Types Of White Blood Cells Research Articles

The Role of CD8+ T Cell in Lung Cancer

Abstract Background and Objective Lung cancer is a common and difficult-to-diagnose form of cancer. CD8+ T cells, a type of white blood cell, play a crucial role in the immune system’s ability to recognize and eliminate cancerous cells. The presence of CD8+ T cells in lung cancer patients is associated with a better prognosis. Immunotherapy utilizing CD8+ T cells has shown promise in treating lung cancer and improving patient survival rates. However, there are challenges to using CD8+ T cells as a therapy. This article aims to explore the role of CD8+ cell in lung cancer. Methods Literature searches were conducted using four databases, namely PubMed, Scopus, Central, and EBSCOhost. We used mesh terms and Boolean operators for optimal results. Key Content and Findings T cells and B cells are important components of the immune system in fighting cancer. T cells, specifically CD8+ T cells, can identify and kill cancer cells, but they can also be inhibited by cancer cells. B cells recognize cancer cells and produce antibodies to fight them, but their effectiveness can be limited. CD8+ T cells play a role in lung cancer prognosis, and their activity is influenced by the tumor microenvironment. Understanding these immune system mechanisms can help develop more effective cancer treatments. Conclusions CD8+ T cells play an important role in the body’s immune system to fight lung cancer cells, through antigen recognition, effector mechanisms (granzyme and perforin), cytokine production, and activation of natural killer (NK) cells.

Incidence and Risk Factors of Surgical Site Infections after Elective Gastrointestinal Surgery: A Prospective Cohort Study

Introduction: Surgical Site Infection (SSI) is defined as an infection that occurs within an incisional wound within 30 days following the procedure. SSI continues to be a significant contributor to extended hospital stays and elevated morbidity, with significant mortality even with well-equipped operation theaters with modern sterilisation techniques, surgical equipment, proper antibiotic cover, and newer surgical methods. Proper identification of the risk factors and modification, if possible, is the only way to optimise surgical outcomes and reduce SSI. Aim: To assess the incidence of SSI and analyse the risk factors associated with SSI. Materials and Methods: A prospective cohort study was conducted from March 2020 to March 2022 at a rural medical college in the coastal area of Alappuzha district of Kerala, India. Patients over 18 years of age admitted for elective gastrointestinal (GI) surgery for various indications were included. Those presenting with pre-existing infections other than the indication for surgery and those undergoing laparoscopic surgeries were excluded. Operations were performed on the third day after admission for all patients. The incidence of SSI and its relation with age, gender, smoking, diabetes, preoperative serum albumin, White Blood Cell (WBC) count, type and duration of surgery, American Society of Anaesthesiologists (ASA) physical status, and surgical wound were analysed using the Chi-square test and Fisher’s exact test. Results: On analysis of 216 patients, variables such as smoking, diabetes, preoperative low serum albumin, higher ASA physical status, contaminated and dirty wound, and prolonged duration of surgery were strongly associated with the development of SSI. Conclusion: Patients with risk factors such as smoking, diabetes, preoperative low serum albumin, higher ASA physical status, contaminated and dirty wound, and prolonged duration of surgery should receive special attention to optimise surgical outcomes and mitigate the risk of infection.

Analysis of interactions between human immune system and a pathogenic virus

"In this paper we present a mathematical model for studying the interactions between human immune system and a pathogenic virus, such as Covid-19. A mathematical analysis based on dynamical systems theory is performed. More exactly, we model the interactions between the immune system and the virus by a modified predator-prey method. Several conclusions emerge from this study, and the main two of them are the followings: 1) a deficiency in the concentration of a single type of white blood cells in the early stages of virus proliferation may lead to the virus victory, and 2) if the number of at least one type of white blood cells can be increased beyond the normal threshold by medical interventions in the early stages of virus infection, then the immune system has a better chance to win against the virus."

