Evaluation of modified clear Twin Block aligner in treating adolescents with skeletal class II malocclusion: A two-centre cephalometric study.

INTRODUCTION: The purpose of this study was to evaluate the therapeutic effect of modified clear Twin Block (CTB) aligner and traditional twin block (TB) appliance from skeletal, dentoalveolar and soft tissue changes in adolescents with skeletal class II malocclusion. METHODS: A total of 80 adolescents, included in this study from two medical centres, were distributed into CTB group, TB group and control group based on the treatment they received. Lateral cephalograms at pre-treatment (T1) and post-treatment (T2) were measured by modified Pancherz's cephalometric analysis, and dentoskeletal and soft tissue changes were analysed by independent-sample t-test, paired-sample t-test, ANOVA test and Scheffe's Post Hoc test. RESULTS: Seventy-five adolescents completed the study, including 32 in the CTB group, 32 in the TB group and 11 in the control group. Both CTB and TB treatment showed significant differences in most dentoskeletal and soft tissue measurements. Compared with the control group, improvements were observed in class II molar relationship through significant different in S Vert/Ms-S Vert/Mi in the CTB group (P < .01) and the TB group (P < .001), as well as deep overjet through significant different in S Vert/Is-S Vert/Ii in the CTB group (P < .001) and the TB group (P < .001). Besides, the CTB group also showed less protrusion of lower incisors and resulted in a more significant improvement in profile with fewer adverse effects on speaking, eating and social activities. CONCLUSIONS: For adolescents with skeletal class II malocclusion, CTB appliance was as effective as TB on improving dentoskeletal and soft tissue measurements, featuring more reliable teeth control and patient acceptance.

Revealing the Influence of SiC Particle Size on the Hot Workability of SiCp/6013 Aluminum Matrix Composites.

SiC particle (SiCp) size has been found to significantly influence the hot workability of particle-reinforced aluminum matrix composites (AMC). In this work, therefore, three types of SiCp/6013 composites with different SiCp sizes (0.7, 5 and 15 µm) were prepared and then subjected to isothermal hot compression tests. In addition, constitutive analysis, processing maps and microstructural characterizations were used to reveal the influence of SiCp size on the hot workability of SiCp/6013 composite. The results showed that the values of hot deformation activation energy Q increased with decreasing SiCp size. Specifically, at lower temperatures (e.g., 350 and 400 °C), the highest peak stress was shown in the AMC with SiCp size of 0.7 µm (AMC-0.7), while in the AMC with SiCp size of 5 µm (AMC-5) at higher temperatures (e.g., 450 and 500 °C). This is because a finer SiCp size would lead to stronger dislocation pinning and grain refinement strengthening effects, and such effects would be weakened at higher temperatures. Further, dynamic softening mechanisms were found to transform from dynamic recovery to dynamic recrystallization with increasing SiCp size, and the dynamic recrystallization occurred more easily at higher temperatures and lower strain rates. Consequently, the instability zones of the composites are all mainly located in the deformation region with lower temperature and higher strain rate, and smaller SiCp results in larger instability zones.

Improvement in Mechanical Properties of Al2024 Alloy Using Mechanical Working and Heat Treatment.

Extrusion speed has a significant influence on the extrusion temperature, microstructure and mechanical properties of the material in the repetitive continuous extrusion forming (RCEF) process. In this work, the mechanical properties of Al2024 were improved by adjusting the speed (with a general range of 2-10 rpm) of repetitive continuous extrusion and applying subsequent heat treatment. During the RCEF process, an increase in the extrusion speed from 4 to 8 rpm was found to increase the extrusion temperature and then enhance the solid solution function. The grain size was affected by the combined effect of deformation speed and its induced temperature. A high-strength Al2024 (ultimate tensile strength of 497.6 MPa) with good elongation (12.93%) was obtained by increasing the extrusion speed and conducting solid solution and artificial aging treatments. The main strengthening mechanisms could be attributed to finer grain size and a larger amount of S (Al2CuMg) precipitates.

Evaluation of optimal anterior-posterior position of upper incisors through ANS point: a retrospective study on a Chinese sample.

