UNASSIGNED: This study aimed to evaluate the association between six complete blood count (CBC)-derived inflammatory markers [neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and pan-immune inflammation value (PIV)] and the risk of frailty and mortality.
UNASSIGNED: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Mortality was identified using the National Death Index until December 31, 2019. Multiple logistic regression analysis was conducted to evaluate the association between six CBC-derived inflammatory markers and frailty. The Cox regression model assessed the association between six CBC-derived inflammatory markers and mortality in frail populations. Restricted cubic spline (RCS) was used to visualize the association of the six CBC-derived inflammatory markers with mortality risk. The predictive value of CBC-derived inflammatory markers for mortality was further assessed using a random survival forest (RSF) approach.
UNASSIGNED: This study analyzed data from a total of 16,705 middle-aged and older participants. Among them, 6,503 participants were frail, with a mortality rate of 41.47%. Multiple logistic regression analysis showed that NLR, MLR, PLR, SII, SIRI, and PIV were positively associated with frailty risk. The Cox regression model revealed that participants in the highest quartile had a significantly increased risk of death compared to those in the lowest quartile: NLR (HR = 1.73, 95% CI:1.54, 1.94), MLR (HR = 1.71, 95% CI:1.51, 1.93), PLR (HR = 1.28, 95%CI: 1.15, 1.43), SII (HR = 1.50, 95%CI:1.34, 1.68), SIRI (HR = 1.88, CI 95%:1.67, 2.12), PIV (HR = 1.55, 95%CI:1.38, 1.73). Random survival forest (RSF) analyses demonstrated that MLR had the highest predictive value for mortality risk middle-aged and older adult frail participants.
UNASSIGNED: The results suggest that CBC-derived inflammatory markers are associated with a higher risk of frailty as well as mortality in the middle and old-aged population of the United States.

Background: Dengue fever (DF) is a mosquito-borne illness with substantial economic and societal impact. Understanding laboratory trends of hospitalized Dominican Republic (DR) pediatric patients could help develop screening procedures in low-resourced settings. We sought to describe laboratory findings over time in DR children with DF and DF severity from 2018 to 2020. Methods: Clinical information was obtained prospectively from recruited children with DF. Complete blood count (CBC) laboratory measures were assessed across Days 1-10 of fever. Participants were classified as DF-negative and DF-positive and grouped by severity. We assessed associations of DF severity with demographics, clinical characteristics, and peripheral blood studies. Using linear mixed-models, we assessed if hematologic values/trajectories differed by DF status/severity. Results: A total of 597 of 1101 with a DF clinical diagnosis were serologically evaluated, and 574 (471 DF-positive) met inclusion criteria. In DF, platelet count and hemoglobin were higher on earlier days of fever (p < = 0.0017). Eighty had severe DF. Severe DF risk was associated with thrombocytopenia, intraillness anemia, and leukocytosis, differing by fever day (p < = 0.001). Conclusions: In a pediatric hospitalized DR cohort, we found marked anemia in late stages of severe DF, unlike the typically seen hemoconcentration. These findings, paired with clinical symptom changes over time, may help guide risk-stratified screenings for resource-limited settings.

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.

Myelodysplastic syndromes (MDS) present with polymorphic and non-specific diagnostic features Research parametersfrom hematology analyzers may be useful to discriminate MDS-related cytopenia.Parameters such as Neu X (related to the cytoplasmic complexity) and Neu Y (related to nucleic acid content) show promise to detect dysplasia of MDS and aid to recognize MDS from cytopenias of other etiologies.

The evolution of complete blood count (CBC) methodology from manual calculations to sophisticated high throughput hematology analyzers is the focus of this article. In recent years, hematology testing has greatly benefitted from the combination of various technologies with automated neural networks. In addition to an increasing complexity of the laboratory instrumentation, there is a demand on point of care CBC testing with its benefits and drawbacks. This article highlights exciting advancements of hematology testing from the past to the present and into the future.