HEMATOLOGICAL PROFILE IN CHRONIC KIDNEY DISEASE PATIENTS – A RETROSPECTIVE STUDY

Background: Chronic kidney disease (CKD) is linked to a variety of alterations in haematological parameters. The most common abnormality is anaemia, but white cell and platelet counts as well as their functionality may be compromised. Poor overall clinical prognosis is linked to these abnormalities. Aim: To evaluate the hematological changes in CKD patients. Materials and Method: Retrospective study conducted was for a period of 2 months in 60 chronic kidney disease patients who attended the nephrology department of our hospital were recruited for the present study. Data was collected from case record of patients from medical records department. Hematological details such as Hemoglobin (Hb), red Blood Cell (RBC) count, Mean Corpuscular Volume (MCV), Mean corpuscular Hemoglobin(MCH), Mean cell hemoglobin Concentration(MCHC), Red Cell Distribution width (RDW), White blood cell (WBC) count, platelet count and type of anemia were noted. Results were analysed using SPSS 20.0 version. Results: Of the 60 CKD patients, the most common age group was 5th to 6th decade with male predominance (68.3%). Mean age was 56 years. The study revealed that Hemoglobin (Hb), Red Blood Cells (RBC) were low in CKD patients. The most common type of anemia was normocytic normochromic anemia (68.3%). Leucocytosis and thrombocytopenia was seen in 15% and 40% patients respectively. Conclusion: Anaemia was highly prevalent in CKD patients and was signicantly. Hematological parameters can be evaluated for the underlying cause and treated appropriately. The present study might help clinicians understand haematological prole and take necessary steps in the management.

White blood cells and chronic rhinosinusitis: a Mendelian randomization study

BackgroundRisk factors for the pathogenesis of chronic rhinosinusitis (CRS) remain largely undetermined, which is likely due to the heterogeneity of the disease. White blood cell counts have been largely unexplored as a risk factor for CRS even though different types of white blood cells are involved in the inflammatory process of CRS.ObjectiveTo investigate causal associations between different types of white blood cells on risk of CRS utilizing a Mendelian randomization (MR) analysis.MethodsA two-sample MR analysis was performed using respective GWAS summary statistics for the exposure traits (neutrophil count, eosinophil count, basophil count, lymphocyte count, and monocyte count) and outcome trait (CRS). For the exposure traits, the European Bioinformatics Institute database of complete GWAS summary data was used. For the outcome trait, summary statistics for CRS GWAS were obtained from FinnGen. Primary analysis for MR was performed using inverse-variance weighted two-sample MR. Sensitivity analyses included weighted median, MR-Egger, and MR-PRESSO (raw and outlier-corrected).ResultsEosinophils were associated with CRS (OR = 1.55 [95% CI 1.38, 1.73]; p = 4.3E-14). Eosinophil results were similar across additional MR methods. MR results did not demonstrate significant causal relationships between neutrophils, lymphocytes, monocytes, or basophils with CRS. No significant pleiotropic bias was observed.ConclusionsIn a two-sample MR analysis, a potential causal link between blood eosinophil counts and CRS has been demonstrated. In addition, causal relationships between blood counts among other white blood cell types and CRS were not found. Further studies involving genetic variation in CRS are needed to corroborate genetic causal effects for CRS.

Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks.

White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.

NETome: A model to Decode the Human Genome and Proteome of Neutrophil Extracellular Traps

Neutrophils are the most abundant type of white blood cells in humans with biological roles relevant to inflammation, and fighting off infections. Neutrophil Extracellular Traps (NETs) act as enxogenous agents controlling invasion by bacteria, viruses, fungi, metabolic, and traumatic agents. Traditionally, studies have focused on elucidating molecular and cellular pathways preceding NET formation. Here, we developed a model to decode the human genome and proteome of developted NETs. Via in vitro system to differentiate HL-60 human myeloid cell line into neutrophil extracellular trap (ecTrap) producing cells, we isolated and captured ectrap derived DNA and proteins for shotgun sequencing. The genomic sequences revealed accurate delineation of gene composition including immune response genes and mitochondrial enrichment, while providing a reference database for future interrogation. Shotgun proteomics showed global proteins in differentiated cells with specific immune pathways when compared to undifferentiated counterparts. Coupled with omics’ approaches, we validated our system by functional assays and began to dissect host-microbial interactions. Our work provides a new understanding of the genomic and proteomic sequences, establishing the first human database deposition of neutrophil extracellular traps.

Label-free deep-UV microscopy detection and grading of neutropenia using a passive microfluidic device.

