Search Results (84)

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

The rapid and accurate quantitative analysis of cell chemotaxis, which is essential in biology, medicine, and drug development, enables the evaluation of the directional migration capability of cells and the simulation of in vivo cell chemotaxis. However, traditional methods for studying cell chemotaxis often depend on complex experimental procedures, which are not only time-consuming and labor-intensive but also prone to human error. Recently, the rapid advancement of microfluidic technology and deep learning has provided a new way for evaluation of cell chemotaxis. In this study, a chemotaxis evaluation method based on microfluidics and deep learning is proposed. A microfluidic device was designed to simulate cell chemotaxis, allowing for the controlled assessment of cell chemotaxis by generating chemical gradients within microchannels and shear stress. Concurrently, deep learning technology was introduced to identify the migrated and non-migrated states of cell images, thereby enabling the automatic counting and analysis of chemotactic cells. Compared with traditional manual assays, this method not only reduced time and labor costs but also achieved higher accuracy and reproducibility. This innovative approach, which integrates microfluidics and deep learning, provides a novel perspective and tool for cell chemotaxis research. This method not only offers a fresh perspective on cell migration analysis but also has the potential to significantly advance the field of biomedical research, particularly in biosensor development related to drug discovery and disease diagnosis. Full article

Cervical cancer is one of the most common malignant tumors, with high incidence and mortality rates. Recent studies mainly adopt Artificial Intelligence (AI) models to detect cervical cells. Yet, due to the imperceptible symptoms of cervical cells, there are three problems that may hinder the performance of the existing approaches: (a) poor quality of the whole-slide image (WSI) performed on cervical cells may lead to undesirable performance; (b) several types of abnormal cervical cells are involved in the progression of cervical cells from normal to cancer, which requires extensive clinical data for training; and (c) the diagnosis of the WSI is medical-rule-driven and requires the AI model to provide interpretability. To address these issues, we propose an integrated automatic cervical cancer screening (IACCS) framework. First, the IACCS framework incorporates a quality assessment module utilizing binarization-based cell counting and a Support Vector Machine (SVM) approach to identify fuzzy regions, ensuring WSI suitability for analysis. Second, to overcome the data limitations, the framework employs data enhancement techniques alongside incremental learning (IL) and active learning (AL) mechanisms, allowing the model to adapt progressively and learn efficiently from new data and expert feedback. Third, recognizing the need for interpretability, the diagnostic decision process is modeled as a multi-objective optimization problem. A multi-objective optimization algorithm is used to generate a set of interpretable diagnostic rules that offer explicit trade-offs between sensitivity and specificity. Extensive experiments demonstrate the effectiveness of the proposed IACCS framework. Applying our comprehensive framework yielded significant improvements in detection accuracy, achieving, for example, a 6.34% increase in mAP_50:95 compared to the baseline YOLOv8 model. Furthermore, the generated Pareto-optimal diagnostic rules provide superior and more flexible diagnostic options compared to traditional manually defined rules. This research presents a validated pathway towards more robust, adaptable, and interpretable AI-assisted cervical cancer screening. Full article

Hematological studies provide essential information about the health of animals, which is crucial for veterinary medicine, scientific research, and aquaculture. Automatic hematological analyzers are an alternative to manual methods, offering faster and more reliable results. The objective of this study was to validate the Sysmex XN-1000V automatic hematology analyzer for blood samples from rainbow trout (Oncorhynchus mykiss), examine the effects of two anticoagulants (K₂EDTA and lithium heparin), and establish normal blood reference values for this fish species. Additionally, comparative studies were conducted between the Sysmex XN-1000V and manual methods (hemocytometer cell count and blood smear estimation), and reference intervals were established. Ninety-nine heparinized blood samples were analyzed for validation and sample stability tests. The results showed extremely good precision, with a coefficient of variation (CV) below 3% for RBCs, HGB, and HCT and less than 5% for non-RBC cells (leukocytes plus thrombocytes). However, heterophils (%) exhibited higher variability, with a CV of 15.08%. Linearity was excellent, and the carry-over was below 1% for all parameters. The sample stability test indicated that samples could be analyzed for up to 48 h when stored at 4 °C and up to 24 h at room temperature. Non-RBC cells were the first to degrade over time. The automated and manual methods demonstrated good correlation and agreement, validating the analyzer’s accuracy. The effects of two anticoagulants, K₂EDTA and lithium heparin, on the blood samples were also studied. Heparin was the preferred anticoagulant for routine hematological analysis of rainbow trout blood with the Sysmex XN-1000V analyzer. In conclusion, the Sysmex XN-1000V enables complete hemogram analyses to be performed quickly and accurately, standardizing techniques, harmonizing results, and providing reliable reference intervals with O mykiss blood. Full article

