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Simple Summary: Genomic rearrangements are chromosomal abnormalities that alter the arrangement of genes and are important for diagnosing multiple cancer types and guiding therapy selection. However, current diagnostic tests may not detect all genomic rearrangements. Hi-C sequencing is a promising new method for detecting genomic rearrangements. First, we examined whether Hi-C can detect known genomic rearrangements in pediatric leukemia and sarcoma specimens. Next, we evaluated whether Hi-C can detect genomic rearrangements that were not found using standard diagnostic testing. Hi-C showed complete agreement with other diagnostic methods in identifying known rearrangements. Hi-C also detected previously unknown genomic rearrangements in 5/11 pediatric leukemia cases that could impact diagnosis, prognosis, or treatment choices. These data suggest Hi-C could be beneficial for medical diagnostic testing of pediatric cancers, but more extensive clinical validation is needed. Hi-C sequencing is a DNA-based next-generation sequencing method that preserves the 3D genome conformation and has shown promise in detecting genomic rearrangements in translational research studies. To evaluate Hi-C as a potential clinical diagnostic platform, analytical concordance with routine laboratory testing was assessed using primary pediatric leukemia and sarcoma specimens. Archived viable and non-viable frozen leukemic cells and formalin-fixed paraffin-embedded (FFPE) tumor specimens were analyzed. Pediatric acute myeloid leukemia (AML) and alveolar rhabdomyosarcoma (A-RMS) specimens with known genomic rearrangements were subjected to Hi-C to assess analytical concordance. Subsequently, a discovery cohort consisting of AML and acute lymphoblastic leukemia (ALL) cases without known genomic rearrangements based on prior clinical diagnostic testing was evaluated to determine whether Hi-C could detect rearrangements. Using a standard sequencing depth of 50 million raw read-pairs per sample, or approximately 5X raw genomic coverage, we observed 100% concordance between Hi-C and previous clinical cytogenetic and molecular testing. In the discovery cohort, a clinically relevant gene fusion was detected in 45% of leukemia cases (5/11). This study provides an institutional proof of principle evaluation of Hi-C sequencing to medical diagnostic testing as it identified several clinically relevant rearrangements, including those that were missed by current clinical testing workflows. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) and schizoaffective disorder (SHZ) are psychiatric disorders commonly identified in individuals in their late adolescence or early adulthood. Comorbidities are common, though a concurrent diagnosis of leukemia, one of the most frequently occurring cancers of adolescence, has not yet been described in such cases. This case study outlines the clinical presentation, course, and treatment response of two 17-year-old male adolescents whose psychotic disorders complicated their leukemia treatment. The first patient was diagnosed with leukemia and subsequently with SCZ while undergoing leukemia treatment. The second patient was diagnosed with SHZ prior to the onset of leukemia. The case study will follow the methodology of Robert E. Stake (Abma & Stake, 2014), as the two cases share a leukemia diagnosis and the reported mental health impact connected with cancer-directed treatment. Early identification and treatment are critical for both psychotic disorders and cancers, often impacting the long-term prognosis. However, when co-occurring, their interplay can present unique challenges to care. [ABSTRACT FROM AUTHOR]

Objective: Therapy‐related myeloid neoplasms (t‐MNs) are often fatal and arise as late complications of previous anticancer drug treatment. No single‐center case series has examined t‐MNs in epithelial ovarian cancer (EOC). Methods: All patients with EOC treated at Chiba University Hospital between 2000 and 2021 were included. We retrospectively analyzed the characteristics, clinical course, and outcomes of patients who developed t‐MNs. Results: Among 895 cases with EOC, 814 cases were treated with anticancer drugs. The median follow‐up period was 45 months (interquartile range, 27–81) months. Ten patients (1.2%) developed t‐MNs (FIGO IIIA in one case, IIIC in three, IVA in one, and IVB in five). Nine patients were diagnosed with myelodysplastic syndrome and one with acute leukemia. One patient with myelodysplastic syndrome developed acute leukemia. The median time from the first chemotherapy administration to t‐MN onset was 42 months (range, 21–94 months), with t‐MN diagnoses resulting from pancytopenia in four cases, thrombocytopenia in three, and blast or abnormal cell morphology in four. The median number of previous treatment regimens was four (range, 1–7). Paclitaxel + carboplatin therapy was administered to all patients, gemcitabine and irinotecan combination therapy to nine, bevacizumab to eight, and olaparib to four. Six patients received chemotherapy for t‐MN. All patients died (eight cancer‐related deaths and two t‐MN‐related deaths). None of the patients was able to restart cancer treatment. The median survival time from t‐MN onset was 4 months. Conclusions: Patients with EOC who developed t‐MN were unable to restart cancer treatment and had a significantly worse prognosis. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explores the detection of gene fusions in acute leukemia using targeted RNA sequencing and compares its effectiveness with traditional methods like karyotyping and RT-PCR. Gene fusions, which result from chromosomal rearrangements, are crucial for diagnosing and treating leukemia. Recent updates in diagnostic criteria emphasize the importance of identifying these gene fusions. Targeted RNA sequencing offers advantages such as rapid diagnosis, minimal training requirements, and cost-effectiveness, making it a practical method in clinical settings. Our findings suggest that targeted RNA sequencing can accurately identify gene fusions, potentially benefiting nearly half of leukemia patients and improving diagnostic precision. This method may significantly impact the research community by providing a reliable and efficient tool for leukemia diagnosis. Gene fusions are key drivers in acute leukemia, impacting diagnosis and treatment decisions. We analyzed 264 leukemia patients using targeted RNA sequencing with conventional karyotyping and reverse transcription polymerase chain reaction (RT-PCR). Leukemic fusions were detected in 127 patients (48.1%). The new guidelines introduced additional diagnostic criteria, expanding the spectrum of gene fusions. We discovered three novel fusions (RUNX1::DOPEY2, RUNX1::MACROD2, and ZCCHC7::LRP1B). We analyzed recurrent breakpoints for the KMT2A and NUP98 rearrangements. Targeted RNA sequencing showed consistent results with RT-PCR in all tested samples. However, when compared to conventional karyotyping, we observed an 83.3% concordance rate, with 29 cases found only in targeted RNA sequencing, 7 cases with discordant results, and 5 cases found only in conventional karyotyping. For the five cases where known leukemic gene rearrangements were suspected only in conventional karyotyping, we conducted additional messenger RNA sequencing in four cases and proved no pathogenic gene rearrangements. Targeted RNA sequencing proved advantageous for the rapid and accurate interpretation of gene rearrangements. The concurrent use of multiple methods was essential for a comprehensive evaluation. Comprehensive molecular analysis enhances our understanding of leukemia's genetic basis, aiding diagnosis and classification. Advanced molecular techniques improve clinical decision-making, offering potential benefits. [ABSTRACT FROM AUTHOR]

