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Chimeric antigen receptor (CAR) engineering of natural killer (NK) cells has shown promising results in early-phase clinical studies. However, advancing CAR-NK cell therapeutic efficacy is imperative. In this study, we investigated the impact of a fourth-generation CD19-targeted CAR (CAR.19) coexpressing IL-27 on NK-92 cells. We observed a significant improvement in NK-92 cell proliferation and cytotoxicity activity against B-cell cancer cell lines, both in vitro and in a xenograft mouse B-cell lymphoma model. Our systematic transcriptome analysis of the activated NK-92 CAR variants further supports the potential of IL-27 in fourth-generation CARs to overcome limitations of NK cell-based targeted tumor therapies by providing essential growth and activation signals. Integrating IL-27 into CAR-NK cells emerges as a promising strategy to enhance their therapeutic potential and elicit robust responses against cancer cells. These findings contribute substantially to the mounting evidence supporting the potential of fourth-generation CAR engineering in advancing NK cell-based immunotherapies. [Display omitted] [ABSTRACT FROM AUTHOR]

AKT2 is crucial for cancer cells' invasion, metastasis, and survival. It is a possible downstream gene target of cancer glycolysis-related microRNAs. The study investigated the role of miRNA-4716–3p, rs2304186, and the AKT2 gene in blood cancer pathogenesis. RT-qPCR was used to analyze AKT2 gene mRNA and miRNA-4716–3p expression in 200 blood cancer samples and 200 healthy controls. Furthermore, Tetra-ARMS PCR was used to examine the rs2304186 AKT2 SNP in 300 patients and 290 control samples. miRNA-4716–3p was shown to be significantly downregulated (p = 0.0294), whereas mRNA expression of the AKT2 gene was found to be significantly upregulated (p = 0.0034) in blood cancer patients compared to healthy individuals. miRNA-4716–3p downregulation (p = 0.0466) was more pronounced, while AKT2 upregulation was non-significant (p = 0.1661) in untreated patients compared to chemotherapy-treated patients. Blood cancer risk was significantly associated with the rs2304186 GT genotype (p = 0.0432), TT genotype (p = 0.0502), and mutant allele (T) frequency (p = 0.0008). Polymorphism rs2304186 was associated with an increased risk of blood cancer in dominant (p = 0.0011), recessive (p = 0.0502), and additive (p = 0.0008) genetic models. The results suggested that the rs2304186 and the deregulated expression of miRNA-4716–3p and AKT2 gene at the mRNA level may significantly increase the incidence of blood cancer, particularly in the Pakistani population. Therefore, these may function as suitable biomarkers for blood cancer diagnosis and prognosis. Additional, larger-scale investigations may be required to affirm these results.

Purpose of Review: The landscape of myelofibrosis (MF) has changed since the discovery of the JAK2 V617F mutation and subsequent development of JAK inhibitors (JAKis). However, treatment with JAKis remain a challenge. In this review we critically analyze the strengths and limitations of currently available JAK inhibitors. Recent Findings: In MF patients, JAK inhibitors have been associated with reduced symptom burden and spleen size, as well as improved survival. However, durability of response and development of treatment resistance remain an issue. Recently, there has been increased efforts to optimize treatment with the development of highly selective JAK inhibitors, as well as use of combination agents to counter disease resistance through targeting aberrant signaling pathways. Summary: Treatment of MF patients with JAKi therapy can be challenging but the development of more potent and selective JAK inhibitors, as well as combination therapies, represent exciting treatment advances in this field. [ABSTRACT FROM AUTHOR]

In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach's results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions. [ABSTRACT FROM AUTHOR]

