Classification or non-classification of substances with positive tumor findings in animal studies: Guidance by the German MAK commission.

Regul Toxicol Pharmacol

Dept. of Experimental and Clinical Pharmacology and Toxicology, Dept. Toxicology, Eberhard Karls University, Wilhelmstr. 56, 72074, Tübingen, Germany. Electronic address:

Published: November 2019

One of the important tasks of the German Senate Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area (known as the MAK Commission) is in the evaluation of a potential for carcinogenicity of hazardous substances at the workplace. Often, this evaluation is critically based on data on carcinogenic responses seen in animal studies and, if positive tumor responses have been observed, this will mostly lead to a classification of the substance under investigation into one of the classes for carcinogens. However, there are cases where it can be demonstrated with a very high degree of confidence that the tumor findings in the experimental animals are not relevant for humans at the workplace and, therefore, the MAK Commission will not classify the respective substance into one of the classes for carcinogens. This paper will summarize the general criteria used by the MAK Commission for the categorization into "carcinogen" and "non-carcinogen" and compare this procedure with those used by other national and international organizations.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.yrtph.2019.104444DOI Listing

Publication Analysis

Top Keywords

mak commission
16
positive tumor
8
tumor findings
8
animal studies
8
classes carcinogens
8
commission
5
classification non-classification
4
non-classification substances
4
substances positive
4
findings animal
4

Similar Publications

Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis.

BMC Med Inform Decis Mak

January 2025

Joint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.

Objective: This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment.

Methods: This study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40-50 samples for use in clinical experiments.

View Article and Find Full Text PDF

Digital-based emergency prevention and control system: enhancing infection control in psychiatric hospitals.

BMC Med Inform Decis Mak

January 2025

Department of Nutritional and Metabolic Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, No. 36 Fangcun Mingxin Road, Liwan District, Guangzhou, 510370, China.

Background: The practical application of infectious disease emergency plans in mental health institutions during the ongoing pandemic has revealed significant shortcomings. These manifest as chaotic management of mental health care, a lack of hospital infection prevention and control (IPC) knowledge among medical staff, and unskilled practical operation. These factors result in suboptimal decision-making and emergency response execution.

View Article and Find Full Text PDF

A new risk assessment model of venous thromboembolism by considering fuzzy population.

BMC Med Inform Decis Mak

December 2024

Department of Respiration, Peking Union Medical College Hospital, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.

Background: Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance problem due to the low incidence rate of VTE, resulting in inferior and unstable model performance, which hinders their ability to replace the Padua model, a widely used linear weighted model in clinic. Our study aims to develop a new VTE risk assessment model suitable for Chinese medical inpatients.

View Article and Find Full Text PDF

Target informed client recruitment for efficient federated learning in healthcare.

BMC Med Inform Decis Mak

December 2024

Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.

Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data.

View Article and Find Full Text PDF
Article Synopsis
  • - A
  • nomogram model
  • was created to predict the risk of prolonged hospital stays for patients who underwent spinal fusion surgery, based on a study of 6,272 patients and various statistical methods like LASSO regression and random forest analysis.
  • Model 1
  • emerged as the best predictor of prolonged hospital stay, influenced by factors such as blood transfusion, operation type, diabetes, BMI, and surgical procedure, with a satisfactory predictive performance indicated by AUC values of 0.784 and 0.795 in internal and external validations.
  • - The model showed
  • good discrimination
  • ability for both validation sets, with C-statistics of 0.811 and 0.814, and included thorough validation both internally
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!