AIMC Topic: Drug Contamination

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Support Vector Machine Classification of Adulterated Illicit Opioids Using Paper-Spray Mass Spectrometry.

Analytical chemistry
The complexity and potency of the illicit opioid supply in North America has become increasingly concerning for people who use drugs. Drug checking efforts aim to keep up with the evolving psychoactive components present in the illicit drug supply. W...

Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products.

Scientific reports
We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse ...

High-precision identification of highly similar Pinelliae Rhizoma and adulterated Rhizoma pinelliae pedatisectae through deep neural networks based on vision transformers.

Journal of food science
Pinelliae Rhizoma is a key ingredient in botanical supplements and is often adulterated by Rhizoma Pinelliae Pedatisectae, which is similar in appearance but less expensive. Accurate identification of these materials is crucial for both scientific an...

Assessment of chemical contamination by cancer drugs during use of the RIVA compounding robot: A pilot study.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
IntroductionMany hospitals are now investing in robotic compounding system for the preparation of cytotoxic agents. The objective of the present study was to describe contamination by cytotoxics inside and outside the RIVA robot (ARxIUM, Winnipeg, Ca...

Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

Journal of medicinal food
Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds i...

Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

Biotechnology and bioengineering
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches ...

Analysis of Training Deep Learning Models for PCB Defect Detection.

Sensors (Basel, Switzerland)
Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep...

Orthogonal projection to latent structures and first derivative for manipulation of PLSR and SVR chemometric models' prediction: A case study.

PloS one
Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising fir...

Qualification of a chemotherapy-compounding robot.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
KIRO® Oncology (Kiro Grifols, Spain) is a robotic system for automated compounding of sterile injectable drugs including intravenous cytotoxic treatments. The present article describes the qualification procedure applied prior to production phases. P...

Microbiological validation of a robot for the sterile compounding of injectable non-hazardous medications in a hospital environment.

European journal of hospital pharmacy : science and practice
OBJECTIVES: To design and execute a comprehensive microbiological validation protocol to assess a brand-new sterile compounding robot in a hospital pharmacy environment, according to ISO and EU GMP standards.