Purpose: Survivorship care often refers to continued healthcare after cancer treatment. Jacobsen and colleagues advocated to expand this to include patients on extended treatments and maintenance/prophylactic therapies, recognizing the care continuum as more complex. Transitions of care for individuals diagnosed with a blood cancer can be complicated. We sought to better understand blood cancer caregivers' experiences as their diagnosed family member encountered "survivorship transitions" across the continuum. Methods: We conducted semi-structured interviews with adults caring for a parent or a child with a blood cancer. Caregivers were segmented into survivorship groups based on two transitional contexts: (1) when patients transitioned to a new line of therapy (active treatment or maintenance therapy); (2) when patients ended treatment. We conducted a thematic analysis and triangulated findings to compare transitional experiences. Results: Caregivers in both groups reported experiencing a "new normal," which included personal, relational, and environmental adjustments. Caregivers in the treatment transitions group (n = 23) also described uncertainty challenges (e.g., losing their "safety net") and disrupted expectations (e.g., feeling "caught off guard" by challenges). Whereas caregivers in the end-of-treatment transitions group (n = 15) described relief coupled with worry (e.g., feeling hopeful yet worried). Conclusions: Survivorship transitions for caregivers are riddled with challenges that include difficult readjustments, uncertainty/worry, and unmet expectations. While there seems to be a cohesive experience of "survivorship transitions," each transition group revealed nuanced distinctions. Implications for Cancer Survivors: Tailored supportive resources are needed for caregivers throughout survivorship transitions. [ABSTRACT FROM AUTHOR]

The prospect of discovering a cure for cancer has been present for the past two to three decades. Annually, this illness affects approximately 10 million individuals worldwide. Anticancer medications are pivotal in treating cancer and other malignant diseases. In this paper, the physical properties and chemical reactions associated with the anticancer medications of various topological indices (TIs) have been established. Additionally, we have discussed the degree-based TIs and their Quantitative Structure-Property Relationship (QSPR) analysis. In mathematical chemistry, molecular descriptors are essential, particularly for researchers investigating Quantitative Structure-Activity Relationship (QSAR) and QSPR models. The goal of the QSPR study is to establish a mathematical relationship between the properties under investigation (such as boiling point and flash point) and various descriptors related to the molecular structure of the drugs. Furthermore, we show the correlation with the physicochemical properties of anticancer medications. [ABSTRACT FROM AUTHOR]

The increasing incidence of multidrug-resistant (MDR) Pseudomonas aeruginosa is a worldwide problem, particularly among critically ill patients. Since it is not anticipated that any new treatments will be available in the near feature, our research aims to assess the efficacy and safety of colistin in the treatment of infections caused by P. aeruginosa in neutropenic leukemia patients. A study was conducted at two hospitals (i.e., Beit-Jala Hospital/Bethlehem; n = 78 and Augusta Victoria Hospital/Jerusalem; n = 61) over a period of 18 months. Using a confidence interval of 95%, a margin of error of 5%, and a response rate of 50%, demographic and clinical characteristics were analyzed. One of the major results of our study was that colistin-treated patients had a favorable clinical response at day six and less nephrotoxicity outcomes compared to the control group. Data analyses revealed a high incidence (50–63%; n = 79) of leukemia in both hospital groups. Microbiologic response, infection-related mortality, and relapse rates were not statistically significant between both groups. Our study demonstrated that colistin is highly useful and effective in the treatment of MDR P. aeruginosa in blood cancer patients. Colistin has proven superior to control group in terms of clinical response at day six. Our study has also shown lower nephrotoxicity rates, which is further encouraging and could support the potential of using colistin as an alternative therapy for such infections. As multidrug resistance continues to be a worldwide concern, the need for effective therapies such as colistin remains of great importance. [ABSTRACT FROM AUTHOR]

Introduction: Blood cancer survivors are at increased risk for medical complications. Methods: Our questionnaire-based study involved 1,551 blood cancer survivors with a ≥3-year interval since the last intense treatment. Its goal was to quantify health-related complications during follow-up and assess their impact on the patients' lives. Results: A total of 20.4% of the responding survivors reported a disease relapse, most often in indolent lymphomas. Second primary malignancies occurred in 14.1%, primarily in lymphoma and allogeneic transplantation survivors. The most frequent malignancy was basal cell carcinoma of the skin, but myeloid malignancies, melanoma, bladder, head-and-neck, and thyroid cancer also appeared disproportionately frequent. An increased infection rate was reported by 43.7%, most often after allogeneic transplantation. New cardiovascular diseases were reported by 30.2%, with a high rate of thromboembolic events in multiple myeloma (MM) and myeloproliferative diseases. Polyneuropathies were reported by 39.1%, most often by survivors with a history of MM or aggressive lymphoma. Disease relapse was perceived as the highest burden, followed by second primary malignancy, increased infection frequency, and polyneuropathy. In each area investigated, the range of perceived severities was wide. Conclusions: Health-related complications are frequent during blood cancer follow-up, with significant repercussions on the patients' lives. [ABSTRACT FROM AUTHOR]

