Platelet and white blood cell counts correlate with leptin and body mass index in Japanese adolescents.

Obesity is associated with mild chronic inflammation, frequently observed along with increased platelet and white blood cell (WBC) levels in adults. We aimed to clarify the relationship between peripheral blood cell count, body mass index standard deviation score (BMI-SDS), and adipocytokine levels in obese adolescents. Participants included 31 patients with obesity (age: 13.1 ± 3.1 yr) and 28 normal-weight controls (age: 13.3 ± 1.9 yr). Obesity was defined as a percentage of overweight ≥ 20%; patients with type 2 diabetes were excluded. As sex differences were observed in blood cell counts, the analysis was performed after adjusting for sex differences. The obese group has significantly higher WBC, red blood cell, and platelet counts, as well as high serum leptin levels and Homeostasis Model Assessment of insulin resistance (HOMA-IR) scores compared with those of the control group. In all participants, BMI-SDS significantly correlated with WBC and platelet counts. Platelet count correlated with serum leptin and glucose levels, whereas WBC count correlated with serum leptin, insulin, HOMA-IR, and glucose levels. Statistical analysis showed that serum leptin level significantly influenced the platelet count and HOMA-IR score affected WBC count. Increased platelet and WBC counts in adolescents with obesity may increase the risk of thrombosis.

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

Comparing Human-Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment.

INTRODUCTION: There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This study aims to assess the interobserver variability and human-level performance in blood cell morphologic assessment. METHODS: A dataset of 1000 single white blood cell images were independently labelled by 10 doctors and morphology scientists. Interobserver variability was calculated using Fleiss' kappa. Observers' labels were then separated into consensus labels used to determine ground truth, and performance labels used to assess observer performance. A machine learning model was trained and assessed using the same cell images. Explainability images (XRAI and IG) were generated for each of the test images. RESULTS: The Fleiss kappa for all 10 observers was 0.608, indicating substantial agreement between observers. The accuracy of human observers was 95%, with sensitivity 72% and specificity 97%. The accuracy of the machine learning model was 95%, with sensitivity 71% and specificity 97%. The model shared similar performance across labels when compared to humans. Explainability metrics demonstrated that the machine learning model was able to differentiate between the cytoplasm and nucleus of the cells, and used these features to perform predictions. CONCLUSION: The substantial, though not perfect, agreement between human observers highlights the inherent subjectivity in white blood cell morphologic assessment. A machine learning model performed similarly to human observers in single white blood cell identification. Further research is needed to compare human-level and machine learning performance in ways that more closely reflect the typical process of morphologic assessment.

The association of organochlorine pesticides and polychlorinated biphenyls exposure with dyslipidemia and blood lipids: The mediating effect of white blood cell counts.

Epidemiological evidence regarding the associations of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) with lipid metabolism and its potential biological mechanisms remain largely unknown. We intended to explore the associations of OCPs and PCBs with dyslipidemia and blood lipid levels, and further evaluate the mediating role of total and differential white blood cell (WBC) counts. We measured the blood lipid levels, the concentration of OCPs/PCBs and WBC counts in serum among 2036 adults in Wuhan city, China. In the multiple-pollutant models, the results showed that ß-hexachlorocyclohexane (HCH), p,p'-dichlorodiphenyldichloroethylene (DDE), and PCB-153 were positively correlated with increased odds of dyslipidemia. p,p'-DDE and PCB-153 were correlated with elevated triglyceride (TG) and lowered high-density lipoprotein cholesterol (HDL-c). A positive relationship was observed between p,p'-DDE and total cholesterol (TC) as well. Meanwhile, weighted quantile sum (WQS) regression analyses revealed that PCB and OCP mixtures were positively related to dyslipidemia risk and TG and negatively associated with HDL-c, to which p,p'-DDE was the major contributor. BMI, gender and age might modify the associations of OCPs and PCBs with dyslipidemia and TG. Furthermore, we found that WBC counts were significantly associated with dyslipidemia and blood lipid levels, and a positive correlation was also found between p,p'-DDE and lymphocyte count. Mediation analysis further indicated that lymphocyte count might mediate the associations of p,p'-DDE with dyslipidemia, TG, and TC. Accordingly, our results showed that OCPs and PCBs were related to abnormal lipid metabolism, which was partially mediated by WBC counts.

Optimizing Prognostic Precision in Hepatocellular Carcinoma: Integrating PWR with ALBI and PALPI Scores.