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient's health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet's superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains. [ABSTRACT FROM AUTHOR]

Background Due to chemotherapy-induced neutropenia or hematologic malignancies, immunocompromised cancer patients may have higher incidence of febrile nonhemolytic transfusion reactions compared with the general population and frequently require platelet transfusions. This quality improvement project compared the safety of transfusion using prestorage leukocyte-reduced and pooled whole blood–derived platelets (Acrodose/WBD) with conventionally produced poststorage WBD platelets (RDP) using an active hemovigilance system. Methods Every patient receiving a blood product at the hospital was virtually monitored in real time by trained nurses from a remote hemovigilance unit. These nurses monitor a digital dashboard, which populates a watch list of patients from the time blood product administration is initiated until 12 hours posttransfusion. Over the course of 6 months, 371 patients receiving 792 RDP transfusions and 423 patients receiving 780 Acrodose/WBD platelets transfusions were monitored for transfusion reactions. Results We identified 26 transfusion reactions in RDP but only 12 transfusion reactions in the Acrodose/WBD platelet group. Conclusion Acrodose platelet transfusion was associated with fewer transfusion reactions, which resulted in significant cost savings. [ABSTRACT FROM AUTHOR]

Background/Aim: Since acute myeloid leukemias still represent the most aggressive type of adult acute leukemias, the profound understanding of disease pathology is of paramount importance for diagnostic and therapeutic purposes. Hence, this study aimed to explore the real-time disease fate with the establishment of an experimental myelomonoblastic leukemia (My1/De) rat model using preclinical positron emission tomography (PET) and wholebody autoradiography. Materials and Methods: In vitro [18F]F-FDG uptake studies were performed to compare the tracer accumulation in the newly cultured My1/De tumor cell line (blasts) with that in healthy control and My1/De bone marrow suspensions. Post transplantation of My1/De cells under the left renal capsule of Long-Evans rats, primary My1/De tumorigenesis, and metastatic propagation were investigated using [18F]F-FDG PET imaging, whole-body autoradiography and phosphorimage analyses. To assess the organ uptake profile of the tumor-carrying animals we accomplished ex vivo biodistribution studies. Results: The tracer accumulation in the My1/De culture cells exceeded that of both the tumorous and the healthy bone marrow suspensions (p<0.01). Based on in vivo imaging, the subrenally transplanted My1/De cells resulted in the development of leukemia in the abdominal organs, and metastasized to the mesenterial and thoracic parathymic lymph nodes (PTLNs). The lymphatic spread of metastasis was further confirmed by the significantly higher %ID/g values of the metastatic PTLNs (4.25±0.28) compared to the control (0.94±0.34). Cytochemical staining of the peripheral blood, autopsy findings, and wright-Giemsa-stained postmortem histological sections proved the leukemic involvement of the assessed tissues/organs. Conclusion: The currently established My1/De model appears to be wellsuited for further leukemia-related therapeutic and diagnostic investigations. [ABSTRACT FROM AUTHOR]

The goal of this study is to improve blood smear image-based blood cancer prediction through medical diagnostic advancements. Blood cancers, particularly leukemia, are challenging to diagnose because of the complexity of biological data and the dimensionality of medical images. There are interpretability and computational problems with each currently in use. We suggest the Random Forest-Recurrent Feature Elimination (RF-RFE) model to increase the precision and dependability of blood cancer diagnosis. This model integrates machine learning and image processing, optimizes feature selection and refinement from high-dimensional data, and applies the XGBoost algorithm to guarantee diagnosis accuracy. Recent model analysis reveals that RF-RFE performs better than them on a wide range of metrics. The RF-RFE offered a sensible, well-rounded strategy. More research on medical diagnostics is made possible by its adaptability in multi-class classification and effectiveness in handling highdimensional feature values. The optimized feature set and computational efficiency of the model, which may enhance leukemia detection and diagnostics, are highlighted in this study. [ABSTRACT FROM AUTHOR]

Introduction: D-dimer is a marker of coagulation and fibrinolysis widely used in clinical practice for assessing thrombotic activity. While it is commonly ordered in the Emergency Department (ED) for suspected venous thromboembolism (VTE), elevated D-dimer levels can occur due to various other disorders. The aim of this study was to find out the causes of elevated D-dimer in patients presenting to a large ED in Saudi Arabia and evaluate the accuracy of D-dimer in diagnosing these conditions. Methods: Data was collected from an electronic hospital information system of patients who visited the ED from January 2016 to December 2022. Demographic information, comorbidities, D-dimer levels, and diagnoses were analyzed. Statistical analysis was performed using the SPSS software. The different diagnoses associated with D-dimer levels were analyzed by plotting the median D-dimer levels for each diagnosis category and their interquartile ranges (IQR). The receiver operating characteristic (ROC) curves were calculated and their area under the curve (AUC) values were demonstrated. The optimal cut-off points for specific diseases were determined based on the ROC analysis, along with their corresponding sensitivities and specificities. Results: A total of 19,258 patients with D-dimer results were included in the study. The mean age of the participants was 50 years with a standard deviation of ± 18. Of the patients, 66% were female and 21.2% were aged 65 or above. Additionally, 21% had diabetes mellitus, 20.4% were hypertensive, and 15.1% had been diagnosed with dyslipidemia. The median D-dimer levels varied across different diagnoses, with the highest level observed in aortic aneurysm 5.46 g/L. Pulmonary embolism (PE) and deep vein thrombosis (DVT) were found in 729 patients (3.8%) of our study population and their median D-dimer levels 3.07 g/L (IQR: 1.35–7.05 g/L) and 3.36 g/L (IQR: 1.06–8.38 g/L) respectively. On the other hand, 1767 patients (9.2%) were diagnosed with respiratory infections and 936 patients (4.9%) were diagnosed with shortness of breath (not specified) with median D-dimer levels of 0.76 g/L (IQR: 0.40–1.47 g/L) and 0.51 g/L (IQR: 0.29–1.06 g/L), respectively. D-dimer levels showed superior or excellent discrimination for PE (AUC = 0.844), leukemia (AUC = 0.848), and aortic aneurysm (AUC = 0.963). DVT and aortic dissection demonstrated acceptable discrimination, with AUC values of 0.795 and 0.737, respectively. D-dimer levels in respiratory infections and shortness of breath (not specified) exhibited poor to discriminatory performance. Conclusion: This is the first paper to identify multiple causes of elevated D-dimer levels in Saudi Arabia population within the ED and it clearly highlights their accurate and diagnostic values. These findings draw attention to the importance of considering the specific clinical context and utilizing additional diagnostic tools when evaluating patients with elevated D-dimer levels. [ABSTRACT FROM AUTHOR]

