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Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

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Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

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Soldiers of the Mexican Army with obesity were subjected to an intense 60-day weight-loss course consisting of a controlled diet, daily physical training, and psychological sessions. The nutritional treatment followed the European Society of Cardiology (ESC) recommendations, incorporating elements of the traditional milpa diet in the nutritional intervention. The total energy intake was reduced by 200 kcal every 20 days, starting with 1,800 kcal and ending with 1,400 kcal daily. On average, the participants reduced their body weight by 18 kg. We employed an innovative approach to monitor the progress of the twelve soldiers who completed the entire program. We compared the untargeted metabolomics profiles of their urine samples, taken before and after the course. The data obtained through liquid chromatography and high-resolution mass spectrometry (LC-MS) provided insightful results. Classification models perfectly separated the profiles pre and post-course, indicating a significant reprogramming of the participants' metabolism. The changes were observed in the C1-, vitamin, amino acid, and energy metabolism pathways, primarily affecting the liver, biliary system, and mitochondria. This study not only demonstrates the potential of rapid weight loss and metabolic pathway modification but also introduces a non-invasive method for monitoring the metabolic state of individuals through urine mass spectrometry data.

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Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.

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Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

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The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents.

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The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

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OBJECTIVE: To determine reference intervals (RI) for fasting blood insulin (FBI) in Brazilian adolescents, 12 to 17 years old, by direct and indirect approaches, and to validate indirectly determined RI. METHODS: Two databases were used for RI determination. Database 1 (DB1), used to obtain RI through a posteriori direct method, consisted of prospectively selected healthy individuals. Database 2 (DB2) was retrospectively mined from an outpatient laboratory information system (LIS) used for the indirect method (Bhattacharya method). RESULTS: From DB1, 29345 individuals were enrolled (57.65 % female) and seven age ranges and sex partitions were statistically determined according to mean FBI values: females: 12 and 13 years-old, 14 years-old, 15 years-old, 16 and 17 years-old; and males: 12, 13 and 14 years-old, 15 years-old, 16 and 17 years-old. From DB2, 5465 adolescents (67.5 % female) were selected and grouped according to DB1 partitions. The mean FBI level was significantly higher in DB2, on all groups. The RI upper limit (URL) determined by Bhattacharya method was slightly lower than the 90 % CI URL directly obtained on DB1, except for group female 12 and 13 years old. High agreement rates for diagnosing elevated FBI in all groups on DB1 validated indirect RI presented. CONCLUSION: The present study demonstrates that Bhattacharya indirect method to determine FBI RI in adolescents can overcome some of the difficulties and challenges of the direct approach.

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Background: The in-hospital treatment for COVID-19 may include medicines from various therapeutic classes, such as antiviral remdesivir and immunosuppressant tocilizumab. Safety data for these medicines are based on controlled clinical trials and case reports, limiting the knowledge about less frequent, rare or unique population adverse events excluded from clinical trials. Objective: This study aims at analyzing the reports of Adverse Drug Events (ADEs) related to these two medicines, focusing on events in pregnant women and foetuses. Methods: Data from the open-access FDA Adverse Event Reporting System (FAERS) from 2020 to 2022 were used to create a dashboard on the Grafana platform to ease querying and analyzing report events. Potential safety signals were generated using the ROR disproportionality measure. Results: Remdesivir was notified as the primary suspect in 7,147 reports and tocilizumab in 19,602. Three hundred and three potential safety signals were identified for remdesivir, of which six were related to pregnant women and foetuses (including abortion and foetal deaths). Tocilizumab accumulated 578 potential safety signals, and three of them were associated with this population (including neonatal death). Discussion: None of the possible signals generated for this population were found in the product labels. According to the NIH and the WHO protocols, both medicines are recommended for pregnant women hospitalized with COVID-19. Conclusion: Despite the known limitations of working with open data from spontaneous reporting systems (e.g., absence of certain clinical data, underreporting, a tendency to report severe events and recent medicines) and disproportionality analysis, the findings suggest concerning associations that need to be confirmed or rejected in subsequent clinical studies.

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Cyclosporine is an immunosuppressant used to prevent organ rejection in kidney, liver, and heart allogeneic transplants. This study aimed to assess the safety of cyclosporine through the analysis of adverse events (AEs) related to cyclosporine in the US Food and Drug Administration Adverse Event Reporting System (FAERS). To detect AEs associated with cyclosporine, a pharmacovigilance analysis was conducted using four algorithms on the FAERS database: reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM). A statistical analysis was performed on data extracted from the FAERS database, covering 19,582 case reports spanning from 2013 to 2022. Among these cases, 3,911 AEs were identified, with 476 linked to cyclosporine as the primary suspected drug. Cyclosporin-induced AEs targeted 27 System Organ Classes (SOCs). Notably, the highest case at the SOC level included eye disorders, injury, poisoning, and procedural complications, as well as immune system disorders, all of which are listed on the cyclosporine label. Furthermore, we discovered novel potential AEs associated with hepatobiliary disorders, among others. Moreover, unexpected adverse drug reactions (ADRs), such as biliary anastomosis complication and spermatozoa progressive motility decrease, were identified. Importantly, these newly identified ADRs were not mentioned on the cyclosporine label, which were involved in injury, poisoning, and procedural complications, and investigations at the SOC level. The study used pharmacovigilance analysis of FAERS database to identify new and unexpected potential ADRs relating to cyclosporine, which can provide safety tips for the safe use of cyclosporine.