Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder influenced by both genetic and environmental factors, remains poorly understood regarding how its polygenic risk score (PRS) impacts functional networks and symptomology. This study capitalized on data from 11,430 children in the Adolescent Brain Cognitive Development study to explore the interplay between PRS_ADHD, brain function, and behavioral problems, along with their interactive effects. The results showed that children with a higher PRS_ADHD exhibited more severe attention deficits and rule-breaking problems, and experienced sleep disturbances, particularly in initiating and maintaining sleep. We also identified the central executive network, default mode network, and sensory-motor network as the functional networks most associated with PRS and symptoms in ADHD cases, with potential mediating roles. Particularly, the impact of PRS_ADHD was enhanced in children experiencing heightened sleep disturbances, emphasizing the need for early intervention in sleep issues to potentially mitigate subsequent ADHD symptoms.
Humans ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Male ; Female ; Sleep Wake Disorders/physiopathology* ; Adolescent ; Child ; Brain/diagnostic imaging* ; Multifactorial Inheritance ; Genetic Predisposition to Disease

Human exhaled breath has great application prospects,e.g.,monitoring pharmacokinetics,disease diagnosis,due to its advantages such as non-invasive and high-frequency sampling.Breath samples can be collected from the oral and nasal cavity.However,the oral and nasal environment affect the chemical composition of breath sample.Therefore,the investigation on the chemical composition of mouth-exhaled breath and nose-exhaled breath is crucial for selection of appropriate sampling strategy for individual studies.In this work,secondary electrospray ionization-high resolution mass spectrometry(SESI-HRMS)was applied to analysis of respiratory metabolomics in real time.A quantitative analysis approach was established for 9 kinds of volatile organic compounds(VOCs)e.g.2-butanone,2-pentanone,ethyl acetate,methyl methacrylate,toluene,styrene,mesitylene,isoprene and limonene.The limit of detection was 2.3?240.8 ng/m3.The intra-day(n=6)and inter-day(n=18)relative standard deviations were 0.6%?4.6%and 4.3%?12.2%,respectively.Nine healthy subjects were recruited to investigate the chemical composition of mouth-exhaled and nose-exhaled breath.The results showed the good performance in quantitative analysis of 9 VOCs in breath air.It was found that the number of unique component(m/z)detected in mouth-exhaled breath(167)was 2.2 times greater than that detected in nose-exhaled breath(76),which might result from the complex environment in oral cavity.The signal intensity of commun component(163)was significantly different between mouth-exhaled breath and nose-exhaled breath.Additionally,the elemental composition analysis showed that the proportion of polar compounds detected in nose-exhaled breath was higher than that in mouth-exhaled breath.This study demonstrated that there was significant differences in the chemical composition between mouth-exhaled and nose-exhaled breath,which provided a theoretical basis for selection of exhalation mode.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.

Objective:To summarize the clinical features of reversible posterior encephalopathy syndrome(PRES)after allogeneic hematopoietic stem cell transplantation(allo-HSCT)in children.Methods:The clinical data of six children who developed PRES after undergoing allo-HSCT in the Department of Hematology of Wuhan Children's Hospital from June 2016 to December 2022 were retrospectively analyzed,and their clinical characteristics,imaging examination,laboratory examination,and treatment regression were summarized.Results:Among 281 children underwent allo-HSCT,6 cases(2.14％)developed PRES,with a median age of 5.1(1.5-9.7)years old.4 cases underwent related haploidentical donor transplantation,and 2 cases underwent sibling allografting and unrelated donor allografting donor transplantation,respectively.All six children had an acute onset of illness,with clinical manifestations of nausea and vomiting,seizures,psychiatric disorders,visual disturbances.The five cases elevated blood pressure.All children with PRES were treated with oral immunosuppressive drugs during seizures,and 3 cases were combined with different degrees of graft-versus-host disease.Most of the children showed effective improvement in clinical symptoms and imaging after adjusting/discontinuing suspected medications(cyclosporine,etc.)and symptomatic supportive treatments(oral antihypertensive,diazepam for antispasmodic,mannitol to lower cranial blood pressure),and one of them relapsed more than 8 months after the first seizure.Conclusion:PRES is rare after hematopoietic stem cell transplantation in children,and its onset may be related to hypertension,cytotoxic drugs,graft-versus-host disease,etc.Most of them can be recovered after active treatment,but not completely reversible,and the prognosis of those who combined with TMA is poor.

