Background::B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM.Methods::Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM at the First Affiliated Hospital, School of Medicine, Zhejiang University was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells.Results::In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow ( P = 0.0125), lower percentages of CAR-T cells in the peripheral blood at peak ( P = 0.0375), and higher percentages of CD8 + T cells ( P = 0.0340). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) ( P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [ P = 0.004], IRF4 [ P = 0.024], and CREBBP [ P = 0.041]), number of multisite mutations, and resistance-related mutation ( ERBB4, P = 0.040) were independent risk factors for PFS. Conclusion::Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy.

Given that ovarian stimulation is vital for assisted reproductive technology (ART) and results in elevated serum estrogen levels, exploring the impact of elevated estrogen exposure on oocytes and embryos is necessary. We investigated the effects of various ovarian stimulation treatments on oocyte and embryo morphology and gene expression using a mouse model and estrogen-treated mouse embryonic stem cells (mESCs). Female C57BL/6J mice were subjected to two types of conventional ovarian stimulation and ovarian hyperstimulation; mice treated with only normal saline served as controls. Hyperstimulation resulted in high serum estrogen levels, enlarged ovaries, an increased number of aberrant oocytes, and decreased embryo formation. The messenger RNA (mRNA)‍-sequencing of oocytes revealed the dysregulated expression of lysine-specific demethylase 6b (Kdm6b), which may be a key factor indicating hyperstimulation-induced aberrant oocytes and embryos. In vitro, Kdm6b expression was downregulated in mESCs treated with high-dose estrogen; treatment with an estrogen receptor antagonist could reverse this downregulated expression level. Furthermore, treatment with high-dose estrogen resulted in the upregulated expression of histone H3 lysine 27 trimethylation (H3K27me3) and phosphorylated H2A histone family member X (γ-H2AX). Notably, knockdown of Kdm6b and high estrogen levels hindered the formation of embryoid bodies, with a concomitant increase in the expression of H3K27me3 and γ-H2AX. Collectively, our findings revealed that hyperstimulation-induced high-dose estrogen could impair the demethylation of H3K27me3 by reducing Kdm6b expression. Accordingly, Kdm6b could be a promising marker for clinically predicting ART outcomes in patients with ovarian hyperstimulation syndrome.
Female ; Mice ; Demethylation/drug effects* ; Embryonic Stem Cells ; Estrogens/administration & dosage* ; Gene Expression/drug effects* ; Histones/metabolism* ; Jumonji Domain-Containing Histone Demethylases/metabolism* ; Mice, Inbred C57BL ; Oocytes ; Ovary/drug effects* ; Reproductive Techniques, Assisted ; Animals

Purpose To investigate the feasibility of multi-sequence MRI-based radiomics for predicting epidermal growth factor receptor(EGFR)mutation status in brain metastases from non-small cell lung cancer(NSCLC).Materials and Methods This retrospective study included 237 patients with NSCLC brain metastases from the Second Xiangya Hospital of Central South University(January 1,2017 to December 31,2023)who underwent EGFR genetic testing.All patients underwent pretreatment brain MRI including contrast-enhanced T1-weighted,T2-weighted FLAIR,and T2-weighted sequences,along with chest CT for primary lung lesions.EGFR mutations were identified in 120 patients.Using December 31,2021 as the cutoff date,patients were divided into training(n=146)and validation(n=91)cohorts.Senior radiologists delineated brain metastases on multi-sequence MRI and primary lesions on CT.A total of 851 radiomic features were extracted using PyRadiomics.Following feature selection,machine learning models were constructed using support vector machine algorithm and compared with least absolute shrinkage and selection operator-derived radiomic signatures.Five models were developed:three single-sequence MRI models,a multi-sequence MRI fusion model,and a CT model,with diagnostic performance evaluated by area under the receiver operating characteristic curve.Results The multi-sequence MRI fusion model demonstrated superior performance across all imaging types.The least absolute shrinkage and selection operator and support vector machine models achieved training set area under the curve of 0.854(95%CI 0.748-0.960)and 0.948(95%CI 0.923-0.973),respectively,and validation set area under the curve of 0.810(95%CI 0.751-0.869)and 0.951(95%CI 0.917-0.985),respectively.The optimal prediction model utilized support vector machine algorithm with multi-sequence MRI features.Conclusion Pretreatment multi-sequence MRI radiomics combined with machine learning accurately predicts EGFR mutation status in NSCLC patients with brain metastases.

Purpose To investigate the feasibility of multi-sequence MRI-based radiomics for predicting epidermal growth factor receptor(EGFR)mutation status in brain metastases from non-small cell lung cancer(NSCLC).Materials and Methods This retrospective study included 237 patients with NSCLC brain metastases from the Second Xiangya Hospital of Central South University(January 1,2017 to December 31,2023)who underwent EGFR genetic testing.All patients underwent pretreatment brain MRI including contrast-enhanced T1-weighted,T2-weighted FLAIR,and T2-weighted sequences,along with chest CT for primary lung lesions.EGFR mutations were identified in 120 patients.Using December 31,2021 as the cutoff date,patients were divided into training(n=146)and validation(n=91)cohorts.Senior radiologists delineated brain metastases on multi-sequence MRI and primary lesions on CT.A total of 851 radiomic features were extracted using PyRadiomics.Following feature selection,machine learning models were constructed using support vector machine algorithm and compared with least absolute shrinkage and selection operator-derived radiomic signatures.Five models were developed:three single-sequence MRI models,a multi-sequence MRI fusion model,and a CT model,with diagnostic performance evaluated by area under the receiver operating characteristic curve.Results The multi-sequence MRI fusion model demonstrated superior performance across all imaging types.The least absolute shrinkage and selection operator and support vector machine models achieved training set area under the curve of 0.854(95%CI 0.748-0.960)and 0.948(95%CI 0.923-0.973),respectively,and validation set area under the curve of 0.810(95%CI 0.751-0.869)and 0.951(95%CI 0.917-0.985),respectively.The optimal prediction model utilized support vector machine algorithm with multi-sequence MRI features.Conclusion Pretreatment multi-sequence MRI radiomics combined with machine learning accurately predicts EGFR mutation status in NSCLC patients with brain metastases.

