1,2,3-triazole and chiral Schiff base hybrids as potential anticancer agents: DFT, molecular docking and ADME studies.

A series of novel 1,2,3-triazole and chiral Schiff base hybrids 2-6 were synthesized by Schiff base condensation reaction from pre-prepared parent component of the hybrids (1,2,3-triazole 1) and series of primary chiral amines and their chemical structure were confirmed using NMR and FTIR spectroscopies, and CHN elemental analysis. Compounds 1-6 were evaluated for their anticancer activity against two cancer PC3 (prostate) and A375 (skin) and MRC-5 (healthy) cell lines by Almar Blue assay method. The compounds exhibited significant cytotoxicity against the tested cancer cell lines. Among the tested compounds 3 and 6 showed very good activity for the inhibition of the cancer cell lines and low toxicity for the healthy cell lines. All the compounds exhibited high binding affinity for Androgen receptor modulators (PDB ID: 5t8e) and Human MIA (PDB ID: 1i1j) inhibitors compared to the reference anticancer drug (cisplatin). Structure activity relationships (SARs) of the tested compounds is in good agreement with DFT and molecular docking studies. The compounds exhibited desirable physicochemical properties for drug likeness.

Comparative investigation of derivatives of (E)-N-((E)-3-phenylallylidene)aniline: Synthesis, structural characterization, biological evaluation, density functional theory analysis, and in silico molecular docking.

Bacterial resistance to antibiotics poses a significant global challenge for the public sector. Globally, researchers are actively investigating solutions to tackle the issue of bacterial resistance to antibiotics, with Schiff bases standing out as promising contenders in the fight against antimicrobial resistance. This study focused on synthesizing a series of Schiff bases (CA1-CA10) by reacting cinnamaldehyde with various aniline derivatives. Various analytical techniques, such as NMR, FTIR, UV-Vis, elemental analysis, and mass spectrometry, were employed to elucidate the structures of the synthesized compounds. Furthermore, crystal structure of CA8 was obtained using single crystal X-ray spectroscopy. The compounds were subjected to in vitro testing to assess their antibacterial and antifungal properties against eleven bacterial strains and four fungal strains. The results revealed diverse activity levels against the pathogens at varying concentrations, with notable potency observed in compounds CA3, CA4, CA9, and CA10, as indicated by their minimum inhibitory concentrations (MIC) values. The observed activity of the compounds seemed to be influenced by the specific substituents attached to their molecular structure. By conducting computational and molecular docking studies, the electronic properties of the compounds were investigated, further substantiating their potential as effective antimicrobial agents.

Poultry fecal imagery dataset for health status prediction: A case of South-West Nigeria.

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources.

Enhancing poultry health management through machine learning-based analysis of vocalization signals dataset.

Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death. Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy. 100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format. This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way.