One step toward precision dosing of caffeine in preterm infants: An external evaluation of published population pharmacokinetic models.

BACKGROUND: Several population pharmacokinetic (PopPK) models of caffeine in preterm infants have been published, but the extrapolation of these models to facilitate model-informed precision dosing (MIPD) in clinical practice is uncertain. This study aimed to comprehensively evaluate their predictive performance using an external, independent dataset. METHODS: Data used for external evaluation were based on an independent cohort of preterm infants. Currently available PopPK models for caffeine in preterm infants were identified and re-established. Prediction- and simulation-based diagnostics were used to assess model predictability. The influence of prior information was assessed using Bayesian forecasting. RESULTS: 120 plasma samples from 76 preterm infants were included in the evaluation dataset. Twelve PopPK models of caffeine in preterm infants were re-established based on our previously published study. Although two models showed superior predictive performance, none of the 12 PopPK models met all the clinical acceptance criteria of these external evaluation items. Besides, the external predictive performances of most models were unsatisfactory in prediction- and simulation-based diagnostics. Nevertheless, the application of Bayesian forecasting significantly improved the predictive performance, even with only one prior observation. CONCLUSIONS: Two models that included the most covariates had the best predictive performance across all external assessments. Inclusion of different covariates, heterogeneity of preterm infant characteristics, and different study designs influenced predictive performance. Thorough evaluation is needed before these PopPK models can be implemented in clinical practice. The implementation of MIPD for caffeine in preterm infants could benefit from the combination of PopPK models and Bayesian forecasting as a helpful tool.

A national-wide survey on clinical implementation of PGx testing into precision therapeutics for Chinese children: a long way before standard clinical practice.

BACKGROUND: Pharmacogenetics/pharmacogenomics (PGx) focuses on the genetic variation that causes the heterogeneity of pharmacokinetics and drug response among individuals and has the potential to predict individual efficacy and/or side effects. This study aims to investigate and understand the implementation of genetic testing for the personalized medication (GTPM) in children's hospitals in Mainland China. METHODS: A survey was conducted on 50 children's hospitals from 31 provinces, municipalities, and autonomous regions across Mainland China, and statistical analysis and recommendations were made. RESULTS: Questionnaire response was rate of 76.0% (38/50). Data from 15 hospitals conducting GTPM were included in this study, but only 6 hospitals had offered PGx tests for no less than five drug-related genes, and only 5 hospitals had covered more than ten drugs, which was a small scale overall. 20.0% of the laboratories did not conduct internal quality control, and 33.3% did not participate in inter-laboratory quality assessment. 46.7% of the practitioners did not receive external training. The primary goal for GTPM was to optimize drug dosage in the 15 hospitals, while the main challenge for GTPM was the implementation cost. CONCLUSION: Although GTPM in pediatrics has made major progress in Mainland China in recent years, there were still various problems in terms of software, hardware configuration, personnel allocation, business scale, quality control, and result interpretation. This requires joint efforts of health administration, medical insurance departments, researchers, and hospitals to promote and improve GTPM.

CYP2C19 genotype and sodium channel blockers in lacosamide-treated children with epilepsy: two major determinants of trough lacosamide concentration or clinical response.

Background: The widespread clinical use of lacosamide (LCM) has revealed significant individual differences in clinical response, with various reported influencing factors. However, it remains unclear how genetic factors related to the disposition and clinical response of LCM, as well as drug-drug interactions (DDIs), exert their influence on pediatric patients with epilepsy. Objectives: To evaluate the impact of genetic variations and DDIs on plasma LCM concentrations and clinical response. Design: Patients with epilepsy treated with LCM from June 2021 to March 2023 in the Children's Hospital of Nanjing Medical University were included in the analysis. Methods: The demographic information and laboratory examination data were obtained from the hospital information system. For the pharmacogenetic study, the left-over blood specimens, collected for routine plasma LCM concentration monitoring, were used to perform genotyping analysis for the selected 26 single nucleotide polymorphisms from 14 genes. The trough concentration/daily dose (C 0/D) ratio and efficacy outcomes were compared. Results: Patients achieved 90.1% and 68.9% responder rates in LCM mono- and add-on therapy, respectively. The genetic variant in the CYP2C19 *2 (rs4244285) was associated with a better responsive treatment outcome (odds ratio: 1.82; 95% confidence interval: 1.05-3.15; p = 0.031). In monotherapy, 36% of patients were CYP2C19 normal metabolizers (NMs), 49% were intermediate metabolizers (IMs), and 15% were poor metabolizers (PMs) carrying CYP2C19 *2 or *3. Of note, the C 0/D ratios of IMs and PMs were 9.1% and 39.6% higher than those of NMs, respectively. Similar results were in the add-on therapy group, and we also observed a substantial decrease in the C 0/D ratio when patients were concomitant with sodium channel blockers (SCBs). Conclusion: This study was the first to confirm that CYP2C19 *2 or *3 variants impact the disposition and treatment response of LCM in children with epilepsy. Moreover, concomitant with SCBs, particularly oxcarbazepine, also decreased plasma LCM concentration.

