Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches.

Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train-independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train-dependent dataset and 92.82% for train-independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train-independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

Environmental chemical-wide associations with immune biomarkers in US adults: A cross-sectional analysis.

Environmental chemical exposures influence immune system functions, and humans are exposed to a wide range of chemicals, termed the chemical "exposome". A comprehensive, discovery analysis of the associations of multiple chemical families with immune biomarkers is needed. In this study, we tested the associations between environmental chemical concentrations and immune biomarkers. We analyzed the United States cross-sectional National Health and Nutrition Examination Survey (NHANES 1999-2018). Chemical biomarker concentrations were measured in blood or urine (196 chemicals, 17 chemical families). Immune biomarkers included counts of lymphocytes, neutrophils, monocytes, basophils, eosinophils, red blood cells, white blood cells, and mean corpuscular volume. We conducted separate survey-weighted, multivariable linear regressions of each log2-transformed chemicals on immune measures, adjusted for relevant covariates. We accounted for multiple comparisons using a false discovery rate (FDR). Among 45,528 adult participants, the mean age was 45.7 years, 51.4% were female, and 69.3% were Non-Hispanic White. 71 (36.2%) chemicals were associated with at least one of the eight immune biomarkers. The most chemical associations (FDR<0.05) were observed with mean corpuscular volume (36 chemicals) and red blood cell counts (35 chemicals). For example, a doubling in the concentration of cotinine was associated with 0.16 fL (95% CI: 0.15, 0.17; FDR<0.001) increased mean corpuscular volume, and a doubling in the concentration of blood lead was associated with 61,736 increased red blood cells per µL (95% CI: 54,335, 69,138; FDR<0.001). A wide variety of chemicals, such as metals and smoking-related compounds, were highly associated with immune system biomarkers. This environmental chemical-wide association study identified chemicals from multiple families for further toxicological, immunologic, and epidemiological investigation.

Analysis of Factors Influencing Prognosis and Assessment of 60 Cases of Decompensated Cirrhotic Patients with Portal Hypertension.

Objective: To investigate the risk factors for the development of portal hypertension in patients with decompensated cirrhosis and analyze their prognosis. Methods: Patients with decompensated cirrhosis who were admitted to our hospital and Qu fu People's Hospital from June 2022 to June 2023 were included in this study. Among them, there were 45 male and 15 female patients, with a median age of 56 (range: 35-77) years. A comparative analysis was performed between Group A (hepatic venous pressure gradient, HVPG <16 mmHg) and Group B (HVPG ≥16 mmHg) patients, along with various clinical outcomes. Multivariate analysis was conducted to explore the risk factors influencing the occurrence of portal hypertension and adverse prognosis in patients with cirrhosis. Results: In Group A patients with portal hypertension, we observed lower levels of aspartate aminotransferase, laminin, serum hyaluronic acid, type III procollagen N-terminal peptide, total bile acids, and cholylglycine acid compared to Group B. On the other hand, levels of alanine aminotransferase, white blood cells, and serum albumin were higher in Group A than in Group B. These differences between the groups were statistically significant (P < 0.05). Multivariate analysis of the aforementioned risk factors indicated that low white blood cell count, high cholylglycine acid levels, and high serum hyaluronic acid levels were identified as independent risk factors for the occurrence of difficult-to-control complications in decompensated portal hypertension among patients with liver cirrhosis (P < 0.05). Conclusion: Liver cirrhosis patients with portal hypertension and multiple risk factors like low white blood cell count and high liver transaminase levels should be cautious regarding the progression of portal hypertension when combined with splenomegaly, liver fibrosis, and bile stasis, as it often indicates a poor prognosis.

Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset.

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.

The Prognostic Significance of Pretreatment White Blood Cell and Platelet Counts for Survival Outcome in Primary Lung Cancer.