BACKGROUND: Enhancing prognostication in Hepatocellular Carcinoma (HCC) remains an unmet need, especially in patients with preserved liver function. This study aimed to integrate the Platelet-to-White Blood Cell Ratio (PWR) with albumin-bilirubin (ALBI) and platelets-albumin-bilirubin (PALBI) scores for improved assessment of mortality and treatment responses in hepatocellular carcinoma (HCC) patients. METHODS: In this prospective study, 262 patients with hepatocellular carcinoma (HCC) were included, with basic data collected and followed up for one year or until death. All prognostic scores were calculated by integrating the PWR with the ALBI and PALBI scores, examining their relationship with treatment responses and mortality rates. RESULTS: The patients were mainly males (69.5%), aged 59.6 ± 8.09 years. The predictive power of the integrated PALBI+PWR score at different time points 1 (P 0.004), 3 months, and 6 months (P 0.004) overpowered all other scores. However, late at the 12-month follow-up, ALBI score had reported superiority on PALPI+PWR (AUC 0.631, 0.617), respectively. Regression analyses confirmed the high performance of PALBI+PWR factors in influencing treatment response (P 0.009-OR 0.562 (0.365 - 0.867)). Regarding mortality prediction, PALPI+PWR proved the highest efficacy in regression analysis (P <0.001) OR (2.451 (1.555 - 3.862). CONCLUSION: Integrating PWR with the PALBI score enhances prognostic precision in patients with HCC, offering improved predictive power for treatment responses and mortality in the early stages of HCC with preserved liver function.

Intra and Inter-Rater Variability in the Interpretation of White Blood Cell Scintigraphy of Hip and Knee Prostheses.

Background: White blood cell (WBC) scintigraphy plays a major role in the diagnostic approach to periprosthetic infections. Although the procedure has been standardized by the publication of several guidelines, the interpretation of this technique may be susceptible to intra and inter-variability. We aimed to assess the reproducibility of interpretation between nuclear medicine physicians and by the same physician and to demonstrate that Cohen's coefficient is more unstable than Gwet's coefficient, as the latter is influenced by the prevalence rates. Methods: We enrolled 59 patients who performed a Technetium-99m WBC (99mTc-WBC) scintigraphy for suspected hip or knee prosthesis infection. Three physicians, blinded to all patient clinical data, performed two image readings. Each WBC study was assessed both visually and semi-quantitatively according to the guidelines of the European Association of Nuclear Medicine (EANM). For semi-quantitative analysis, readers drew an irregular Region of Interest (ROI) over the suspected infectious lesion and copied it to the normal contralateral bone. The mean counts per ROI were used to calculate lesion-to-reference tissue (LR) ratios for both late and delayed images. An increase in LR over time (LRlate> LRdelayed) of more than 20% was considered indicative of infection. Agreement between readers and between readings was assessed by the first-order agreement coefficient (Gwet's AC1). Reading time for each scan was compared between the three readers in both the first and the second reading, using the Generalized Linear Mixed Model. Results: An excellent agreement was found among all three readers: 0.90 for the first reading and 0.94 for the second reading. Both inter- and intra-variability showed values ≥0.86. Gwet's method demonstrated greater robustness than the Cohen coefficient when assessing the intra and inter-rater variability, since it is not influenced by the prevalence rate. Conclusions: These studies can contribute to improving the reliability of nuclear medicine imaging techniques and to evaluating the effectiveness of trainee preparation.

An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.

Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model's performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells.

Increased white blood cell count is associated with an increased demand for unfractionated heparin during veno-arterial extracorporeal oxygenation in lung transplantation.

