TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages.

Influence of early progesterone removal on follicular development, expression of estrus, and pregnancy rates in presynchronized postpartum beef cows.

The objective of this study was to evaluate the impact of early progesterone removal on pregnancy rates to fixed-time artificial insemination (FTAI) in presynchronized beef cows. Postpartum beef cows (n = 882) were randomly assigned to 1 of 2 treatments: 1) 7&7 Synch: cows received a controlled internal drug release insert (CIDR) and a 25-mg injection of prostaglandin F on day 0, 100 μg of GnRH on day 7, a second injection of prostaglandin F (PG2) at CIDR removal on day 14, and a second injection of GnRH at FTAI 60-66 h after PG2 (day 17); 2) 7&6 Synch: cows received the same treatment as 7&7 Synch; however, CIDR removal occurred in conjunction with PG2 on day 13, while FTAI remained at 60-66 h after CIDR removal (day 16). Ovarian ultrasonography was performed to determine follicle diameter at PG2 and FTAI in a subset of cows (n = 40).

Effects of bacterial direct-fed microbial combinations on beef cattle growth performance, feeding behavior, nutrient digestibility, ruminal morphology, and carcass characteristics.

The effects of the dietary inclusion of a mixture of bacterial direct-fed microbial (DFM) on feedlot beef cattle growth performance, carcass characteristics, nutrient digestibility, feeding behavior, and ruminal papillae morphology were evaluated. Crossbred-Angus steers (n = 192; initial body weight (BW) = 409 kg ± 8 kg) were blocked by BW and randomly assigned into 48 pens (4 steers/pen and 16 pens/treatment) following a randomized complete block design. A steam-flaked corn-based fishing diet was offered to ad libitum intake once daily for 153 d containing the following treatments: (1) Control (no DFM, lactose carrier only); (2) treat-A (Lactobacillus animalis, Propionibacterium freudenreichii, Bacillus subtilis, and Bacillus licheniformis), at 1:1:1:3 ratio, respectively; totaling 6 × 109 CFU (50 mg)/animal-daily minimum; and (3) treat-B, the same DFM combination, but with doses at 1:1:3:1 ratio.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

Introduction: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

Areas Covered: This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.