Background: In Vietnam, lung cancer ranks second among common types of cancer. Although there have been many advances in the diagnosis and treatment of lung cancer, it is still one of the deadliest types of cancer. Objective: We investigated the prognostic value of pretreatment white blood cell (WBC) and platelet counts of patients with lung cancer. Methods: This was a prospective, descriptive study with longitudinal follow-up. Data from 203 patients with stage IIIA-IV lung cancer presenting at Can Tho City Oncology Hospital between June 2020 and June 2022 were analyzed. Complete blood cell counts were obtained using standard methods. Lung cancer diagnoses and histological classifications were obtained from cancer registries. The optimal overall survival cutoff point for pretreatment WBC and platelet counts was determined using maximally selected rank statistics. Results: The median follow-up was 6 (interquartile range 4-8) months and the median age was 61.3 years. The number of male patients was higher than the number of female patients. Most (71.4%) patients had adenocarcinoma; 62.1% of the patients had a WBC count of > 10 × 109/L and 38.4% had a platelet count of > 400 × 109/L. The median overall survival (OS) of all patients was 8 months. The 3-month, 6-month, and 1-year OS was 88.7%, 62.4%, and 28.3%, respectively. Patients with a WBC count of <9.18 × 109/L had a higher OS than those with a count of ≥ 9.18 × 109/L (17 months versus 8 months; p < 0.001) Patients with a platelet count of < 453 × 109/L had a higher OS than those with a count of ≥ 453 × 109/L (8 months versus 7 months; p < 0.001). Conclusion: White blood cell and platelet count tests are routine investigations that are valuable, in combination with other factors, for predicting OS of lung cancer patients. They can help clinicians to monitor treatment response and survival.

Nucleus segmentation of white blood cells in blood smear images by modeling the pixels' intensities as a set of three Gaussian distributions.

The precise segmentation of white blood cells (WBCs) within blood smear images is a significant challenge with implications for both medical research and image processing. Of particular importance is the often neglected task of accurately segmenting WBC nuclei, an aspect that currently lacks dedicated methodologies. This paper introduces a straightforward and efficient method designed to fill this critical gap, providing an effective solution for the efficient segmentation of WBC nuclei. In blood smear imagery, the distinctive coloration of WBCs contrasts with the hues of other blood components. The inherent obscurity of WBCs prompts their segmentation by isolating pixels with minimal intensities. To streamline this process, our proposed method employs the Laplacian pyramid technique to decorrelate pixels in blood smear images, thereby amplifying the contrast. Subsequently, the intensities of pixels constituting blood cells, encompassing WBCs and the background, are modeled using three Gaussian random variables. Capitalizing on this feature, we implement the Gaussian mixture model (GMM) clustering method to determine the optimal threshold value, facilitating a highly precise segmentation of WBC nuclei. The proposed method demonstrates the capability to process images containing a single WBC as well as effectively functioning with images containing multiple cells of this type. Evaluation of the method on the ALL-IDB, ALL-IDB2, CellaVision, and JTSC datasets yielded accuracy values of 0.9802, 0.9725, 0.9772, and 0.9730, respectively. Comparative analysis with state-of-the-art methods revealed a notably comparable performance, underscoring the effectiveness of the proposed approach. The method presented in this article is highly competitive for segmenting the nuclei of WBCs compared to state-of-the-art methods. The three main advantages of our method are its ability to process images containing one or more WBCs, the automatic calculation of threshold values for each processed image, eliminating the need for manual parameter adjustments. Lastly, the method is efficient, as its algorithmic complexity is approximately O ( n m ) .

Association between white blood cells and ultra-early hematoma growth in patients with spontaneous intracerebral hemorrhage.

Background: Ultra-early inflammatory reaction after spontaneous intracerebral hemorrhage (sICH) plays an important role in the coagulation process and is closely related to early hematoma expansion. However, the relationship between ultra-early hematoma growth (uHG) and ultra-early inflammatory reaction remains unknown. Objective: To evaluate the association between ultra-early inflammatory indicators and uHG in patients with sICH. Methods: We retrospectively included 225 patients with acute sICH who were divided into the uHG ≤4.7 ml/h group and the uHG >4.7 ml/h group, respectively. The uHG was defined as hematoma volume (milliliter) at the primary computed tomography (CT) scan divided by time (hour) from onset to the performance of primary CT within 6 h after onset. The white blood cells (WBC), blood hypersensitive C-reactive protein, National Institutes of Health Stroke Scale (NIHSS) score and other related baseline data were collected and compared between the two groups. The multivariate regression analysis and receiver operating characteristic (ROC) curve were used to evaluate the independent risk factors for uHG >4.7 ml/h. Results: NIHSS score and WBC were independent risk factors for uHG in patients with acute sICH (OR 1.188, 95% CI: 1.111-1.271, p < 0.001; OR 1.151, 95% CI: 1.018-1.300, p = 0.024; respectively). The area under curve of ROC for WBC and NIHSS score was 0.658 and 0.754, respectively (all p < 0.001), while the WBC combined with NIHSS score was 0.773 (p < 0.001). Conclusion: WBC count within 6h after onset might be an independent risk factor for the increase of uHG in patients with sICH.

