Magnetic solid-phase extraction-based surface-enhanced Raman spectroscopy for label-free therapeutic drug monitoring of carbamazepine and clozapine in human serum.

Determination of antiepileptic drugs and antipsychotics in human serum is significant in individualized drug administration and therapeutic drug monitoring (TDM). In this study, we developed a rapid label-free TDM method for the antiepileptic drug carbamazepine (CBZ) and the antipsychotic clozapine (CLO) in human serum. This detection strategy is based on the combination of surface-enhanced Raman scattering (SERS) and magnetic solid-phase extraction (MSPE). Initially, Fe3O4@SiO2@MIL-101(Fe) nanocomposites were synthesized by the layer-by-layer self-assembly method and characterized using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Brunauer-Emmett-Teller, ultraviolet-visible, and Fourier transform infrared analyses. Subsequently, CBZ and CLO were detected in human serum using Fe3O4@SiO2@MIL-101(Fe) as the solid-phase extraction adsorbent and Ag nanoparticles as SERS substrates. The potential of the MSPE-SERS method for the label-free TDM of CBZ and CLO was then investigated. Fe3O4@SiO2@MIL-101(Fe) prevents magnetic particle aggregation and demonstrates rapid magnetic separation capability that simplifies the pretreatment process and reduces interference from complex matrices. Its large surface area can effectively enrich targets in complex matrices, thereby improving the SERS detection sensitivity. The linearity between CBZ and CLO was excellent over the concentration range of 0.1-100 µg/mL (calculated as the intensity of the SERS characteristic peaks of CBZ and CLO at 728 cm and 1054 cm-1, respectively), with correlation coefficients (R2) of 0.9987 and 0.9957, and detection limits of 0.072 and 0.12 µg/mL, respectively. The recoveries of CBZ with CLO ranged from 94.0 % to 105.0 %, and their relative standard deviations were <6.8 %. Compared to other assays, the developed MSPE-SERS method has the advantages of simple sample pretreatment, rapid detection, and good reproducibility, which provides a novel approach for the TDM of other drugs.

Can salivary lactate be used as an anaerobic biomarker?

Background: Salivary lactate has been suggested as a non-invasive anaerobic biomarker in sports medicine for decades, yet has not been widely applied until now. This study aimed to explore possible issues related to its application and suggest directions for future method improvement. Methods: A liquid chromatography-mass spectrometry (LC-MS) method for the determination of salivary lactate was developed, validated and applied on saliva samples collected from a group of professional sprinters (n = 20). The samples were collected via chewing a cotton ball for one minute and centrifuging it afterwards. The evaluation included variation with mouth rinse times, consistency at different sampling times, change after treadmill or cycle ergometer trainings, and association with blood lactate. Sample sizes were calculated prior to the study. One-way analysis of variance (ANOVA), intra-class correlation coefficients (ICC) and relative standard deviation (RSD) were used to evaluate data variances. Pearson correlation was applied to show correlation between salivary and blood lactate. Effect sizes and power were calculated following ANOVA and correlation analyses. Results: The RSD of the LC-MS method was 19.70%. Salivary lactate concentration was affected by mouth rinse times before sampling (ANOVA p = 0.025, η 2 = 0.40, 1 - ß = 0.99, ICC = 0.23, mean RSD of four sampling = 55.30%), and stabilized after mouth rinsing for three times. The concentrations at resting state across three weeks were consistent at group level (ANOVA p = 0.57, η 2 = 0.03, 1 - ß = 0.20), but varied greatly individually (ICC = 0.22, mean RSD = 56.16%). Salivary lactate level significantly increased after treadmill and cycle ergometer trainings (ANOVA p = 0.0002, η 2 = 0.46, 1 - ß = 0.9999 and ANOVA p = 0.0019, η 2 = 0.40, 1 - ß = 0.9993, respectively), and displayed positive correlation with blood lactate concentration (r = 0.61, p = 0.0004, 1 - ß = 0.9596). Significant difference between male and female participants was observed in none of the tests conducted in this study. Discussion: Salivary lactate was found to be a potential anaerobic biomarker. However, reproducible methods for sample collection and analysis, as well as more knowledge on the secretion mechanism and pattern of salivary lactate are required to make it a practical anaerobic biomarker.

Review on the Formulation, Existing Problems, and Practical Effects of Fitness Exercise Prescriptions for People With Intellectual Disabilities.

