.The principal direction of the needle was judged using principal component analysis,and a cylindrical model was constructed to enclose the needle data.Then random sample consensus algorithm was used for needle trajectory fitting to accurately extract the spatial position and orientation of the puncture needle.The efficacy of the above method was evaluated using a 3D-printed anatomical model based on common clinical combinations of puncture depths and angles.Results The anatomical model experiments showed a 100％success rate in puncture needle identification,with angular error of(1.013±0.424)° and positional error of(2.023±1.553)mm,indicating that this method had good accuracy and stability.Conclusion The puncture needle's position in chest CT images could be extracted with high precision based on point cloud processing.

Objective:To investigate the feasibility of flight fatigue being detected via photoplethysmography (PPG) and regional cerebral oxygen saturation (rScO 2) in order to address the challenges posed by flight fatigue during prolonged or multiple consecutive flights. Methods:A total of 16 healthy male volunteers were enrolled. A wireless cerebral oximetry monitor headband was employed to collect PPG and rScO 2 data from the forehead while a multi-lead physiological data acquisition system was used concurrently to record three-lead electrocardiograms (ECGs). After 18 h of sleep deprivation, each volunteer performed a flight-simulating task, which was divided into 4 stages: the baseline period (T1), relaxation period (T2), early fatigue period (T3) and severe fatigue period (T4). Five-minute data was collected from each stage for analysis using AcqKnowledge 6.0. Heart rate (HR) and 3 HR variability (HRV) metrics, namely standard deviation of NN intervals (SDNN), root mean square of successive differences (RMSSD), and low frequency to high frequency power ratio (LF/HF), were computed independently from both ECG and PPG traces. The mean rScO 2 value for each stage was used to represent the cerebral oxygen saturation during that stage. The intra-class correlation coefficient (ICC) was employed to assess the consistency of the measurements, and the differences in HR and HRV indicators of the volunteers in the 4 stages of the experiment were analyzed. Results:The HR measured by ECG and PPG was highly consistent across the 4 stages among the 14 volunteers ( ICC=0.951, 0.963, 0.962, 0.963, P=0.013, 0.011, 0.021, 0.015), so were SDNN, RMSSD and LF/HF values ( ICC=0.935-0.983, all P<0.05). HR values calculated with either method showed significant differences across the 4 stages in the 14 volunteers ( F=21.63, 20.52, P=0.007, 0.008). HR gradually declined from T1 to T4, and was significantly lower at T4 than at T1 ( P=0.011, 0.009). There were significant differences in SDNN ( F=22.31, 24.26, P=0.006, 0.003), RMSSD ( F=22.30, 22.26, P=0.006, 0.006), and LF/HF ( F=20.37, 25.13, P=0.009, 0.002) across the 4 stages among the 14 volunteers. SDNN and RMSSD kept increasing as fatigue was intensified, while LF/HF decreased correspondingly. Statistically significant differences were found in SDNN, RMSSD and LF/HF values between T4 and T1 (all P<0.01). rScO 2 measured during the flight-simulating trial also differed significantly across the 4 stages ( F=21.39, P=0.007). rScO? at both T3 and T4 was significantly lower than at T1 ( P=0.009, 0.007). Conclusions:PPG can replace ECG for monitoring HR and HRV indicators under flight fatigue. Furthermore, the combination of PPG with rScO 2 monitoring allows for earlier detection of flight fatigue. This study is expected to offer a user-friendly and non-invasive approach to management of pilot fatigue.

Objective To explore the efficacy of extracting biopsy needle pose in chest CT images based on point cloud processing.Methods Three-dimensional point clouds were generated through segmentation of chest CT images and surface reconstruction.Spatial point cloud clustering and geometric constraints were applied to filter regions contained the puncture needle in space.The principal direction of the needle was judged using principal component analysis,and a cylindrical model was constructed to enclose the needle data.Then random sample consensus algorithm was used for needle trajectory fitting to accurately extract the spatial position and orientation of the puncture needle.The efficacy of the above method was evaluated using a 3D-printed anatomical model based on common clinical combinations of puncture depths and angles.Results The anatomical model experiments showed a 100％success rate in puncture needle identification,with angular error of(1.013±0.424)° and positional error of(2.023±1.553)mm,indicating that this method had good accuracy and stability.Conclusion The puncture needle's position in chest CT images could be extracted with high precision based on point cloud processing.