OBJECTIVES: This study was designed to determine the optimal anterior-posterior (AP) position of upper incisors through Anterior Nasal Spine (ANS) point. MATERIALS AND METHODS: Lateral cephalometric radiographic images of 690 patients were collected and divided into a derivation group and a validation group, and the former were subdivided into a proper AP position (PAP) group and an improper AP position (iPAP) group. The distance from facia-axis (FA) point of upper incisors to the line perpendicular to Frankfort horizontal (FH) plane through ANS (FA-ANS) was measured, and the relationship between FA-ANS and several cephalometric indices were studied through Pearson correlation analysis. Receiver operating characteristic (ROC) curves for different clinical indices were analyzed to evaluate the diagnostic efficiency of optimal AP position of upper incisors. RESULTS: The average value of FA-ANS in PAP group was 0.57±1.99, which was significantly different from FA-ANS in iPAP group. Cephalometric indices such as U1-NA, U1-SN, AB-NPo, UL-TVL, Wits, and ANB were found to be correlated with FA-ANS. The receiver operating characteristic (ROC) curves represented a greater diagnostic efficiency of FA-ANS compared with other clinical indices. CONCLUSIONS: ANS point, as a stable skeletal landmark, could be used to access an optimal AP position of upper incisors, providing aids to clinical diagnosis and treatment goal determination for clinical practice. CLINICAL RELEVANCE: A new index FA-ANS, together with other traditional indices, could help determine the optimal position of upper incisors and provide a personalized therapeutic plan.

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn's disease in Chinese population.

BACKGROUND: Thalidomide is an effective treatment for refractory Crohn's disease (CD). However, thalidomide-induced peripheral neuropathy (TiPN), which has a large individual variation, is a major cause of treatment failure. TiPN is rarely predictable and recognized, especially in CD. It is necessary to develop a risk model to predict TiPN occurrence. AIM: To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables. METHODS: A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model. The National Cancer Institute Common Toxicity Criteria Sensory Scale (version 4.0) was used to assess TiPN. With 18 clinical features and 150 genetic variables, five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, sensitivity (recall rate), precision, accuracy, and F1 score. RESULTS: The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248 [P = 0.0004, odds ratio (OR): 8.983, 95% confidence interval (CI): 2.497-30.90], dose (mg/d, P = 0.002), brain-derived neurotrophic factor (BDNF) rs2030324 (P = 0.001, OR: 3.164, 95%CI: 1.561-6.434), BDNF rs6265 (P = 0.001, OR: 3.150, 95%CI: 1.546-6.073) and BDNF rs11030104 (P = 0.001, OR: 3.091, 95%CI: 1.525-5.960). In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. Among these models, XGBoost and GBDT obtained the first two highest AUROC (0.90 and 1), AUPRC (0.98 and 1), accuracy (0.96 and 0.98), precision (0.90 and 0.95), F1 score (0.95 and 0.98), specificity (0.94 and 0.97), and sensitivity (1). In the validation set, XGBoost algorithm exhibited the best predictive performance with the highest specificity (0.857), accuracy (0.818), AUPRC (0.86) and AUROC (0.89). ET and GBDT obtained the highest sensitivity (1) and F1 score (0.8). Overall, compared with other state-of-the-art classifiers such as ET, GBDT and RF, XGBoost algorithm not only showed a more stable performance, but also yielded higher ROC-AUC and PRC-AUC scores, demonstrating its high accuracy in prediction of TiPN occurrence. CONCLUSION: The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables. With the ability to identify high-risk patients using single nucleotide polymorphisms, it offers a feasible option for improving thalidomide efficacy in CD patients.

µPESI-MS/MS System for Screening and Quantitating Drugs in Plasma Samples.

Recently, we developed a novel microprobe electrospray ionization (µPESI) source and its coupled MS (µPESI-MS/MS) system. Here, we aimed to widely validate the µPESI-MS/MS method for quantitative analysis of drugs in plasma samples. Furthermore, the relationship between the quantitative performance of the µPESI-MS/MS method and the physicochemical properties of target drugs was analyzed. The µPESI-MS/MS methods for quantitative analysis of 5 representative drugs with a relatively wide range of molecular weight, pKa, and log P values were developed and validated. The results showed that the linearity, accuracy, and precision of these methods met the requirements of the European Medicines Agency (EMA) guidance. Then a total of 75 drugs from plasma samples were primarily detected using the µPESI-MS/MS methods, among which 48 drugs could be quantitatively measured. Logistics regression suggested that drugs with significantly greater log P and physiological charge had better quantitative performance using the µPESI-MS/MS method. Collectively, these results clearly demonstrate the practical application of the µPESI-MS/MS system as a rapid approach to the quantitative analysis of drugs in plasma samples.

Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques.

BACKGROUND: Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for clinicians. This non-interventional retrospective study aims to develop two deep learning (DL) systems to efficiently, accurately, and instantly detect the head position on LCRs. METHODS: LCRs from 13 centers were reviewed and a total of 3000 radiographs were collected and divided into 2400 cases (80.0 %) in the training set and 600 cases (20.0 %) in the validation set. Another 300 cases were selected independently as the test set. All the images were evaluated and landmarked by two board-certified orthodontists as references. The head position of the LCR was classified by the angle between the Frankfort Horizontal (FH) plane and the true horizontal (HOR) plane, and a value within - 3°- 3° was considered normal. The YOLOv3 model based on the traditional fixed-point method and the modified ResNet50 model featuring a non-linear mapping residual network were constructed and evaluated. Heatmap was generated to visualize the performances. RESULTS: The modified ResNet50 model showed a superior classification accuracy of 96.0 %, higher than 93.5 % of the YOLOv3 model. The sensitivity&recall and specificity of the modified ResNet50 model were 0.959, 0.969, and those of the YOLOv3 model were 0.846, 0.916. The area under the curve (AUC) values of the modified ResNet50 and the YOLOv3 model were 0.985 ± 0.04 and 0.942 ± 0.042, respectively. Saliency maps demonstrated that the modified ResNet50 model considered the alignment of cervical vertebras, not just the periorbital and perinasal areas, as the YOLOv3 model did. CONCLUSIONS: The modified ResNet50 model outperformed the YOLOv3 model in classifying head position on LCRs and showed promising potential in facilitating making accurate diagnoses and optimal treatment plans.

Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping.

BACKGROUND: Sexual dimorphism is obvious not only in the overall architecture of human body, but also in intraoral details. Many studies have found a correlation between gender and morphometric features of teeth, such as mesio-distal diameter, buccal-lingual diameter and height. However, it's still difficult to detect gender through the observation of intraoral photographs, with accuracy around 50%. The purpose of this study was to explore the possibility of automatically telling gender from intraoral photographs by deep neural network, and to provide a novel angle for individual oral treatment. METHODS: A deep learning model based on R-net was proposed, using the largest dataset (10,000 intraoral images) to support the automatic detection of gender. In order to reverse analyze the classification basis of neural network, Gradient-weighted Class Activation Mapping (Grad-CAM) was used in the second step, exploring anatomical factors associated with gender recognizability. The simulated modification of images based on features suggested was then conducted to verify the importance of characteristics between two genders. Precision (specificity), recall (sensitivity) and receiver operating characteristic (ROC) curves were used to evaluate the performance of our network. Chi-square test was used to evaluate intergroup difference. A value of p < 0.05 was considered statistically significant. RESULTS: The deep learning model showed a strong ability to learn features from intraoral images compared with human experts, with an accuracy of 86.5% and 82.5% in uncropped image data group and cropped image data group respectively. Compared with hard tissue exposed in the mouth, gender difference in areas covered by soft tissue was easier to identify, and more significant in mandibular region than in maxillary region. For photographs with simulated removal of lips and basal bone along with overlapping gingiva, mandibular anterior teeth had similar importance for sex determination as maxillary anterior teeth. CONCLUSIONS: Deep learning method could detect gender from intraoral photographs with high efficiency and accuracy. With assistance of Grad-CAM, the classification basis of neural network was deciphered, which provided a more precise entry point for individualization of prosthodontic, periodontal and orthodontic treatments.

Influence of Pre-Heat Treatment on the Deformation Behaviors, Microstructural Characteristics, and Mechanical Properties of a Continuously Cast Al-Cu-Mg Alloy during Continuous Extrusion Process.