This Paper guides a review platform which allows to evaluate Artificial intelligence assisted diagnosis for blood cancer using machine learning. Advanced medical and technology-based research has fuelled the adoption of latest technologies for the sake of advancement in medical science application and overall improvement in detection, diagnosis, prevention and treatment of diseases. AI technology is being used widely in medicine, economy and daily life; in medicine, artificial intelligence is used for mainly treatment, diagnosis and prediction of disease prognosis. This review effectively highlights the wide-ranging applications of AI in medicine, with a specific focus on its contribution to treatment, diagnosis, prognosis and prediction. [ABSTRACT FROM AUTHOR]

Background: Hematological cancers impose a complex burden on individuals, affecting their physical health and mental and emotional well-being. This study evaluated the health-related Quality of Life (HRQOL) and its determinants among adults with hematological cancers in Qatar in 2023. Methods: A cross-sectional study used a validated structured questionnaire conducted among adult patients diagnosed with hematological cancers. All adult patients who attended The National Centre for Cancer Care and Research (NCCCR) in Qatar during the Data collection period (January to March 2023) and agreed to participate were included in the study. Results: A total of 257 participants were enrolled in the study. The highest median (IQR) score of the HRQOL domain was observed in the functionating score of 90.6 (13.8), followed by the global health score of 83.3(25. 0). The median (IQR) of the symptoms burden score was 07.4(12.3). Gender significantly affects HRQOL, with males reporting better functioning and lower symptom burden than females. Employment status is positively associated with functioning scores. Regular exercise correlates with higher global health and functioning scores and lower symptom burden, while depressive symptoms are linked to poorer HRQOL outcomes. Patients experiencing cancer recurrence or active disease report lower global health and functioning scores and higher symptom burden. Treatment modalities such as chemotherapy and bone marrow transplant (BMT) timing also influence HRQOL, with recent treatment recipients showing lower global health and higher symptom burden scores. Depressive symptoms were the primary factor, lowering the global health score by 15.2%. Regarding the low functioning score predictors, female gender, depressive symptoms, and cancer recurrence emerged as significant predictors of the low functioning score. Furthermore, Regular exercise increased the functioning score by 03.4 units (p-value=0.018). Finally, Multiple linear regression analysis reinforced the significance of depressive symptoms, active disease status, and recurrence within the past five years as substantial predictors of higher symptom scores. Conclusions: The study emphasizes the profound impact of depressive symptoms on all aspects of Health-Related Quality of Life (HRQOL), mainly affecting global health. It highlights the positive role of regular exercise in enhancing global health, functioning, and symptom burden scores. [ABSTRACT FROM AUTHOR]

Introduction: The Philadelphia chromosome-negative myeloproliferative neoplasms are a group of slowly progressing haematological malignancies primarily characterised by an overproduction of myeloid blood cells. Patients are treated with various drugs, including the JAK1/2 inhibitor ruxolitinib. Mathematical modelling can help propose and test hypotheses of how the treatment works. Materials and methods: We present an extension of the Cancitis model, which describes the development of myeloproliferative neoplasms and their interactions with inflammation, that explicitly models progenitor cells and can account for treatment with ruxolitinib through effects on the malignant stem cell response to cytokine signalling and the death rate of malignant progenitor cells. The model has been fitted to individual patients' data for the JAK2 V617F variant allele frequency from the COMFORT-II and RESPONSE studies for patients who had substantial reductions (20 percentage points or 90% of the baseline value) in their JAK2 V617F variant allele frequency (n = 24 in total). Results: The model fits very well to the patient data with an average root mean square error of 0.0249 (2.49%) when allowing ruxolitinib treatment to affect both malignant stem and progenitor cells. This average root mean square error is much lower than if allowing ruxolitinib treatment to affect only malignant stem or only malignant progenitor cells (average root mean square errors of 0.138 (13.8%) and 0.0874 (8.74%), respectively). Discussion: Systematic simulation studies and fitting of the model to the patient data suggest that an initial reduction of the malignant cell burden followed by a monotonic increase can be recapitulated by the model assuming that ruxolitinib affects only the death rate of malignant progenitor cells. For patients exhibiting a long-term reduction of the malignant cells, the model predicts that ruxolitinib also affects stem cell parameters, such as the malignant stem cells' response to cytokine signalling. [ABSTRACT FROM AUTHOR]