CYP2C19 genotype and sodium channel blockers in lacosamide-treated children with epilepsy: two major determinants of plasma lacosamide concentration or treatment efficacy This study examined the impact of genetic factors and drug combinations on the effectiveness and plasma concentrations of lacosamide, an antiseizure medication, in patients under 18. Analyzing blood samples from 316 patients at the Children's Hospital of Nanjing Medical University, researchers discovered that genetic variations in the CYP2C19 (i.e. *2 and *3), along with metabolic capacity, and co-medication with sodium channel blockers, all influence plasma lacosamide concentration. Understanding these genetic influences could inform personalized dosing strategies, improving the medication's management for pediatric epilepsy patients.

AISOA-SSformer: An Effective Image Segmentation Method for Rice Leaf Disease Based on the Transformer Architecture.

Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model's adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.

Development and application of a population pharmacokinetic model repository for caffeine dose tailoring in preterm infants.

BACKGROUND: Considerable interindividual variability for the pharmacokinetics of caffeine in preterm infants has been demonstrated, emphasizing the importance of personalized dosing. This study aimed to develop and apply a repository of currently published population pharmacokinetic (PopPK) models of caffeine in preterm infants to facilitate model-informed precision dosing (MIPD). RESEARCH DESIGN AND METHODS: Literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases. Relevant publications were screened, and their quality was assessed. PopPK models were reestablished to develop the model repository. Covariate effects were evaluated and the concentration-time profiles were simulated. An online simulation and calculation tool was developed as an instance. RESULTS: Twelve PopPK models were finally included in the repository. Preterm infants' age and body size, especially the postnatal age and current weight, were identified as the most clinically critical covariates. Simulated blood concentration-time profiles across these models were comparable. Caffeine citrate-dose regimen should be adjusted according to the age and body size of preterm infants. The developed online tool can be used to facilitate clinical decision-making. CONCLUSIONS: The first developed repository of PopPK models for caffeine in preterm infants has a wide range of potential applications in the MIPD of caffeine.

A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD.

Precise disease detection is crucial in modern precision agriculture, especially in ensuring the health of tomato crops and enhancing agricultural productivity and product quality. Although most existing disease detection methods have helped growers identify tomato leaf diseases to some extent, these methods typically target fixed categories. When faced with new diseases, extensive and costly manual annotation is required to retrain the dataset. To overcome these limitations, this study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology within the VLDet framework, which can accurately identify new tomato leaf diseases without manual annotation by using only image-text pairs. First, we developed a progressive visual transformer-convolutional pyramid module (PVT-C) that effectively extracts tomato leaf disease features and optimizes anchor box positioning using the self-supervised learning algorithm DINO, suppressing interference from irrelevant backgrounds. Then, a context feature guided module (CFG) was adopted to address the low adaptability and recognition accuracy of the model in data-scarce environments. To validate the model's effectiveness, we constructed a tomato leaf disease image dataset containing 4 base classes and 2 new categories. Experimental results show that the PDC-VLD model achieved 61.2% on the main evaluation metric mAP novel 50 , and 56.4% on mAP novel 75 , 87.7% on mAP base 50 , 81.0% on mAP all 50 , and 45.5% on average recall, outperforming existing OVD models. Our research provides an innovative solution for efficiently and accurately detecting new diseases, substantially reducing the need for manual annotation, and offering critical technical support and practical reference for agricultural workers.

Population pharmacokinetics of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets.

Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (Ctrough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20-50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (ka) was set at 2.64 and 0.46 h-1 for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target Ctrough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.

Effect of Chinese Medicine in Patients with COVID-19: A Multi-center Retrospective Cohort Study.