The presence of a second phase in Al-Cu-MG alloys, with various sizes and supersaturation-solid-solubility, which can be changed by pre-heat-treatment, could have remarkable influence on hot workability and mechanical performance. In the present work, a continuously cast 2024 Al alloy was homogenized and then subjected to hot compression and continuous extrusion (Conform) along with the initial as-cast alloy. The results showed that the 2024 Al alloy specimen with pre-heat treatment had a higher resistance to deformation and dynamic recovery (DRV) during hot compression process compared with the as-cast sample. Meanwhile, dynamic recrystallization (DRX) was advanced in the pre-heat-treated sample. After the Conform Process, the pre-heat-treated sample also attained better mechanical properties without additional solid solution treatment. The higher supersaturation solid solubility and dispersoids generated during pre-heat treatment was proved to play a key role in restricting boundary migration, tangling dislocation motion and promoting the precipitation of S phase, which raised resistance to DRV and plastic deformation and enhanced the mechanical properties.

The interplay between the nerves and skeleton: a 30-year bibliometric analysis.

Background: The mechanisms and effects of the interplay between the nerves and skeleton remain a popular research topic. This study aimed to analyze and evaluate publications on nerve-bone interactions using bibliometrics and to identify the state of the art of current research, hotspots, and future directions. Methods: This study included 1989 articles and reviews from the Web of Science Core Collection (WoSCC) published from January 1, 1991, to June 22, 2022. The Bibliometrix package of R 4.2.0 (The R Foundation for Statistical Computing, Vienna, Austria) was used to analyze basic information about the publications, including the annual number of publications, institution analysis, author influence analysis, journal analysis, and the national cooperation network. We also used CiteSpace 5.8.R3 for bibliometric analysis, including co-occurrence, co-citation, and cluster analysis. Results: We discovered a significant increase in the number of articles on nerve-bone interactions published over the last 10 years. The most active country and institution were the United States and the University of Minnesota, respectively. In terms of journals and cocited journals, Bone was ranked highest with respect to the number of publications, while Journal of Bone and Mineral Research was ranked highest among cited journals. Wang Lei was the author with the most publications, and Bjurholm A was the most cited author. The analysis of references and keywords revealed that the impact of nerve- and neuromodulation-related factors on stem cell differentiation was a persistently hot topic. Osteoarthritis, neuropeptide Y, and osteoclastogenic process are likely to be the next era of research hotspots. The neurovascular crosstalk within bone has received great attention, especially in skeletal diseases, which may provide potential targets for future treatments. Conclusions: We used a bibliometric method to provide an efficient, objective, and comprehensive assessment of existing research about the interplay between the skeletal and nervous systems and to accurately identify hotspots and research frontiers, providing valuable information for future research.

Hot Workability of the Multi-Size SiC Particle-Reinforced 6013 Aluminum Matrix Composites.

The size and distribution of ceramic particles in aluminum matrix composites have been reported to remarkably influence their properties. For a single ceramic particle, the particle size is too small and prone to agglomeration, which makes the mechanical properties of the composites worse. When the ceramic particle size is too large, the particles and alloy at the interface are not firmly bonded, and the effect of dispersion distribution is not achieved, which will also reduce the mechanical properties of the composites. The multi-size ceramic particles are expected to improve this situation, while their effect on hot workability is less studied. In this study, the hot deformation behavior, constitutive model, processing map and SEM microstructure were investigated to evaluate the hot workability of multi-size SiC particle-reinforced 6013 aluminum matrix composites. The results showed that the increased deformation temperature and decreased strain rate could decrease flow stresses. The flow stress behaviors of the composites can be described by the sine-hyperbolic Arrhenius equation with the deformation activation energy of Q = 205.863 kJ/mol. The constitutive equation of the composites is ε ˙=3.11592×1013sinh0.024909σ4.12413exp−205863RT. Then, the hot processing map of the SiCp/6013 composites was constructed and verified by SEM observations. The rheological instability zone was in the region of a high strain rate. The optimal processing zone for composites was 450~500 °C and 0.03~0.25 s−1. In addition, the strain level was found to increase both the Q value and the area of the instability zone.