The high FKBP1A expression in WBCs as a potential screening biomarker for pancreatic cancer.

Given the limitation of current routine approaches for pancreatic cancer screening and detection, the mortality rate of pancreatic cancer cases is still critical. The development of blood-based molecular biomarkers for pancreatic cancer screening and early detection which provide less-invasive, high-sensitivity, and cost-effective, is urgently needed. The goal of this study is to identify and validate the potential molecular biomarkers in white blood cells (WBCs) of pancreatic cancer patients. Gene expression profiles of pancreatic cancer patients from NCBI GEO database were analyzed by CU-DREAM. Then, mRNA expression levels of three candidate genes were determined by quantitative RT-PCR in WBCs of pancreatic cancer patients (N = 27) and healthy controls (N = 51). ROC analysis was performed to assess the performance of each candidate gene. A total of 29 upregulated genes were identified and three selected genes were performed gene expression analysis. Our results revealed high mRNA expression levels in WBCs of pancreatic cancer patients in all selected genes, including FKBP1A (p < 0.0001), PLD1 (p < 0.0001), and PSMA4 (p = 0.0002). Among candidate genes, FKBP1A mRNA expression level was remarkably increased in the pancreatic cancer samples and also in the early stage (p < 0.0001). Moreover, FKBP1A showed the greatest performance to discriminate patients with pancreatic cancer from healthy individuals than other genes with the 88.9% sensitivity, 84.3% specificity, and 90.1% accuracy. Our findings demonstrated that the alteration of FKBP1A gene in WBCs serves as a novel valuable biomarker for patients with pancreatic cancer. Detection of FKBP1A mRNA expression level in circulating WBCs, providing high-sensitive, less-invasive, and cost-effective, is simple and feasible for routine clinical setting that can be applied for pancreatic cancer screening and early detection.

Biomonitoring of polycyclic aromatic hydrocarbons exposure and short-time health effects in wildland firefighters during real-life fire events.

Human biomonitoring data retrieved from real-life wildland firefighting in Europe and, also, worldwide are scarce. Thus, in this study, 176 Portuguese firefighters were biomonitored pre- and post- unsimulated wildfire combating (average:12-13 h; maximum: 55 h) to evaluate the impact on the levels of urinary polycyclic aromatic hydrocarbons hydroxylated metabolites (OHPAH; quantified by high-performance liquid chromatography with fluorescence detection) and the associated short-term health effects (symptoms, and total and differentiated white blood cells). Correlations between these variables and data retrieved from the self-reported questionnaires were also investigated. Firefighters were organized into four groups according to their exposure to wildfire emissions and their smoking habits: non-smoking non-exposed (NSNExp), non-smoking exposed (NSExp), smoking non-exposed (SNExp), and smoking and exposed (SExp). The most abundant metabolites were 1-hydroxynaphthalene and 1-hydroxyacenaphthene (1OHNaph + 1OHAce) (98-99 %), followed by 2-hydroxyfluorene (2OHFlu) (0.2-1.1 %), 1-hydroxyphenanthrene (1OHPhen) (0.2-0.4 %), and 1-hydroxypyrene (1OHPy) (0.1-0.2 %); urinary 3-hydroxybenzo(a)pyrene was not detected. The exposure to wildfire emissions significantly elevated the median concentrations of each individual and total OHPAH compounds in all groups, but this effect was more pronounced in non-smoking (1.7-4.2 times; p ≤ 0.006) than in smoking firefighters (1.3-1.6 times; p ≤ 0.03). The greatest discriminant of exposure to wildfire emissions was 1OHNaph + 1OHAce (increase of 4.2 times), while for tobacco smoke it was 2OHFlu (increase of 10 times). Post-exposure, white blood cells count significantly increased ranging from 1.4 (smokers, p = 0.025) to 3.7-fold (non-smokers, p < 0.001), which was accompanied by stronger significant correlations (0.480 < r < 0.882; p < 0.04) between individual and total OHPAH and total white blood cells (and lymphocytes > monocytes > neutrophils in non-smokers), evidencing the impact of PAH released from wildfire on immune cells. This study identifies Portuguese firefighters with high levels of biomarkers of exposure to PAH and points out the importance of adopting biomonitoring schemes, that include multiple biomarkers of exposure and biomarkers of effect, and implementing mitigations strategies.