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of leukemia at any stage is essential. The current traditional approaches rely mainly on field experts' knowledge, which is time consuming. A lengthy testing interval combined with inadequate comprehension could harm a person's health. In this situation, an automated leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic leukemia from blood smear pictures, a new strategy based on traditional image analysis techniques with machine learning techniques and a composite learning approach were constructed in this experiment. The diagnostic process is separated into two parts: detection and identification. The traditional image analysis approach was utilized to identify leukemia cells from smear images. Finally, four widely recognized machine learning algorithms were used to identify the specific type of acute leukemia. It was discovered that Support Vector Machine (SVM) provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with this model. Finally, it was revealed that this composite approach achieved 99.9% accuracy. [ABSTRACT FROM AUTHOR]

Chronic myeloid leukemia (CML) is a cancer in the bone marrow caused by the proliferation of granulocyte cells at all the maturation stages. Late diagnosis of CML decreases the patient survival rate, makes diagnosing CML is mandatory before entering the blastic phase. CD 19 is an important target for CML and is effectively utilized for therapeutic and diagnosis purposes. This research was focused on developing an aptamer-mediated circular interdigitated electrode (IDE) sensor for detecting the level of CD 19 and measured at 0–2 V with the step of 0.1 V. To improve the surface functionalization on IDE, the surface of IDE was modified with a single-walled carbon nanotube (SWCN) to enhance the aptamer immobilization. SWCN increased the aptamer attachment and also enhanced the analytical performances on IDE. This SWCN-aptamer modified IDE detected the CD 19 as low as 10 nM on a linear co-regression range from 10 to 100 nM [y = 2.0126x - 2.3857; R2 = 0.9749]. Furthermore, control performances with CD 33, and complementary aptamer did not show the increment of current, and CD 19 spiked human serum increased the current flow without significant interference, demonstrating the specific and selective detection of CD 19. This biosensor quantifies CD 19 biomarker at its lower level and diagnoses CML and its associated complications.

AbstractLeukemia is a type of cancer that affects the body’s blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals.

Introduction: The Philadelphia chromosome-negative myeloproliferative neoplasms are a group of slowly progressing haematological malignancies primarily characterised by an overproduction of myeloid blood cells. Patients are treated with various drugs, including the JAK1/2 inhibitor ruxolitinib. Mathematical modelling can help propose and test hypotheses of how the treatment works. Materials and methods: We present an extension of the Cancitis model, which describes the development of myeloproliferative neoplasms and their interactions with inflammation, that explicitly models progenitor cells and can account for treatment with ruxolitinib through effects on the malignant stem cell response to cytokine signalling and the death rate of malignant progenitor cells. The model has been fitted to individual patients' data for the JAK2 V617F variant allele frequency from the COMFORT-II and RESPONSE studies for patients who had substantial reductions (20 percentage points or 90% of the baseline value) in their JAK2 V617F variant allele frequency (n = 24 in total). Results: The model fits very well to the patient data with an average root mean square error of 0.0249 (2.49%) when allowing ruxolitinib treatment to affect both malignant stem and progenitor cells. This average root mean square error is much lower than if allowing ruxolitinib treatment to affect only malignant stem or only malignant progenitor cells (average root mean square errors of 0.138 (13.8%) and 0.0874 (8.74%), respectively). Discussion: Systematic simulation studies and fitting of the model to the patient data suggest that an initial reduction of the malignant cell burden followed by a monotonic increase can be recapitulated by the model assuming that ruxolitinib affects only the death rate of malignant progenitor cells. For patients exhibiting a long-term reduction of the malignant cells, the model predicts that ruxolitinib also affects stem cell parameters, such as the malignant stem cells' response to cytokine signalling. [ABSTRACT FROM AUTHOR]