BACKGROUND: Pneumoconiosis mostly combines pulmonary and cardiovascular diseases, among which pulmonary heart disease (PHD) is of major concern due to its significant impact on the survival of pneumoconiosis patients. White cell count (WCC), red cell distribution width (RDW) and platelet parameters are thought to affect inflammatory responses and may be predictors of various cardiovascular diseases. However, very few studies have focused on PHD.
OBJECTIVE: To examine the relationship between baseline complete blood count parameters (WCC, RDW, platelet parameters) and the risk of incident PHD in pneumoconiosis patients.
METHODS: A retrospective cohort study.
METHODS: This was a single-centre, retrospective cohort study that used data from an Occupational Disease Hospital, Chengdu, Sichuan.
METHODS: A total of 946 pneumoconiosis patients from January 2012 to November 2021 were included in the study. Female patients and patients who had PHD, coronary heart disease, hypertensive heart disease, cardiomyopathy, heart failure, oncological disease, multiple organ dysfunction, AIDS at baseline and follow-up time of less than 6 months were also excluded.
METHODS: We identified PHD according to the patient\'s discharge diagnosis. We constructed Cox proportional hazard regression models to assess the HR of incident PHD in pneumoconiosis, as well as 95% CIs.
RESULTS: In the multiple Cox proportional hazard regression analysis, platelet count (PLT) and plateletcrit (PCT) above the median at baseline were associated with an increased risk of PHD in pneumoconiosis with adjusted HR of 1.52 (95% CI 1.09 to 2.12) and 1.42 (95% CI 1.02 to 1.99), respectively.
CONCLUSIONS: Higher baseline PLT and PCT are associated with a higher risk of PHD in pneumoconiosis.

Background and Objectives: Differentiation between brucella spondylodiscitis and Modic type I changes (MC1) includes difficulties. Hematological inflammatory indices (HII) such as neutrophil to lymphocyte ratio (NLR) and aggregate index of systemic inflammation (AISI) are suggested as indicators of inflammation and infection and have diagnostic, prognostic, and predictive roles in various diseases. This study aimed to evaluate differences between brucella spondylodiscitis and MC1 in terms of HII. Materials and Methods: Thirty-five patients with brucella spondylodiscitis and thirty-seven with MC1 were enrolled in the study. Brucella spondylodiscitis and MC1 were diagnosed by microbiological, serological, and radiological diagnostic tools. HII (NLR, MLR, PLR, NLPR, SII, SIRI, AISI) were derived from baseline complete blood count. Results: The two groups were similar for age (p = 0.579) and gender (p = 0.092), leukocyte (p = 0.127), neutrophil (p = 0.366), lymphocyte (p = 0.090), and monocyte (p = 0.756) scores. The Brucella spondylodiscitis group had significantly lower pain duration (p < 0.001), higher CRP and ESR levels (p < 0.001), and lower platelet count (p = 0.047) than the MC1 group. The two groups had similarity in terms of HII: NLR (p = 0.553), MLR (p = 0.294), PLR (p = 0.772), NLPR (p = 0.115), SII (p = 0.798), SIRI (p = 0.447), and AISI (p = 0.248). Conclusions: Increased HII can be used to differentiate infectious and non-infectious conditions, but this may be invalid in brucellosis. However, pain duration, CRP and ESR levels, and platelet count may be useful to distinguish brucella spondylodiscitis from MC1.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are associated with immune-inflammatory activation. Recently, complete blood count (CBC)-based inflammation indexes such as the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), and the platelet-to-lymphocyte ratio (PLR) have emerged as reproducible and cost-effective inflammation markers in mental disorders. In this study, we aimed at investigating the relationship of NLR, MLR, and PLR with symptom severity in people with SSDs, testing interactions with relevant clinical variables.
METHODS: We included inpatients with SSDs aged 18-65 consecutively hospitalized from May 2020 to March 2024. Socio-demographic and clinical data were recorded. CBC-based ratios were estimated from routinely collected blood samples. Structural equation modelling (SEM) was performed to test relationships involving symptom severity constructs and CBC-based ratios, accounting for substance use disorder, antipsychotic treatment, and obesity.
RESULTS: Two hundred sixty-six participants met inclusion criteria. The SEM analysis uncovered a significant relationship of MLR with positive (coeff.: 0.19, p=0.048) and negative (coeff.: 0.27, p=0.004) symptoms, also showing a significant link of substance use disorder and antipsychotic treatment with symptom severity as well as of antipsychotic treatment with obesity.
CONCLUSIONS: Notwithstanding the cross-sectional design and the somewhat limited sample representativeness, this study showed a significant relationship between the MLR - but not the NLR or the PLR - and the severity of both positive and negative symptoms, testing at the same time the interactions with other clinical variables. Considering the insufficiency and inconsistency of data in this field, further research is needed to validate our findings and elucidate the underlying mechanisms driving the observed relationships between the MLR and SSD symptoms.