Subclinical mastitis (SM) is the most economically damaging yet often visually undetectable disease of dairy cows. Early detection and treatment can reduce the loss caused by the disease; thus, the continuous improvement of SM diagnostic methods is necessary. Although milk’s somatic cell count (SCC) is commonly measured for diagnostic purposes, its direct determination is not widely used in everyday practice. The primary objective of our work was to investigate whether the predictive value of SM diagnostics can be improved by training artificial neural networks (ANNs) on data generated using typical conventional milking systems. The best ANN classifier had a sensitivity of 0.54 and a specificity of 0.77, which is comparable to performances of various California Mastitis Tests (CMT) found in the literature. Combining two diagnostic tests, ANN and CMT, we concluded that the positive predictive value could be up to 50% higher than the value provided by the individual CMT. While implementing CMT is a labor-intensive process on herd-level, in milking machines where milk properties or milk yield data can be measured automatically, similar to our work, SCC-increase predictions for all individuals could be obtained daily basis. Full article

This study compared two types of automatic milking systems (AMSs) and their relationship with epidemiological indices of subclinical mastitis (SCM) and prevalence of mastitis-causing pathogens. Conducted between 2020 and 2023 on a dairy farm in Vacaria, Rio Grande do Sul, Brazil, this study analyzed data from 464 lactating cows housed in compost-bedded pack barns (CBPBs) and milked by eight AMS units: four from DeLaval (which utilizes teat cup for teat cleaning) and four from Lely (which utilizes brushes for teat cleaning). SCM incidence, prevalence, percentage of chronic, and cured cows were determined using somatic cell counts (SCCs) and microbiological cultures. Statistical analyses included the Wilcoxon signed-rank test and Chi-square test to evaluate SCM indices and pathogen associations with AMSs. No significant difference was observed in SCM prevalence (p = 0.3371), percentage of chronic (p = 0.3590) and cured cows (p = 0.4038), SCC (p = 0.1290), and total bacterial count (TBC) (p = 0.8750) between AMS types. However, the SCM incidence was higher in the Lely (14.7%) than in the DeLaval AMS (9.1%) (p = 0.0032). The Chi-square results revealed that the Lely AMS was associated with major pathogens like Staphylococcus aureus and Escherichia coli, whereas DeLaval showed associations with minor environmental and contagious pathogens, particularly non-aureus Staphylococci. The findings indicate a relationship between AMS-cleaning systems and pathogen spread, suggesting that Lely AMS may contribute to more aggressive infections due to its cleaning system. Full article

Background: This study presents a young man with hereditary spherocytosis (HS) who underwent intensive chemotherapy for newly diagnosed diffuse large B-cell lymphoma (DLBCL) and achieved complete remission. This case challenges the idea of HS as a barrier to standard DLBCL treatment. Discussion: By meticulously monitoring blood counts and providing timely transfusions, the team successfully mitigated potential complications associated with chemotherapy-induced stress on red blood cells. Conclusions: This experience underscores the importance of a multidisciplinary approach and tailored treatment plans for patients with co-existing conditions, suggesting that HS should not automatically disqualify them from potentially curative therapies for aggressive lymphomas. Full article

Adopting a new milking system at a dairy farm causes various changes. This study examined the impact on udder health when changing from a conventional milking system to an automatic milking system. For this purpose, quarter milk samples were taken six times from 138 cows at one conventional dairy farm in Northern Germany over a five-week period around the time of the milking system changeover. To assess udder health, the absolute number of new intramammary infections and the causative pathogen genera and species were analysed for each individual study time point. Pathogen species were detected using matrix-assisted laser desorption ionisation time-of-flight, and the infection dynamics were analysed using two Poisson regression models. In addition, the prevalence and incidence of new intramammary infections and the infection dynamics of the four most frequently isolated pathogen species were calculated. Mixed models were used to determine the development of the new infection rate, the somatic cell count, the teat-end condition, and the udder hygiene between the individual study time points and to compare the new infection rate before and after the changeover of the milking system. After the automatic milking system had been installed, a significant increase in the quarter-level somatic cell count occurred (p < 0.001). Two days before the installation of the automatic milking system, the mean quarter-level somatic cell count was 11,940 cells/mL milk; one sampling date later, 8 days after the changeover, a mean quarter-level somatic cell count of 60,117 cells/mL milk was measured. The significant increase in somatic cell count was probably caused by the time between the last milking and the quarter milk sampling. Additionally, significantly more udders were scored as clean 8 days (95%) and 15 days (96%) after the changeover of the milking system compared to at the last sampling date (88%). Also, significantly more teat ends were classified as free of hyperkeratosis 15 days (80%) compared to 22 days (67%) after the changeover of the milking system. The highest number of absolute new intramammary infections was detected 8 days before the transition of the milking system (28.6%). The lowest number of absolute new intramammary infections occurred 8 days after the change to the automatic milking system (11.0%). Minor mastitis pathogens, such as non-aureus staphylococci and coryneform bacteria, were mainly responsible for the development of new intramammary infections. The most frequently isolated pathogen species were Staphylococcus sciuri, Staphylococcus chromogenes, Staphylococcus haemolyticus, and Corynebacterium amycolatum, with a prevalence of up to 23.9, 10.7, 8.4, and 5.3%, respectively. By comparing the new infection rate before and after the changeover of the milking system, it was possible to establish that the changeover to the automatic milking system had no significant influence on the new intramammary infection rate (p = 0.988). Therefore, this trial confirmed that the changeover from a conventional milking system to an automatic milking system had no negative influence on udder health. Full article