Leukemia recognition and classification contain the identification of dissimilar kinds of leukemia, a group of blood cancers that affects the bone marrow and blood. A classical model containing microscopic analysis of blood smears to classify abnormal cells analytic of leukemia. Leukemia recognition employing a united technique of neutrosophic logic and deep learning (DL) signifies a new and complete approach to handling uncertainty and difficulty in medical data. Neutrosophic logic permits the representation of unstated or imperfect data, which is general in medical analyses. DL mainly convolutional neural networks (CNN) or recurrent neural networks (RNN), which can mechanically remove difficult patterns from medicinal imageries, improving the accuracy of leukemia recognition. The neutrosophic logic module accommodates the characteristic uncertainty in medicinal data, offering a formalism to manage imperfect or inaccurate data linked with the analysis procedure. The combination of these dual techniques generates a robust structure which capable of leveraging both the control of DL in image analysis and the flexibility of neutrosophic logic in dealing with uncertainties, contributing to more trustworthy and interpretable leukemia recognition methods. This study develops a new Salp Swarm Algorithm with a Neutrosophic Logic SVM (SSANSVM) model for Leukemia Detection and Classification. The SSA-NSVM technique mainly exploits Neutrosophic Logic (NL) concepts with the DL model for the detection of leukemia. To attain this, the SSA-NSVM model uses bilateral filtering (BF) based image pre-processing. In addition, the SSA-NSVM approach applies a modified densely connected networks (DenseNet) technique for learning complex and intrinsic feature patterns. Besides, the hyperparameter range of the modified DenseNet system takes place utilizing a SSA. At last, the NSVM technique is employed for the detection and identification of leukemia. The performance validation of the SSA-NSVM algorithm is verified utilizing a benchmark medicinal image dataset. The simulation values emphasized that the SSA-NSVM model reaches better detection outcomes than other existing approaches. [ABSTRACT FROM AUTHOR]

Opinion statement: Cardiotoxicity has emerged as a serious outcome catalyzed by various therapeutic targets in the field of cancer treatment, which includes chemotherapy, radiation, and targeted therapies. The growing significance of cancer drug–induced cardiotoxicity (CDIC) and radiation-induced cardiotoxicity (CRIC) necessitates immediate attention. This article intricately unveils how cancer treatments cause cardiotoxicity, which is exacerbated by patient-specific risks. In particular, drugs like anthracyclines, alkylating agents, and tyrosine kinase inhibitors pose a risk, along with factors such as hypertension and diabetes. Mechanistic insights into oxidative stress and topoisomerase-II-B inhibition are crucial, while cardiac biomarkers show early damage. Timely intervention and prompt treatment, especially with specific agents like dexrazoxane and beta-blockers, are pivotal in the proactive management of CDIC. [ABSTRACT FROM AUTHOR]