A team conducted semistructured interviews and developed case reports about financial toxicity (FT) and healthcare transitions (HCTs) with two adolescent and young adult (AYA) cancer survivors. These reports found poor HCTs from pediatric specialty care to adult primary care. Nurses can address FT and facilitate smooth HCTs by screening AYA survivors and caregivers for financial stressors, engaging in cost-of-care conversations and interprofessional care coordination, and supporting access to navigation services. AT A GLANCE * AYAs are vulnerable to FT and challenges with HCTs and access to care throughout the cancer care continuum. * Income loss, limited assets, restrictions on eligibility for financial aid, caregiver dependence, and limited support from providers contribute to FT in AYAs with cancer. * Nurses can support AYA cancer survivors by coordinating care among providers and facilitating access to financial, legal, and social services. [ABSTRACT FROM AUTHOR]

Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow's ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemiainfected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement of a trained pathologist, the decision process is critical, which leads to the development of an automated software framework for diagnosis. Researchers utilized state-of-the-art machine learning approaches, such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes, K-Nearest Neighbor (KNN), and others, to provide limited accuracies of classification. More advanced deep-learning methods are also utilized. Due to constrained dataset sizes, these approaches result in over-fitting, reducing their outstanding performances. This study introduces a deep learning-machine learning combined approach for leukemia diagnosis. It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers. The transfer learning frameworks such as VGGNet, Xception, InceptionResV2, Densenet, and ResNet are employed as feature extractors. The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells. For the experimentation, a very popular ALL-IDB dataset is used, approaching a maximum accuracy of 100%. A private real images dataset with three subclasses of leukemia images, including Acute Myloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myloid Leukemia (CML), is also employed to generalize the system. This dataset achieves an impressive multi-class classification accuracy of 97.08%. The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size (520 images). Hence, thismethod can be explored further for leukemia diagnosis having a limited number of dataset samples. [ABSTRACT FROM AUTHOR]

The article focuses on the genetic analysis of de novo variants in the KMT2A gene associated with mixed-lineage leukemia within a family affected by Wiedemann-Steiner syndrome. Topics include the identification of these genetic variants, their implications for understanding mixed-lineage leukemia, and their role in the broader context of Wiedemann-Steiner syndrome.

Background. Climatic and geomorphological characteristics of a region are vital factors in the regional spread of diseases. Geographic Information System (GIS) is a useful tool for studying cancer epidemiology. This study was conducted with the aim of investigating the prevalence of childhood leukemia in Khuzestan based on 10-year GIS data of temperature, wind speed and dust level. Methods. The medical characteristics of all cases of leukemia in children admitted to Bagai 2 Ahvaz Hospital, which were diagnosed with leukemia for the first time. We collected what was diagnosed. Results. Five hundred and twelve children had acute lymphoblastic leukemia (ALL) and 100 children had acute myeloblastic leukemia (AML). The year 2015 had the highest and the year 2018 the lowest ALL rate. The peak of AML was in 2013 and the lowest was in 2018. Central cities had the highest mean particulate matter, temperature, wind speed, and the highest rate of leukemia. Conclusion. The results show that dust, temperature and wind speed are related to the prevalence of childhood leukemia in Khuzestan, Iran. Practical Implications. A wide range of environmental and genetic factors can play a role in the occurrence of cancer, so additional studies are needed to confirm the effect of particles and environmental factors such as temperature and dust on the occurrence of cancer. [ABSTRACT FROM AUTHOR]

Background This study examined the trends and patterns of opioid and non-opioid pharmacotherapy use among a large national sample of privately insured pediatric patients with cancer in the United States. Materials and Methods We identified pediatric (aged < 21) patients diagnosed with central nervous system (CNS), lymphoma, gonadal, leukemia, or bone cancer from MarketScan data 2005-2019. We examined the proportion of patients who filled a prescription for the following 5 types of pharmacotherapy: opioid, anticonvulsant, non-steroidal anti-inflammatory drug (NSAID), antidepressant, and muscle relaxant during active cancer treatment. We assessed the trends and patterns in pharmacotherapy using multivariable logistic regressions. Results Among 4174 patients included, 2979 (71%) had an opioid prescription; 746 (18%), 384 (9%), 202 (5%), and 169 (4%) had anticonvulsant, NSAID, antidepressant and muscle relaxant prescriptions, respectively. Multivariable logistic regression showed a nonlinear trend in the use of opioids among pediatric patients with cancer over time such that use slightly increased until 2012 (OR of 1.40 [95% CI, 1.12-1.73] for 2012 vs. 2006) but then decreased thereafter (OR of 0.51 [0.37-0.68] for 2018 vs. 2012). The use of anticonvulsants, NSAIDs, and muscle relaxants increased significantly linearly over time (all P  < .005). Conclusion There has been a downward trend in the use of opioids in recent years among pediatric patients with cancer and an upward trend in the use of non-opioid pharmacotherapy for pain management potentially as an alternative to opioids. [ABSTRACT FROM AUTHOR]

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland–Altman analysis confirmed the model's reliability, with a mean bias of −2.29 and 95% limits of agreement between 18.49 and −23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise. [ABSTRACT FROM AUTHOR]

The study aimed to examine the prevalence of pneumonia in pediatric children diagnosed with leukemia at King Hussein Medical Center, Royal Medical Services, Amman, Jordan. The study was conducted from January 2019 to March 2020. A total of 100 hospitalized leukemia patients with febrile neutropenic episodes were evaluated for the presence of pneumonia. Samples were collected from all patients and tested for microbial growth. Univariate analysis revealed that age (P =.033) and packed cell volume (P =.006) were statistically significant risk factors, associated with the prevalence of pneumonia in leukemia patients with febrile neutropenia episodes. Similarly, as the absolute neutrophil count counts increased with an odds ratio and a 95% confidence interval of 2.386 (0.859-6.625), the odds of pneumonia in febrile neutropenic patients were more prevalent. The study reported the prevalence of pneumonia in immunocompromised febrile neutropenic patients with leukemia, which could lead to the development of evidence-based febrile neutropenic treatment protocol development. It will assure more responsive patient management and treatment. [ABSTRACT FROM AUTHOR]

