OBJECTIVE To establish a method for simultaneous determination of plasma concentration of lamotrigine（LTG）， levetiracetam（LEV） and perampanel（PER） in children with epilepsy and apply this method in clinical practice. METHODS Plasma proteins were precipitated with acetonitrile. Using PER-D 5 as internal standard， ultra-high performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） method was adopted. The determination was performed on ACQUITY UPLC HSS T3 C 18 column with mobile phase consisted of 0.1% formic acid with 5 mmol/L ammonium acetate-acetonitrile （gradient elution） at the flow rate of 0.3 mL/min. The column temperature was 40 ℃， and sample size was 5 μL. The analysis time was 5 min. The electrospray ionization source and multiple reaction monitoring mode were used for positive ion scanning. The ion pairs used for quantitative analysis of LTG， LEV， PER and internal standard were m / z 255.9→144.9， m / z 171.1→126.1， m / z 350.1→219.0 and m / z 354.9→220.2， respectively. The steady-state trough concentrations of the aforementioned drugs in the plasma of 14 pediatric epilepsy patients receiving combination therapy were determined using the same UPLC-MS/MS method as above. RESULTS The linear ranges of LTG， LEV and PER were 0.15-24 μg/mL （ R 2 ＞0.993）， 0.312 5-50 μg/mL （ R 2 ＞0.997） and 6.25-1 000 ng/mL （ R 2 ＞0.997）， respectively. The lower limits of quantification were 0.15 μg/mL， 0.312 5 μg/mL and 6.25 ng/mL， respectively. RSDs of intraday and interday precision tests of the three drugs were no more than 9.83%， and the accuracies （relative errors） were between －9.33% and 13.72%（ n ＝6 or n ＝18）； the average extraction recovery rates were 86.4%-97.9%， and the average matrix effects were 86.9%-110.0% （ n ＝6）. The absolute values of the relative errors in the stability tests were all below 15%. The steady-state trough concentrations of LTG， LEV and PER were （5.64±4.03）μg/mL， （10.67±8.78）μg/mL and（450.20±251.27）ng/mL， respectively； the rates of achieving target trough concentrations were 71.4%， 37.5% and 84.6%， respectively. CONCLUSIONS The established UPLC-MS/MS method is specific， rapid and suitable for the plasma concentration monitoring in epileptic children receiving combination therapy.

With the aging population and the widespread implementation of lung cancer screening programs, an increasing number of elderly patients are undergoing curative lung resection. Due to diminished physiological reserve and complex comorbidities, this demographic exhibits a significantly higher incidence of severe perioperative complications (defined as Clavien-Dindo grade≥Ⅲ), which adversely affects both perioperative safety and long-term prognosis. In recent years, the research paradigm has shifted from univariate analysis toward multidimensional risk integration and the development of predictive models. This article systematically reviews the key risk factors for severe perioperative complications in elderly lung cancer patients, encompassing biological aging processes, frailty and sarcopenia, cardiopulmonary and renal functional reserves, inflammatory-immune and coagulation status, and perioperative interventions. Furthermore, it traces the evolution of risk assessment tools from traditional regression models to machine learning models that integrate multimodal data. The review also discusses common challenges in this field, including the standardization of outcome definitions, external validation, calibration assessment, and clinical translation. Future efforts should prioritize the deep integration of predictive tools with clinical decision support systems to establish a closed-loop care pathway from risk identification to stratified intervention, thereby effectively reducing complication rates and enhancing surgical outcomes for elderly lung cancer patients.

Cancer immunotherapy is currently a very promising therapeutic strategy for treating tumors. However, its effectiveness is restricted by insufficient antigenicity and an immunosuppressive tumor microenvironment (ITME). Pyroptosis, a unique form of programmed cell death (PCD), causes cells to swell and rupture, releasing pro-inflammatory factors that can enhance immunogenicity and remodel the ITME. Nanomaterials, with their distinct advantages and different techniques, are increasingly popular, and nanomaterial-based delivery systems demonstrate significant potential to potentiate, enable, and augment pyroptosis. This review summarizes and discusses the emerging field of nanomaterials-induced pyroptosis, focusing on the mechanisms of nanomaterials-induced pyroptosis pathways and strategies to activate or enhance specific pyroptosis. Additionally, we provide perspectives on the development of this field, aiming to accelerate its further clinical transition.