Cancer is the most fatal diseases in the world which has highest mortality rate as compared to other type's human diseases. The most common and dangerous types of cancers are lung cancer, skin cancer, brain tumors, breast cancer, Colorectal cancer, Prostate Cancer, Blood cancer and many other. The millions of person lose their life due to these highly dangerous, fatal types of disease. Hence it is required to provide the solution using computer added automatic cancer detection technique in early stage, for the research gap analysis is required. In this paper we have studied the various cancer detection techniques based on traditional machine learning (ML) and deep learning (DL) techniques and summarize the research gap for the various cancers detection techniques. The study has been conducted based the types of technique uses, types of features utilized, dataset used and accuracy of the cancer detection achieved using best technique. In this study we have conducted the reviewed over 100 recently published research papers and focused on 7 types of most fatal cancer types such as lung cancer, breast cancer, skin cancer, brain tumor, colorectal, prostate, and Leukemia (Blood cancer). The study also used the state-of-art table to compare the previous and current study conducted on cancer detection techniques. We have visualized using separate comparison table for 7 types of cancer detection using traditional ML method and DL methods also visualized through the bar chart. The best accuracy result obtained using ML and DL methods are 100% and the most commonly used ML classifier is Support Vector Machine whereas CNN is most commonly used DL classifier. The main challenges we observed are the data imbalance issue, varieties of feature extraction techniques, small medical dataset, classifier parameters optimizations, Execution time, Adaptive classifier, and common technique for segmentation and classification. The main objective of this review is to investigate the existing methods used for the various types of cancer detection and finding the research gap, challenges, and recent advancement mainly in the use of ML and DL models which may help the researchers to find the better solutions. [ABSTRACT FROM AUTHOR]

Summary: Obesity has taken the face of a pandemic with less direct concern among the general population and scientific community. However, obesity is considered a low‐grade systemic inflammation that impacts multiple organs. Chronic inflammation is also associated with different solid and blood cancers. In addition, emerging evidence demonstrates that individuals with obesity are at higher risk of developing blood cancers and have poorer clinical outcomes than individuals in a normal weight range. The bone marrow is critical for hematopoiesis, lymphopoiesis, and myelopoiesis. Therefore, it is vital to understand the mechanisms by which obesity‐associated changes in BM adiposity impact leukemia development. BM adipocytes are critical to maintain homeostasis via different means, including immune regulation. However, obesity increases BM adiposity and creates a pro‐inflammatory environment to upregulate clonal hematopoiesis and a leukemia‐supportive environment. Obesity further alters lymphopoiesis and myelopoiesis via different mechanisms, which dysregulate myeloid and lymphoid immune cell functions mentioned in the text under different sequentially discussed sections. The altered immune cell function during obesity alters hematological malignancies and leukemia susceptibility. Therefore, obesity‐induced altered BM adiposity, immune cell generation, and function impact an individual's predisposition and severity of leukemia, which should be considered a critical factor in leukemia patients. [ABSTRACT FROM AUTHOR]

Abstract While paediatric blood cancers are deadly, modern medical advances have enabled clinicians to measure levels of residual cancer cells to manage therapeutic strategies for patients. However, blood cancers, including leukaemias and lymphomas, are highly heterogeneous and is comprised of complex clonal populations that can hinder efforts in detecting the cancer cells as well as managing treatments. Furthermore, the tumour microenvironment is comprised of heterogenous immune dynamics that may be different between patients. High‐throughput sequencing has constributed to new discoveries in genetic and transcriptomic alterations underpinning cancer, including blood cancers, and has changed how patients are monitored and managed. Here we discuss the recent efforts using single‐cell approach, particularly on efforts to track clonal heterogenity of paediatric blood cancer and the underlying immune response, highlighting avenues for novel biomarker discovery that may have significant impact on clinical oncology practice.

Background: Administrative burdens have been identified as a major issue impacting patient care, professional practice, and the overall efficiency of healthcare systems. The aim of this study is to assess the administrative burden faced by Italian hematologists. Methods: A cross-sectional survey that included both closed-ended quantitative questions and open-ended free text answer options was administered to 1,570 hematologists working with malignancies and members of Italian GIMEMA Foundation – Franco Mandelli ONLUS and the Italian Linfomi Foundation (FIL). The survey was conducted online from May 24 to June 30, 2023. Descriptive statistics were computed for the quantitative data to clearly summarize the responses and descriptive analysis of free text responses was carried out. Results: Surveyed hematologists spend an average of 47.07% of their time on administrative tasks, with 63.22% (n = 110) of respondents reporting spending at least half of their time on these activities. More than half (57.47%, n = 100) reported that “Patient care” is the medical task most affected by a lack of time. Additionally, 55.17% (n = 96) reported experiencing burnout in the past 6 months, with filling out “Forms” being identified as the top contributing administrative task by 27.59% (n = 48) of respondents, followed by “Scheduling” (24.71%, n = 43) and “Managing IT system failures” (21.84%, n = 38). Nearly half of the surveyed hematologists (45.40%, n = = 79) identified patient care as the top priority requiring more time. Conclusions: The study confirms that the administrative workload of hematologists has a significant impact on patient care, communication, and burnout risk, reducing the time available for patient care, leading to exhaustion and concern about clinical errors.