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human's immune system and significantly affect bone marrow's production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient's blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are also utilized while examining the patient for leukemia. However, all these methods are labor-intensive, slow, inaccurate, and require a lot of human experience and dedication. Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations. They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells. However, these systems are more efficient, reliable, and fast than previous manual diagnosing methods. However, more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity. In this paper, we have proposed a robust automated system to diagnose leukemia and its sub-types. We have classified ALL into its sub-types based on FAB classification, i.e., L1, L2, and L3 types with better performance. We have achieved 96.06% accuracy for subtypes classification, which is better when compared with the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Simple Summary: Treatments used to cure childhood cancer can have negative long-term impacts on physical health and well-being. Here, we present the Alberta Childhood Cancer Survivorship Research Program, its foundational cohort, and descriptive statistics of outcomes ascertained through data linkage. To this end, 2580 survivors of childhood cancer were included in the cohort, the majority of which were male, diagnosed between the ages of 0 and 4 years, and had leukemia, central nervous system tumor, or lymphoma. By the study exit date, the median time since diagnosis was 5.5 years overall and 10.4 years for 5-year survivors. During the follow-up time, 82 subsequent cancers were diagnosed, 20,355 inpatient and 555,425 ambulatory/outpatient events occurred, 606,773 claims were reported, and 437 survivors died. The results from this research program seek to inform and improve clinical care and reduce cancer-related sequelae. Adverse outcomes after childhood cancer have been assessed in a range of settings, but most existing studies are historical and ascertain outcomes only after 5-year survival. Here, we describe the Alberta Childhood Cancer Survivorship Research Program and its foundational retrospective, population-based cohort of Albertan residents diagnosed with a first primary neoplasm between the ages of 0 and 17 years from 1 January 2001 to 31 December 2018. The cohort was established in collaboration with the Alberta Cancer Registry and Cancer in Young People in Canada program and has been linked to existing administrative health databases and patient-reported outcome questionnaires. The cohort comprised 2580 survivors of childhood cancer, 1379 (53.4%) of whom were 5-year survivors. Approximately 48% of the cohort was female, 47% of the cohort was diagnosed between 0 and 4 years of age, and the most frequent diagnoses were leukemias (25.4%), central nervous system tumors (24.0%), and lymphomas (14.9%). Detailed treatment information was available for 1741 survivors (67.5%), with manual abstraction ongoing for those with missing data. By the study exit date, the median time since diagnosis was 5.5 years overall and 10.4 years for 5-year survivors. During the follow-up time, 82 subsequent primary cancers were diagnosed, 20,355 inpatient and 555,425 ambulatory/outpatient events occurred, 606,773 claims were reported, and 437 survivors died. The results from this research program seek to inform and improve clinical care and reduce cancer-related sequelae via tertiary prevention strategies. [ABSTRACT FROM AUTHOR]

An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%-12% higher than the conventional technique. [ABSTRACT FROM AUTHOR]

In this day and age, surrounded by innumerable forms of technology, the use of various autonomous systems to recognize various ailments has tremendously benefited the medical industry. An important medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears. Invaluable details regarding the patient's health may be revealed, such as the discovery of acute lymphatic leukaemia or other serious disorders. This study provides a paradigm for detecting acute lymphoblastic leukemia from a microscopic vision of white blood cells. Microscopic images must go through a thorough pre-processing phase before being classified. In this study, WBCs are separated from blood smear images using morphological techniques, and the segmented region is then searched for a set of textural, geometrical, and statistical properties. Four different machine learning techniques are used to examine the performance of these algorithms: random forest (RF), support vector machine (SVM), naive Bayes classifier (NB), and K nearest neighbor (KNN). The SVM is effective in classifying and identifying the acute lymphoblastic cell that produces leukemia malignancy, as can be observed after careful comparison. A single classifier is virtually completely useless given the variety of blood smear pictures. As a result, we considered using EMC-SVM to classify leukocytes. The suggested method successfully distinguishes white blood cells from sample blood smear images, and accurately categorizes each segmented cell into the relevant group. [ABSTRACT FROM AUTHOR]

The article presents the case study of 45-year-old woman with hypoxic respiratory failure and general asthenia. Topics include indicative of an active erythropoietic response, correlating with the clinical suspicion of microangiopathic hemolytic anemia (MAHA); and high side fluorescence typically expected in blasts, malignant lymphocytes, and reactive lymphocytes.

Simple Summary: Hormonal contraception is widely used among reproductive-aged women. In spite of the high effectiveness of hormonal contraception, some users become pregnant, probably due to irregular use. We previously reported an increased morbidity of childhood cancer in a nationwide cohort of offspring exposed in utero to maternal use of hormonal contraception. However, it remains unknown if mortality is increased in offspring with and without cancer after in utero exposure to hormonal contraception. The present study indicates that in utero exposure to hormonal contraception has an influence on long-term child mortality and survival after a diagnosis of leukemia. These novel findings have potential use in guidelines for hormonal contraception use in relation to pregnancy and expand our understanding of the etiology and prognosis of childhood leukemia. Approximately 400 million women of reproductive age use hormonal contraceptives worldwide. Eventually, pregnancy sometimes occurs due to irregular use. Use in early pregnancy is found to be associated with child morbidities including cancer, the main reason for disease-related death in children. Here, we add the missing piece about in utero exposure to hormonal contraception and mortality in offspring, including assessments of prognosis in children with cancer. In utero exposure to hormonal contraception may be associated with death since we found a hazard ratio (HR) of 1.22 (95% confidence interval (CI) 1.01–1.48) compared to children of mothers with previous use. The HRs were 1.22 (95% CI 0.99–1.13) for oral combined products and 2.92 (95% CI 1.21–7.04) for non-oral progestin-only products. A poorer prognosis was also found in exposed children with leukemia (3.62 (95% CI: 1.33–9.87)). If causal, hormonal contraception in pregnancy seems detrimental for offspring health and a marker of poorer prognosis in children with leukemia. [ABSTRACT FROM AUTHOR]

Simple Summary: In this work, an improved pipeline for Leukocytes subtype classification is proposed which uses transfer learning for deep feature extraction and a quantum inspired evolutionary algorithm for feature selection. The proposed system achieves a high accuracy with smaller number of features as compared to the classical methods. Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works. [ABSTRACT FROM AUTHOR]