No evidence linking sleep traits with white blood cell counts: Multivariable-adjusted and Mendelian randomization analyses.

BACKGROUND: Disturbances in habitual sleep have been associated with multiple age-associated diseases. However, the biological mechanisms underpinning these associations remain largely unclear. We assessed the possible involvement of the circulating immune system by determining the associations between sleep traits and white blood cell counts using multivariable-adjusted linear regression and Mendelian randomization. METHODS: Cross-sectional multivariable-adjusted linear regression analyses were done using participants within the normal range of total white blood cell counts (>4.5 × 109 and <11.0 × 109 /µL) from UK Biobank. For the sleep traits, we examined (short and long) sleep duration, chronotype, insomnia symptoms and daytime dozing. Two-sample Mendelian randomization analyses were done using instruments for sleep traits derived from European-ancestry participants from UK Biobank (over 410,000 participants) and using SNP-outcome data derived from European-ancestry participants from the Blood Cell Consortium (N = 563,946) to which no data from UK Biobank contributed. RESULTS: Using data from 357,656 participants (mean [standard deviation] age: 56.5 [8.1] years, and 44.4% men), we did not find evidence that disturbances in any of the studied sleep traits were associated with differences in blood cell counts (total, lymphocytes, neutrophiles, eosinophiles and basophiles). Also, we did not find associations between disturbances in any of the studied sleep traits and white blood cell counts using Mendelian Randomization. CONCLUSION: Based on the results from two different methodologies, disturbances in habitual sleep are unlikely to cause changes in blood cell counts and thereby differences in blood cell counts are unlikely to be underlying the observed sleep-disease associations.

Isolating Circulating Cancer Stem Cells (CCSCs) from Human Whole Blood.

Measuring circulating tumor cells (CTCs) or circulating cancer stem cells (CCSCs) in blood, which shed from primary tumors, is a noninvasive method to screen and/or diagnose patients with a high risk of developing metastatic cancers or relapse. Here, we describe an optimized and standardized laboratory method for isolating CCSCs from human blood samples, using cancer-specific stem cell biomarkers (Kantara et al., Lab Invest 95:100-112, 2015). When performing this technique, 0-1 circulating epithelial tumor cells/mL blood should be expected in patients free of malignant adenocarcinomas compared to over 3 circulating cancer stem cells/mL blood in patients positive for malignant adenocarcinomas (Kantara et al., Lab Invest 95:100-112, 2015).

Sitting Less, Recovering Faster: Investigating the Relationship between Daily Sitting Time and Muscle Recovery following Intense Exercise: A Pilot Study.

(1) There is growing concern surrounding the adverse effects of prolonged sitting on health, yet its impact on post-exercise recovery remains relatively unexplored. This study aimed to better understand the potential influence of habitual prolonged sitting on recovery time and the unfavorable impact prolonged sitting may have on time to recovery, as assessed by muscle damage and inflammatory markers and an isokinetic dynamometer. (2) Nine college-age men (mean age ± SD = 22.1 ± 3.1 years, body mass = 80.9 ± 15.7 kg, height = 171 ± 9.0 cm, Body Mass Index (BMI) = 27.6 ± 4.9 kg·m2) participated in an exhaustive exercise protocol. Creatine Kinase (CK), Myoglobin (Mb), C-Reactive Protein (CRP), White Blood Cell Count (WBC), Peak Torque (PT), and muscle soreness were measured at baseline and 0, 24, 48, and 72 h post-exercise. Dietary and exercise logs were maintained during the 5-day testing procedure. (3) No significant differences were observed in muscle damage markers (CK [p = 0.068] and Mb [p = 0.128]), inflammatory markers (CRP [p = 0.814] and WBC [p = 0.140]), or PT [p = 0.255]) at any time point. However, a significant positive correlation was found between daily sitting time and the percent increase in CK concentration from 0 h to 72 h (r = 0.738, p = 0.023). Strong correlations were also noted between prolonged sitting and percent change in Mb concentration at 48 h (r = 0.71, p = 0.033) and 72 h (r = 0.889, p = 0.001). There was a significant two-way interaction for time × velocity (p = 0.043) for PT with a simple main effect for time at 60°·s-1 (p = 0.038). No significant associations were detected between daily carbohydrate or protein intake and recovery markers (p > 0.05). (4) The findings suggest minimizing daily sitting time may expedite and potentially aid muscle recovery after an intense exercise bout, although further research is warranted to validate these findings.

