BACKGROUND: Alectinib is a second-generation anaplastic lymphoma kinase (ALK) inhibitor indicated for ALK-mutated non-small-cell lung cancer. Recently, the association between alectinib and red cell morphological abnormalities has been reported in a few case series. This retrospective observational study aims to determine the frequency of occurrence of acanthocytosis in patients taking alectinib and to evaluate the red cell indices, biochemical markers of haemolysis and eosin-5-maleimide (EMA) binding assay results in patients receiving alectinib.
METHODS: Patients who were on alectinib and had a complete blood count test performed in Queen Elizabeth Hospital Haematology Laboratory between 1 May 2021 and 31 August 2021 were included in the study. Haematological investigations that had been performed before and after the commencement of alectinib were reviewed.
RESULTS: Fifty patients receiving alectinib were evaluated in this analysis. One hundred per cent of patients showed 3+ acanthocytes on the peripheral blood smears. Compared with the test results before starting alectinib, the post-alectinib blood tests showed a significantly lower haemoglobin concentration, red blood cell count and haematocrit; and a significantly higher mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration and red cell distribution width. All the tested patients showed a marked reduction in EMA mean channel fluorescence compared with normal control.
CONCLUSIONS: Our cohort revealed that alectinib caused significant acanthocytosis in all patients. Alectinib was also associated with changes in red cell indices and biochemical markers of haemolysis, compatible with a spherocytic and anisopoikilocytic morphology with haemolysis. Patients on alectinib had reduced EMA binding.

BACKGROUND: Predicting an individual\'s risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy.
METHODS: The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions.
RESULTS: The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups.
CONCLUSIONS: The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction.

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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: Identifying patients presenting with nonspecific abdominal symptoms who have underlying cancer is a challenge. Common blood tests are widely used to investigate these symptoms in primary care, but their predictive value for detecting cancer in this context is unknown. We quantify the predictive value of 19 abnormal blood test results for detecting underlying cancer in patients presenting with 2 nonspecific abdominal symptoms.
RESULTS: Using data from the UK Clinical Practice Research Datalink (CPRD) linked to the National Cancer Registry, Hospital Episode Statistics and Index of Multiple Deprivation, we conducted a population-based cohort study of patients aged ≥30 presenting to English general practice with abdominal pain or bloating between January 2007 and October 2016. Positive and negative predictive values (PPV and NPV), sensitivity, and specificity for cancer diagnosis (overall and by cancer site) were calculated for 19 abnormal blood test results co-occurring in primary care within 3 months of abdominal pain or bloating presentations. A total of 9,427/425,549 (2.2%) patients with abdominal pain and 1,148/52,321 (2.2%) with abdominal bloating were diagnosed with cancer within 12 months post-presentation. For both symptoms, in both males and females aged ≥60, the PPV for cancer exceeded the 3% risk threshold used by the UK National Institute for Health and Care Excellence for recommending urgent specialist cancer referral. Concurrent blood tests were performed in two thirds of all patients (64% with abdominal pain and 70% with bloating). In patients aged 30 to 59, several blood abnormalities updated a patient\'s cancer risk to above the 3% threshold: For example, in females aged 50 to 59 with abdominal bloating, pre-blood test cancer risk of 1.6% increased to: 10% with raised ferritin, 9% with low albumin, 8% with raised platelets, 6% with raised inflammatory markers, and 4% with anaemia. Compared to risk assessment solely based on presenting symptom, age and sex, for every 1,000 patients with abdominal bloating, assessment incorporating information from blood test results would result in 63 additional urgent suspected cancer referrals and would identify 3 extra cancer patients through this route (a 16% relative increase in cancer diagnosis yield). Study limitations include reliance on completeness of coding of symptoms in primary care records and possible variation in PPVs if extrapolated to healthcare settings with higher or lower rates of blood test use.
CONCLUSIONS: In patients consulting with nonspecific abdominal symptoms, the assessment of cancer risk based on symptoms, age and sex alone can be substantially enhanced by considering additional information from common blood test results. Male and female patients aged ≥60 presenting to primary care with abdominal pain or bloating warrant consideration for urgent cancer referral or investigation. Further cancer assessment should also be considered in patients aged 30 to 59 with concurrent blood test abnormalities. This approach can detect additional patients with underlying cancer through expedited referral routes and can guide decisions on specialist referrals and investigation strategies for different cancer sites.

BACKGROUND: Errors associated with failures in filing, actioning and communicating blood test results can lead to delayed and missed diagnoses and patient harm. This study aimed to audit how blood tests in primary care are filed, actioned and communicated in primary care, to identify areas for patient safety improvements.
METHODS: UK primary care clinicians were recruited through the Primary Care Academic CollaboraTive (PACT). PACT members audited 50 recent sets of blood tests from their practice and retrospectively extracted data on blood test result coding, actioning and communication. PACT members received a practice report, showing their own results, benchmarked against other participating practices.
RESULTS: PACT members from 57 general practices across all four UK nations collected data on 2572 patients who had blood tests in April 2021. In 89.9% (n=2311) they agreed with the initial clinician\'s actioning of blood tests; 10.1% disagreed, either partially (7.1%) or fully (3.0%).In 44% of patients (n=1132) an action (eg, \'make an appointment\') was specified by the filing clinician. This action was carried out in 89.7% (n=1015/1132) of cases; in 6.8% (n=77) the action was not carried out, in 3.5% (n=40) it was unclear. In the 117 cases where the test result had not been actioned 38% (n=45) were felt to be at low risk of harm, 1.7% (n=2) were at high risk of harm, 0.85% (n=1) came to harm.Overall, in 47% (n=1210) of patients there was no evidence in the electronic health records that results had been communicated. Out of 1176 patients with one or more abnormal results there was no evidence of test communication in 30.6% (n=360). There were large variations between practices in rates of actioning and communicating tests.
CONCLUSIONS: This research demonstrates variation in the way blood test results are actioned and communicated, with important patient safety implications.