Background: Cytogenetics is evolving and different molecular mechanisms we know now have proved to be of diagnostic and prognostic significance in both acute lymphoid (ALL) and myeloid leukaemia (AML). This study aims to find out and compare the occurrence of different cytogenetics in paediatric acute leukaemia. Methods: This is a cross-sectional study of diagnosed B-ALL and AML patients presenting at The Indus Hospital. We studied FISH and karyotype in BALL and FISH in AML patients. FISH analysis shows a total of 69 (12.8%) of B ALL patients had cytogenetic abnormalities. BCR-ABL1 was positive in 5.1%, ETV6/RUNX1T1 in 8.6% and KMT2A in 2.3% individuals. Karyotype reveals hyper diploidy in 24.3%, Monosomy in 1.94%, and t (1:19) and t (17:19) were observed in 5.8% and 0.24% cases respectively. FISH analysis in AML cases reveal positivity of t (8:21) in 26.4%, INV (16) in 6.1% while PML-RARA t(15:17) was done on morphological suspicion in 17 cases; all of which showed positivity; making 7.9% of the total AMLs. The study demonstrated a wide spectrum of heterogeneity in paediatric acute leukaemia. Conclusion: Hyperdiploidy was the most common cytogenetic abnormality. We report a lower incidence of t (12:21), compared to the world. We showed a higher prevalence of RUNX1/RUNX1T1 in young children. The prevalence of core binding factor AML was 32.5%. [ABSTRACT FROM AUTHOR]

Diagnostic and molecular genetic testing are key in advancing the treatment of acute myeloid leukemia (AML), yet little is known about testing patterns outside of clinical trials, especially in older patients. We analyzed diagnostic and molecular testing patterns over time in 565 patients aged ≥ 55 years with newly diagnosed AML enrolled in the Connect® MDS/AML Disease Registry (NCT01688011) in the United States. Diagnostic data were recorded at enrolment and compared with published guidelines. The percentage of bone marrow blasts was reported for 82.1% of patients, and cellularity was the most commonly reported bone marrow morphological feature. Flow cytometry, karyotyping, molecular testing, and fluorescence in situ hybridization were performed in 98.8%, 95.4%, 75.9%, and 75.7% of patients, respectively. Molecular testing was done more frequently at academic than community/government sites (84.3% vs 70.2%; P

Simple Summary: Mast cell leukemia (MCL) is a rare and aggressive subtype of systemic mastocytosis (SM). MCL diagnosis requires fulfillment of diagnostic criteria for SM plus at least 20% immature/atypical mast cells on bone marrow smear. Based on different clinicopathological characteristics, MCL is subdivided into the following variants: primary (de novo) or secondary (from a prior SM form); acute with C-findings (where C-findings represent organ damage requiring cytoreductive treatment) or chronic without C-findings; and finally, MCL with or without an associated hematological neoplasm. The latter category was renamed MCL with an associated myeloid neoplasm in the 2022 International Consensus Classification (ICC). The relevance of the distinction between the leukemic and aleukemic forms, based on the percentage of circulating mast cells, is under discussion. In this study, we review the current knowledge on MCL with the aim of increasing clinicians' and pathologists' awareness of this rare, still incompletely understood disease with a grim prognosis. Mast cell leukemia (MCL) is the leukemic form of SM with at least 20% mostly immature mast cells on bone marrow aspirate. MCL may develop de novo, in the absence of a prior SM, or it may represent a progression from a previous SM. MCL may be sub-divided into the more frequent, aggressive acute form with signs of organ damage (C-findings) and the chronic form lacking C-findings and presenting a more stable course, although over time, progression to acute MCL is common. The 2022 WHO subtype of MCL with an associated hematological neoplasm was renamed MCL with an associated myeloid neoplasm in the 2022 International Consensus Classification (ICC). The relevance of the distinction between the leukemic and aleukemic forms based on the percentage of circulating mast cells is a matter of debate. The current knowledge on MCL is restricted mainly to single reports or case series with a limited number of larger studies. Our aim is to provide a comprehensive overview of this rare disease in terms of clinical manifestations, morphology, phenotype, molecular characteristics, differential diagnosis, outcome and treatment. A general overview on mastocytosis is also included. [ABSTRACT FROM AUTHOR]

Objective: We undertook a rapid review of literature relating to the diagnosis of blood cancers, to find out what factors contribute to delays in diagnosis, including symptom recognition, appraisal and help-seeking behaviours. Methods: We used rapid review methodology following Tricco et al. to synthesise current literature from two electronic databases. We searched for studies about symptom appraisal help-seeking for all blood cancers published between 2001 and 2021, written in English. Results: Fifteen studies were included in the review, of which 10 were published in the United Kingdom. We found a number of factors associated with delays in blood cancer diagnosis. These included patient factors such as gender, age and ethnicity, as well as health system factors such as poor communication and seeing a locum clinician in primary care. A narrative synthesis of the evidence produced four types of symptom interpretation by patients: (1) symptoms compatible with normal state of health, (2) event-linked problems, (3) mild or chronic illness and (4) non-specific unwell state. These four interpretations were linked to different help-seeking behaviours. After seeking help, patients often experienced delays due to healthcare professionals' (HCPs') non-serious interpretation of symptoms, misleading blood tests, discontinuity of care and other barriers in the diagnostic pathway. Conclusion: Blood cancers are difficult to diagnose due to non-specific heterogeneous symptoms, and this is reflected in how those symptoms are interpreted by patients and managed by HCPs. It is important to understand how different interpretations affect delays in help-seeking, and what HCPs can do to support timely follow-up for patients. [ABSTRACT FROM AUTHOR]

The loss of CD38 expression has been described in multiple myeloma (MM) patients with relapsed/refractory disease and patients receiving CD38 monoclonal antibodies. However, the loss of CD38 at diagnosis is extremely rare with very few reports describing such finding in newly diagnosed MM patients. Only two detailed case reports described CD38-negative clonal plasma cells at diagnosis. However, none of these cases was primary plasma cell leukemia (PCL). We describe an elderly patient with primary PCL presenting mainly with a negative CD38 expression, which, to the best of our knowledge, has never been described. The patient did not do well and died within 3 months of diagnosis. The loss of CD38 expression at diagnosis could indicate more aggressive disease biology. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 50-year-old male patient with primary plasma cell leukemia (pPCL) characterized by atypical flower-like morphology in plasma cells, posing a diagnostic challenge. Topics discussed include the clinical presentation, diagnostic challenges associated with unusual morphological variants, and the importance of comprehensive diagnostics to distinguish pPCL from other hematological disorders.

The article presents a case study of a 69-year-old man initially diagnosed with myelodysplastic syndrome (MDS) but later developing mast cell leukaemia (MCL) after acquiring a KIT mutation. It reports on the progression of the disease from MDS to MCL. It mentions the identification of mast cell infiltration and mutations through molecular testing, and it highlights the challenges in treating aggressive forms of systemic mastocytosis (SM) like MCL.