Neutropenia is a condition comprising an abnormally low number of neutrophils, a type of white blood cell, which puts patients at an increased risk of severe infections. Neutropenia is especially common among cancer patients and can disrupt their treatment or even be life-threatening in severe cases. Therefore, routine monitoring of neutrophil counts is crucial. However, the current standard of care to assess neutropenia, the complete blood count (CBC), is resource-intensive, time-consuming, and expensive, thereby limiting easy or timely access to critical hematological information such as neutrophil counts. Here, we present a simple technique for fast, label-free neutropenia detection and grading via deep-ultraviolet (deep-UV) microscopy of blood cells in polydimethylsiloxane (PDMS)-based passive microfluidic devices. The devices can potentially be manufactured in large quantities at a low cost, requiring only 1 μL of whole blood for operation. We show that the absolute neutrophil counts (ANC) obtained from our proposed microfluidic device-enabled deep-UV microscopy system are highly correlated with those from CBCs using commercial hematology analyzers in patients with moderate and severe neutropenia, as well as healthy donors. This work lays the foundation for the development of a compact, easy-to-use UV microscope system to track neutrophil counts that is suitable for low-resource, at-home, or point-of-care settings.

DETEKSI SEL DARAH SECARA OTOMATIS DENGAN EXTREME LEARNING MACHINE

Blood tests in humans play an important role not only to determine a person's blood type but also to detect diseases. Blood tests are carried out by composing blood preparations and checking them in the laboratory. With the development of technology in computer vision, the examination of blood preparations can be done through digital images of the microscope preparat. This study was conducted to detect blood cells automatically from digital images using the Extreme Learning Machine (ELM) method. The most obvious difference between ELM and other Neural Networks is in the number of its hidden layer. The ELM architecture only consists of 1 layer, while the hidden layer architecture in other Neural Networks may contain more than 1 layer. In the learning process, ELM utilizes the Moore Penrose Pseudoinverse matrix inverse theory which has the best generalization results with faster computation time. Experiments were carried out by digitizing the image of the blood cells. These images consisted of two types, the stained images and the unstained images. In the stained microscopic images, each component of the blood cells can be clearly seen and easily identified. Meanwhile, in the image of the unstained preparations, the components of the blood cells cannot be distinguished or seen directly by the human eye. The experimental results show that the developed method can achieve 95.83% accuracy for the stained image and 68.6% accuracy for the unstained image. Furthermore, the average testing accuracy of varied white blood cell types has an accuracy of 81.36%.

A Method for Expanding the Training Set of White Blood Cell Images.

In medicine, the count of different types of white blood cells can be used as the basis for diagnosing certain diseases or evaluating the treatment effects of diseases. The recognition and counting of white blood cells have important clinical significance. But the effect of recognition based on machine learning is affected by the size of the training set. At present, researchers mainly rely on image rotation and cropping to expand the dataset. These methods either add features to the white blood cell image or require manual intervention and are inefficient. In this paper, a method for expanding the training set of white blood cell images is proposed. After rotating the image at any angle, Canny is used to extract the edge of the black area caused by the rotation and then fill the black area to achieve the purpose of expanding the training set. The experimental results show that after using the method proposed in this paper to expand the training set to train the three models of ResNet, MobileNet, and ShuffleNet, and comparing the original dataset and the method trained by the simple rotated image expanded dataset, the recognition accuracy of the three models is obviously improved without manual intervention.

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

An Overview of Conventional Drugs and Nano Therapeutic Options for the Treatment and Management of Pediatric Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia (ALL) is a common form of pediatric cancer affecting the lymphoblast, a type of white blood cell found in the bone marrow. In this disease, the normal lymphoblast cells transform into leukemic cells and subsequently enter the bloodstream. Leukemic cells found in patients with ALL have shown differences in cholesterol uptake and utilization. Current treatment consists of chemotherapy, chimeric antigen receptor (CAR) therapy, and hematopoietic stem cell transplantation (HSCT). In addition, minimal residual disease (MRD) has become an effective tool in measuring treatment efficacy and the potential for relapse. Chemotherapy resistance remains a significant barrier in the treatment of ALL. Biomarkers such as an upregulated Akt signaling pathway and an overexpressed VLA-4 integrin-protein have been associated with drug resistance. Nanoparticles have been used to favorably alter the pharmacokinetic profile of conventional drug agents. These drug-delivery systems are designed to selectively deliver their drug payloads to desired targets. Therefore, nanoparticles offer advantages such as improved efficacy and reduced toxicity. This review highlights conventional treatment options, distinctive characteristics of pediatric ALL, therapeutic challenges encountered during therapy, and the key role that nanotherapeutics play in the treatment of ALL.