BACKGROUND: This retrospective observational study aimed to examine whether clinical inflammatory parameters were associated with the requirement dosage of unfractionated heparin (UFH) to maintain the range of ACT in veno-arterial extracorporeal membrane oxygenation (V-A ECMO) during lung transplantation surgery. METHODS: Among all patients who underwent lung transplantation using V-A ECMO from January 2021 to May 2022, 27 patients were included. These patients were divided into two groups based on whether the infusion rate of UFH was increased from the initial infusion rate (7-8 units/kg/h) (increased group, n = 10) or the infusion rate was maintained or decreased (non-increased group, n = 17). The infusion rate was adjusted with an activated clotting time (ACT) target of 160-200 s. RESULTS: At 1-2 h after starting ECMO, ACT was significantly lower (179.0 (166.5-188.5) versus 224.0 (193.0-242.0) sec, p = 0.006) and white blood cell (WBC) counts were higher in the increased group (12.6 ± 3.3 versus 9.5 ± 4.0 × 103/µL, p = 0.046). The UFH infusion rates were higher in the increased group during the surgery. The cutoff value of WBC count at 1-2 h after starting ECMO for discriminating the need for increasing the UFH dosage was determined as 10.2 × 103/µL (sensitivity 90.0%, specificity 58.8%, area under the curve 0.712) and discrimination of this cut-off value was confirmed as statistically significant (p = 0.018). CONCLUSION: These data suggested that WBC count was associated with the requirement of an increase in the UFH infusion rate of V-A ECMO during lung transplantation surgery. Further evaluation is necessary to clarify the role of WBC count in determining the optimal UFH dosage.

Clinical value of smear review of flagged samples analyzed with the Sysmex XN hematology analyzer.

OBJECTIVES: A smear review is typically made in flagged differential counts performed with hematology analyzers although the clinical value of such reviews is uncertain. Therefore, we evaluated the differences in differential counts between Sysmex XN-9000 and a smear review in flagged samples. Furthermore, the clinical value of blasts identified was investigated. METHODS: Data on all differential counts performed in a two-year period were identified at two laboratories. In patients with blasts, the electronic health record was reviewed. Agreement between automated and manual differential counts was evaluated by Bland-Altman plots. Concordance between the two methods categorized according to reference intervals was evaluated and adjusted for irrelevant non-concordance caused by random analytical error. RESULTS: In total, 5,500 flagged differential counts were identified from 4,092 patients. A good agreement between the automated and manual differential count was found for all cell types (-0.480 × 109/L to 0.297 × 109/L). The concordance between the two methods was excellent for all cell types, except for monocytes (82 %) where the automated estimates were higher than the manual in 19 % of samples. Blasts were identified in 241 (1 %) of smear reviews. Acute leukemia was diagnosed in 13 (5 %) patients, and only in one patient contributed the detection of blasts to the suspicion of acute leukemia. CONCLUSIONS: Our findings indicate that routine smear review of all flagged samples do not contribute with additional, significant information. After local validation and dialogue with clinical departments, such reviews may potentially be omitted to increase cost-effectiveness and reduce turn-around-time.

Hematological Markers as Predictors of ICU Admission in COVID-19 Patients: A Case-Control Study From a Tertiary Hospital.

BACKGROUND: COVID-19 illness severity ranges from mild- to life-threatening cases necessitating critical care. Rapid prediction of disease severity and the need for critical care support in COVID-19 patients remain essential, not only for current management but also for preparedness in future pandemics. This study aimed to assess hematological parameters as predictors of intensive care unit (ICU) admission and survival in COVID-19 patients, providing insights applicable to a broad range of infectious diseases. METHODS: A case-control study was conducted at Hospital Raja Perempuan Zainab II, a tertiary referral hospital in Kelantan, Malaysia, from March 2020 to August 2021. Demographics, clinical, and laboratory data were retrieved from patients' medical records. Statistical analyses, including the Chi-square (χ2) test, independent t-tests, and simple and multiple logistic regressions, were used to analyze the data. A receiver operating characteristic (ROC) curve analysis was conducted to assess the accuracy of the predictors. RESULTS: The median age was 51 years, with females comprising 56.7% (n=148) and males 43.3% (n=113). A total of 88.5% of patients were admitted to non-ICU wards, with a mortality rate of 5.7%. Significant differences were observed in the distribution of hematological parameters between ICU-admitted and non-admitted patients. Neutrophil (OR: 23.96, 95% CI: 7.296-78.675) and white blood cell (WBC) count (OR: 36.677, 95% CI: 2.086-644.889) were the most significant predictors for ICU admission and survival, respectively. CONCLUSIONS: WBC and neutrophil counts exhibited high predictive value for ICU admission, while WBC, neutrophil, lymphocyte, and immature granulocyte (IG) counts were significant predictors of survival status among COVID-19 patients. These findings underscore the continued relevance of hematological markers in managing severe respiratory infections and improving critical care triage, with implications for current and future healthcare challenges.

Exploring the relationship between blood platelet and other components utilizing count regression: A cross-sectional study in Bangladesh.