Simple Summary: Positron emission tomography (PET); typically combined with computed tomography (CT), has become a critical advanced imaging technique in oncology. With PET-CT; a radioactive molecule (radiotracer) is injected in the bloodstream and localizes to sites of tumor because of specific cellular features of the tumor that accumulate the targeting radiotracer. The CT scan; performed at the same time; facilitates better visualization of radioactivity from deep or dense structures; and to provide detailed anatomic information. PET-CT has a variety of applications in oncology, including staging, therapeutic response assessment, restaging, and evaluation of suspected recurrence. This series of six articles provides an overview of the value, applications, and imaging and interpretive strategies of PET-CT in the more common adult malignancies. The current article addresses the use of PET-CT in hematologic malignancies. PET-CT is an advanced imaging modality with many oncologic applications, including staging, assessment of response to therapy, restaging and evaluation of suspected recurrence. The goal of this 6-part series of review articles is to provide practical information to providers and imaging professionals regarding the best use of PET-CT for the more common adult malignancies. In the first article of this series, hematologic malignancies are addressed. The classification of these malignancies will be outlined, with the disclaimer that the classification of lymphomas is constantly evolving. Critical applications, potential pitfalls, and nuances of PET-CT imaging in hematologic malignancies and imaging features of the major categories of these tumors are addressed. Issues of clinical importance that must be reported by the imaging professionals are outlined. The focus of this article is on [18F] fluorodeoxyglucose (FDG), rather that research tracers or those requiring a local cyclotron. This information will serve as a resource for the appropriate role and limitations of PET-CT in the clinical management of patients with hematological malignancy for health care professionals caring for adult patients with hematologic malignancies. It also serves as a practical guide for imaging providers, including radiologists, nuclear medicine physicians and their trainees. [ABSTRACT FROM AUTHOR]

Leukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enables to detect morphology and WBC count which consequently helps in the diagnosis and prognosis of leukemia. Manual WBC assessment methods are tedious, subjective, and less accurate. To overcome these problems, we propose a multi-scale information fusion network (MIF-Net) for WBC segmentation. MIF-Net is a shallow architecture with internal and external spatial information fusion mechanisms. In WBC images, the cytoplasm is with low contrast compared to the background, whereas nuclei shape can be complex with an indistinctive boundary for some cases, therefore accurate segmentation becomes challenging. Spatial features in the initial layers of the network include fine boundary information and MIF-Net splits and propagates this boundary information on multi-scale for external information fusion. Multi-scale information fusion in our network helps in preserving boundary information and contributes to segmentation performance improvement. MIF-Net also uses internal information fusion after intervals for feature empowerment in different stages of the network. We evaluated our network for four publicly available datasets and achieved state-of-the-art segmentation performance. In addition, the proposed architecture exhibits superior computational efficiency by using only 2.67 million trainable parameters.

• Electrochemical biosensors are a new technology for leukemia diagnosis, offering early detection and sensitivity to biomarkers. • The use of nanomaterials has improved the biosensors' detection limits, sensitivity, and specificity. • These biosensors could revolutionize leukemia diagnostics, enabling early, accurate detection and personalized treatment strategies. Leukemia, a type of blood cancer marked by an abnormal increase in white blood cells, poses a significant challenge to healthcare. The key to successful treatment lies in early detection. However, traditional methods often fall short. This review investigates the potential of electrochemical biosensors for a more accurate and earlier diagnosis of leukemia. Electrochemical biosensors are compact devices that transform biological interactions into electrical signals. Their small size, ease of use, and minimal sample requirements make them perfectly suited for point-of-care applications. Their remarkable sensitivity and specificity enable the detection of subtle biomolecular changes associated with leukemia, which is crucial for early disease detection. This review delves into studies that have utilized these biosensors to identify various types of leukemia. It examines the roles of electrodes, biorecognition elements, and signal transduction mechanisms. The discussion includes the integration of nanomaterials such as gold nanoparticles and nitrogen-doped graphene into biosensor design. These materials boost sensitivity, enhance signal amplification, and facilitate multi-analyte detection, thereby providing a more holistic view of the disease. Beyond technical advancements, the review underscores the practical benefits of these biosensors. Their portability makes them a promising tool for resource-constrained settings, enabling swift diagnosis in remote areas or at a patient's bedside. The potential for monitoring treatment effectiveness and detecting minimal residual disease to prevent relapse is also explored. This review emphasizes the transformative potential of electrochemical biosensors in combating leukemia. By facilitating earlier and more accurate diagnosis, these biosensors stand to revolutionize patient care and enhance treatment outcomes. [ABSTRACT FROM AUTHOR]

Background: Involvement of the central nervous system (CNS) by acute leukemias (ALs) has important implications for risk stratification and disease outcome. The clinical laboratory plays an essential role in assessment of cerebrospinal fluid (CSF) specimens from patients with ALs at initial diagnosis, at the end of treatment, and when CNS involvement is clinically suspected. The two challenges for the laboratory are 1) to accurately provide a cell count of the CSF and 2) to successfully distinguish blasts from other cell types. These tasks are classically performed using manual techniques, which suffer from suboptimal turnaround time, imprecision, and inconsistent inter‐operator performance. Technological innovations in flow cytometry and hematology analyzer technology have provided useful complements and/or alternatives to conventional manual techniques. Aims: We performed a PRISMA‐compliant systematic review to address the medical literature regarding the development and current state of the art of CSF blast identification using flow cytometry and laboratory hematology technologies. Materials and Methods: We searched the peer reviewed medical literature using MEDLINE (PubMed interface), Web of Science, and Embase using the keywords "CSF or cerebrospinal" AND "blasts(s)". Results: 108 articles were suitable for inclusion in our systematic review. These articles covered 1) clinical rationale for CSF blast identification; 2) morphology‐based CSF blast identification; 3) the role of flow cytometry; 4) use of hematology analyzers for CSF blast identification; and 5) quality issues. 9/L, which is much lower than the original machine count and platelet transfusion was warranted. Discussion: 1) Clinical laboratory testing plays a central role in risk stratification and clinical management of patients with acute leukemias, most clearly in pediatric ALs; 2) studies focused on other patient populations, including adults and patients with AML are less prevalent in the literature; 3) improvements in instrumentation may provide better performance for the classification of CSF specimens. Conclusion: Current challenges include: 1) more precisely characterizing the natural history of AL involvement of the CNS, 2) improvements in automated cell count technology of low cellularity specimens, 3) defining the role of flow MRD testing of CSF specimens and 4) improved recognition of specimen quality by clinicians and laboratory personnel. [ABSTRACT FROM AUTHOR]