Hypofractionated vs. Conventionally Fractionated Radiotherapy for Glioblastoma: Peripheral Blood Leukocyte Comparison prior to and Following Chemoradiation

<h3>Purpose/Objective(s)</h3> The diagnosis of glioblastoma portends a poor prognosis with extremely low 5-year survival rates. Immune suppression related to the tumor, radiotherapy (RT), and chemotherapy has been documented following treatment and appears associated with poor tumor outcome. Previous study suggests radiotherapy contributes to lymphopenia, and that a shorter course of treatment may better spare the lymphocyte population. However, immunosuppressive monocytes, such as myeloid-derived suppressor cells, expand in number during treatment and may also affect tumor control directly and by contributing to lymphopenia. We analyzed clinical measures of peripheral blood leukocytes in patients receiving conventionally fractionated (CF) RT and hypofractionated (HF) RT for glioblastoma. We hypothesized monocyte quantities expand more with CF RT, compared to HF RT following concurrent chemoradiation. <h3>Materials/Methods</h3> We retrospectively reviewed records from patients treated for diagnosis of glioblastoma between 7/1/2011 and 6/2/2021 at our institution. Patients over the age of 18 who completed their glioblastoma RT course, had not received prior RT, and had sufficient lab data for comparison were included. Clinical peripheral blood counts were recorded prior to and following chemoradiation. Values were analyzed using descriptive summary statistics and t-confidence intervals. CF RT for newly diagnosed glioblastoma usually consists of approximately 6000 centigray (cGy) in 30 fractions. HF RT schemes used were generally 3400-4500 cGy in 10-15 fractions. <h3>Results</h3> Lab results from 80 patients (65 CF, 15 HF) were analyzed prior to and following RT. The median patient age was 59 years (Q1, Q3: 47, 66). 57% were male. All patients received concurrent temozolomide. Absolute leukocyte count decreased by 4.8 10⁹/L (3.7, 5.9) and 1.9 10⁹/L (-0.1, 3.8) for CF RT and HF RT, respectively. Absolute lymphocytes decreased by 7.0 10⁹/L (5.8, 8.1) in CF RT and 5.3 10⁹/L (3.5, 7.1) in HF RT, while the percent lymphocytes increased by 1.1% (-1.8%, 4.1%) for CF RT and 2.1% (-3.9%, 8.0%) for HF RT, respectively. Absolute monocytes decreased by 0.1 10⁹/L (0.0, 0.2) for CF RT and 0.1 10⁹/L (-0.1, 0.3) for HF RT. The percent monocytes increased by 2.7% (1.3%, 4.1%) in CF RT and by 0.2% (-2.2%, 2.6%) in HF RT. <h3>Conclusion</h3> We observed different effects in peripheral blood leukocyte populations in glioblastoma patients receiving CF RT and HF RT. Percent monocytes had the greatest increase of the white blood cell types in CF RT, while percent lymphocytes had the greatest increase after concurrent chemoradiation in those receiving HF RT. Further study will better define the effects of RT on the various cell populations important for anti-neoplastic immunity and assess impact on survival.

Plasma iron controls neutrophil production and function.

Low plasma iron (hypoferremia) induced by hepcidin is a conserved inflammatory response that protects against infections but inhibits erythropoiesis. How hypoferremia influences leukocytogenesis is unclear. Using proteomic data, we predicted that neutrophil production would be profoundly more iron-demanding than generation of other white blood cell types. Accordingly in mice, hepcidin-mediated hypoferremia substantially reduced numbers of granulocytes but not monocytes, lymphocytes, or dendritic cells. Neutrophil rebound after anti-Gr-1–induced neutropenia was blunted during hypoferremia but was rescued by supplemental iron. Similarly, hypoferremia markedly inhibited pharmacologically stimulated granulopoiesis mediated by granulocyte colony-stimulating factor and inflammation-induced accumulation of neutrophils in the spleen and peritoneal cavity. Furthermore, hypoferremia specifically altered neutrophil effector functions, suppressing antibacterial mechanisms but enhancing mitochondrial reactive oxygen species–dependent NETosis associated with chronic inflammation. Notably, antagonizing endogenous hepcidin during acute inflammation enhanced production of neutrophils. We propose plasma iron modulates the profile of innate immunity by controlling monocyte-to-neutrophil ratio and neutrophil activity in a therapeutically targetable system.