BACKGROUND: B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM. METHODS: Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells. RESULTS: In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow (P = 0.013), lower percentages of CAR-T cells in the peripheral blood at peak (P = 0.037), and higher percentages of CD8+ T cells (P = 0.034). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) (P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [P = 0.004], IRF4 [P = 0.024], and CREBBP [P = 0.041]), number of multisite mutations, and resistance-related mutation (ERBB4, P = 0.040) were independent risk factors for PFS. CONCLUSION: Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy. REGISTERATION Chinese Clinical Trial Registry (ChiCTR2100046474) and National Clinical Trial (NCT04670055, NCT05430945).

Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.

Aconitum kusnezoffii is a traditional Chinese medicine of Ranunculaceae family. Its toxicity is relatively strong, and its dosage is similar to that of poisoning. In clinical practice, poisoning events are often caused by excessive dosage or improper use. There is no specific antidote for kusnezoff root poisoning. Severe kusnezoff root poisoning can cause malignant arrhythmia and even death.A case of severe kusnezoff monkshood poisoning was reported in January 2021, which was treated with nificaran hydrochloride for injection in the emergency medicine department of the First Hospital of Handan City. The patient developed ventricular tachycardia, ventricular fibrillation and AS syndrome. In addition to conventional treatment, the patient did not have arrhythmia again after intravenous injection of 25 mg of nifekalan load and continuous pumping of 0.4 mg/kg/h for 7 hours, and did not relapse after discontinuation of nifekalan 24 hours later. It is suggested that the malignant arrhythmia caused by clinical severe kusnezoff monkshood poisoning can be controlled by nifekalan. Whether nifekalan is superior to conventional antiarrhythmic drugs still needs more accumulation and verification of clinical application data.

Aconitum kusnezoffii is a traditional Chinese medicine of Ranunculaceae family. Its toxicity is relatively strong, and its dosage is similar to that of poisoning. In clinical practice, poisoning events are often caused by excessive dosage or improper use. There is no specific antidote for kusnezoff root poisoning. Severe kusnezoff root poisoning can cause malignant arrhythmia and even death.A case of severe kusnezoff monkshood poisoning was reported in January 2021, which was treated with nificaran hydrochloride for injection in the emergency medicine department of the First Hospital of Handan City. The patient developed ventricular tachycardia, ventricular fibrillation and AS syndrome. In addition to conventional treatment, the patient did not have arrhythmia again after intravenous injection of 25 mg of nifekalan load and continuous pumping of 0.4 mg/kg/h for 7 hours, and did not relapse after discontinuation of nifekalan 24 hours later. It is suggested that the malignant arrhythmia caused by clinical severe kusnezoff monkshood poisoning can be controlled by nifekalan. Whether nifekalan is superior to conventional antiarrhythmic drugs still needs more accumulation and verification of clinical application data.

OBJECTIVE: To assess the association of CYP2C19 and CYP3A5 gene polymorphisms with the risk of myocardial infarction. METHODS: Five hundred patients with myocardial infarction and 500 healthy controls were randomly selected. Fluorescent PCR and Sanger sequencing were used to detect the CYP2C19 and CYP3A5 gene polymorphisms. Logistic regression was used to analyze the correlation between the polymorphisms and myocardial infarction. Quanto software was used to evaluate the statistical power. RESULTS: The two groups had significant difference in the frequency of AG, GG genotypes and A allele of the CYP2C19 gene rs4986893 locus and the AA, AG, GG genotypes and G allele of the CYP3A5 gene rs776746 locus ( P<0.05), but not in the frequency of genotypes and alleles of CYP2C19 gene rs4244285 and rs12248560 loci, and the AA genotype of the rs4986893 locus. After correction for age, gender, and body mass index, Logistic regression indicated that the AG genotype and A allele of the CYP2C19 gene rs4986893 locus, and the GG genotype and G allele of CYP3A5 gene rs776746 locus are associated with susceptibility of myocardial infarction, while rs4986893 GG genotype and AA and AG genotypes of rs776746 may confer a protective effect. Based on the sample size and allele frequency, analysis with Quanto software suggested that the result of this study has a statistical power of 99%. CONCLUSION CYP2C19 and CYP3A5 gene polymorphisms may increase the risk for myocardial infarction.
Cytochrome P-450 CYP2C19/genetics* ; Cytochrome P-450 CYP3A/genetics* ; Gene Frequency ; Genotype ; Humans ; Myocardial Infarction/genetics* ; Polymorphism, Genetic ; Polymorphism, Single Nucleotide