This study aimed to investigate the incidence of subclinical mastitis (SCM), the implicated pathogens, and their impact on milk quality in dairy sheep in Greece. Furthermore, we preliminarily evaluated infrared thermography and the application of AI tools for the early, non-invasive diagnosis of relevant cases. In total, 660 milk samples and over 2000 infrared thermography images were obtained from 330 phenotypically healthy ewes. Microbiological investigations, a somatic cell count (SCC), and milk chemical analyses were performed. Infrared images were analyzed using the FLIR Research Studio software (version 3.0.1). The You Only Look Once version 8 (YOLOv8) algorithm was employed for the automatic detection of the udder’s region of interest. A total of 157 mammary glands with SCM were identified in 122/330 ewes (37.0%). The most prevalent pathogen was staphylococci (136/160, 86.6%). Considerable resistance was detected to tetracycline (29.7%), ampicillin (28.6%), and sulfamethoxazole–trimethoprim (23.6%). SCM correlated with high total mesophilic count (TMC) values and decreased milk fat, lactose, and protein content. A statistically significant variation (p < 0.001) was identified in the unilateral SCM cases by evaluating the mean temperatures of the udder region between the teats in the thermal images. Finally, the YOLOv8 algorithm was employed for the automatic detection of the udder’s region of interest (ROI), achieving 84% accuracy in defining the ROI in this preliminary evaluation. This demonstrates the potential of infrared thermography combined with AI tools for the diagnosis of ovine SCM. Nonetheless, more extensive sampling is essential to optimize this diagnostic approach. Full article

Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in devices is laborious, time-consuming, and operator-dependent, a computational program called the Automatic Quantification of Spheres Algorithm (ASQA) that detects, identifies, counts, and measures spheres automatically was developed. The algorithm and manual counts were compared, and there was no statistically significant difference (p = 0.167). The performance of the AQSA is better when the input image has a uniform background, whereas, with a nonuniform background, artifacts can be interpreted as spheres according to image characteristics. The areas of spheres derived from LN229 cells and CSCs from primary cultures were measured. For images with one sphere, area measurements obtained with the AQSA and SpheroidJ were compared, and there was no statistically significant difference between them (p = 0.173). Notably, the AQSA detects more than one sphere, compared to other approaches available in the literature, and computes the sphere area automatically, which enables the observation of treatment response in the sphere derived from the human glioblastoma LN229 cell line. In addition, the algorithm identifies spheres with numbers to identify each one over time. The AQSA analyzes many images in 0.3 s per image with a low computational cost, enabling laboratories from developing countries to perform sphere counts and area measurements without needing a powerful computer. Consequently, it can be a useful tool for automated CSC quantification from cancer cell lines, and it can be adjusted to quantify CSCs from primary culture cells. CSC-derived sphere detection is highly relevant as it avoids expensive treatments and unnecessary toxicity. Full article

Bifidobacterium is among the dominant flora in the healthy intestine of the human body. It has many probiotic effects such as lowering cholesterol, inhibiting tumors, and regulating immunity. However, fluctuations in culture conditions during cultivation will lead to a decrease in the number of active bacteria. Therefore, more precise control of culture conditions is required to reduce the activity damage caused by environmental fluctuations. Based on this, this study utilized a fully automatic intelligent fermentation tank to develop a cultivation technique suitable for improving the activity and biomass of Bifidobacterium breve B2798. The results show that, under a cultivation temperature of 38.0 °C, the highest viable cell count, which is (2.56 ± 0.04) × 10¹⁰ CFU/mL, can be achieved in the culture medium, with the conclusion that the fermentation endpoint should be controlled at the end period of bacteria logarithmic growth when there is the highest viable cell count and bacterial activity in the culture medium. This study has elucidated the influences of different temperatures on the biomass, viable cell count, and activity of Bifidobacterium breve B2798, providing basic data for the later development of industrialized processing techniques for this bacteria strain. Full article