P030: Baseline IgM amount can identify patients with adverse outcome despite a PET-2 adapted treatment in classical Hodgkin Lymphoma: results from a real-life single-center study i

Background: Hodgkin Lymphoma is characterized by an inflammatory background in which the reactive myeloid cells may exert an immune suppressive effect that advances progression of disease. Immunoglobulin M is first antibody isotype to be produced during an immune response, but it plays also an immunoregulatory role, therefore we investigated if it could have any clinical impact on prognosis. Experimental design: In this retrospective, observational, single – center study, we evaluated 212 newly diagnosed Hodgkin Lymphoma (HL) patients, including 132 advanced stage cases, with a median follow up of 60.3 months (range 60–204); 109 (51%) were men, with a median age of 31 years (range 15–77), 64 patients showed bulky disease, while 119 suffered from B symptoms at the onset. The median baseline values for IgM, IgA and IgG were, respectively: 86.0 (range 5.0–336.0 mg/dL), 197.5 (range 5.0–336.0 mg/dL) and 1110.00 (range 157.0–2763.0 mg/dL). 49/212 (23%) patients had a baseline IgM value lower than 50mg/dL. White blood cell count and type were determined by electrical impedance method. Nephelometry was used for immunoglobulins quantification. Results: PFS at 60 months was 54.1% versus 81.1 % respectively in patients with IgM ≤50 mg/dL or IgM>50 mg/dL (p<0.001). A level of 50 mg/dl of IgM at baseline resulted in 84.1% sensitivity and 45.5% specificity in predicting achievement of complete response in the whole cohort (area under curve - AUC - 0.62, p=0.013). In multivariate analysis, only baseline IgM ≤50 mg/dl and presence of large nodal mass (< 7 cm) were independent baseline variables able to predict clinical outcome while after two cycles of treatment only IgM ≤50 mg/dl at baseline and PET-2 status were independent predictors of PFS. Thus, we stratified the advanced stage patients in three groups based on clinical variables available at diagnosis: low risk group was defined as absence of LNM and baseline IgM >50; standard-risk was defined by either presence of LNM or baseline IgM ≤50 mg/dL; high risk group was defined by both presence of LNM and IgM ≤50 mg/dL at baseline. The 60-months PFS estimates were significantly different among the three groups, respectively 83.5, 59.5 and 40.0%, p=<0.0001. Conclusion: IgM amount at diagnosis is a valuable prognostic factor much earlier than PET-2 and it can also provide information in PET-2 negative patients. This can help identify at baseline different HL classes at risk of treatment failure.

Molecular characterization of multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy that occurs when plasma cells, a type of white blood cell, grow out of control and start to overproduce antibodies that accumulates in blood and bone marrow. Despite the recent advance the survival rate for MM has not been increased significantly, which opens the need for identifying new molecular targets. This review article presents the most frequently observed gene mutations (KRAS (22.0%), NRAS (18.0%), DIS3 (9.3%), TTN (8.3%), ZNF717 (8.3%), TENT5C (7.3%), TP53 (7.3%) %), BRAF (6.3%), MUC16 (6.3%), RYR2 (5.4%), LRP1B (5.4%)) in MM patients and its rates, correlations, clinical significance, importance in the framework of MM disease and potential novel targets which are collected from the literature. The genes that are mutated in MM patients (211) taken from cBioportal data set. In conclusion, in the study conducted in MM patients, the 3 genes with the most frequent mutations were recorded as KRAS, NRAS and DIS3, respectively. In addition, in the context of our literature reviews and the data obtained, it appears that the TZNF717, TTN, MUC16, RYR2 genes need further study within the framework of MM.