Signal Suppression in LC-ESI-MS/MS from Concomitant Medications and Its Impact on Quantitative Studies: An Example Using Metformin and Glyburide.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been widely used in the quantitative analysis of drugs. The ubiquitous concomitant drug scenario in the clinic has spawned a large number of co-analyses based on this technique. However, signal suppression caused by concomitant drugs during electrospray ionization may affect the quantification accuracy of analytes, which has not received enough attention. In this study, metformin (MET) and glyburide (GLY) were co-eluted by the conventional optimization of chromatographic conditions to illustrate the effect of signal suppression caused by the combined drugs on the quantitative analysis. The response of MET was not affected by GLY over the investigated concentration range. However, the GLY signal could be suppressed by about 30% in the presence of MET, affecting its pharmacokinetic analysis in simulated samples. As an attempt to solve the suppression of GLY by co-eluting MET, dilution can alleviate the suppression. However, this method still has limitations due to the sacrifice of sensitivity. The stable isotope-labeled internal standard could play a role in correction and improve the quantitative accuracy of GLY, which was further confirmed in the pharmacokinetic study of simulated samples. This study provided an example model to illustrate the possible effect of clinical drug combination on LC-MS/MS drug quantitative analysis and investigated the effective methods to solve this problem.

Artificial intelligence system for automated landmark localization and analysis of cephalometry.

OBJECTIVES: Cephalometric analysis is essential for diagnosis, treatment planning and outcome assessment of orthodontics and orthognathic surgery. Utilizing artificial intelligence (AI) to achieve automated landmark localization has proved feasible and convenient. However, current systems remain insufficient for clinical application, as patients exhibit various malocclusions in cephalograms produced by different manufacturers while limited cephalograms were applied to train AI in these systems. METHODS: A robust and clinically applicable AI system was proposed for automatic cephalometric analysis. First, 9870 cephalograms taken by different radiography machines with various malocclusions of patients were collected from 20 medical institutions. Then 30 landmarks of all these cephalogram samples were manually annotated to train an AI system, composed of a two-stage convolutional neural network and a software-as-a-service system. Further, more than 100 orthodontists participated to refine the AI-output landmark localizations and retrain this system. RESULTS: The average landmark prediction error of this system was as low as 0.94 ± 0.74 mm and the system achieved an average classification accuracy of 89.33%. CONCLUSIONS: An automatic cephalometric analysis system based on convolutional neural network was proposed, which can realize automatic landmark location and cephalometric measurements classification. This system showed promise in improving diagnostic efficiency in clinical circumstances.

Effect of Grain Size on the Plastic Deformation Behaviors of a Fe-18Mn-1.3Al-0.6C Austenitic Steel.

Grain size is a microscopic parameter that has a significant impact on the macroscopic deformation behavior and mechanical properties of twinning induced plasticity (TWIP) steels. In this study, Fe-18Mn-1.3Al-0.6C steel specimens with different grain sizes were first obtained by combining cold rolling and annealing processes. Then the influence of grain size on the plastic deformation mechanisms was investigated by mechanical testing, X-ray diffraction-based line profile analysis, and electron backscatter diffraction. The experimental results showed that the larger grain size could effectively promote twinning during plastic straining, produce an obvious TWIP effect, and suppress the rate of dislocation proliferation. The continuous contribution of dislocation strengthening and twinning functions led to a long plateau in the work-hardening rate curve, and increased the work-hardening index and work-hardening ability. At the same time, the strain could be uniformly distributed at the grain boundaries and twin boundaries inside the grain, which effectively relieved the stress concentration at the grain boundaries and improved the plasticity of deformed samples.

Automated calibration system for length measurement of lateral cephalometry based on deep learning.

Objective. Cephalometric analysis has been significantly facilitated by artificial intelligence (AI) in recent years. For digital cephalograms, linear measurements are conducted based on the length calibration process, which has not been automatized in current AI-based systems. Therefore, this study aimed to develop an automated calibration system for lateral cephalometry to conduct linear measurements more efficiently.Approach. This system was based on deep learning algorithms and medical priors of a stable structure, the anterior cranial base (Sella-Nasion). First, a two-stage cascade convolutional neural network was constructed based on 2860 cephalograms to locate sella, nasion, and 2 ruler points in regions of interest. Further, Sella-Nasion distance was applied to estimate the distance between ruler points, and then pixels size of cephalograms was attained for linear measurements. The accuracy of automated landmark localization, ruler length prediction, and linear measurement based on automated calibration was evaluated with statistical analysis.Main results. First, for AI-located points, 99.6% ofSand 86% ofNpoints deviated less than 2 mm from the ground truth, and 99% of ruler points deviated less than 0.3 mm from the ground truth. Also, this system correctly predicted the ruler length of 98.95% of samples. Based on automated calibration, 11 linear cephalometric measurements of the test set showed no difference from manual calibration (p > 0.05).Significance. This system was the first reported in the literature to conduct automated calibration with high accuracy and showed high potential for clinical application in cephalometric analysis.