Compared with normal people, patients with intellectual disability have poor cardiopulmonary and muscle fitness levels, and their daily physical activity generally cannot reach the "guideline-recommended amount," which increases the risk of obesity and cardiovascular disease in this group. From the perspective of six elements of exercise prescription (frequency, intensity, time, form of exercise, amount of exercise, and progressive rate), this paper systematically reviews the current situation of the formulation and implementation of exercise prescription for patients with intellectual disabilities. The results show that the design idea of aerobic fitness exercise prescription for patients with intellectual impairment follows the six-element 5paradigm, but the insufficient recommended amount of each element is a common problem. In the design of muscle fitness exercise prescription, due to the differences of different exercise forms, the description of the six elements is very inconsistent. Although most prescription execution effects show that it is beneficial to improve cardiopulmonary and muscle fitness, there is a great debate on whether it is beneficial to improve body composition. People with intellectual disabilities are highly heterogeneous groups. In the initial stage of exercise intervention, the elements of exercise prescription need to be adjusted individually to obtain sustainable positive benefits.

Effects of 4-Week Tangeretin Supplementation on Cortisol Stress Response Induced by High-Intensity Resistance Exercise: A Randomized Controlled Trial.

Objective: This study aimed to investigate the effects of 4-week tangeretin supplementation on the cortisol stress response induced by high-intensity resistance exercise. Methods: A randomized controlled trial of twenty-four soccer players was conducted during the winter training season. The experimental group (EG) took the oral supplement with tangeretin (200 mg/day) and the control group (CG) took placebo for 4 weeks. Before and after the 4-week intervention, all players performed a high intensity bout of resistance exercise to stimulate their cortisol stress responses. Serum cortisol, adreno-corticotropic hormone (ACTH) and superoxide dismutase (SOD) were obtained by collecting blood samples before (PRE), immediately after (P0), and 10 (P10), 20 (P20) and 30 minutes (P30) after the exercise. Results: The serum cortisol level (PRE, p = 0.017; P10, p = 0.010; P20, p = 0.014; P30, p = 0.007) and ACTH (P10, p = 0.037; P30, p = 0.049) of experimental group significantly decreased after the 4-week intervention. Compared with control group, EG displayed a significantly lower level of the serum cortisol (PRE, p = 0.036; P10, p = 0.031) and ACTH (P30, p = 0.044). Additionally, EG presented significantly higher superoxide dismutase activity level compared with CG at P30 (p = 0.044). The white blood cell of EG decreased significantly (PRE, p = 0.037; P30, p = 0.046) and was significantly lower than CG at P20 (p = 0.01) and P30 (p = 0.003). Conclusion: Four-week tangeretin supplementation can reduce serum cortisol and ACTH, which may ameliorate the cortisol stress response in soccer players during high-intensity resistance exercise training. It can also enhance antioxidant capacity, accelerate the elimination of inflammation throughout the body, and shorten recovery time after high-intensity exercise.

Vertebrae Labeling via End-to-End Integral Regression Localization and Multi-Label Classification Network.

Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use heatmaps to locate the vertebrae's centroid. However, the process of obtaining vertebrae's centroid coordinates using heatmaps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebrae coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. First, a novel end-to-end integral regression localization and multi-label classification network is developed, which can capture multi-scale features and also utilize the residual module and skip connection to fuse the multi-level features. Second, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure of location being destroyed, an integral regression module is used in the localization network. It combines the advantages of heatmaps representation and direct regression coordinates to achieve end-to-end training and can be compatible with any key point detection methods of medical images based on heatmaps. Finally, multi-label classification of vertebrae is carried out to improve the identification rate, which uses bidirectional long short-term memory (Bi-LSTM) online to enhance the learning of long contextual information of vertebrae. The proposed method is evaluated on a challenging data set, and the results are significantly better than state-of-the-art methods (identification rate is 91.1% and the mean localization error is 2.2 mm). The method is evaluated on a new CT data set, and the results show that our method has good generalization.

[Effects of the combination of tangeretin and whey protein on testosterone and cortisol in sprinters at winter training season].

Objective: To evaluate the regulation efficacy of oral tangeretin on testosterone and cortisol in sprinters at winter training season. Methods: Twenty-four sprinters were paired and randomly divided into experimental group (EG) and control group (CG). During winter training season, EG were treated with 200 mg tangeretin by oral, and CG were treated with placebo for 4 weeks. Blood samples were collected on the first day of each week (T1, T2, T3, T4) and after the intervention (T5) to detect serum levels of testosterone, cortisol, superoxide dismutase (SOD), and adrenocorticotropic hormone (ACTH). The body composition was tested at T1 and T5. Results: After 4 weeks, ①the serum cortisol level of CG was increased, and the serum levels of testosterone and SOD were decreased significantly (P＜0.05). ②In EG, the serum levels of cortisoland ACTH were decreased significantly (P＜0.05, P＜ 0.01), while the serum testosterone level was remained stable, and the level of SOD was increased slightly. ③The muscle mass of EG and CG were increases, but that of EG was increased higher than that of CG. Conclusion: Tangeretin reduces the oxidative stress response that caused by high-intensity exercise during winter training, which maintain the serum testosterone level and inhibit cortisol excessive secretion and promote muscle synthesis.

Computer-aided detection in chest radiography based on artificial intelligence: a survey.

As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.