Objective:To describe the body mass index (BMI) level and long-term trends of Chinese older adults aged 65 and above.Methods:Older adults aged 65 and above from six waves (2002-2018) of the China Longitudinal Healthy Longevity Survey were selected as the study population. Multiple cross-sectional design with six survey waves conducted in 2002, 2005, 2008, 2011, 2014, and 2018 was adopted, enrolling 15 647, 15 358, 15 622, 9 166, 6 302, and 12 417 participants, respectively. Additionally, a total of 13, 755 participants were included in the cohort study design. Relevant information was collected through questionnaires and physical examinations. The χ2 trend test was used to compare the changes in the rates of underweight and overweight/obesity over the years, and the linear mixed-e?ects model (LMM) was used to fit trajectory curves of BMI changes with advancing age in older adults. Results:The baseline ages of the participants included in 2002, 2005, 2008, 2011, 2014, and 2018 were (85.16±11.26), (84.23±11.83), (84.99±12.16), (81.10±11.86), (78.89±11.30), and (83.08±12.42) years, respectively, with a relatively high proportion of females and rural residents. In the cohort study, the 13 755 participants had a median ( Q1, Q3) follow-up time of 6.5 (5.2, 10.0) years, with a cumulative follow-up duration of 109 041 person-years. In each wave, males had higher BMI than females, urban residents had higher BMI than rural residents, and BMI gradually decreased with increasing age (all P<0.001). The mean BMI of older adults in China increased from (19.37±3.80) kg/m2 in 2002 to (22.04±4.01) kg/m2 in 2018 ( P<0.001). Across all survey years, the prevalence of underweight was consistently higher in women than in men and in rural areas than in urban areas, with an upward trend as age increased (all P<0.001). In 2018, the underweight rates in the 65-79, 80-89, 90-99, and ≥100-year-old age groups were 8.0%, 16.7%, 26.2%, and 35.5%, respectively. Meanwhile, the prevalence of overweight/obesity was higher in men than in women and in urban areas than in rural areas, showing a declining trend with advancing age (all P<0.001). The prevalence of underweight among the older adults decreased significantly from 45.2% in 2002 to 18.9% in 2018 ( P<0.001), while the prevalence of overweight/obesity increased from 11.0% in 1998 to 29.6% in 2018 ( P<0.001). The trajectory curves fitted by the LMM model showed that individuals born in later decades had higher BMI levels at the same age compared to earlier cohorts. Conclusion:From 2002 to 2018, the BMI level among Chinese older adults showed an increasing trend. The prevalence of underweight showed a declining trend, while the rates of obesity and overweight increased. However, the underweight rate remained notably high among the oldest old.

Objective:To describe the body mass index (BMI) level and long-term trends of Chinese older adults aged 65 and above.Methods:Older adults aged 65 and above from six waves (2002-2018) of the China Longitudinal Healthy Longevity Survey were selected as the study population. Multiple cross-sectional design with six survey waves conducted in 2002, 2005, 2008, 2011, 2014, and 2018 was adopted, enrolling 15 647, 15 358, 15 622, 9 166, 6 302, and 12 417 participants, respectively. Additionally, a total of 13, 755 participants were included in the cohort study design. Relevant information was collected through questionnaires and physical examinations. The χ2 trend test was used to compare the changes in the rates of underweight and overweight/obesity over the years, and the linear mixed-e?ects model (LMM) was used to fit trajectory curves of BMI changes with advancing age in older adults. Results:The baseline ages of the participants included in 2002, 2005, 2008, 2011, 2014, and 2018 were (85.16±11.26), (84.23±11.83), (84.99±12.16), (81.10±11.86), (78.89±11.30), and (83.08±12.42) years, respectively, with a relatively high proportion of females and rural residents. In the cohort study, the 13 755 participants had a median ( Q1, Q3) follow-up time of 6.5 (5.2, 10.0) years, with a cumulative follow-up duration of 109 041 person-years. In each wave, males had higher BMI than females, urban residents had higher BMI than rural residents, and BMI gradually decreased with increasing age (all P<0.001). The mean BMI of older adults in China increased from (19.37±3.80) kg/m2 in 2002 to (22.04±4.01) kg/m2 in 2018 ( P<0.001). Across all survey years, the prevalence of underweight was consistently higher in women than in men and in rural areas than in urban areas, with an upward trend as age increased (all P<0.001). In 2018, the underweight rates in the 65-79, 80-89, 90-99, and ≥100-year-old age groups were 8.0%, 16.7%, 26.2%, and 35.5%, respectively. Meanwhile, the prevalence of overweight/obesity was higher in men than in women and in urban areas than in rural areas, showing a declining trend with advancing age (all P<0.001). The prevalence of underweight among the older adults decreased significantly from 45.2% in 2002 to 18.9% in 2018 ( P<0.001), while the prevalence of overweight/obesity increased from 11.0% in 1998 to 29.6% in 2018 ( P<0.001). The trajectory curves fitted by the LMM model showed that individuals born in later decades had higher BMI levels at the same age compared to earlier cohorts. Conclusion:From 2002 to 2018, the BMI level among Chinese older adults showed an increasing trend. The prevalence of underweight showed a declining trend, while the rates of obesity and overweight increased. However, the underweight rate remained notably high among the oldest old.

