Comparison of Serum Metabolite Changes of Radiated Mice Administered with Panax quinquefolium from Different Cultivation Regions Using UPLC-Q/TOF-MS Based Metabolomic Approach

Chemometric analysis of bioactive compounds revealed that American ginsengs (AGs) from different cultivation regions of China had a difference in quality, which indicates their possible pharmacological difference. A UPLC-Q/TOF-MS-based untargeted metabolomic approach was used to uncover serum metabolite changes in radiated mice pre-administered with AG root decoctions from seven cultivation regions and to further assess their quality difference. OPLS-DA revealed that 51 metabolites (ESI−) and 110 (ESI+) were differentially expressed in sera between the control and the radiated model mice. Heatmap analysis further revealed that AG could not reverse most of these radiation-altered metabolites, which indicates dietary supplement of AG before cobalt radiation had the weak potential to mediate serum metabolites that were altered by the sub-lethal high dose radiation. In addition, 83 (ESI−) and 244 (ESI+) AG altered metabolites were detected in radiated mice under radiation exposure. Both OPLS-DA on serum metabolomes and heatmap analysis on discriminant metabolites showed that AGs from different cultivation regions differentially influenced metabolic alterations in radiated mice, which indicates AGs from different cultivation regions showed the pharmacological difference in modulation of metabolite changes. AGs from Shandong, Shanxi, and Beijing provinces had more similar pharmacological effects than AGs from USA, Canada, Jilin, and Heilongjiang. Finally, 28 important potential biomarkers were annotated and assigned onto three metabolic pathways including lipid, amino acid, and energy metabolisms

Table3_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Table1_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Table2_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Evolocumab loaded Bio-Liposomes for efficient atherosclerosis therapy

Abstract PCSK9, which is closely related to atherosclerosis, is significantly expressed in vascular smooth muscle cells (VSMCs). Moreover, Proprotein Convertase Subtilisin/Kexin type 9 (PCSK9) mediated phenotypic transformation, abnormal proliferation, and migration of VSMCs play key roles in accelerating atherosclerosis. In this study, by utilizing the significant advantages of nano-materials, a biomimetic nanoliposome loading with Evolocumab (Evol), a PCSK9 inhibitor, was designed to alleviate atherosclerosis. In vitro results showed that (Lipo + M)@E NPs up-regulated the levels of α-SMA and Vimentin, while inhibiting the expression of OPN, which finally result in the inhibition of the phenotypic transition, excessive proliferation, and migration of VSMCs. In addition, the long circulation, excellent targeting, and accumulation performance of (Lipo + M)@E NPs significantly decreased the expression of PCSK9 in serum and VSMCs within the plaque of ApoE−/− mice