Seasonal assessment of mastitis using thermogram analysis in murrah buffaloes.

Mastitis is a global threat that challenges dairy farmers' economies worldwide. Sub-clinical mastitis (SCM) beholds the lion's share in it, as its visible clinical signs are not evident and are challenging to diagnose. The treatment of intramammary infection (IMI) demands antimicrobial therapy and subsequent milk withdrawal for a week or two. This context requires a non-invasive diagnostic tool like infrared thermography (IRT) to identify mastitis. It can form the basis of precision dairy farming. Therefore, the present study focuses on thermal imaging of the udder and teat quarters of Murrah buffaloes during different seasons to identify SCM and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 30-45 lactating Murrah buffalo cows were screened out using IRT regularly throughout the year 2021-22. The IMI was further screened using the California mastitis test. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat skin surface temperature of Murrah buffaloes between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 30.28 and 36.81 °C, 32.54 to 38.61 °C, and 34.32 to 40.02 °C among healthy, SCM, and CM-affected quarters. Correspondingly, the mean values of teat skin surface temperature (TSST) were 30.52 to 35.96 °C, 32.92 to 37.55 °C, and 34.51 to 39.05 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.26, 4.04; 2.19, 3.35; 1.80, 3.21; and 1.45, 2.64 °C and TSST as 2.40, 3.99; 2.28, 3.26; 1.59, 3.09; and 1.68, 2.92 °C of SCM, CM-affected quarters to healthy quarters, respectively. The highest incidence of SCM was observed during autumn and CM during winter. Henceforth, irrespective of the seasons studied in the present study, IRT is an efficient, supportive tool for the early identification of SCM.

Effect of stoichiometry upon the characteristics of quercetin-arginine cocrystals formulated through solution crystallization.

OBJECTIVE: The aim of this study is to demonstrate the effect of stoichiometry upon characteristics of quercetin-arginine (QCT-Arg) cocrystals. SIGNIFICANCE: Quercetin (QCT) is a most abundant flavonoid in vegetables and fruits and has been widely used as an antioxidant. However, its oral bioavailability remains low due to poor aqueous solubility. We illustrate that QCT-Arg cocrystals formulated through an optimized stoichiometry can be a useful approach for its solubilization. METHOD: Cocrystals were prepared using solvent evaporation method. Characterizations were performed through microscopic, spectroscopic, and thermal techniques. The stoichiometry was confirmed from the binary phase diagram which was prepared using thermograms derived from differential scanning calorimetric experiments. RESULT: Cocrystal formation was accompanied by the conversion of isotropic phase into anisotropic one. Thread-like cocrystals were formed, regardless of QCT-Arg stoichiometry and solvent's polarity. Spectral analyses suggested that cocrystal structure was held together by hydrogen bonding between QCT and Arg. We ruled out the existence of eutectic mixture based on the observation of two eutectic points in the binary phase diagram. CONCLUSION: Morphology of cocrystals remained unaffected by the solvent type, stoichiometry and the presence of surfactant. We noticed that the cocrystals could improve the aqueous solubility of QCT.

Seasonal assessment of mastitis using thermogram analysis in Sahiwal cows.

"India is the world's leading producer of milk" and demands a non-invasive diagnostic tool like infrared thermography (IRT) to identify the costliest production disease, mastitis. It can form the basis of precision dairy farming. Therefore, the present study focuses on thermal imaging of the udder and teat quarters of Sahiwal cows during different seasons to identify subclinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 24-69 lactating Sahiwal cows were screened out using IRT regularly throughout the year. The intramammary infection status was further assessed using the CMT. The receiver operating characteristic analysis was carried out to develop the current study's cut-off for various thermographic parameters. The incidence for SCM and CM ranged from 26.47 to 38.75% and 17.83-22.79%, respectively during different seasons in Sahiwal udder quarters. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat surface temperature of Sahiwal cows between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 29.07 and 36.91 °C, 31.51 to 37.88 °C and 32.42 to 38.79 °C among healthy, SCM, and CM-affected quarters, and correspondingly, the mean values of teat skin surface temperature (TSST) were 28.28 to 36.77 °C, 30.68 to 37.88 °C and 31.70 to 38.73 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.44, 3.35; 0.97, 1.88; 1.06, 1.83; 1.29, 2.39 °C and TSST as 2.4, 3.42; 1.11, 1.96; 1.21, 2.19, 1.3, 2.4 °C of SCM, CM-affected quarters to healthy quarters, respectively, in Sahiwal cows. Thermograms showed a strong positive correlation with the CMT scores of SCM, CM cases, and healthy samples. Henceforth, irrespective of the seasons studied in the present work, IRT is an efficient, supportive tool for the early identification of subclinical mastitis.