The rapid and reliable diagnosis of anaerobic bacteria constitutes one of the key procedures in clinical microbiology. Automatic jar gassing systems are commonly used laboratory instruments for this purpose. The most critical factors affecting the cultivation performance of these systems are the level of residual oxygen remaining in the anaerobic jar and the reaction rate determined by the Pd/Al₂O₃ catalyst. The main objective of the presented study is to design and manufacture an enhanced jar gassing system equipped with an extremum seeking-based estimation algorithm that combines real-time data and a reaction model of the Pd/Al₂O₃ catalyst. The microkinetic behavior of the palladium catalyst was modeled through a learning-from-experiment methodology. The majority of microkinetic model parameters were derived from material characterization analysis. A comparative validation test of the designed cultivation system was conducted using conventional gas pouches via six different bacterial strains. The results demonstrated high cell viability, with colony counts ranging from 1.26 × 10⁵ to 2.17 × 10⁵ CFU mL⁻¹. The favorable catalyst facets for water formation on Pd surfaces and the crystal structure of Pd/Al₂O₃ pellets were identified by X-Ray diffraction analysis (XRD). The doping ratio of the noble metal (Pd) and the support material (Al₂O₃) was validated via energy-dispersive spectroscopy (EDS) measurements as 0.68% and 99.32%, respectively. The porous structure of the catalyst was also analyzed by scanning electron microscopy (SEM). During the reference clinical trial, the estimation algorithm was terminated after 878 iterations, having reached its predetermined termination value. The measured and modelled reaction rates were found to converge with a root-mean-squared error (RMSE) of less than 10⁻⁴, and the Arrhenius parameters of ongoing catalytic reaction were obtained. Additionally, our research offers a comprehensive analysis of anaerobic jar gassing systems from an engineering perspective, providing novel insights that are absent from the existing literature. Full article

Nowadays, continuous monitoring is a difficult issue in microscopy. A chip-sized microscope was developed, composed only of microelectronic components, with high optical resolution and a wide field of view. Due to its miniaturized size, it can be placed on or attached to the sample for continuous monitoring in the sample environment. An example of an application of this microscope for the food and beverage industry is described, referring to the study of the fermentation process of white wine. The comparison of the images acquired with conventional optical microscopy reveals similar results. To automatically count yeast cells, the traditional image postprocessing is compared with deep learning. Neural networks achieve similar cell recognition characteristics but with an ~100× speed improvement, by directly processing the obtained holograms. Full article

The complete blood cell count (CBC) was screened in a group of 15 donkeys, of which 8 were of Asinara breed (oculocutaneous albinism type 1, OCA1) and 7 of Sardo breed (gray coat). All donkeys were kept under same management and dietary conditions and underwent periodic health monitoring in the month of June 2024, at the peak of the positive photoperiod, at Mediterranean latitudes. One aliquot of whole blood, drawn from each individual into K₂-EDTA containing tubes, was analyzed for the complete blood cell count through an automatic analyzer, within two hours of sampling. Data were analyzed and compared by one-way ANOVA, where the breed was an independent variable. All animals appeared clinically healthy, though mild eosinophilia was observed in Sardo donkeys. The red blood cell line showed peculiar traits for Asinara donkeys, which displayed significantly higher circulating red blood cell numbers than gray coat Sardo donkeys (RBC, 5.19 vs. 3.80 10¹²/mL ± 0.98 pooled-St. Dev, respectively; p = 0.017). RBCs also exhibited a smaller diameter and higher degree of anisocytosis in Asinara donkeys, along with lower hematocrit value, albeit within physiological ranges. Taken all together, such hematological profile depicts a peculiar trait of the red blood cell line in albino donkeys during the positive photoperiod. Full article

(1) Background: Heat stress detrimentally restricted economic growth in dairy production. In particular, the cooling mechanism of the spraying system effectively reduced both environmental and shell temperatures. This study was designed to investigate the underlying modulatory mechanism of an automatic cooling system in alleviating heat-stressed dairy cows. (2) Methods: A total of 1208 multiparous dairy cows was randomly allocated into six barns, three of which were equipped with automatic sprinklers (SPs), while the other three were considered the controls (CONs). Each barn was considered a replicate. (3) Results: Body temperatures and milk somatic cell counts significantly decreased, while DMI, milk yield, and milk fat content significantly increased under SP treatment. Rumen fermentability was enhanced, embodied by the increased levels of total VFA, acetate, propionate, and butyrate after SP treatment. The rumen microbiota results showed the relative abundances of fiber-degrading bacteria, including the Fibrobacters, Saccharofermentans, Lachnospira, Pseudobutyrivibrio, Selenomonas, and Succinivibrio, which significantly increased after receiving the SP treatment. (4) Conclusions: This study demonstrated that SP effectively alleviated heat stress and improved production performances and milk quality through modulating the rumen microbiota composition and fermentation function of dairy cows. Full article