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.

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva.

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA).

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

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.

Turning chaotic sample group clusterization into organized ones by feature selection: Application on photodiagnosis of Brucella abortus serological test.

Bovine brucellosis diagnosis is a major problem to be solved; the disease has a tremendous economic impact with significant losses in meat and dairy products, besides the fact that it can be transmitted to humans. The sanitary measures instituted in Brazil are based on disease control through diagnosis, animal sacrifice, and vaccination. Although the currently available diagnostic tests show suitable quality parameters, they are time-consuming, and the incidence of false-positive and/or false-negative results is still observed, hindering effective disease control. The development of a low-cost, fast, and accurate brucellosis diagnosis test remains a need for proper sanitary measures at a large-scale analysis. In this context, spectroscopy techniques associated with machine learning tools have shown great potential for use in diagnostic tests. In this study, bovine blood serum was investigated by UV-vis spectroscopy and machine learning algorithms to build a prediction model for Brucella abortus diagnosis. Here we first pre-treated the UV raw data by using Standard Normal Deviate method to remove baseline deviation, then apply principal component analysis - a clustering method - to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution, then we properly select the main principal components to improve clusterization. Finally, by using machine learning algorithms (SVM and KNN), the predicting models achieved a 92.5% overall accuracy. The present methodology provides a test result in an average time of 5 min, while the standard diagnosis, with the screening and confirmatory tests, can take up to 48 h. The present result demonstrates the method's viability for diagnosing bovine brucellosis, which can significantly contribute to disease control programs in Brazil and other countries.

Using infrared spectroscopy of serum and chemometrics for diagnosis of paracoccidioidomycosis.

Paracoccidioidomycosis (PCM) is a systemic granulomatous mycosis endemic to Latin America, whose etiologic agents are fungi of the genus Paracoccidioides. PCM is usually diagnosed by microscopic observation of the fungus in biological samples, combined or not with other techniques such as serological methods. However, all currently used diagnostic methods have limitations. The objective of this study was to develop a method based on Fourier transform infrared spectroscopy (FTIR) and chemometric analysis for PCM diagnosis. We included 224 serum samples: 132 PCM sera, 24 aspergillosis sera, 10 cryptococcosis sera, 8 histoplasmosis sera, and 50 sera from healthy blood donors. Samples were analyzed by attenuated total reflection (ATR), and chemometric analyses including exploratory analysis through principal component analysis (PCA) and a classification method (PCM and non-PCM) through orthogonal partial least squares discriminant analysis (OPLS-DA). The spectra were similar, with the main bands up to approximately 1652 cm-1 and 1543 cm-1 (amide I and amide II bands). This same region was mainly responsible for the partial separation of the samples in PCA. The OPLS-DA model correctly classified all serum samples with only one latent variable, with a determination coefficient (R²) higher than 0.999 for both the calibration set and prediction set. Sensitivity and specificity were 100% for both sets, showing better performance than the reference diagnostic methods. Therefore, the use of FTIR/ATR together with OPLS-DA modeling proved to be a promising method for PCM diagnosis.

Target selectivity of cholesterol-phosphatidylcholine liposome loaded with phthalocyanine for breast cancer diagnosis and treatment by photodynamic therapy.

This study investigated the ability of cholesterol-phosphatidylcholine liposomes loaded with chloride aluminum phthalocyanine (CL-AlClPc) to discriminate between healthy (MCF-10A) and neoplastic (MCF-7 and MDA-MB-231) breast cells for breast cancer diagnosis and treatment by photodynamic therapy (PDT) using a new drug delivery system consisting of CL-AlClPc. When PDT treatment was applied at an energy fluence of 700 mJ/cm², CL-AlClPc was more cytotoxic to neoplastic cells than to healthy breast cells because CL-AlClPc was better internalized by the tumor cells. An even higher fluorescence signal is expected for neoplastic cells during clinical treatment than for healthy cells, which will be useful for precise and targeted tumor cell detection. CL-AlClPc also facilitated better drug distribution and targeting of essential organelles inside the cells. This selectivity is critical for future in vivo diagnosis and treatment; it prevents side effects because it prioritizes tumor cells and tissues instead of healthy ones. The CL-AlClPc system designed herein had a small size (150 nm), low zeta potential (-6 mV), low polydispersity (0.16), high encapsulation rate efficiency (82.83%), and high shelf stability (12 months).