Objective·To investigate the changes in transcriptome and chromatin accessibility during the differentiation of human embryonic stem cells(hESCs)into neural progenitor cells(NPCs)using in vitro differentiation models and high-throughput multi-omics sequencing technologies.Methods·hESCs were first induced to differentiate into NPCs in vitro using the embryoid body formation method,and cells at both stages were collected.The cell phenotypes were identified by reverse transcription-quantitative real-time PCR(RT-qPCR)and immunofluorescence(IF)staining.Transcriptome sequencing(RNA-seq)was conducted to detect and analyze the differentially expressed genes(DEGs)between hESCs and NPCs.The assay for transposase-accessible chromatin with high-throughput sequencing(ATAC-seq)was employed to assess chromatin accessibility changes between hESCs and NPCs.Motif enrichment analysis was performed on differentially accessible chromatin regions to discover potential regulatory transcription factors.Finally,an integrated analysis of RNA-seq and ATAC-seq data and the protein-protein interaction(PPI)network were performed to identify key genes and regulatory pathways involved in the early stages of neural differentiation in vitro.Results·Both RT-qPCR and IF results indicated that the expression levels of pluripotency markers(NANOG and POU5F1)were high at the hESC stage but significantly decreased at the NPC stage,while early neural differentiation markers(PAX6,SOX1,and NES)were minimally expressed at the hESC stage but markedly upregulated at the NPC stage.RNA-seq analysis revealed that compared to the hESC stage,there were 5 597 genes upregulated and 3 654 genes downregulated at the NPC stage.Gene function enrichment analysis showed that the upregulated genes at the NPC stage were enriched in the functions related to neural development.ATAC-seq analysis demonstrated a total of 27 491 genomic regions had significant changes in chromatin accessibility during the differentiation from hESC to NPC,with 12 381 regions showing increased accessibility and 15 110 regions showing decreased accessibility.Motif enrichment analysis revealed that transcription factor genes such as DLX1 and LHX2 might play an important role in the differentiation process from hESCs into NPCs.Integrated analysis of RNA-seq and ATAC-seq data revealed that overlapping genes with high expression at the NPC stage were mainly enriched in axon guidance,forebrain development,and neuron migration.After neural differentiation,the expression levels of CTNND2 and LHX2 genes increased,and the chromatin accessibility of related genomic regions also increased.PPI network analysis indentified candidate downstream genes including PRKACA,CDH2,and ERBB4.Conclusion·The in vitro differentiation model of hESCs combined with high-throughput multi-omics sequencing technologies can be used to depict the changes in transcriptome and chromatin accessibility during the differentiation of hESCs into NPCs.In this process,the expression levels of genes related to axon guidance,forebrain development,and neuronal migration pathways increase and related chromatin accessibility is enhanced.