Do Men and Women Differ in Hematological Adaptations to 24 Weeks of Crossfit® Training?

Regular exercise can modulate the immune system functioning through changes in the number and function of leukocytes as well as in red blood cells and other typical blood markers. High intensity exercise promotes increases in cytotoxic activity, phagocytic capacity, chemotaxis and cell apoptosis. The aim of the study was to compare the chronic effects of a 24-week training program using CrossFit® methodology on hematological variables of men vs. women. Twenty-nine CrossFit® athletes (35.3 ± 10.4 years, 175.0 ± 9.2 cm, 79.5 ± 16.4 kg) participated in the study. The blood count, the lipid profile and glucose markers were measured every two months during the study period. The erythrocyte count and hemoglobin concentrations increased in months 4 and 6 in men and women, respectively. Hematocrit levels increased in men in months 2, 4 and 6, while in women only in month 6. Red cell distribution width increased in men in month 6 when compared to the value in month 2. Segmented neutrophils increased in men in month 6 and eosinophil levels increased in women in month 6. Differences between the two sexes were observed in monocytes levels at baseline, as well as in months 2, 4 and 6. Cross-Fit® training increased red cell count indicators in both sexes, which may be related to increased erythropoiesis. Some white blood cell counts were altered and these differed between sexes. The number of lymphocytes remained stable throughout the experiment.

RNA Analysis of Circulating Leukocytes in Patients with Alzheimer's Disease.

Background: One of the key symptoms of Alzheimer's disease (AD) is the impairment of short-term memory. Hippocampal neurogenesis is essential for short-term memory and is known to decrease in patients with AD. Impaired short-term memory and impaired neurogenesis are observed in aged mice alongside changes in RNA expression of gap junction and metabolism-related genes in circulating leukocytes. Moreover, after penetrating the blood-brain barrier via the SDF1/CXCR4 axis, circulating leukocytes directly interact with hippocampal neuronal stem cells via gap junctions. Objective: Evaluation of RNA expression profiles in circulating leukocytes in patients with AD. Methods: Patients with AD (MMSE≧23, n = 10) and age-matched controls (MMSE≧28, n = 10) were enrolled into this study. RNA expression profiles of gap junction and metabolism-related genes in circulating leukocytes were compared between the groups (jRCT: 1050210166). Results: The ratios of gap junction and metabolism-related genes were significantly different between patients with AD and age-matched controls. However, due to large inter-individual variations, there were no statistically significant differences in the level of single RNA expression between these groups. Conclusions: Our findings suggest a potential connection between the presence of circulating leukocytes and the process of hippocampal neurogenesis in individuals with AD. Analyzing RNA in circulating leukocytes holds promise as a means to offer novel insights into the pathology of AD, distinct from conventional markers.

Validation of the Sysmex XN analyser and Blood Bank mode for the quality and safety of donor blood and transfusion products.

OBJECTIVES: Our objective was to compare the measurement of residual white blood cell (rWBC) and residual red blood cell (rRBC) counts in blood products using the XN Blood Bank mode and the laboratory standard operating procedures for manual counts. In addition, to compare the whole blood complete blood count (CBC) values of blood donors and the quality of blood products using the Sysmex XN analyser versus the XS-1000i analyser. MATERIALS AND METHODS: For blood donors, 190 samples from blood or apheresis donors were analysed on both the Sysmex XS-1000i and XN-1000 analysers and the mean values of six CBC parameters were compared: the white blood cell count (WBC), the red blood cell count (RBC), haemoglobin (HGB), haematocrit (HCT), the mean corpuscular volume (MCV), the platelet count (PLT). For blood products, 164 samples were collected: 13 Plasma products - whole blood, 9 Plasma products - apheresis, 36 RBC concentrates - whole blood, 30 PLT concentrates - buffy coats, 36 PLT concentrates - buffy coats - pooled and 55 PLT concentrates - apheresis. RESULTS: All CBC parameters of the blood donors tested showed similar performance, with excellent correlation coefficients (r) ranging from 0.821 to 0.995. The majority of the blood products did not have a quantifiable number of residual cells, meaning the number of rWBC and rRBC, if present, was below the limit of quantitation (LoQ) of the different methods. rWBC were detected by Blood Bank mode in Plasma products - whole blood with a mean rWBC of 0.012 × 109 /L and in PLT concentrates - buffy coats with a mean rWBC of 0.19 × 109 /L. The correlation coefficient in both analysers for all three parameters (HGB, HCT, RBC) in RBC concentrates - whole blood was excellent, ranging from 0.95 to 0.99. For platelet count, r ranged from 0.98 to 0.99. CONCLUSION: The XN-Series analyser, equipped with a Blood Bank mode, demonstrated reliable performance when used for blood donor evaluation, rWBC enumeration and measurement of end blood products.

