Aberrant activation of Receptor Tyrosine Kinases(RTKs)is a well-established trigger of tumorigenesis,and the over-use of RTK inhibitors often leads to drug resistance and tumor recurrence.While current Drug-Target Int...Aberrant activation of Receptor Tyrosine Kinases(RTKs)is a well-established trigger of tumorigenesis,and the over-use of RTK inhibitors often leads to drug resistance and tumor recurrence.While current Drug-Target Interaction(DTI)prediction methods(including those based on heterogeneous information networks)have shown promise,they remain limited in their ability to fully capture the nature of DTIs and often lack interpretability.To overcome these limitations,this study introduces a novel hybrid optimization model termed MDBO-RF,which integrates a Modified Dung Beetle Optimizer(MDBO)with Random Forest(RF).The key innovation lies in the enhancement of the DBO algorithm through a quaternion-based learning mechanism and the Cauchy mutation strategy,specifically designed to overcome the slow convergence and susceptibility to local optima that plague traditional metaheuristic algorithms used for hyperparameter tuning.The model leverages commonly used molecular descriptors to enhance the prediction of Tyrosine Kinase(TK)inhibitory activity and enable efficient compound screening.Our results demonstrate that MDBO-RF achieves a 3.41%increase in prediction accuracy compared to the standard RF model and outperforms several other contemporary machine learning approaches.The model effectively streamlines the RTK inhibitor screening process by improving prediction accuracy in multi-target competitive binding scenarios and reducing false-positive screening due to off-target effects.This work underscores the value of hybrid optimization strategies in bioinformatics and provides a robust,interpretable tool for accelerating drug discovery.展开更多
目的建立咪喹莫特(IMQ)和环磷酰胺(CTX)诱导的C57BL/6J小鼠斑秃模型并比较其特点。方法将104只C57BL/6J小鼠分为IMQ组(头部/背部涂抹5%0.05 g IMQ,每周4次,持续4周)与CTX组(脱毛后腹腔注射3 mg/20 g CTX),对照组对应区域涂抹凡士林(0.05...目的建立咪喹莫特(IMQ)和环磷酰胺(CTX)诱导的C57BL/6J小鼠斑秃模型并比较其特点。方法将104只C57BL/6J小鼠分为IMQ组(头部/背部涂抹5%0.05 g IMQ,每周4次,持续4周)与CTX组(脱毛后腹腔注射3 mg/20 g CTX),对照组对应区域涂抹凡士林(0.05 g)或仅脱毛。在建模过程中,持续监测小鼠的体质量、毛发变化。HE染色观察皮肤毛囊变化和表皮厚度。采用ELISA法检测小鼠头、背部皮损和血清中干扰素-γ(IFN-γ)、白细胞介素-15(IL-15)、肿瘤坏死因子-α(TNF-α)的水平。结果IMQ建模周期为4周,第4周时头、背部皮损处形成约1 cm×1 cm面积大小的片状裸露区,并伴有少量鳞屑;CTX建模周期为14 d,腹腔注射CTX后5 d,头、背部皮损毛发脱落,皮肤呈暗灰色。组织病理学显示,两种模型组小鼠的毛囊较对照组有明显改变,且IMQ组小鼠的表皮增厚更为显著。IMQ和CTX两种建模方式均可导致皮肤组织和血清中的IFN-γ、IL-15、TNF-α显著升高(P<0.0001),其中IMQ建模方式的炎症因子升高更为明显(P<0.0001,P<0.001,P<0.01)。结论IMQ和CTX均能在C57BL/6J小鼠头、背部成功诱导斑秃模型,相较于IMQ建模方式,CTX的建模方式具有建模时间短、存活率高、成模率高的优势,是一种相对稳定的斑秃动物建模方法。展开更多
文摘目的分析达拉非尼联合曲美替尼治疗黑色素瘤致发热综合征的发生特点及规律,为临床安全用药提供参考。方法收集13例北京大学肿瘤医院(以下简称“某院”)应用达拉非尼联合曲美替尼方案治疗BRAF V600突变的黑色素瘤术后辅助或不可切除晚期患者的病例资料,同时检索从建库起至2024年6月30日中国知网、万方数据、Web of Science、Elsevier、PubMed数据库收载的相关个案报道,汇总相关数据进行分析。结果纳入分析的患者共33例,其中某院相关病例13例,国内外文献相关报道20例。男性18例,女性15例,年龄为35~77岁。首次中位发热时间16.5(1~240)d,3例患者发热后未进行处理自行好转,30例患者经中断药物治疗或非甾体抗炎药及类固醇处理后不再发热,随后20例患者以单药达拉非尼、达拉非尼联合曲美替尼原剂量或减量重启治疗,继续治疗后仍有18例患者再次出现发热。其中有20例患者经历2次及以上发热,3例患者因发生严重不良反应致永久停药。结论达拉非尼联合曲美替尼致首次发热的时间大多在用药后前30 d内(69.69%),60.60%患者经历2次及以上发热,本研究发现中断治疗、减少药物剂量、加用非甾体抗炎药或类固醇药物是改善发热综合征的关键管理策略。
基金National Key Research and Development Program of China(No.2022YFD1802104).
文摘Aberrant activation of Receptor Tyrosine Kinases(RTKs)is a well-established trigger of tumorigenesis,and the over-use of RTK inhibitors often leads to drug resistance and tumor recurrence.While current Drug-Target Interaction(DTI)prediction methods(including those based on heterogeneous information networks)have shown promise,they remain limited in their ability to fully capture the nature of DTIs and often lack interpretability.To overcome these limitations,this study introduces a novel hybrid optimization model termed MDBO-RF,which integrates a Modified Dung Beetle Optimizer(MDBO)with Random Forest(RF).The key innovation lies in the enhancement of the DBO algorithm through a quaternion-based learning mechanism and the Cauchy mutation strategy,specifically designed to overcome the slow convergence and susceptibility to local optima that plague traditional metaheuristic algorithms used for hyperparameter tuning.The model leverages commonly used molecular descriptors to enhance the prediction of Tyrosine Kinase(TK)inhibitory activity and enable efficient compound screening.Our results demonstrate that MDBO-RF achieves a 3.41%increase in prediction accuracy compared to the standard RF model and outperforms several other contemporary machine learning approaches.The model effectively streamlines the RTK inhibitor screening process by improving prediction accuracy in multi-target competitive binding scenarios and reducing false-positive screening due to off-target effects.This work underscores the value of hybrid optimization strategies in bioinformatics and provides a robust,interpretable tool for accelerating drug discovery.