OBJECTIVE: To evaluate the effectiveness and safety of Chinese medicine (CM) in the treatment of coronavirus disease 2019 (COVID-19) in China. METHODS: A multi-center retrospective cohort study was carried out, with cumulative CM treatment period of ⩾3 days during hospitalization as exposure. Data came from consecutive inpatients from December 19, 2019 to May 16, 2020 in 4 medical centers in Wuhan, China. After data extraction, verification and cleaning, confounding factors were adjusted by inverse probability of treatment weighting (IPTW), and the Cox proportional hazards regression model was used for statistical analysis. RESULTS: A total of 2,272 COVID-19 patients were included. There were 1,684 patients in the CM group and 588 patients in the control group. Compared with the control group, the hazard ratio (HR) for the deterioration rate in the CM group was 0.52 [95% confidence interval (CI): 0.41 to 0.64, P<0.001]. The results were consistent across patients of varying severity at admission, and the robustness of the results were confirmed by 3 sensitivity analyses. In addition, the HR for all-cause mortality in the CM group was 0.29 (95% CI: 0.19 to 0.44, P<0.001). Regarding of safety, the proportion of patients with abnormal liver function or renal function in the CM group was smaller. CONCLUSION: This real-world study indicates that the combination of a full-course CM therapy on the basic conventional treatment, may safely reduce the deterioration rate and all-cause mortality of COVID-19 patients. This result can provide the new evidence to support the current treatment of COVID-19. Additional prospective clinical trial is needed to evaluate the efficacy and safety of specific CM interventions. (Registration No. ChiCTR2200062917).

LC-MS/MS and EMIT measure the whole blood concentration of cyclosporine A: The two methods yield concordant results within the dynamic range of the latter, but the former shows broader application scenarios.

Cyclosporine A (CsA) is a widely used immunosuppressive drug with a narrow therapeutic index and large individual differences. Its therapeutic and toxic effects are closely related to blood drug concentrations, requiring routine therapeutic drug monitoring (TDM). The current main methods for TDM of CsA are enzyme multiplied immunoassay technique (EMIT) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). However, few study on the method comparison of the EMIT and LC-MS/MS for the measurement of whole blood CsA concentration in children has been reported. In this study, we developed a simple and sensitive LC-MS/MS assay for the determination of CsA, and 657 cases of CsA concentrations were determined from 197 pediatric patients by a routine EMIT assay and by the validated in-house LC-MS/MS method on the same batch of samples, aimed to address the aforementioned concern. Consistency between the two assays was evaluated using linear regression and Bland-Altman analysis. The linear range of LC-MS/MS was 0.500-2000 ng/mL and that of the EMIT was 40-500 ng/mL, respectively. Overall, the correlation between the two methods was significant (r-value ranging from 0.8842 to 0.9441). Unsatisfactory consistency was observed in the concentrations < 40 ng/mL (r = 0.7325) and 200-500 ng/mL (r = 0.6851). Bland-Altman plot showed a mean bias of -18.0 % (±1.96 SD, -73.8 to 37.8 %) between EMIT and LC-MS/MS. For Passing-Bablok regression between EMIT and LC-MS/MS did not differ significantly (p > 0.05). In conclusion, the two methods were closely correlated, but the CsA concentration by LC-MS/MS assay was slightly higher than that by EMIT method. Switching from the EMIT assay to the LC-MS/MS method was acceptable, and the LC-MS/MS method will receive broader application in clinical settings due to its better analytical capabilities, but the results need to be further verified in different laboratories.

Which factors affect the live birth outcome of the first single euploid frozen-thawed blastocyst transfer in couples with balanced chromosomal translocations?

Objective: The objective of this study is to investigate the factors that influence the live birth rate (LBR) of the first single euploid frozen-thawed blastocyst transfer (FBT) cycles after preimplantation genetic testing for structural rearrangements (PGT-SR) in couples with balanced chromosomal translocations (BCT). Design: Single center, retrospective and observational study. Methods: A total of 336 PGT-SR and the first single euploid FBT cycles between July 2016 and December 2022 were included in this study. The patients were divided into two groups according to the live birth outcomes. The parameters of the study population, controlled ovarian stimulation cycles, and FBT cycles were analyzed. Multivariable binary logistic regression was performed to find the factors that affected the LBR. Results: The percentage of blastocysts at developmental stage Day 5 compared to Day 6 (51.8% vs. 30.8%; P<0.001) and with morphology ≥BB compared to