UNASSIGNED: We aimed to integrate MR radiomics and dynamic hematological factors to build a model to predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in esophageal squamous cell carcinoma (ESCC).
UNASSIGNED: Patients with ESCC receiving NCRT and esophagectomy between September 2014 and September 2022 were retrospectively included. All patients underwent pre-treatment T2-weighted imaging as well as pre-treatment and post-treatment blood tests. Patients were randomly divided to training set and testing set at a ratio of 7:3. Machine learning models were constructed based on MR radiomics and hematological factors to predict pCR, respectively. A nomogram model was developed to integrate MR radiomics and hematological factors. Model performances were evaluated by areas under curves (AUCs), sensitivity, specificity, positive predictive value and negative.
UNASSIGNED: A total of 82 patients were included, of whom 39 (47.6 %) achieved pCR. The hematological model built with four hematological factors had an AUC of 0.628 (95%CI 0.391-0.852) in the testing set. Two out of 1106 extracted features were selected to build the radiomics model with an AUC of 0.821 (95%CI 0.641-0.981). The nomogram model integrating hematological factors and MR radiomics had best predictive performance, with an AUC of 0.904 (95%CI 0.770-1.000) in the testing set.
UNASSIGNED: An integrated model using dynamic hematological factors and MR radiomics is constructed to accurately predicted pCR to NCRT in ESCC, which may be potentially useful to assist individualized preservation treatment of the esophagus.

BACKGROUND: In order to meet the demand for laboratory talents in the clinical laboratory industry and address the current curriculum characteristics and shortcomings of the teaching mode of \"Clinical Hematology Laboratory Technology\", we investigated the effectiveness of the bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary model combined with problem-based learning (BOPPPS-PBL) in undergraduate teaching of this course.
METHODS: Seventy students majoring in Medical Laboratory Technology from the Army Medical University in the past 5 years have been selected and divided into two groups with the same teaching content and time. The control group (2015 and 2016 grades) used traditional teaching methods, while the experimental group (2017, 2018 and 2019 grades) used the BOPPPS-PBL model. After class, diverse evaluation methods were used to analyze the formative and summative exam scores of the two groups of students.
RESULTS: After the reform, students performed significantly better in exams than before. In addition, the new teaching methods have had a positive impact, with students demonstrating high motivation for self-directed learning and problem-solving abilities.
CONCLUSIONS: Compared to traditional teaching methods. The BOPPPS-PBL integrated case study education model is a relatively effective teaching method to improve students\' problem-solving ability and comprehensive practical ability.

BACKGROUND: Abnormal results in common blood tests may occur several months before lung cancer (LC) and colorectal cancer (CRC) diagnosis. Identifying early blood markers of cancer and distinct blood test signatures could support earlier diagnosis in general practice.
METHODS: Using linked Australian primary care and hospital cancer registry data, we conducted a cohort study of 855 LC and 399 CRC patients diagnosed between 2001 and 2021. Requests and results from general practice blood tests (six acute phase reactants [APR] and six red blood cell indices [RBCI]) were examined in the 2 years before cancer diagnosis. Poisson regression models were used to estimate monthly incidence rates and examine pre-diagnostic trends in blood test use and abnormal results prior to cancer diagnosis, comparing patterns in LC and CRC patients.
RESULTS: General practice blood test requests increase from 7 months before CRC and 6 months before LC diagnosis. Abnormalities in many APR and RBCI tests increase several months before cancer diagnosis, often occur prior to or in the absence of anaemia (in 51% of CRC and 81% of LC patients with abnormalities), and are different in LC and CRC patients.
CONCLUSIONS: This study demonstrates an increase in diagnostic activity in Australian general practice several months before LC and CRC diagnosis, indicating potential opportunities for earlier diagnosis. It identifies blood test abnormalities and distinct signatures that are early markers of LC and CRC. If combined with other pre-diagnostic information, these blood tests have potential to support GPs in prioritising patients for cancer investigation of different sites to expedite diagnosis.

Pig-tailed macaques (PTMs) are commonly used as preclinical models to assess antiretroviral drugs for HIV prevention research. Drug toxicities and disease pathologies are often preceded by changes in blood hematology. To better assess the safety profile of pharmaceuticals, we defined normal ranges of hematological values in PTMs using an Isolation Forest (iForest) algorithm.
Eighteen female PTMs were evaluated. Blood was collected 1-24 times per animal for a total of 159 samples. Complete blood counts were performed, and iForest was used to analyze the hematology data to detect outliers.
Median, IQR, and ranges were calculated for 13 hematology parameters. From all samples, 22 outliers were detected. These outliers were excluded from the reference index.
Using iForest, we defined a normal range for hematology parameters in female PTMs. This reference index can be a valuable tool for future studies evaluating drug toxicities in PTMs.