Optical techniques for the diagnosis and treatment of lesions induced by the human papillomavirus - A resource letter.

Human papillomaviruses (HPV) are the most common sexually-transmitted virus, and carcinogenic HPV strains are reported to be responsible for virtually all cases of cervical cancer and its precursor, the cervical intraepithelial neoplasia (CIN). About 30% of the sexually active population are considered to be affected by HPV. Around 600 million people are estimated to be infected worldwide. Diseases related to HPV cause significant impact from both the personal welfare point of view and public healthcare perspective. This resource letter collects relevant information regarding HPV-induced lesions and discusses both diagnosis and treatment, with particular attention to optical techniques and the challenges involved to the implementation of those approaches.

Evaluation of theranostic nanocarriers for near-infrared imaging and photodynamic therapy on human prostate cancer cells.

This paper evaluates how effectively chloroaluminum phthalocyanine (ClAlPc) entrapped in colloidal nanocarriers, such as nanocapsule (NC) and nanoemulsion (NE), induces photodamage in human prostate cancer cells (LNCaP) during photodynamic therapy (PDT). The MTT cell viability assay showed that both ClAlPc-NC and ClAlPc-NE induced phototoxicity and efficiently killed LNCaP cells at low ClAlPc-NC and ClAlPc-NE concentrations (0.3µgmL-1) as well as under low light doses of 4Jcm-2 and 7Jcm-2, respectively, upon PDT with a 670-nm diode laser line. Confocal imaging studies indicated that ClAlPc-NC and ClAlPc-NE were preferentially localized in the perinuclear region of LNCaP cells both in the dark and upon irradiation with laser light. After PDT treatment, ClAlPc-NC-treated LNCaP cells exhibited a higher green fluorescence signal, possibly due to the larger shrinkage of the actin cytoskeleton, compared to ClAlPc-NE-treated LNCaP cells. Additionally, ClAlPc-NC or ClAlPc-NE and mitochondria showed a relatively high co-localization level. The cellular morphology did not change in the dark, but confocal micrographs recorded after PDT revealed that LNCaP cells treated with ClAlPc-NC or ClAlPc-NE underwent morphological alterations. Our preliminary in vitro studies reinforced the hypothesis that biocompatible theranostic ClAlPc-loaded nanocarriers could act as an attractive photosensitizer system in PDT and could serve as an interesting molecular probe for the early diagnosis of prostate cancer and other carcinomas.

The optical properties of mouse skin in the visible and near infrared spectral regions.

Visible and near-infrared radiation is now widely employed in health science and technology. Pre-clinical trials are still essential to allow appropriate translation of optical methods into clinical practice. Our results stress the importance of considering the mouse strain and gender when planning pre-clinical experiments that depend on light-skin interactions. Here, we evaluated the optical properties of depilated albino and pigmented mouse skin using reproducible methods to determine parameters that have wide applicability in biomedical optics. Light penetration depth (δ), absorption (µa), reduced scattering (µ's) and reduced attenuation (µ't) coefficients were calculated using the Kubelka-Munk model of photon transport and spectrophotometric measurements. Within a broad wavelength coverage (400-1400nm), the main optical tissue interactions of visible and near infrared radiation could be inferred. Histological analysis was performed to correlate the findings with tissue composition and structure. Disperse melanin granules present in depilated pigmented mouse skin were shown to be irrelevant for light absorption. Gender mostly affected optical properties in the visible range due to variations in blood and abundance of dense connective tissue. On the other hand, mouse strains could produce more variations in the hydration level of skin, leading to changes in absorption in the infrared spectral region. A spectral region of minimal light attenuation, commonly referred as the "optical window", was observed between 600 and 1350nm.