UNASSIGNED: Blood counts and biochemical markers are among the most common tests performed in hospitals and most readily accepted by patients, and are widely regarded as reliable biomarkers in the literature. The aim of this study was to assess the causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension (PAH).
UNASSIGNED: A two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between blood counts and biochemical indicators with PAH. The genome-wide association study (GWAS) for blood counts and biochemical indicators were obtained from the UK Biobank (UKBB), while the GWAS for PAH were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was used as the primary analysis method, supplemented by three sensitivity analyses to assess the robustness of the results. And we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 2003-2018 to verify the relationship.
UNASSIGNED: The MR analysis primarily using the IVW method revealed genetic variants of platelet count (OR=2.51, 95% CI 1.56-4.22, P<0.001), platelet crit(OR=1.87, 95% CI1.17-7.65, P=0.022), direct bilirubin (DBIL)(OR=1.71, 95%CI 1.18-2.47,P=0.004), insulin-like growth factor (IGF-1)(OR=0.51, 95% CI 0.27-0.96, P=0.038), Lipoprotein A (Lp(a))(OR=0.66, 95% CI 0.45-0.98, P=0.037) and total bilirubin (TBIL)(OR=0.51, 95% CI 0.27-0.96, P=0.038) were significantly associated with PAH. In NHANES, multivariate logistic regression analyses revealed a significant positive correlation between platelet count and volume and the risk of PAH, and a significant negative correlation between total bilirubin and PAH.
UNASSIGNED: Our study reveals a causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension. These findings offer novel insights into the etiology and pathological mechanisms of PAH, and emphasizes the important value of these markers as potential targets for the prevention and treatment of PAH.

Differential white blood cell counts are frequently used in diagnosis, patient stratification, and treatment selection to optimize therapy responses. Referral laboratories are often used but challenged with use of different hematology platforms, variable blood shipping times and storage conditions, and the different sensitivities of specific cell types. To extend the scientific literature and knowledge on the temporal commutability of blood samples between hematology analyzers, we performed a comparative ex-vivo study using four of the most utilized commercial platforms, focusing on the assessment of eosinophils given its importance in asthma management. Whole blood from healthy volunteers with and without atopy (n = 6+6) and participants with eosinophilic asthma (n = 6) were stored under different conditions (at 4, 20, 30, and 37°C, with or without agitation) and analyzed at different time points (3, 6, 24, 48 and 72h post-sampling) in parallel on the Abbott CELL-DYN Sapphire, Beckman Coulter DxH900, Siemens ADVIA 2120i and Sysmex XN-1000V. In the same blood samples, eosinophil-derived neurotoxin (EDN), eosinophil activation and death markers were analyzed. All platforms gave comparable measurements of cell differentials on fresh blood within the same day of sampling. However, by 24 hours, significant temporal and temperature-dependent differences were observed, most markedly for eosinophils. None of the platforms performed perfectly across all temperatures tested during the 72 hours, showing that handling conditions should be optimized depending on the cell type of interest and the hematology analyzer. Neither disease status (healthy vs. asthma) nor agitation of the sample affected the cell quantification result or EDN release. The eosinophil activation markers measured by flow cytometry increased with time, were influenced by temperature, and were higher in those with asthma versus healthy participants. In conclusion, hematology analyzer, time window from sampling until analysis, and temperature conditions must be considered when analyzing blood cell differentials, particularly for eosinophils, via central labs to obtain counts comparable to the values obtained in freshly sampled blood.

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

UNASSIGNED: Myasthenia gravis (MG) is a chronic autoimmune disease caused by neuromuscular junction (NMJ) dysfunction. Our current understanding of MG\'s inflammatory component remains poor. The systemic inflammatory response index (SIRI) presents a promising yet unexplored biomarker for assessing MG severity. This study aimed to investigate the potential relationship between SIRI and MG disease severity.
UNASSIGNED: We conducted a retrospective analysis of clinical data from 171 MG patients admitted between January 2016 and June 2021. Patients with incomplete data, other autoimmune diseases, or comorbidities were excluded. Disease severity was evaluated using the Myasthenia Gravis Foundation of America (MGFA) classification and Myasthenia Gravis Activities of Daily Living (MG-ADL) on admission. The association between SIRI and disease severity was assessed through logistic regression analysis, along with receiver operating characteristic (ROC) curve and decision curve analysis (DCA) comparisons with established inflammation indicators.
UNASSIGNED: After exclusion, 143 patients were analyzed in our study. SIRI levels significantly differed between patients with higher and lower disease severity (p < 0.001). Univariate logistic regression showed that SIRI had a significant effect on high disease severity (OR = 1.376, 95% CI 1.138-1.664, p = 0.001). This association remained significant even after adjusting for age, sex, disease duration, history of MG medication and thymoma (OR = 1.308, 95% CI 1.072-1.597, p = 0.008). Additionally, a positive correlation between SIRI and MG-ADL was observed (r = 0.232, p = 0.008). Significant interactions were observed between SIRI and immunosuppressor (p interaction = 0.001) and intravenous immunoglobulin (p interaction = 0.005). DCA demonstrated the superior net clinical benefit of SIRI compared to other markers when the threshold probability was around 0.2.
UNASSIGNED: Our findings indicate a strong independent association between SIRI and disease severity in MG, suggesting SIRI\'s potential as a valuable biomarker for MG with superior clinical benefit to currently utilized markers.

Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the full blood count (FBC) and C-reactive protein (CRP) values. Several models (i.e. multivariable logistic regression, linear discriminant analysis, K nearest neighbors, support vector machine, random forest model and decision tree) were trained using FBC and CRP values of 2693 infants aged 7 to 60 days with suspected BSI between 2005 and 2022 in a tertiary paediatric hospital in Dublin, Ireland. All models tested showed similar sensitivities (range 47% - 62%) and specificities (range 85%-95%). A trained decision tree and random forest model were applied to the full dataset and to a dataset containing infants with suspected BSI in 2023 and showed good segregation of a low-risk and high-risk group. Negative predictive values for these two models were high for the full dataset (> 99%) and for the 2023 dataset (> 97%), while positive predictive values were low in both dataset (4%-20%).   Conclusion: We identified several models that can predict positive blood cultures in infants with suspected BSI aged 7 to 60 days. Application of these models could prevent administration of antimicrobial treatment and burdensome diagnostics in infants who do not need them. What is Known: • Bloodstream infection (BSI) in infants cause non-specific symptoms and may be difficult to diagnose. • Results of blood cultures can take up to 48 hours. What is New: • Machine learning models can contribute to clinical decision making on BSI in infants while blood culture results are not yet known.

The interpretation of a laboratory test result requires an appropriate reference range established in healthy subjects, and normal ranges may vary by factors such as geographic region, sex, and age. We examined hematological and clinical chemistry parameters in healthy residents at two rural vaccine trial sites: Bancoumana and Doneguebougou in Mali, West Africa. During screening of clinical studies in 2018 and 2019, peripheral blood samples from 1,192 apparently healthy individuals age 6 months to 82 years were analyzed at a laboratory accredited by the College of American Pathologists for a complete blood count, and creatinine and/or alanine aminotransferase levels. Based on manufacturers\' reference range values, which are currently used in Malian clinical laboratories, abnormal values were common in this healthy population. In fact, 30.4% of adult participants had abnormal neutrophil levels and 19.8% had abnormal hemoglobin levels. Differences by sex were observed in those who were older, but not in those younger than 10 years, for several parameters, including hemoglobin, platelet, and absolute neutrophil counts in hematology, and creatinine in biochemistry. The site-specific reference intervals we report can be used in malaria vaccine clinical trials and other interventional studies, as well as in routine clinical care, to identify abnormalities in hematological and biochemical parameters among healthy Malian trial participants.

OBJECTIVE: To determine the median perioperative blood loss (PBL) during minimally invasive surgical (MIS) myomectomy.
METHODS: Prospective pilot study.
METHODS: Large academic teaching hospital.
METHODS: Thirty-one patients underwent laparoscopic or robotic myomectomy and completed a postoperative complete blood count (CBC) from November 2020 to August 2022. Patients had to have at least one fibroid greater than or equal to 3 cm on preoperative imaging.
METHODS: A CBC was collected preoperatively within 7 days of surgery. Estimated blood loss (EBL) was determined by the surgeon intraoperatively. A repeat CBC was drawn between postoperative days 2 through 4. PBL was calculated using the equation PBL = (patient weight in kg × 65 cc/kg) × (preoperative hematocrit - postoperative hematocrit)/preoperative hematocrit.
RESULTS: Median PBL (536.3 cc [270.0, 909.3]) was greater than median EBL (200.0 cc [75.0, 500.0]). PBL ranged from a net gain of 191.5 cc to net loss of 2362.5 cc. Median size of the largest fibroid on preoperative imaging was 8.8 cm (6.6, 11.5), and median weight of fibroids removed was 321 g (115, 519). About half of patients (51.6%) had one fibroid removed, and 48.4% had 2 or more fibroids removed. Five patients were converted to laparotomy, 4 from robotic approaches. Two patients required a blood transfusion.
CONCLUSIONS: Calculated PBL was greater than intraoperative EBL. This suggests there is continued blood loss post myometrial bed closure. Blood loss should be evaluated both during and after myomectomy, as intraoperative EBL underestimates total PBL.

