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Drug Abuse and Addiction: Pharmacological Phenomena01:15

Drug Abuse and Addiction: Pharmacological Phenomena

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Drug dependence, abuse, and addiction are complex phenomena that can precipitate various abnormal states. Physical dependence refers to a state of pharmacological adaptation to a drug. This adaptation often results in tolerance—a reduced response to the drug after repeated administrations. When the drug use is abruptly stopped, withdrawal symptoms occur due to the body's need to readjust from the pharmacologically induced imbalance. However, tolerance and withdrawal symptoms do not...
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Drug Concentrations: Measurements01:23

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Drug concentration is the quantity of a drug present in a biological sample. Measuring drug amounts in biological samples allows the clinician to understand how a drug is absorbed, distributed, metabolized, and excreted. Samples can be obtained through invasive or non-invasive methods. Invasive techniques involve surgical or parenteral interventions to gather blood, cerebrospinal fluid, or tissue biopsy. Conversely, non-invasive approaches provide samples like urine, feces, and saliva.
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Updated: Sep 12, 2025

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
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可解释的非法药物滥用预测使用血液学差异.

Aijun Chen1, Yinchu Shen1, Yu Xu2

  • 1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.

Scientific reports
|August 7, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种机器学习模型,使用血液检测结果来预测非法药物使用 (IDU). 可解释模型准确地识别了IDU,有助于临床查和器官功能评估.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 临床化学 临床化学
  • 医疗保健中的机器学习

背景情况:

  • 非法药物使用 (IDU) 带来了重大的公共卫生挑战.
  • 准确和早期识别注射药物使用是及时干预和治疗的关键.
  • 血液学参数为预测IDU提供了潜在的生物标志物,但需要强大的分析模型.

研究的目的:

  • 开发和验证可靠和可解释的机器学习 (ML) 模型,用于预测非法药物使用 (IDU).
  • 确定有助于预测IDU的关键血液学特征.
  • 评估预测模型对初步IDU查的临床实用性.

主要方法:

  • 利用了286名非法毒品使用者 (IDUr) 和302名非使用者 (n-IDUr) 的血液学数据.
  • 对比了八个ML算法的性能来预测IDU.
  • 开发了一个可解释的光梯度增强机 (LGB) 模型,使用13个选定的功能.
  • 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

  • 可解释的LGB模型实现了高预测准确性,在内部验证中,曲线下的面积 (AUC) 为0.925,在外部验证中为0.915.
  • 发现的关键预测特征包括化物 (Cl),β-基酸盐 (BHB) 和离子间隙 (AG).
  • 这些特征与脏,肝脏和甲状腺功能有关,表明IDUr的潜在器官功能障碍.

结论:

  • 一个可解释的ML模型有效地根据血液学参数预测非法药物使用情况.
  • 化物,BHB和离子间隙是IDU预测的重要指标.
  • 该模型有可能在初步IDU查和指导进一步的器官特异性检查中进行临床应用.