Background and Aims: Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues-low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. Methods: A cross-sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non-parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data-gathering technique. Count regression, negative binomial regression, and quasi-Poisson regression techniques have been employed for examining relationship of the data sets. Results: Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. Conclusion: From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts.

Target Values for 25-Hydroxy and 1,25-Dihydroxy Vitamin D Based on Their Associations with Inflammation and Calcium-Phosphate Metabolism.

Target values for 25-hydroxy vitamin D and 1,25(OH)2D or 1,25-dihydroxy vitamin D remain a topic of debate among clinicians. We analysed data collected from December 2012 to April 2020 from two cohorts. Cohort A, comprising 455,062 subjects, was used to investigate the relationship between inflammatory indicators (white blood cell [WBC] count and C-reactive protein [CRP]) and 25(OH)D/1,25(OH)2D. Cohort B, including 47,778 subjects, was used to investigate the connection between 25(OH)D/1,25(OH)2D and mineral metabolism markers (phosphate, calcium, and intact parathyroid hormone [iPTH]). Quadratic models fit best for all tested correlations, revealing U-shaped relationships between inflammatory indicators and 25(OH)D and 1,25(OH)2D. Minimal CRP and WBC counts were observed at 1,25(OH)2D levels of 60 pg/mL and at 25(OH)D levels of 32 ng/mL, as well as of 42 ng/mL, respectively. iPTH correlated inversely with both 1,25(OH)2D and 25(OH)D, while phosphate as well as calcium levels positively correlated with both vitamin D forms. Calcium-phosphate product increased sharply when 25(OH)D was more than 50 ng/mL, indicating a possible risk for vascular calcification. Multiple regression analyses confirmed that these correlations were independent of confounders. This study suggests target values for 25(OH)D between 30-50 ng/mL and for 1,25(OH)2D between 50-70 pg/mL, based particularly on their associations with inflammation but also with mineral metabolism markers. These findings contribute to the ongoing discussion around ideal levels of vitamin D but require support from independent studies with data on clinical endpoints.

Ultra-sensitive molecular residual disease detection through whole genome sequencing with single-read error correction.

While whole genome sequencing (WGS) of cell-free DNA (cfDNA) holds enormous promise for detection of molecular residual disease (MRD), its performance is limited by WGS error rate. Here we introduce AccuScan, an efficient cfDNA WGS technology that enables genome-wide error correction at single read-level, achieving an error rate of 4.2 × 10-7, which is about two orders of magnitude lower than a read-centric de-noising method. The application of AccuScan to MRD demonstrated analytical sensitivity down to 10-6 circulating variant allele frequency at 99% sample-level specificity. AccuScan showed 90% landmark sensitivity (within 6 weeks after surgery) and 100% specificity for predicting relapse in colorectal cancer. It also showed 67% sensitivity and 100% specificity in esophageal cancer using samples collected within one week after surgery. When AccuScan was applied to monitor immunotherapy in melanoma patients, the circulating tumor DNA (ctDNA) levels and dynamic profiles were consistent with clinical outcomes. Overall, AccuScan provides a highly accurate WGS solution for MRD detection, empowering ctDNA detection at parts per million range without requiring high sample input or personalized reagents.

White blood cell counts, ratios, and C-reactive protein among individuals with schizophrenia spectrum disorder and associations with long-term outcomes: a population-based study.

BACKGROUND: Immune mechanisms are associated with adverse outcomes in schizophrenia; however, the predictive value of various peripheral immune biomarkers has not been collectively investigated in a large cohort before. OBJECTIVE: To investigate how white blood cell (WBC) counts, ratios, and C-Reactive Protein (CRP) levels influence the long-term outcomes of individuals with schizophrenia spectrum disorder (SSD). METHODS: We identified all adults in the Central Denmark Region during 1994-2013 with a measurement of WBC counts and/or CRP at first diagnosis of SSD. WBC ratios were calculated, and both WBC counts and ratios were quartile-categorized (Q4 upper quartile). We followed these individuals from first diagnosis until outcome of interest (death, treatment resistance and psychiatric readmissions), emigration or December 31, 2016, using Cox regression analysis to estimate adjusted hazard ratios (aHRs). RESULTS: Among 6,845 participants, 375 (5.5 %) died, 477 (6.9 %) exhibited treatment resistance, and 1470 (21.5 %) were readmitted during follow-up. Elevated baseline levels of leukocytes, neutrophils, monocytes, LLR, NLR, MLR, and CRP increased the risk of death, whereas higher levels of lymphocytes, platelets, and PLR were associated with lower risk. ROC analysis identified CRP as the strongest predictor for mortality (AUC=0.84). Moreover, elevated levels of leukocytes, neutrophils, monocytes, LLR, NLR and MLR were associated with treatment resistance. Lastly, higher platelet counts decreased the risk of psychiatric readmissions, while elevated LLR increased this risk. CONCLUSIONS: Elevated levels of WBC counts, ratios, and CRP at the initial diagnosis of SSD are associated with mortality, with CRP demonstrating the highest predictive value. Additionally, certain WBC counts and ratios are associated with treatment resistance and psychiatric readmissions.