The purpose of this study was to describe the stress experienced and coping approaches exhibited by Jordanian parents of children recently diagnosed with leukemia. This cross-sectional study collected data from parents of children who had been diagnosed with leukemia within the previous three months (N = 130; 57 fathers (43.8%) and 73 mothers (56.2%), age (Mean = 37.5, SD = 8.4)). The Parenting Stress Index–Short Form was used. Similarly, parents' coping approaches were assessed with the Coping Health Inventory for Parents. Parents' mean total stress and coping strategies scores were 108 (± 26.8) and 93.9 (± 15.6), respectively. There were significant negative relationships between parents' stress levels and age (r = −0.46, p =.01), education level (f = 28.5, p <.05), and income (r = −0.65, p =.01). Additionally, there were significant positive relationships between parents' coping strategies and age (r = 0.34, p =.01) and income (r = 0.53, p <.01). There were non-significant differences between fathers' and mothers' stress levels and coping strategies. Parents of children with leukemia experienced high stress. The findings also confirmed the negative relationship between parents' stress, age, education level, and income; and the positive relationship between parents' coping strategies, age, and income. Several strategies are needed to manage said stress; for example, respite care for parents who spend long hours at the hospital. Volunteers to care for children at home or hospital might also relieve parents' stress. [ABSTRACT FROM AUTHOR]

Objectives: Classification of acute leukemia involves assigning lineage by resemblance to normal progenitor cells. This approach provides descriptive information about the blast cells that is useful for disease monitoring, provides clues to pathogenesis, and can help clinicians select effective chemotherapeutic regimens. Acute leukemias of ambiguous lineage (ALALs) are those leukemias that either fail to show evidence of myeloid, B-, or T-lymphoid lineage commitment or show evidence of commitment to more than 1 lineage. The different treatment regimens for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) make ALAL a challenge both diagnostically and therapeutically.Methods: Current classification criteria have reduced the reported incidence of mixed-lineage leukemias by emphasizing fewer markers and categorizing some biphenotypic leukemias with recurrent cytogenetic abnormalities as other entities. Several recent studies have explored the genomic and epigenetic landscape of mixed-phenotype acute leukemia (MPAL) and have suggested a further refinement of the World Health Organization classification to emphasize the genomic heterogeneity of MPAL.Results: Genomic and expression profile data for MPAL reveal mutations commonly seen in both AML and ALL, with T-/myeloid MPAL showing overlapping features with early T-cell precursor lymphoblastic leukemia.Conclusions: Our review aimed to discuss the diagnostic challenges, recent genomic studies, and therapeutic strategies in this poorly understood disease. [ABSTRACT FROM AUTHOR]

Pyoderma gangrenosum (PG) is an uncommon, idiopathic, neutrophilic dermatosis characterised by large necrotic ulcers. Occasionally, patients develop atypical presentations, including pustular, bullous, and vegetative lesions. Bullous pyoderma gangrenosum (BPG) is considered an extremely rare form. We describe a case of BPG in a 76-year-old man, with active oncological history, including a recent diagnosis of hairy cell leukemia. Diagnosis of PG was delayed because of atypical clinical presentation that mimicked necrotising fasciitis. The patient was treated with diverse intravenous antibiotics and several surgical procedures. The suspicion of neutrophilic dermatosis arose from the histopathological studies. In the setting of mandatory clinico-pathological correlation, the aim of this report is to point out the morphological characteristics that allow recognition of this uncommon variant of pyoderma gangrenosum. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of cancer, followed by lengthy treatment, poses a significant threat to patients' mental health. The current study aimed to examine the effectiveness of writing therapy on depression and anxiety of patients with leukemia. Methods: This study involved two groups in a randomized clinical trial. The statistical population of the current study comprised 384 patients of Baghdad Medical City, Iraq, who met the inclusion criteria, in 2019. A simple random sampling method was used to select a statistical sample of 110 individuals, who were then divided into experimental and control groups (55 members in each group). Data collection tools included Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI). Multivariate analysis of covariance (MANCOVA) was used in SPSS software for data analysis. Results: The writing therapy intervention approach reduced the anxiety (F = 67.864, P < 0.001) and depression (F = 54.218, P < 0.001) of patients with leukemia. In addition, none of the demographic variables of the study's participants had a significant relationship with the findings. Conclusion: Implementing the writing therapy intervention can improve the psychological symptoms of patients with leukemia. [ABSTRACT FROM AUTHOR]

Abstract: Fusion oncogenes can be cancer-defining molecular alterations that are essential for diagnosis and therapy selection.1,2 Rapid and accessible molecular diagnostics for fusion-driven leukemias such as acute promyelocytic leukemia (APL), Philadelphia chromosome-positive acute lymphoblastic leukemia, and chronic myeloid leukemia (CML) are unavailable, creating a barrier to timely diagnosis and effective targeted therapy in many health care settings, including community hospitals and low-resource environments. We developed CRISPR-based RNA-fusion transcript detection assays using SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) for the diagnosis of fusion-driven leukemias. We validated these assays using diagnostic samples from patients with APL and CML from academic centers and dried blood spots from low-resource environments, demonstrating 100% sensitivity and specificity. We identified assay optimizations to enable the use of these tests outside of tertiary cancer centers and clinical laboratories, enhancing the potential impact of this technology. Rapid point-of-care diagnostics can improve outcomes for patients with cancer by expanding access to therapies for highly treatable diseases that would otherwise lead to serious adverse outcomes due to delayed or missed diagnoses., (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and detection of leukaemia, i.e., the accurate difference of malignant leukocytes with little expense at the beginning of the disease, is a primary challenge in the disease analysis field. Despite the higher occurrence of leukemia, there is a lack of flow cytometry tools, and the procedures accessible at medical diagnostics centres are time-consuming. Distinct researchers have implemented computer-aided diagnostic (CAD) and machine learning (ML) methods for laboratory image analysis, aiming to manage the restrictions of late leukemia analysis. This study proposes a new Falcon optimization algorithm with deep convolutional neural network for Leukemia detection and classification (FOADCNN-LDC) technique. The main objective of the FOADCNN-LDC technique is to classify and recognize leukemia. The FOADCNN-LDC technique utilizes a median filtering (MF) based noise removal process to eradicate the image noise. Besides, the FOADCNN-LDC technique employs the ShuffleNetv2 model for the feature extraction process. Moreover, the detection and classification of the leukemia process are performed by utilizing the convolutional denoising autoencoder (CDAE) model. The FOA is implemented to select the hyperparameter of the CDAE model. The simulation process of the FOADCNN-LDC approach is performed on a benchmark medical dataset. The investigational analysis of the FOADCNN-LDC approach highlighted a superior accuracy value of 99.62% over existing techniques., (© 2024. The Author(s).)