Simultaneous absolute protein quantification of seven cytochrome P450 isoforms in rat liver microsomes by LC-MS/MS-based isotope internal standard method.

The cytochrome P450 (CYP) enzymes play a pivotal role in drug metabolism. LC-MS/MS-based targeting technology has been applied to the analysis of CYP enzymes, promoting drug development and drug-drug interaction studies. Rat is one of the most commonly used models for drug metabolism assessment, but LC-MS/MS assay quantifying the abundance of CYP enzymes in rats is rarely reported. Herein, an accurate and stable LC-MS/MS based method was developed and validated for the simultaneous quantification of seven major rat CYP isoforms (CYP1A2, 2B1, 2C6, 2C11, 2D1, 2E1, and 3A1) in liver microsomes. The careful optimization of trypsin digestion and chromatography combined with isotope-labeled peptide as internal standard improved the efficiency and accuracy of the analysis. Highly specific surrogate peptides were obtained by a procedure including trypsin digestion for six hours and separated on a Hypersil Gold C18 column (100 × 2.1 mm, 3 µm) using gradient elution for 15 min with a mobile phase of water containing 0.1% formic acid and acetonitrile. In the method validation, linearity, matrix effect, recovery, stability, accuracy, and precision all meet the requirements. Subsequently, this method was applied to detect seven enzymes in rat liver microsomes from four different sources, and the correlation between the abundance and activity of CYP enzymes was further analyzed. The high-throughput detection method provided in this study will provide support for pertinent pharmaceutical research based on rat models.

Population Pharmacokinetics and Initial Dosage Optimization of Tacrolimus in Pediatric Hematopoietic Stem Cell Transplant Patients.

Background: There is a substantial lack of tacrolimus pharmacokinetic information in pediatric hematopoietic stem cell transplant (HSCT) patients. This study aimed to develop population pharmacokinetics (PopPK) of tacrolimus in pediatric HSCT patients and to devise model-guided dosage regimens. Methods: A retrospective analysis was performed on 86 pediatric HSCT patients who received tacrolimus intravenously or orally. A total of 578 tacrolimus trough concentrations (C0) were available for pharmacokinetic analysis using a non-linear mixed-effects modeling method. Demographic and clinical data were included and assessed as covariates via the stepwise method. Bayesian estimators were used to devise pediatric dosage regimens that targeted C0 of 5-15 ng mL-1. Results: A one-compartment model with first-order absorption adequately described the tacrolimus pharmacokinetics. Clearance (CL), volume of distribution (V), and typical bioavailability (F) in this study were estimated to be 2.42 L h-1 (10.84%), 79.6 L (16.51%), and 19% (13.01%), respectively. Body weight, hematocrit, post-transplantation days, and caspofungin and azoles concomitant therapy were considered significant covariates for tacrolimus CL. Hematocrit had a significant impact on the V of tacrolimus. In the subgroup cohort of children (n = 24) with CYP3A5 genotype, the clearance was 1.38-fold higher in CYP3A5 expressers than in non-expressers. Simulation indicated that the initial dosage optimation of tacrolimus for intravenous and oral administration was recommended as 0.025 and 0.1 mg kg-1 d-1 (q12h), respectively. Conclusion: A PopPK model for tacrolimus in pediatric HSCT patients was developed, showing good predictive performance. Model-devised dosage regimens with trough tacrolimus concentrations provide a practical strategy for achieving the therapeutic range.

Equilibration for Electrospray Ionization Mass Spectrometry in Quantitative Analysis.

