Approach to the Diagnosis and Classification of Blood Cell Disorders. Dacie and Lewis Practical Haematology. Elsevier; 2017. pp. 497-510. [DOI: 10.1016/b978-0-7020-6696-2.00023-0]

Integration of a battery of biomarkers to evaluate the health status of field-collected frogs of Leptodactylus luctator living in ecosystems with different anthropogenic disturbances

Amphibians are the most threatened group of vertebrates because they have certain biological and ecological characteristics that make them sensitive to environmental changes. The aim of this study was to evaluate the health status of field-collected adult frogs of Leptodactylus luctator (Amphibia, Anura) living in sites with different anthropogenic disturbances (florihorticulture, petrochemical industry and sewage discharges) and a reference site without any detectable influence of such activities. To this end, a battery of 21 biomarkers (hematological, biochemical and individual biomarkers) was studied using a multivariate approach that allows us to evaluate the relationship between them and provide information on their usefulness. The frogs at the florihorticulture, petrochemical and sewage discharges sites exhibited several biomarkers far from homeostasis. In addition, we identified 11 of 21 biomarkers that were useful indicators of the health status of the frogs and allowed discrimination between study sites in the following order: lymphocytes (98 %), neutrophils (45 %), hemoglobin (42 %), monocytes (41 %), fat body index (35 %), eosinophils (35 %), hepatosomatic index (33 %), mean corpuscular hemoglobin (32 %), thrombocytes (27 %), catalase in liver (26 %), and GST in liver (26 %). The results suggest that hematological biomarkers contribute the most to site separation, whereas biochemical biomarkers contribute the least. The integral interpretation of the results also allowed us to diagnose the different health status of L. luctator: The frogs from the petrochemical industry were the most negatively affected, followed by the frogs from the sewages discharges and finally the frogs from the florihorticulture and reference sites. This is the first field study with anurans in which so many biomarkers were examined.

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.

Applying artificial neural network in predicting sepsis mortality in the emergency department based on clinical features and complete blood count parameters

A complete blood count (CBC) is routinely ordered for emergency department (ED) patients with infections. Certain parameters, such as the neutrophil-to-lymphocyte ratio (NLR), might have prognostic value. We aimed to evaluate the prognostic value of the presenting CBC parameters combined with clinical variables in predicting 30-day mortality in adult ED patients with infections using an artificial neural network (ANN). We conducted a retrospective study of ED patients with infections between 17 December 2021 and 16 February 2022. Clinical variables and CBC parameters were collected from patient records, with NLR, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) calculated. We determined the discriminatory performance using the area under the receiver operating characteristic curve (AUROC) and performed a 70/30 random data split and supervised ANN machine learning. We analyzed 558 patients, of whom 144 (25.8%) had sepsis and 60 (10.8%) died at 30 days. The AUROCs of NLR, MLR, PLR, and their sum were 0.644 (95% CI 0.573-0.716), 0.555 (95% CI 0.482-0.628), 0.606 (95% CI 0.529-0.682), and 0.610 (95% CI 0.534-0.686), respectively. The ANN model based on twelve variables including clinical variables, hemoglobin, red cell distribution width, NLR, and PLR achieved an AUROC of 0.811 in the testing dataset.

Toxicity study of pro-angiogenic casein manganese oxide nanoparticles: an in vitro and in vivo approach

Recently, we have demonstrated casein manganese oxide nanoparticles (CMnNP) that exhibit pro-angiogenic property established through different in vitro and in vivo experiments. The CMnNP was explored for therapeutic angiogenesis for treatment of wounds and recovery of hindlimb ischemia in pre-clinical mouse prototypical. It is well known that to translate any therapeutic nanoparticle for future clinical applications, their biosafety evaluation in small and large animals is essential. Herein, in the current study, the biosafety and bioavailability of the CMnNP have been explored by a systematic toxicity profiling study in C57BL/6J mice model. Initially, the in vitro cytotoxic effects of CMnNP were validated in RAW 264.7 cells. Later, the CMnNP was administered intraperitoneally with different doses (50, 300, and 2000 mg/kg b.wt./day) at different time points of exposure (acute: 2 weeks, sub-chronic: 4 weeks as well as chronic exposure: 8 and 20 weeks) with reference to the maximum tolerable dose (MTD) of CMnNP as per the OECD guidelines. The blood hematological and serum biochemical parameters of CMnNP treatment groups indicate negligible changes similar to untreated group. The histopathological examination of CMnNP-treated vital organs (lung, spleen, liver, brain, kidney, and heart) illustrates no major changes even at higher doses. Further, the biodistribution and excretion study depicts normal clearance of CMnNP. Additionally, the serum cytokine levels were normal in the therapeutic dose of CMnNP. The results altogether indicate that the non-toxic nature of CMnNP makes them useful as future therapeutic angiogenic agent for the treatment of various diseases where angiogenesis plays an important role.

Development and Pilot Testing of a Comprehensive Mobile Application to Assist Cell Count Determination During Peripheral Smear and Bone Marrow Examination

BACKGROUND

In the modern era of complete blood count analysis, manual differential count is performed whenever 'flags' are generated by an automated hematology analyzer. Traditionally, tally counters with five or eight keys are used for manual differential count. A few mobile applications are available to perform this task; however, the application features and cell representation are limited.

OBJECTIVES

The primary objective of our study was to develop an indigenous, comprehensive mobile application to assist with manual blood cell differential count. The secondary objective was to measure the usability of a newly developed application among undergraduate medical students.

MATERIALS AND METHODS

A new mobile application was developed using a Java development kit, Version 11.0.13 (Oracle Corporation, Austin, USA) in Android Studio Dolphin (2021.3.1) (Google, California, USA). The application content was validated by three pathologists with more than five years of experience. The app's usability was tested among 60 participants using a validated mHealth App Usability Questionnaire (MAUQ). The questionnaire had 18 items covering three domains: ease of use, interface & satisfaction, and usefulness.

RESULTS

The newly developed application supports peripheral smear WBC differential count, platelet count, reticulocyte count, malaria parasite quantification, and bone marrow differential count. During usability testing, the app was easy to use in 95% (57/60) of participants, time-efficient in 91.7% (55/60), and helpful for healthcare practice learning in 96.7% (58/60). The total mean score was 6.11, indicating high usability.

CONCLUSION

A comprehensive mobile application to assist manual differential count with adequate cell representation was developed. The mobile application was easy to use, time-efficient, and valuable among the study participants.

Increasing a microscope's effective field of view via overlapped imaging and machine learning

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.