Plasma Thermogram Parameters Differentiate Status and Overall Survival of Melanoma Patients.

Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients.

An approach for carbon content measurement in marine sediment: Application of organic and elemental carbon analyzer.

Organic and Elemental Carbon (OEC) is widely applied in the atmospheric sciences for determining carbon content and distinguishing black carbon contents of aerosols, with an advantage that OEC-based approach can provide thermograms derived from carbonaceous material. It is potential to adopt the advantage to measure the content and composition of organic carbon (OC)% in marine sediments. Here, we utilized the OEC analyzer to measure the OC% in marine sediment based on the pyrolytic oxidation principle, and obtain the OC-derived carbon dioxide (CO2) thermograms. We examined marine sediments and reference materials to understand the stability and reproducibility of OC% measurements using our approach. The findings indicate that the OC% results (ranging from 1.44 to 1.59%, ave. 1.55 ± 0.03%, n = 64) based on this approach are accurate. In addition, CO2 concentration thermograms obtained by repeated measurements exhibit a strong reproducibility. Our approach can thus provide the concentrations of thermally-evolved CO2 with increasing heating temperature to deeply understand the reactivities of OC and the compositions in sediments. We suggest that the OEC-based OC% measurement is credible when samples preparation is well-performed (e.g., suitable sample mass and uniformly distributed loading). To sum up, we provide a means to accurately determine the OC% in marine sediments in terms of the ramped-pyrolysis principle.

Dynamic Vascular Imaging Using Active Breast Thermography.

Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared 'thermogram' breast imaging can map the skin temperature, and signal inversion with components analysis can be used to identify the mechanisms of thermal image generation of the vasculature using dynamic thermal data. This work focuses on using dynamic infrared breast imaging to identify the thermal response of the stationary vascular system and the physiologic vascular response to a temperature stimulus affected by vasomodulation. The recorded data are analyzed by converting the diffusive heat propagation into a virtual wave and identifying the reflection using component analysis. Clear images of passive thermal reflection and thermal response to vasomodulation were obtained. In our limited data, the magnitude of vasoconstriction appears to depend on the presence of cancer. The authors propose future studies with supporting diagnostic and clinical data that may provide validation of the proposed paradigm.

Mode of Antifungal Action of Daito-Gettou (Alpinia zerumbet var. exelsa) Essential Oil against Aspergillus brasiliensis.

Plant-derived essential oils (EOs) are used in medicines, disinfectants, and aromatherapy products. Information on the antifungal activity of EO of Alpinia zerumbet var. exelsa (known as Daito-gettou) found in Kitadaito Island, Okinawa, is limited. Therefore, we aimed to evaluate the antifungal activity of EOs obtained via steam distillation of leaves of Daito-gettou, which is a hybrid of A. zerumbet and A. uraiensis. Daito-gettou EO showed antifungal activity (minimum inhibitory concentration = 0.4%) against Aspergillus brasiliensis NBRC 9455, which was comparable to that of A. zerumbet found in the Okinawa main island. Gas chromatography/mass spectrometry revealed that the main components of Daito-gettou EOs are γ-terpinene, terpinen-4-ol, 1,8-cineole, 3-carene, and p-cymene. Terpinen-4-ol content (MIC = 0.075%) was 17.24%, suggesting that the antifungal activity of Daito-gettou EO was mainly attributable to this component. Daito-gettou EO and terpinen-4-ol inhibited mycelial growth. Moreover, calorimetric observations of fungal growth in the presence of Daito-gettou EO showed a characteristic pattern with no change in the initial growth rate and only a delay in growth. As this pattern is similar to that of amphotericin B, it implies that the action mode of Daito-gettou EO and terpinen-4-ol may be fungicidal. Further studies on the molecular mechanisms of action are needed for validation.