Electrospray ionization mass spectrometry (ESI-MS) is widely used in drug development, therapeutic drug monitoring, and other fields. However, unstable mass spectral signals, especially during the initial stages of instrument operation, plague analysts. Generally, in quantitative experiments, the stability of response can be achieved by running the analytical system for some time. However, the equilibration time required for the responses of different compounds to stabilize has been elusive. To investigate the response stability of the ESI-MS system, 72 compounds with different physicochemical properties were employed on three systems, and flow injection analysis was performed in positive ion mode. With the use of 5.00% (response stable factor, RSF) as the stability limit, about 80% of the compounds were stable within 60 min. Under a 2.00% criterion, the stabilization time was significantly longer. The stabilization time varies with different instruments and physicochemical properties of the compounds. When positive ion detection is performed in an acidic mobile phase, the octanol-water partition coefficient (Log P), molecular weight, and molar volume can all affect the time required to stabilize the response. In general, it is necessary to balance the ESI-MS system for an appropriate time before sample detection, especially for the analysis of compounds with strong hydrophilicity, small molecular weight, or small molar volume under the conditions above.

Nicotine Delivery and Pharmacokinetics of an Electronic Cigarette Compared With Conventional Cigarettes in Chinese Adult Smokers: A Randomized Open-Label Crossover Clinical Study.

INTRODUCTION: To evaluate the nicotine pharmacokinetics of a commercial electronic cigarette (e-cigarette) relative to conventional cigarettes in Chinese adult smokers. AIMS AND METHODS: A randomized, open-label, crossover clinical study was conducted on 23 healthy adult Chinese smokers. In two sessions, subjects used either the e-cigarettes with 30 mg/g nicotine in e-liquid or conventional cigarettes of a given brand, at one puff every 30 seconds for a total of 10 puffs. Blood samples were collected at specified time points for 4 hours after the first puff. Subjective effects on desire-to-smoke and physiological parameters such as heart rate and oxyhemoglobin saturation levels were also examined before and after using the two products. RESULTS: The baseline-adjusted maximum nicotine concentration (Cmax-BL), time-to-peak nicotine concentration (Tmax), and nicotine absorption rate (Cmax-BL divided by Tmax) were found to be similar for the e-cigarette versus those of conventional cigarettes (p > .05). Total nicotine exposure measured as the area-under-curve (AUC0-t-BL) was significantly lower for the e-cigarette relative to that of conventional cigarettes. In addition, the subjects found that e-cigarettes were well tolerated under controlled puffing conditions. CONCLUSIONS: The test e-cigarettes achieved similar nicotine delivery and pharmacokinetic profiles to those of the comparator cigarettes, indicating that this e-cigarette could be a potential alternative to conventional cigarettes for those adult smokers. IMPLICATIONS: There are no data in the published literature on the nicotine pharmacokinetics of e-cigarettes in Chinese smokers. To the best of our knowledge, this is the first study to evaluate the nicotine delivery and pharmacokinetic profile of a commercial e-cigarette brand compared with conventional cigarettes in Chinese adult smokers. After the use of test e-cigarettes, nicotine delivery and pharmacokinetic profile were similar to those of conventional cigarettes in Chinese adult smokers.

Enhanced PDGFR/Wnt/ß-catenin activity of mesenchymal stem cells with high migration ability rescue bone loss of osteoporosis.

Osteoporosis is a widespread disease characterized by bone mass loss and microarchitectural deterioration. The side effects of clinical drugs make mesenchymal stem cells (MSCs)-based therapy gain increasing focus in the treatment of osteoporosis. MSCs need to migrate to the site of damage and undergo differentiation in order to participate in the subsequent bone repair process. Therefore, the homing ability of MSCs may be related to the repair ability. Here, we proposed a novel method to screen MSCs with high migration capacity and confirmed that these MSCs exhibited higher osteogenic differentiation ability both in vivo and in vitro. Further results indicated that MSCs with high migration ability could partly rescue the bone loss of ovarectomized (OVX) rats. Higher expression of Platelet-derived growth factors receptor ß- (PDGFRß) and more nuclear transduction of ß-catenin in MSCs with high migration ability may be responsible for biological functions. This article may provide a method to improve the efficacy of MSCs-based therapy in the clinic.