Effects of dietary supplementation of a blend of Saccharomyces cerevisiae, multiple live probiotic bacteria, and their fermentation products on performance, health, and rumen bacterial community of newly weaned beef steers during a 56-d receiving period.

We examined the effects of a blend of Saccharomyces cerevisiae, multiple live probiotic bacteria, and their fermentation products on performance, health, and the ruminal bacterial community of newly weaned beef steers during a 56-d receiving period. Forty newly weaned Angus crossbred steers (221 ±â€…25.6 kg BW; 180 ±â€…17 d of age) were stratified by body weight (BW) into four pens (10 steers per pen) such that each pen had a similar average BW at the beginning of the experiment. The pens were randomly assigned to receive a corn silage basal diet (CON; n = 20) or the basal diet supplemented with 9 g/steer/d of PRO feed additive (PRO; n = 20). The PRO additive is a blend of S. cerevisiae and the fermentation products of Enterococcus faecium, Bacillus licheniformis, B. subtilis, Lactobacillus animalis, and Propionibacterium freudenreichii. The DMI and water consumed were monitored using the GrowSafe intake nodes and custom flow meters, respectively. BWs were recorded weekly to calculate average daily gain (ADG). Before morning feeding, 10 mL of blood was taken from each steer on days 0-7, and thereafter weekly for analyses of immune cells, plasma glucose, and NEFAs. On day 56, rumen fluid samples (200 mL each) were collected from all the steers for microbiome analysis. Over the 56-d receiving period, the supplemental PRO had no effects on DMI, water intake, or ADG. However, compared to CON, beef steers fed supplemental PRO tended to have greater ADG (P = 0.08) and BW (P = 0.07) during the first 14 d of the study. There was a treatment × day interaction (P ≤ 0.05) for WBC, neutrophils and monocytes over the 56 d such that beef steers fed supplemental PRO had lower blood concentrations on certain days during the first 7 d after weaning, indicating reduced inflammation or stress response. The results of the rumen microbiome analysis revealed that the relative abundance of complex fiber degrading or obligate proton-reducing bacterial genera such as Bacteroides, Ruminococcus gauvreauii group, Desulfovibrio, Syntrophococcus, and Acetitomaculum were greater (P ≤ 0.05) in beef steers fed supplemental PRO compared to CON. This study demonstrated that dietary supplementation of PRO improved the growth performance, reduced stress or inflammatory response during the initial days after weaning, and altered the ruminal bacterial community toward increased relative abundance of bacterial genera associated with improved rumen function.

Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning.

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.

Reagentless Vis-NIR Spectroscopy Point-of-Care for Feline Total White Blood Cell Counts.

Spectral point-of-care technology is reagentless with minimal sampling (<10 µL) and can be performed in real-time. White blood cells are non-dominant in blood and in spectral information, suffering significant interferences from dominant constituents such as red blood cells, hemoglobin and billirubin. White blood cells of a bigger size can account for 0.5% to 22.5% of blood spectra information. Knowledge expansion was performed using data augmentation through the hybridization of 94 real-world blood samples into 300 synthetic data samples. Synthetic data samples are representative of real-world data, expanding the detailed spectral information through sample hybridization, allowing us to unscramble the spectral white blood cell information from spectra, with correlations of 0.7975 to 0.8397 and a mean absolute error of 32.25% to 34.13%; furthermore, we achieved a diagnostic efficiency between 83% and 100% inside the reference interval (5.5 to 19.5 × 109 cell/L), and 85.11% for cases with extreme high white blood cell counts. At the covariance mode level, white blood cells are quantified using orthogonal information on red blood cells, maximizing sensitivity and specificity towards white blood cells, and avoiding the use of non-specific natural correlations present in the dataset; thus, the specifity of white blood cells spectral information is increased. The presented research is a step towards high-specificity, reagentless, miniaturized spectral point-of-care hematology technology for Veterinary Medicine.