Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

Enhancing early identification of high-fertile cattle females using infrared blood serum spectra and machine learning

Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to multifactorial traits, including hormonal, age-related, and body condition factors. Early high-fertility identification is crucial for timely interventions and enhancing AI success. In this study, we present the potential use of Fourier-transform infrared (FTIR) spectroscopy on blood serum for early identification of high-fertile Nellore female cows for AI protocols. Blood serum FTIR spectra were obtained from Nellore female cows before AI. FTIR spectra underwent data analysis and the results demonstrated successful discrimination between animals that exhibit pregnant and non-pregnant diagnoses 30 days after AI. FTIR spectra revealed consistent vibrational modes, emphasizing Amide I and II bands. Principal Component Analysis (PCA) effectively segregated groups based on molecular information. Linear SVM with C = 10 and 4 PCs achieved 100% accuracy in the group classification. This innovative approach using FTIR spectroscopy and ML algorithms offers a promising means of high-fertile cow identification, potentially improving AI outcomes in Nellore cattle. The study presents valuable insights into advancements in reproductive management practices for this economically significant breed.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.