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Objective To explore the characteristics and the cause of variance of pharmacokinetics (PK) of docetaxel (DTX) in patients with solid tumors. Methods Between January 1 of 2018 and December 31 of 2020, 374 patients at two research centers—oncology, breast and thyroid surgery, thoracic surgery, the Third Affiliated Hospital of Chongqing Medical University, and oncology, breast and thyroid surgery, obstetrics and gynecology, Henan Provincial People's Hospital—provided a total of 772 docetaxel plasma samples. Liquid chromatography-mass spectrometry (LC-MS/MS) was used to determine docetaxel plasma concentrations. Nonlinear mixed-effects model (NONMEM) software was used to establish the population phar macokinetics(PPK) model. Using bootstrap and visual predictive check(VPC) for internal validation of the model. Results The current study established a three-compartment PPK model with first-order elimination of docetaxel in patients with solid tumors. DTX blood concentrations ranged from 3.53 ng/mL to 2 491 ng/mL, with a median of 175.5 ng/mL. Albumin (ALB) was identified as a covariate significantly affecting the clearance(CL) of docetaxel, and creatinine(CREA) was a covariate significantly affecting Q3. The model-estimated CL, V2, Q2, and Q3 of docetaxel were 57.2 L/h, 11.8 L, 14.1 L/h, and 8.63 L/h, respectively. Conclusion A three-compartment PPK model with multicenter data is established in patients with solid tumors. ALB is the main factor affecting CL variation. The internal validation results show that the model is stable and reliable.

The ceramifiable silicone rubber (SR) composite is prepared using boron oxide, calcium silicate, and kaolin as ceramifiable fillers. The effects of the content of CaSiO3/B2O3 on the high-temperature properties of composites are investigated. In the process of decomposition and oxidation of the ceramifiable SR composite in air, B2O3, and low-melting-point glass frit that participate in the formation of the residue network structure in different temperature regions, it continuously produces a liquid phase during the process of the ceramifying transformation. Microscopic images reveal that different structures are formed at different temperatures. The network structure of the ceramic residue becomes increasingly compact with the increase in temperature from 600°C to 800°C, which has a better protective effect on heat transfer and mass loss. At 900°C, with the lattice reconstruction of calcium silicate and the change of crystal structure, volume expansion occurs after cooling, alleviating the volume shrinkage caused by ceramic phase formation in the process of ablation. When the ratio of CaSiO3/B2O3 reaches 1:1 (both are 15 phr), the bending strength and linear shrinkage of the composites reach a satisfactory balance, the bending strength and the shrinkage reach 18.5 MPa and 12.1%, respectively.

Boron phenolic resin is widely used in the aerospace field because of its excellent thermal properties. In this article, nitrile rubber powder was added to phenolic resin to modify fiber-reinforced phenolic resin composites. The results showed that the tensile strength continued to decrease; the elongation ratio increased from 20.01% to 32.04%; and flexural strength and flexural modulus reached the highest values of 188 and 9,401 MPa, respectively. Thermal analysis showed that rubber had little effect on the heat resistance at low temperatures, especially below 350°C. Furthermore, the coefficient of thermal expansion of the composites increased from 8.9 × 10−6 to 1.5 × 10−5 K−1, increasing by nearly 70%. The electron microscopy images showed a tortuous fracture path in modified composites, which indicated that rubber powder–modified phenolic composites had a ductile fracture.

During the walking process of lower limb exoskeleton rehabilitation robots, inevitable collision impacts will occur when the swinging leg lands on the ground. The impact reaction force from the ground will induce vibrations in the entire robot's body from bottom to top. To address this phenomenon, considering the limitations of traditional active compliance and passive compliance methods, a variable stiffness and damping actuator (VSDA) leg structure using a magnetorheological damper (MRD) is proposed. Firstly, experimental methods are used to obtain the ground reaction force (GRF) exerted on a normal person during walking. Then, a mathematical model of the VSDA leg structure is constructed, and its working principle is analyzed. Based on human mass and dimensions, a 3D model is designed and selected. Finally, a simulation model is built in the MATLAB/Simulink environment using the fuzzy switch damping control strategy to simulate the acceleration and displacement of the human body under sinusoidal and random excitations. The results indicate that under sinusoidal excitation, employing fuzzy switch damping control optimizes human displacement by 72.47% compared to the high stiffness and high damping system, and by 16.95% compared to the switch damping system. Human acceleration is optimized by 52.09% compared to the high stiffness and high damping system, and by 25.2% compared to the switch damping system. Under random excitation, adopting fuzzy switch damping control optimizes human displacement by 59.09% compared to the high stiffness and high damping system, and by 21.74% compared to the switch damping system. Human acceleration is optimized by 78.74% compared to the high stiffness and high damping system, and by 31.66% compared to the switch damping system. This validates the VSDA design structure and control method, demonstrating certain advantages in improving the compliance and stability of lower limb exoskeleton rehabilitation robots. [ABSTRACT FROM AUTHOR]

Background To date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls. Methods Metabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n =138). Results Eight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients. Conclusion This study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients. [ABSTRACT FROM AUTHOR]