Objective·To investigate the changes in transcriptome and chromatin accessibility during the differentiation of human embryonic stem cells(hESCs)into neural progenitor cells(NPCs)using in vitro differentiation models and high-throughput multi-omics sequencing technologies.Methods·hESCs were first induced to differentiate into NPCs in vitro using the embryoid body formation method,and cells at both stages were collected.The cell phenotypes were identified by reverse transcription-quantitative real-time PCR(RT-qPCR)and immunofluorescence(IF)staining.Transcriptome sequencing(RNA-seq)was conducted to detect and analyze the differentially expressed genes(DEGs)between hESCs and NPCs.The assay for transposase-accessible chromatin with high-throughput sequencing(ATAC-seq)was employed to assess chromatin accessibility changes between hESCs and NPCs.Motif enrichment analysis was performed on differentially accessible chromatin regions to discover potential regulatory transcription factors.Finally,an integrated analysis of RNA-seq and ATAC-seq data and the protein-protein interaction(PPI)network were performed to identify key genes and regulatory pathways involved in the early stages of neural differentiation in vitro.Results·Both RT-qPCR and IF results indicated that the expression levels of pluripotency markers(NANOG and POU5F1)were high at the hESC stage but significantly decreased at the NPC stage,while early neural differentiation markers(PAX6,SOX1,and NES)were minimally expressed at the hESC stage but markedly upregulated at the NPC stage.RNA-seq analysis revealed that compared to the hESC stage,there were 5 597 genes upregulated and 3 654 genes downregulated at the NPC stage.Gene function enrichment analysis showed that the upregulated genes at the NPC stage were enriched in the functions related to neural development.ATAC-seq analysis demonstrated a total of 27 491 genomic regions had significant changes in chromatin accessibility during the differentiation from hESC to NPC,with 12 381 regions showing increased accessibility and 15 110 regions showing decreased accessibility.Motif enrichment analysis revealed that transcription factor genes such as DLX1 and LHX2 might play an important role in the differentiation process from hESCs into NPCs.Integrated analysis of RNA-seq and ATAC-seq data revealed that overlapping genes with high expression at the NPC stage were mainly enriched in axon guidance,forebrain development,and neuron migration.After neural differentiation,the expression levels of CTNND2 and LHX2 genes increased,and the chromatin accessibility of related genomic regions also increased.PPI network analysis indentified candidate downstream genes including PRKACA,CDH2,and ERBB4.Conclusion·The in vitro differentiation model of hESCs combined with high-throughput multi-omics sequencing technologies can be used to depict the changes in transcriptome and chromatin accessibility during the differentiation of hESCs into NPCs.In this process,the expression levels of genes related to axon guidance,forebrain development,and neuronal migration pathways increase and related chromatin accessibility is enhanced.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Objective To investigate the relationships of pre-radiotherapy body weight,gender,age,EBVDNA,hemoglobin,plasma albumin,and induction chemotherapy regimen with the changes of target area and lymph node volume in adaptive radiotherapy,so as to provide a reference for the timing and population selection of adaptive radiotherapy.Methods A retrospective analysis was conducted on 34 patients who received the first course of radiotherapy at Sun Yat-sen University Cancer Center from January 2022 to November 2022.All patients underwent CT scans again after 20 sessions of radiotherapy for developing the secondary radiotherapy plans.The body weight,gender,age,tumor stage,hemoglobin,plasma albumin,induction chemotherapy regimen,and EBVDNA were collected.Results The tumor volume reduction in the primary focus was more evident in patients with pre-treatment plasma albumin≥40 g/L than in those with pre-treatment plasma albumin<40 g/L(t=3.971,P=0.001),and in patients with pretreatment EBVDNA≤4000 copies/mL than in those with pretreatment EBVDNA>4000 copies/mL(t=4.080,P=0.001).Pearson analysis showed that GTVnx volume difference was positively correlated with pre-radiotherapy GTVnx volume(r=0.444,P=0.009),right parotid gland volume difference(r=0.737,P<0.001),left parotid gland volume difference(r=0.435,P=0.010),and hemoglobin(r=0.722,P<0.001).Conclusion The reduction in tumor volume during radiotherapy is more pronounced in nasopharyngeal cancer patients with normal plasma albumin level and those with pretreatment EBVDNA≤4000 copies/mL.The pre-radiotherapy treatment volume of primary focus,parotid gland volume change before and after radiotherapy,and pre-radiotherapy EBVDNA,hemoglobin and plasma albumin levels can be used to predict the degree of tumor volume shrinkage during radiotherapy,providing a reference for the selection of the timing of adaptive radiotherapy for nasopharyngeal carcinoma.