Background: Swift and accurate blood smear analyses are crucial for diagnosing leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation remain time-consuming and prone to errors. Additionally, conventional image processing methods struggle to differentiate cells due to visual similarities between malignant and benign cell morphology., Method: In response to above challenges, we propose Coupled Transformer Convolutional Network (CoTCoNet) framework for leukemia classification. CoTCoNet integrates dual-feature extraction to capture long-range global features and fine-grained spatial patterns, facilitating the identification of complex hematological characteristics. Additionally, the framework employs a graph-based module to uncover hidden, biologically relevant features of leukocyte cells, along with a Population-based Meta-Heuristic Algorithm for feature selection and optimization. Furthermore, we introduce a novel combination of leukocyte segmentation and synthesis, which isolates relevant regions while augmenting the training dataset with realistic leukocyte samples. This strategy isolates relevant regions while augmenting the training data with realistic leukocyte samples, enhancing feature extraction, and addressing data scarcity without compromising data integrity., Results: We evaluated CoTCoNet on a dataset of 16,982 annotated cells, achieving an accuracy of 0.9894 and an F1-Score of 0.9893. We tested CoTCoNet on four diverse, publicly available datasets (including those above) to assess generalizability. Results demonstrate a significant performance improvement over existing state-of-the-art approaches., Conclusions: CoTCoNet represents a significant advancement in leukemia classification, offering enhanced accuracy and efficiency compared to traditional methods. By incorporating explainable visualizations that closely align with cell annotations, the framework provides deeper insights into its decision-making process, further solidifying its potential in clinical settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Leukemia is the fifth most common cancer in Saudi Arabia. The aim of this study was to assess the various patterns of leukemia associated with age, gender, and nationality in this region., Methods: This cross-sectional study was conducted at the regional laboratory in Makkah, Saudi Arabia, from April to November 2023. Descriptive statistics were presented as frequencies and percentages by using the GraphPad Prism software., Results: This study included 107 patients, and the results showed that leukemia cases were higher in males than females and more prevalent in older patients (above 50 years of age). Overall, acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) were the most common types of leukemia among Saudi patients. In addition, AML was the most prevalent type of leukemia in males and females, followed by ALL. Data also revealed that ALL was the most common type in the younger population, whereas AML was highly prevalent in older patients., Conclusions: In conclusion, this study provides valuable information about the patterns of leukemia reported at the regional laboratory in Makkah and will help in designing management and preventive approaches for these patients. This epidemiological investigation is also valuable for establishing proper medical databases.

Purpose: To assess the accuracy of Raman spectroscopy in distinguishing between patients with leukemia and healthy individuals., Method: PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases were searched for relevant articles published from inception of the respective database to November 1, 2023. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), were calculated along with their corresponding 95 % confidence intervals (CI). A summary comprehensive receiver operating characteristic curve (SROC) was constructed and the area under the curve (AUC) was calculated. The degree of heterogeneity was tested and analyzed., Results: Fifteen groups of original studies from 13 articles were included. The pooled SEN and SPE were 0.93 (95 % CI, [0.92 -0.93]) and 0.91(95 % CI, [0.90-0.92]), respectively. The DOR was 613.01 (95 %CI, [270.79-1387.75]), and the AUC was 0.99. The Deeks' funnel plot asymmetry test indicated no significant publication bias among the included studies (bias coefficient, 40.80; P = 0.13 < 0.10). The meta-regression analysis findings indicated that the observed heterogeneity could be attributed to variations in sample categories and Raman spectroscopy techniques., Conclusion: We confirmed that Raman spectroscopy has good accuracy in differentiating patients with leukemia from healthy individuals, and may become a means of leukemia screening in clinical practice. In the case of analysis based on live cells using surface-enhanced Raman spectroscopy (SERS) improved diagnostic efficacy was observed., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Leukemia, a type of blood cell cancer, is categorized by the type of white blood cells affected (lymphocytes or myeloid cells) and disease progression (acute or chronic). In 2020, it ranked 15th among the most diagnosed cancers and 11th in cancer-related deaths globally, with 474,519 new cases and 311,594 deaths (GLOBOCAN2020). Research into leukemia's development mechanisms may lead to new treatments. Ubiquitin-specific proteases (USPs), a family of deubiquitinating enzymes, play critical roles in various biological processes, with both tumor-suppressive and oncogenic functions, though a comprehensive understanding is still needed., Aim: This systematic review aimed to provide a comprehensive review of how Ubiquitin-specific proteases are involved in pathogenesis of different types of leukemia., Methods: We systematically searched the MEDLINE (via PubMed), Scopus, and Web of Science databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) to identify relevant studies focusing on the role of USPs in leukemia. Data from selected articles were extracted, synthesized, and organized to present a coherent overview of the subject matter., Results: The review highlights the crucial roles of USPs in chromosomal aberrations, cell proliferation, differentiation, apoptosis, cell cycle regulation, DNA repair, and drug resistance. USP activity significantly impacts leukemia progression, inhibition, and chemotherapy sensitivity, suggesting personalized diagnostic and therapeutic approaches. Ubiquitin-specific proteases also regulate gene expression, protein stability, complex formation, histone deubiquitination, and protein repositioning in specific leukemia cell types., Conclusion: The diagnostic, prognostic, and therapeutic implications associated with ubiquitin-specific proteases (USPs) hold significant promise and the potential to transform leukemia management, ultimately improving patient outcomes., (© 2024. The Author(s).)

Acute leukemia is the most common hematopoietic stem cell malignant tumor in children, which ranks in the top one of the incidence of tumor in children, it is a major disease that affects the growth and survival of children. With the continuous improvement of medical diagnosis and treatment and the extensive development of immunotherapy, the survival rate and quality of life of children with acute leukemia have been significantly improved. In recent years, three cooperative groups of childhood leukemia have been established in China, and a series of high-level research results have been published. In the future, efforts should be made to promote the process of standardization and homogenization in the diagnosis and treatment of children's acute leukemia, explore the monitoring targets of sensitive residual diseases, and find the best treatment for refractory/recurrent cases. Speeding up the clinical research of new drugs will be an urgent problem and development direction in the field of acute leukemia diagnosis and treatment in children.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.