Chronic spontaneous urticaria (CSU) is an immunological disease that is depicted by high prevalence and eminent burden for patients and society that is attributable to the arbitrary nature of symptoms and inconsistent tools for assessment of activity and severity. Transglutaminase-2 (TG2) is a posttranslational enzyme that is pervasively expressed in many cells and tissue types including mast cells. It has various biological functions, and its role in allergic disorders has been highlighted and delineated through several postulated mechanisms. This case-control study aimed at determining the relationship between serum levels TG2 and severity of CSU. To the best of our knowledge, this is the first study in Egypt to determine the relationship between serum TG2 and severity of CSU. We enrolled 60 adult patients with confirmed diagnosis of CSU. According to urticaria activity score (UAS), patients were categorized into three groups [20 with mild disease; UAS = 0, 20 with moderate disease; UAS = 1-3, 20 with severe disease; UAS = 4-6]. Another 20 healthy individuals (age and gender matched) served as a control group. All patients were subjected to detailed medical history, clinical examination, complete blood count with differential, serum total IgE, CRP, ESR, TSH, ANA, liver and renal function tests. Serum level of TG2 was done by quantitative ELISA for all enrolled patients and controls. Serum TG2 is significantly higher in patients group compared to control group (P value < 0.001). Serum TG2 levels were significantly higher in patients with severe disease compared to patients with moderate or mild disease. This is illustrated by the significant positive correlation between serum TG2 and UAS (r 0.814 and P value 0.000). Moreover, serum TG2 accurately classified CSU patients into mild, moderate and severe subgroups: as regards differentiation between mild and moderate cases (sensitivity 70%, specificity 80%, PPV 77.8, NPV 72.7) and as for the differentiation between moderate and severe cases (sensitivity 95%, specificity 90%, PPV 90.5, NPV 94.7). Serum TG2 may have a pivotal role as a marker of severity in patients with CSU.

Purpose: This study aimed to assess the predictive accuracy of 30-day mortality with delta neutrophil index (DNI) in adult cardiac surgical patients. Methods: This study enrolled patients who underwent cardiac surgery under general anesthesia between March 2016 and May 2022 at a tertiary hospital in the Republic of Korea. DNI was measured preoperatively, on postoperative arrival to the surgical intensive care unit (ICU), and 12, 24, 48, and 72 h postoperatively. Receiver operating characteristic (ROC) analysis was employed to identify the prediction accuracy of DNI. An area under ROC curve (AUROC) ≥0.700 was defined as satisfactory predictive accuracy. An optimal cutoff point for the DNI value to maximize predictive accuracy was revealed in the ROC curve, where [sensitivity + specificity] was maximum. Results: This study included a total of 843 patients in the final analyses. The mean age of the study population was 66.9±12.2 years and 38.4% of them were female patients. The overall 30-day mortality rate was 5.2%. Surgery involving the thoracic aorta, history of prior cardiac surgery, or emergency surgery were associated with a higher mortality rate. The DNI showed satisfactory predictive accuracy at 24 h, 48 h, and 72 h postoperatively, with AUROC of 0.729, 0.711, and 0.755, respectively. The optimal cutoff points of DNI at each time point were 3.2, 3.8, and 2.3, respectively. Conclusions: Postoperative DNI is a good predictor of 30-day mortality after cardiac surgery and has the benefit of no additional financial costs or time.