Relationship between circulating white blood cell count and inflammatory skin disease: a bidirectional mendelian randomization study.

Observational studies have shown a strong association between circulating white blood cell counts (WBC) and inflammatory skin diseases such as acne and psoriasis. However, the causal nature of this relationship is unclear. We performed a two-way two-sample Mendelian randomization (MR) analysis to investigate potential causal relationships between leukocytes and inflammatory skin diseases. The circulating white blood cell count, basophil cell count, leukocyte cell count, lymphocyte cell count, eosinophil cell count, and neutrophil cell count data were obtained from the Blood Cell Consortium (BCX). The data for inflammatory skin disorders, including acne, atopic dermatitis (AD), hidradenitis suppurativa (HS), psoriasis, and seborrheic dermatitis (SD), were obtained from the FinnGen Consortium R10. The primary analysis utilized inverse variance weighting (IVW) along with additional methods such as MR-Egger, weighted mode, and weighted median estimator. To assess heterogeneity among instrument variables, Cochran's Q test was employed, while MR-Egger intercept and MR-PRESSO were used to test for horizontal pleiotropy. IVW demonstrated that an elevated monocyte count was significantly associated with a decreased risk of psoriasis (OR = 0.897, 95% CI: 0.841-0.957, P = 0.001, FDR = 0.016). Additionally, an increased eosinophil count was causally associated with a higher risk of AD (OR = 1.188, 95% CI: 1.093-1.293, P = 0.000, FDR = 0.002). No inverse causal relationship between inflammatory skin disease and circulating white blood cell count was found. In conclusion, this study provides evidence that increased monocyte count is associated with a reduced risk of psoriasis and that there is a causal relationship between increased eosinophil counts and an increased risk of AD. These findings help us understand the potential causal role of specific white blood cell counts in the development of inflammatory skin diseases.

Co-exposure effects of urinary polycyclic aromatic hydrocarbons and metals on lung function: mediating role of systematic inflammation.

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) and metals were associated with decreased lung function, but co-exposure effects and underlying mechanism remained unknown. METHODS: Among 1,123 adults from National Health and Nutrition Examination Survey 2011-2012, 10 urinary PAHs, 11 urinary metals, and peripheral white blood cell (WBC) count were determined, and 5 lung function indices were measured. Least absolute shrinkage and selection operator, Bayesian kernel machine regression, and quantile-based g-computation were used to estimate co-exposure effects on lung function. Mediation analysis was used to explore mediating role of WBC. RESULTS: These models demonstrated that PAHs and metals were significantly associated with lung function impairment. Bayesian kernel machine regression models showed that comparing to all chemicals fixed at median level, forced expiratory volume in 1 s (FEV1)/forced vital capacity, peak expiratory flow, and forced expiratory flow between 25 and 75% decreased by 1.31% (95% CI: 0.72%, 1.91%), 231.62 (43.45, 419.78) mL/s, and 131.64 (37.54, 225.74) mL/s respectively, when all chemicals were at 75th percentile. In the quantile-based g-computation, each quartile increase in mixture was associated with 104.35 (95% CI: 40.67, 168.02) mL, 1.16% (2.11%, 22.40%), 294.90 (78.37, 511.43) mL/s, 168.44 (41.66, 295.22) mL/s decrease in the FEV1, FEV1/forced vital capacity, peak expiratory flow, and forced expiratory flow between 25% and 75%, respectively. 2-Hydroxyphenanthrene, 3-Hydroxyfluorene, and cadmium were leading contributors to the above associations. WBC mediated 8.22%-23.90% of association between PAHs and lung function. CONCLUSIONS: Co-exposure of PAHs and metals impairs lung function, and WBC could partially mediate this relationship. Our findings elucidate co-exposure effects of environmental mixtures on respiratory health and underlying mechanisms, suggesting that focusing on highly prioritized toxicants would effectively attenuate adverse effects.