Quality analysis of a breast thermal images database.

The study and early detection of breast cancer are key for its treatment. We carry out an exhaustive analysis of the most used database for mastology research with infrared images, analyzing the anomalies according to five quality dimensions: completeness, correctness, concordance, plausibility, and currency. We established control queries that looked for these anomalies and that can be used to ensure the quality of the database. Finally, we briefly review the more than 40 papers that use this database and that do not mention any of these anomalies. When analyzing the database, we found 365 anomalies related to personal and clinical data, and thermal images. The errors found in our research may lead to a modification of the results and conclusions made in the articles found in the literature, serve as a basis for improvements in the quality of the database, and help future researchers to work with it.

Dye and metal ion adsorption ability of Asian green mussel byssus thread complex; their microscopic and thermal property characterization.

Various physical, chemical, and biological factors act as pollutants and cause deleterious effects on the environment and living organisms. Several researchers are developing eco-friendly good adsorbent from agriculture waste materials for pollutant removal, especially aromatic dyes and heavy metal removal. We used the Asian green mussel byssus thread, a natural mariculture waste product for the removal of aromatic dyes (methylene blue and eosin Y) and heavy metals (zinc, copper, iron, mercury and lead). We documented the dynamicity of byssus thread dye and metal removal at different pH (pH 2-10) and different concentrations. The highest amount of metal removal was observed at pH 6.0, and the dye removal efficacy is related to the property of the dye (i.e. anionic or cationic dye). The byssus thread had a natural fluorescent property upon UV-excitation; however, microscopic examination revealed that metal and dye coordination significantly alter the byssus plaque region's physical and chemical property rather than the thread regions. It was further confirmed by using DSC and TGA characterization of de-metaled and metal-treated byssus thread complex. We concluded that Perna viridis byssus thread could be used as a strong adsorbent for dye and metal removal from the water.

Isothermal Titration Calorimetry Analysis of a Cooperative Riboswitch Using an Interdependent-Sites Binding Model.

Isothermal titration calorimetry (ITC) is a powerful biophysical tool to characterize energetic profiles of biomacromolecular interactions without any alteration of the underlying chemical structures. In this protocol, we describe procedures for performing, analyzing, and interpreting ITC data obtained from a cooperative riboswitch-ligand interaction.

Development of AI classification model for angiosome-wise interpretive substantiation of plantar feet thermal asymmetry in type 2 diabetic subjects using infrared thermograms.

Diabetic Foot Syndrome (DFS) is the prime impetus for most of the lower extremity complications among the diabetic subjects. DFS is characterized by aberrant variations in plantar foot temperature distribution while healthy subjects exhibit a symmetric thermal pattern between the contralateral and ipsilateral plantar feet. Thus, "asymmetry analysis" of foot thermal distribution is contributory in assessment of overall foot health of diabetic subjects. The study, aims to classify symmetric and asymmetric foot regions angiosome-wise, by comparing minimal number of color image features - color moments and Dissimilarity Index. Further, the asymmetric foot regions are assessed for identifying the hotspots within such angiosomes of the patients that characterize the possibility of onset of diabetic foot ulcer. The color feature based machine learning model developed, achieved an accuracy of 98% for a 10-fold cross validation, test accuracy of 96.07% and 0.96 F1-score thereby convincing that the chosen features are amplest and conducive in the asymmetry analysis. The developed model was validated for generalization by testing on a public benchmark dataset, in which the model achieved 92.5% accuracy and 0.91 F1 score.

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images.

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively.

Effect of Y3+ on the structural and photoluminescence properties of yttrium-doped sodium borate glass.

Glass of the composition 70%B2 O3 -(30-x)%Na2 O-x%Y2 O3 with x = 0, 0.5, 1, 1.5, 2, and 2.5 mol% was manufactured using the melt quenching method. X-ray diffraction tests indicated the amorphous structure of the glass, with the presence of some YBO3 clusters in the high yttrium content samples. Fourier transform infrared spectra analysis proved the presence of different borate groups and linkages, in addition to nonsystematic changes in the ratio BO3 /BO4 in the glass. The forming ability of the glass was found to be approximately stable for the low Y3+ content samples and increased for the heavily doped samples. The density of the samples was found to increase as the Y3+ concentration was increased, whereas the molar volume decreased. The bond strength of the examined glass suggested that a covalent nature was dominant between bonds. All the Y3+ -doped glass was found to emit greenish-cyan light when excited at λ ex = 365 nm . Photoluminescence intensity was shown to be enhanced by the generated YBO3 groups. The obtained correlated colour temperature values with 82.5% purity recommend the suitability of the glass for applications in outdoor illumination.