The healthcare industry is constantly changing because of technological breakthroughs that spur new methods of diagnosing and treating illnesses. This study investigates the development of Ion Sensitive Field Effect Transistor (ISFET) sensors for DNA-based blood cancer diagnosis. This work presents the design of a two-dimensional ion-sensitive field-effect transistor. Concentration fluctuations and transfer characteristics with different oxides are studied using blood from two electrolyte solutions. It is possible to evaluate how the modeled device can be utilized as a pH sensor or a biosensor in healthcare applications by looking at how the pH changes for different oxides. Additionally, several oxides were examined in the simulated ISFET devices' output characteristics. Blood is the electrolyte to study the device's sensitivity for different oxides. When pH 7.4 is considered, SiO2 oxide is significantly more sensitive than other oxides. The resulting 2D-ISFET exhibits remarkable blood electrolyte sensitivity and holds potential as a quick detection tool for blood cancer. The results show that the ISFET possesses drain-induced barrier lowering (DIBL), greater ON-current (ION) and switching ratio (ION/IOFF), and decreased subthreshold swing (SS). The pH sensor's sensitivity and the suggested equipment can detect up to 30 fg/mL of blood cancer biomarkers. An important development in technology-driven healthcare is the emergence of DNA-based blood cancer detection utilizing ISFET sensors. This opens up new avenues for improving cancer diagnosis and patient outcomes.

While paediatric blood cancers are deadly, modern medical advances have enabled clinicians to measure levels of residual cancer cells to manage therapeutic strategies for patients. However, blood cancers, including leukaemias and lymphomas, are highly heterogeneous and is comprised of complex clonal populations that can hinder efforts in detecting the cancer cells as well as managing treatments. Furthermore, the tumour microenvironment is comprised of heterogenous immune dynamics that may be different between patients. High‐throughput sequencing has constributed to new discoveries in genetic and transcriptomic alterations underpinning cancer, including blood cancers, and has changed how patients are monitored and managed. Here we discuss the recent efforts using single‐cell approach, particularly on efforts to track clonal heterogenity of paediatric blood cancer and the underlying immune response, highlighting avenues for novel biomarker discovery that may have significant impact on clinical oncology practice. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to compare psychological well-being, hope, and health concerns in leukemia patients before and after receiving stem cells. Methods: The statistical population of the present study consisted of leukemia patients in Tehran, and the research sample was among the patients referred to the Bone Marrow Transplant Center of Shariati Hospital in Tehran, and they were placed in two experimental and control groups (15 people in each group), which were selected by purposive sampling method. Results: To collect the data for the research, three psychological well-being questionnaires were used by Riff (2002), Schneider's Hope Scale (SHS), and Schneider's (1991) Health Concern Questionnaire (HCQ). To analyze the data in this research, according to the “pre-test-post-test design with the control group,” covariance analysis was used. Conclusion: Based on the research findings, it was concluded that receiving stem cells increases hope and psychological well-being in leukemia patients and significantly reduces health concerns.

Topological indices are obtained from molecular graphs and are real numbers that can forecast the biological and physicochemical properties of several anti-cancer treatments, including skin cancer, breast cancer, and blood cancer. This article focuses on the application of topological indices in predicting the effectiveness of several drugs used to cure blood cancer, such as Pamidronic acid, Alpelisib, Prednisone, Olaparib, Ribociclib, Tucatinib, dexamethasone, docetaxel, Midostaurin, paclitaxel, toremifene, and venetoclax. The article investigates the mathematical relationships between physical and chemical qualities and data encoded in chemical structures under characteristics such as molecular weight, molar volume, and complexity. Several topological indices are used in this context to forecast the physicochemical characteristics of the drugs. [ABSTRACT FROM AUTHOR]