This study was aimed at understanding the molecular characteristics of Plasmodium ovale curtisi and Plasmodi-um ovale wallikeri imported cases in Chongqing,to provide data to support monitoring and control efforts.In a retrospective analysis,26 Plasmodium ovale archival blood samples were characterized with respect to five molecular markers(Cox1,Cytb,Tra,Dhfr,and K13)from 2013 to 2023.PCR amplification of partial fragments of the Cox1,Cytb,and Tra genes of Plas-modium ovale was performed to distinguish the two subspecies.The drug-resistance Dhfr and K13 genes of Plasmodium ovale were amplified with PCR assays followed by DNA sequencing,and the sequences were aligned.The differentiation of 26 cases of Plasmodium ovale(14 cases of curtisi subspecies and 12 cases of wallikeri subspecies)according to ssrRNA was consistent with the classification results of Cox1,Cytb,and TRA genes.Thirteen single nucleotide dimorphism sites were identified in Cox 1,including the 145 and 153 loci,with only variations in amino acids M176I and I288V at loci 528 and 862,and N337H mutation in one sample.Twelve base substitutions were found among Cytb gene subspecies,with only the M248I mutation in amino acid 248.A total of 49 nucleotide dimorphism sites in Tra gene,resulting in 18 amino acid mutations,were identified be-tween the two subspecies.In the curtisi type sample,the poc1 type had more PINTINPINTIN and TITPIS amino acid units than the poc2 type.The mutation rate of the Dhfr gene was rel-atively high:25％of the samples showed S58R mutations.The K13 gene subspecies was not homozygous,and one sample was heterozygous.This study confirmed the dimorphism and mutation sites between Plasmodium ovale curtisi and wallikeri sub-species in Cox1,Cytb,Tra,Dhfr,and K13 gene fragments of imported Plasmodium ovale in Chongqing,thus enriching knowledge regarding gene polymorphisms in Plasmodium ovale curtisi and wallikeri imported cases.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

The construction method and simulation parameter settings for the discrete element model of Jianwei Xiaoshi Granules, as the primary material of Jianwei Xiaoshi Tablets, are not yet clear. The accuracy of the simulation model significantly influences the dynamic response characteristics between granules. Therefore, it is necessary to calibrate the parameters to improve the accuracy of the simulation parameters. Using the repose angle of Jianwei Xiaoshi Granules as the response value, the response surface methodology was employed to optimize and calibrate the discrete element parameters. Physical experiments were conducted to determine the physical properties of Jianwei Xiaoshi Granules. Based on the Hertz-Mindlin with Johnson-Kendall-Roberts(JKR) V2 model and virtual simulation methods, a repose angle determination model was constructed in EDEM software. The repose angle was measured using image analysis and numerical fitting methods. The Plackett-Burman experiment was used to screen the initial parameters for significance in the discrete element simulation. The significant parameters were then subjected to a steepest ascent experiment to determine the optimal parameter range. Furthermore, based on the Box-Behnken experiment, a second-order regression equation between significant parameters and repose angle was established, with the repose angle of 37.64° in the physical experiment as the target value. The regression equation was optimized and solved. The significance screening experiment revealed that the granule-granule static friction coefficient, granule-granule rolling friction, and granule-steel plate rolling friction of Jianwei Xiaoshi Granules significantly influenced the simulated repose angle. The optimal parameter combination was found to be 0.330, 0.222, and 0.229. The simulation results with this optimal parameter combination showed that there was no significant difference between the simulated repose angle and the repose angle obtained in the physical experiment, with a relative error of 0.05%, which further validated the reliability of the calibrated discrete element parameters for Jianwei Xiaoshi Granules.
Drugs, Chinese Herbal/chemistry* ; Calibration ; Computer Simulation

Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
Drugs, Chinese Herbal/chemistry* ; Tablets/chemistry* ; Tensile Strength ; Quality Control ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared

OBJECTIVE: To explore the immunological mechanisms underlying spermatogenetic malfunction in patients with non-obstructive azoospermia (NOA) based on bioinformatics and machine learning, and to screen out the key genes associated with spermatogenesis failure. METHODS: NOA-related datasets were obtained from the GEO database, and the differentially expressed genes identified by differential analysis and weighted gene co-expression network analysis (WGCNA). A model of spermatogenesis scoring was established for analysis of the immunological microenvironment and cell interaction networks related to spermatogenesis failure. The key genes were screened out by machine learning, followed by analysis of their correlation with T cells and macrophages. An NOA mouse model was constructed for validation of transcriptome sequencing. RESULTS: Seventy-five differentially expressed genes were identified for the establishment of the spermatogenesis scoring model. The low spermatogenesis score group showed a higher infiltration of the immune cells, with an increased proportion of T cells and macrophages and a correlation of cell interaction signals with immunity. SOX30, KCTD19, ASRGL1 and DRC7 were identified by machine learning as the key genes related to spermatogenesis, with down-regulated expressions in the NOA group, and their expression levels negatively correlated with the infiltration of T cells and macrophages. The accuracy of the spermatogenesis scoring and machine learning models, as well as the trend of the expression levels of the key genes, was successfully validated with the transcriptome sequencing data on the NOA mouse testis. CONCLUSION The development of NOA is closely associated with enhanced immunological microenvironment in the testis. T cells and macrophages may play important roles in spermatogenesis failure. SOX30, KCTD19, ASRGL1 and DRC7 are potential biomarkers for the diagnosis and treatment of NOA.
Male ; Azoospermia/genetics* ; Machine Learning ; Animals ; Computational Biology ; Mice ; Humans ; Spermatogenesis/genetics* ; Gene Expression Profiling ; Macrophages/immunology* ; Gene Regulatory Networks ; T-Lymphocytes/immunology* ; Transcriptome