Effect of Common Cold on Serum Clozapine Concentrations in Hospitalized Patients with Schizophrenia.

Objective: The present study aims to investigate the effect of common cold on the serum clozapine concentrations in hospitalized patients with schizophrenia. Methods: A total of 65 schizophrenic patients with common cold receiving clozapine treatment were retrospectively enrolled. The demographic data, medication situation, clozapine concentration, and parameters of routine haematological and biochemical laboratory tests were obtained from the medical record system. The serum clozapine concentration and clozapine concentration/dose (C/D) ratios between the baseline period and cold period were compared by paired-sample t tests. Association between the changes in serum concentration and C/D ratios of clozapine and changes in white blood cell (WBC) and neutrophil (NE) counts was evaluated using Pearson correlation analysis. Results: The serum clozapine concentration (t = -9.856, P < 0.001) and clozapine C/D ratios (t = -10.071, P < 0.001) were found to be significantly elevated in the cold period compared to the baseline period. Moreover, the changes in the serum clozapine concentration were found to be significantly elevated in female patients compared to male patients (t = -2.483, P = 0.017). Furthermore, changes in the serum clozapine concentration were positively correlated to the changes in WBC (r = 0.303, P = 0.014) and NE (r = 0.315, P = 0.011) counts. Similarly, changes in clozapine C/D ratios were positively correlated to the changes in WBC (r = 0.275, P = 0.027) and NE (r = 0.328, P = 0.008) counts. Conclusion: The serum clozapine concentrations in patients with schizophrenia during the common cold period were increased, which might by related to the elevated WBC and NE counts.

ResNeXt-CC: a novel network based on cross-layer deep-feature fusion for white blood cell classification.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5-20.43% accuracy, 3.6-23.56% F1-score, 3.5-25.71% AUROC and 8.1-36.98% specificity, respectively.

Differential white blood cell count and epigenetic clocks: a bidirectional Mendelian randomization study.

BACKGROUND: Human aging and white blood cell (WBC) count are complex traits influenced by multiple genetic factors. Predictors of chronological age have been developed using epigenetic clocks. However, the bidirectional causal effects between epigenetic clocks and WBC count have not been fully examined. METHODS: This study employed Mendelian randomization (MR) to analyze summary statistics from four epigenetic clocks involving 34,710 participants, alongside data from the Blood Cell Consortium encompassing 563,946 individuals. We primarily explored bidirectional causal relationships using the random-effects inverse-variance weighted method, supplemented by additional MR methods for comprehensive analysis. Additionally, multivariate MR was applied to investigate independent effects of WBC count on epigenetic age acceleration. RESULTS: In the two-sample univariate MR (UVMR) analysis, we observed that a decrease in lymphocyte count markedly accelerated aging according to the PhenoAge, GrimAge, and HannumAge metrics (all P < 0.01, ß < 0), though it did not affect Intrinsic Epigenetic Age Acceleration (IEAA). Conversely, an increase in neutrophil count significantly elevated PhenoAge levels (ß: 0.38; 95% CI 0.14, 0.61; P = 1.65E-03 < 0.01). Reverse MR revealed no significant causal impacts of epigenetic clocks on overall WBC counts. Furthermore, in multivariate MR, the impact of lymphocyte counts on epigenetic aging metrics remained statistically significant. We also identified a marked causal association between neutrophil counts and PhenoAge, GrimAge, and HannumAge, with respective results showing strong associations (PhenoAge ß: 0.78; 95% CI 0.47, 1.09; P = 8.26E-07; GrimAge ß: 0.55; 95% CI 0.31, 0.79; P = 5.50E-06; HannumAge ß: 0.42; 95% CI 0.18, 0.67; P = 6.30E-04). Likewise, eosinophil cell count demonstrated significant association with HannumAge (ß: 0.33; 95% CI 0.13, 0.53; P = 1.43E-03 < 0.01). CONCLUSION: These findings demonstrated that within WBCs, lymphocyte and neutrophil counts exert irreversible and independent causal effects on the acceleration of PhenoAge, GrimAge, and HannumAge. Our findings highlight the critical role of WBCs in influencing epigenetic clocks and underscore the importance of considering immune parameters when interpreting epigenetic age.