Retrospective Analysis of Infection Factors in Secondary Internal Fixation after External Fixation for Open Fracture of a Long Bone: A Cohort of 117 Patients in a Two-Center Clinical Study

Purpose To investigate infection risk factors after secondary internal fixation (IF) of open fracture of a long bone with removed fixation frame and explore the safe range of feasible operation for abnormal inflammatory indicators. Methods Clinical data of 117 cases of open fracture of a long bone that underwent temporary external fixation (EF) in one stage and IF in two stages were retrospectively analyzed. Collected data included age, sex, Gustilo type, multiple injuries, debridement time, duration of EF, needle infection, interval of conversion to IF after external fixator, preoperative white blood cell (WBC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), albumin (ALB), blood glucose, and prognosis. We selected these factors for univariate analysis of postoperative surgical site infection (SSI) and multivariate logistic regression analysis of statistically significant risk factors and created receiver operating characteristic (ROC) curves to compare the diagnostic efficiency of each index and determine the optimal screening point. Results We followed up 117 patients, with 130 limbs affected. Univariate analysis showed that ESR, CRP, ALB, WBC, EF time, and Gustilo fracture type were significantly associated with SSI. Multivariate logistic regression analysis showed that CRP, duration of EF, and Gustilo fracture type were independently associated with postoperative infection. Area under ROC curves for WBC, ESR, and CRP were 69.7%, 73.2%, and 81.2%. Conclusions We demonstrated the role of Gustilo classification of open fractures in predicting postoperative infection, especially for open fractures above type III. If the inflammatory indexes return to normal or show a downward trend, and the second-stage IF operation is performed within the cutoff values, postoperative recurrent infection was reduced.

Multiple Myeloma: Possible Cure from the Sea.

Simple SummaryMultiple myeloma (MM) is a complex white blood cell (plasma cell, PC) cancer. The aetiology of MM is still unknown, and it is still an incurable disease despite efforts by the scientific community. The high level of PC genetic heterogeneity renders MM a complex puzzle to be solved. Combinations of drugs are generally used to treat MM patients, with a general increase in overall survival. Relapsed and refractory MM patients are the generation of patients who resist or do not respond to first-line therapy and need additional treatments. Exploring new sources, such as marine organisms, for drug discovery is fundamental to fighting MM. Various studies have shown that marine natural products (MNPs) might have antiproliferative and cancer-specific cytotoxic properties, giving MNPs a pivotal role in anticancer drug discovery. This review recaps updated frontline treatment options, including new ones developed from MNP research.Multiple myeloma (MM) is a blood cancer that occurs in the plasma cells (PCs), a type of white blood cell. Despite the progress of several current treatments that prolong the overall patient’s survival, most MM cases are incurable. For this reason, many efforts have been undertaken by the scientific community in the search for new treatments. BLENREPTM and Aplidin® are two marine-derived drugs currently in use for MM. In addition, other natural products have been identified from marine organisms, tested for their possible anticancer properties, and are in preclinical or clinical trials for MM, including cytarabine, a compound in use for leukaemia treatment. Between the most successful marine compounds in fighting MM, there are molecules with specific targets, such as the elongation factor 1-alpha 2 and proteasome inhibitors, and compounds conjugated with antibodies that recognise specific cell types and direct the drug to the correct cell target. Active compounds belong to different chemical classes, from cyclic peptides to alkaloids, highlighting the importance of screening the plethora of compounds produced by marine organisms. In this review, we summarise the current state of art of MM therapies focusing on the marine natural product emerging roles.

WBC image classification and generative models based on convolutional neural network

BackgroundComputer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system.Methods(i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.Results(i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work.ConclusionThis work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.

A comparison of automated classification techniques for image processing in video internet of things

The counts of various types of white blood cells give vital information for identifying a variety of ailments utilizing the video internet of things (VIoT). This technique needs to be automated to save time and minimize counting errors. This paper aims to apply recent image processing techniques to identify and categorize white blood cells (WBCs) found in peripheral blood and to develop a system that would automatically detect and evaluate WBCs. The proposed method consists of steps such as segmentation scanning, feature extraction, and blood cell categorization. First, we segment the cell pictures, which entails grouping white blood cells into clusters. The second stage consists of scanning each input image and preparing the dataset. The third stage involves determining the texture and contour of a scanned picture. Finally, different machine learning algorithms are used to classify the results based on these criteria.