Objective:To investigate the feasibility of flight fatigue being detected via photoplethysmography (PPG) and regional cerebral oxygen saturation (rScO 2) in order to address the challenges posed by flight fatigue during prolonged or multiple consecutive flights. Methods:A total of 16 healthy male volunteers were enrolled. A wireless cerebral oximetry monitor headband was employed to collect PPG and rScO 2 data from the forehead while a multi-lead physiological data acquisition system was used concurrently to record three-lead electrocardiograms (ECGs). After 18 h of sleep deprivation, each volunteer performed a flight-simulating task, which was divided into 4 stages: the baseline period (T1), relaxation period (T2), early fatigue period (T3) and severe fatigue period (T4). Five-minute data was collected from each stage for analysis using AcqKnowledge 6.0. Heart rate (HR) and 3 HR variability (HRV) metrics, namely standard deviation of NN intervals (SDNN), root mean square of successive differences (RMSSD), and low frequency to high frequency power ratio (LF/HF), were computed independently from both ECG and PPG traces. The mean rScO 2 value for each stage was used to represent the cerebral oxygen saturation during that stage. The intra-class correlation coefficient (ICC) was employed to assess the consistency of the measurements, and the differences in HR and HRV indicators of the volunteers in the 4 stages of the experiment were analyzed. Results:The HR measured by ECG and PPG was highly consistent across the 4 stages among the 14 volunteers ( ICC=0.951, 0.963, 0.962, 0.963, P=0.013, 0.011, 0.021, 0.015), so were SDNN, RMSSD and LF/HF values ( ICC=0.935-0.983, all P<0.05). HR values calculated with either method showed significant differences across the 4 stages in the 14 volunteers ( F=21.63, 20.52, P=0.007, 0.008). HR gradually declined from T1 to T4, and was significantly lower at T4 than at T1 ( P=0.011, 0.009). There were significant differences in SDNN ( F=22.31, 24.26, P=0.006, 0.003), RMSSD ( F=22.30, 22.26, P=0.006, 0.006), and LF/HF ( F=20.37, 25.13, P=0.009, 0.002) across the 4 stages among the 14 volunteers. SDNN and RMSSD kept increasing as fatigue was intensified, while LF/HF decreased correspondingly. Statistically significant differences were found in SDNN, RMSSD and LF/HF values between T4 and T1 (all P<0.01). rScO 2 measured during the flight-simulating trial also differed significantly across the 4 stages ( F=21.39, P=0.007). rScO? at both T3 and T4 was significantly lower than at T1 ( P=0.009, 0.007). Conclusions:PPG can replace ECG for monitoring HR and HRV indicators under flight fatigue. Furthermore, the combination of PPG with rScO 2 monitoring allows for earlier detection of flight fatigue. This study is expected to offer a user-friendly and non-invasive approach to management of pilot fatigue.

Objective To investigate the expression of some genes in Pomacea canaliculata infected with Angiostrongylus cantonensis, so as to provide insight into the preliminary understanding of the interactions between Angiostrongylus cantonensis and its intermediate host Pomacea canaliculata. Methods P. canaliculata was fed with rat faces containing the first-stage larvae of A. cantonensis. Three to five P. canaliculata was sampled 1, 10 days and 20 days after feeding, and the hemolymph, hepatopancreas, kidney, intestinal tract, head-foot and gill tissues were collected, while uninfected P. canaliculata served as controls. Total RNA was extracted from various tissues of P. canaliculata at different time points post-infection, and transcribed reversely into cDNA. Based on previous transcriptome sequencing results, 10 genes associated with immune defense, signal transduction, cell growth and metabolism, stress response were selected, and the gene expression was determined in the hemolymph tissues of P. canaliculata 1, 10 days and 20 days post-infection with A. cantonensis using real-time fluorescent quantitative PCR assay, and the α-tubulin gene expression was quantified in the hepatopancreas, kidney, head-foot, intestinal tract and gill tissues of P. canaliculata infected with A. cantonensis. Results Higher CELA1 gene expression was detected in the infection group than in the control group 1 (t = 12.32, P < 0.05), 10 days (t = 23.51, P < 0.05) and 20 days post-infection (t = 34.92, P < 0.05), and the CELA1 expression increased with the time of infection. The GST gene expression was (7.26 ± 1.80) times higher in the infection group than in the control group 1 day post-infection, and was significantly lower in the infection group than in the control group 10 days (t = 23.89, P < 0.05) and 20 days post-infection (t = 19.83, P < 0.05). Higher ferritin gene expression was found in the infection group than in the control group 10 days post-infection (t = 32.76, P < 0.05), and higher CRT gene expression was seen in the infection group than in the control group 1 (t = 7.23, P < 0.05), 10 days (t = 5.78, P < 0.05) and 20 days post-infection (t = 6.32, P < 0.05). In addition, the greatest α-tubulin gene expression was observed in the the hepatopancreatic tissues of P. canaliculata (F = 17.58, P < 0.05), and the α-tubulin gene expression altered in various tissues of P. canaliculata post-infection with A. cantonensis, with the most remarkable reduction of α - tubulin gene expression seen in the hepatopancreatic tissues (P < 0.05). Conclusions Following A. cantonensis infection in P. canaliculata, the expression of multiple genes is altered, and the expression of α-tubulin gene is inhibited in multiple tissues. The findings provide a basis for the further elucidation of the interactions between P. canaliculata and A. cantonensis.