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network.

The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermograms and a classifier based on the mobile CNN model. The segmentation algorithm employing edge detection and second-order polynomial curve fitting techniques can effectively capture the thermograms' region of interest (ROI), thereby facilitating efficient feature extraction. The classifier was developed based on ShuffleNet by adding one block consisting of a convolutional layer with 1028 filters. The modified Shufflenet demonstrated a good fit learning with 6.1 million parameters and 22 MB size. Simulation results showed that modified ShuffleNet alone resulted in a 72% accuracy rate, but the performance excelled to a 100% accuracy rate when integrated with the proposed segmentation algorithm. In terms of diagnostic accuracy of the normal and abnormal test, BreaCNet significantly improves the sensitivity rate from 43% to 100% and specificity of 100%. We confirmed that feeding only the ROI of the input dataset to the network can improve the classifier's performance. On the implementation aspect of BreaCNet, the on-device inference is recommended to ensure users' data privacy and handle an unreliable network connection.

Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model.

Breast cancer, the most common invasive cancer, causes deaths of thousands of women in the world every year. Early detection of the same is a remedy to lessen the death rate. Hence, screening of breast cancer in its early stage is utmost required. However, in the developing nations not many can afford the screening and detection procedures owing to its cost. Hence, an effective and less expensive way of detecting breast cancer is performed using thermography which, unlike other methods, can be used on women of various ages. To this end, we propose a computer aided breast cancer detection system that accepts thermal breast images to detect the same. Here, we use the pre-trained DenseNet121 model as a feature extractor to build a classifier for the said purpose. Before extracting features, we work on the original thermal breast images to get outputs using two edge detectors - Prewitt and Roberts. These two edge-maps along with the original image make the input to the DenseNet121 model as a 3-channel image. The thermal breast image dataset namely, Database for Mastology Research (DMR-IR) is used to evaluate performance of our model. We achieve the highest classification accuracy of 98.80% on the said database, which outperforms many state-of-the-art methods, thereby confirming the superiority of the proposed model. Source code of this work is available here: https://github.com/subro608/thermogram_breast_cancer.

A machine learning model for early detection of diabetic foot using thermogram images.

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.

Thermal heterogeneity of selected retreats in cool-temperate viviparous lizards suggests a potential benefit of future climate warming.

Rocky retreats are limited and geologically constrained resources for rock-dwelling nocturnal lizards. Such lizards should seek retreats that offer thermoregulatory benefits without the risk of overheating during the day, and that protect from predation. For cold-adapted species where air temperature is frequently lower than optimum temperature for performance, factors influencing retreat-site selection and whether future warmer conditions will force superficial rock slabs to be abandoned on hot days remain poorly known. Here, we predicted that retreats selected by a nocturnally foraging, cool-temperate gecko from southern New Zealand would be thermally heterogeneous and that future warmer temperature will force lizards to abandon daytime retreats on hot days. We sampled loose rock slabs (potential retreats) in a tussock-grassland site in all seasons. We measured seasonal rock temperature profiles and field body temperature (Tb) of captured geckos using thermography and quantified the physical characteristics of each potential retreat. We found that both physical characteristics and rock temperatures determine choice of retreats. Field Tb of lizards positively correlated with retreat and air temperatures. Also, retreat temperatures, including those of the substrate below the rock slabs, showed complex heterogeneity enabling lizards to choose microsites within retreats to achieve preferred body temperatures intermittently. Observed seasonal shifts in characteristics of occupied rocks imply that lizards choose retreats to maximise warmth in spring, minimise risk of overheating (remain below voluntary thermal maximum, VTmax) in summer and avoid freezing over winter. Our study demonstrates the importance of microclimatic conditions in influencing retreat-site selection. Climate warming might lead to seasonal changes in use of rock slabs and possibly be beneficial initially, but longer-term implications need to be examined.