BACKGROUND: Although widely used, the ADVIA 120 hematology analyzer has not been previously validated for determining the differential leukocyte count in goats.
OBJECTIVE: The aim of this study was to compare the differential leukocyte counts provided by the ADVIA 120 (A-diff) and the manual method (M-Diff) in goats.
METHODS: EDTA blood samples that were analyzed within 4 h of collection were used in the study. The following exclusion criteria were applied: inappropriately filled tubes or tubes containing clots, erroneous ADVIA peroxidase cytograms, and blood smears of poor quality. The A-Diff was compared with the M-Diff performed by two independent observers on 200 leukocytes.
RESULTS: Forty samples were included after previously excluding eight samples. The correlation between the A-Diff and M-Diff was very strong for eosinophils (r = .870, p < .001) and strong for lymphocytes (r = .796, p < .001) and neutrophils (r = .730, p < .001), while no significant correlation was observed for monocytes (r = .026, p = .872). The Passing-Bablok regression analyses revealed statistically significant constant errors for neutrophils (5.83%; 95% confidence interval [CI]: 0.41%, 12.18%) and eosinophils (1.89%; 95% CI: 1.17%, 2.71%). Bland-Altman analyses showed a statistically significant negative bias for lymphocytes (-5.0%) and a statistically significant positive bias for eosinophils (2.2%). The very low basophil percentages precluded a meaningful method comparison.
CONCLUSIONS: The ADVIA 120 overall demonstrated good performance for the differential WBC count in goats under the conditions of this study. Therefore, it can be considered suitable for routine hematologic screening in goats. Nonetheless, it should be emphasized that any abnormal result should be confirmed with a blood smear evaluation.

Sepsis and septic shock are prevalent and life-threatening complications in burn patients. Despite their severity, existing diagnostic methods are limited. This study aims to evaluate the efficacy of Complete Blood Count (CBC) and CBC ratio markers in diagnosing sepsis and septic shock, and in predicting mortality among burn patients. A cohort of 2757 burn patients was examined to ascertain the correlation between various CBC parameters, their ratios, and the incidence of sepsis and related mortality. Key markers analyzed included Red Cell Distribution Width (RDW), Mean Platelet Volume (MPV), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Mean Platelet Volume-to-Platelet Ratio (MPVPR). Our findings indicate that 65.5% of the patients developed sepsis, and 24.3% succumbed to their conditions. The CBC parameters RDW, MPV, NLR, MPVPR, and MPV-to-Lymphocyte Ratio (MPVLR) were significantly associated with sepsis and mortality. These markers showed considerable temporal variation and yielded an Area Under the Curve (AUC) of over 0.65 in an unadjusted Generalized Estimating Equations (GEE) model. This study underscores the potential of RDW, MPV, NLR, MPVPR, and MPVLR as vital prognostic tools for diagnosing sepsis, septic shock, and predicting mortality in burn patients. Although based on a single-center dataset, our results contribute to the enhancement of sepsis management by facilitating earlier, more precise diagnosis and treatment strategies. Further multi-center research is necessary to confirm these findings and broaden their applicability, establishing a solid base for future explorations in this crucial field.

OBJECTIVE: Evaluation of the association of inflammatory cell ratios, especially neutrophil-to-lymphocyte ratio (NLR), based on preoperative complete blood counts, with postoperative complications in lobectomy surgery.
METHODS: This was a retrospective monocentric cohort study.
METHODS: The study was conducted at Foch University Hospital in Suresnes, France.
METHODS: Patients having undergone a scheduled lobectomy from January 2018 to September 2021.
METHODS: There were no interventions.
RESULTS: The authors studied 208 consecutive patients. Preoperative NLR, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic inflammation index, systemic inflammation response index, and aggregate inflammation systemic index were calculated. Median and (IQR) of NLR was 2.67 (1.92-3.69). No statistically significant association was observed between any index and the occurrence of at least one major postoperative complication, which occurred in 37% of the patients. Median postoperative length of stay was 7 (5-10) days. None of the ratios was associated with prolonged length of stay (LOS), defined as a LOS above the 75th percentile.
CONCLUSIONS: The results suggested that simple available inflammatory ratios are not useful for the preoperative identification of patients at risk of postoperative major complications in elective lobectomy surgery.