Background: Parental overprotection may have an impact on children's emotional and behavioral problems (EBPs). As pediatric hematologic cancer patients have compromised immune systems, parents of such children often worry excessively, interfering with their daily lives. Therefore, avoiding overprotection is crucial for the overall physical and mental health of pediatric hematologic cancer patients. Aims: The aim of this study was to examine the current status of EBPs in pediatric hematologic cancer patients and analyze their associated risk factors. Design: This work was a multicenter cross-sectional observational and correlational study. We collected data anonymously through parental questionnaires from three pediatric hematologic oncology hospitals in China. The Strengths and Difficulties Questionnaire, the Parental Overprotection Measure (POM) scale, and a general information survey designed by the research team were employed to assess children's EBPs, the degree of parental overprotection, as well as basic demographic and disease-related information. Chi-square tests and generalized linear mixedeffects regression analysis were used to analyze the factors influencing EBPs among the pediatric hematologic cancer patients. Setting and participants: Using a convenience sampling method, a total of 202 participants' parents were selected. All participants were invited to complete the questionnaire through one-on-one guidance. Results: Emotional symptoms accounted for the highest proportion of abnormal EBPs in children (27.72%), followed by peer problems (26.24%), prosocial behavior (25.74%), behavioral problems (14.36%), and total difficulties (13.86%). A minority of children had abnormal hyperactivity scores (4.95%). The results of a generalized linear mixed regression analysis showed that age, duration of illness, and parental overprotection were significant factors influencing abnormal EBPs in children (p < 0.05). A POM score threshold of 37 exhibited good sensitivity (74%) and specificity (90%) in predicting abnormal EBPs in children. Conclusion: Pediatric hematologic cancer patients under excessive parental protection are more prone to experiencing EBPs. Healthcare professionals should guide parents to reduce this excessive protection, thus mitigating the occurrence of EBPs in children. [ABSTRACT FROM AUTHOR]

Background: Cancer is the second leading cause of mortality worldwide.19.3 million new cases and 10.3 million deaths from cancer were reported worldwide. Cancer-related fatigue (CFR) has a complicated aetiology. Fatigue has a more detrimental influence on daily tasks and quality of life (QoL) than other ailments like pain or grief. This study aimed to determine the effect of task oriented approach on cancer related fatigue and functional capacity among subjects with blood leukaemia. Methods: A randomised, clinical trial was conducted with a total of 30 blood cancer survivors. Subjects were randomised into a radiation therapy group (RT group) (n=15) and non-radiation therapy group (NRT group) (n=15). Both groups included a task oriented approach for 1section/day, 5 days /week for 4 weeks. The main outcome was fatigue as assessed by the Brief fatigue inventory, Other evaluated outcomes were functional capacity and Quality of life measured using 6 minute walk test and EORTC QLQ C30 (Version 3) . Data were collected at baseline and post-intervention. Result: There is a significant improvement in both group in cancer related fatigue after the intervention, two groups, non-radiation group show much better improvement comparing to participants under radiation with a mean difference of 3.4 in Brief Fatigue Inventory, 5.93 in 6 minute walk test, 16.46 in EORTC QLQ C30 (Version 3). Conclusion: It has been concluded that Task oriented approach has better effect on reducing the fatigue caused by cancer and enhancing the functional capacity, quality of life among blood leukaemia individuals. Participants who are not under radiation therapy have better prognosis than participants under radiation therapy. [ABSTRACT FROM AUTHOR]

Simple Summary: It is well recognized now that the development of drug resistance is one of the leading causes of treatment failure in conventional therapies. In comparison, recent improvements in immunotherapy showed promising results in eradicating cancer. Chimeric antigen receptor (CAR)-T-cell therapy is one of the cancer immunotherapies that uses patient's T cells and genetically modifies them to target cancer cells. Although CAR-T-cell therapy has shown remarkable success in treating blood cancer, it has proven far more limited in the treatment of solid tumors in different organs. This review article discussed the chronological development of CAR-T-cell therapies and their treatment options in different types of cancers. This article also addresses the current clinical challenges in CAR-T-cell therapy and recent advancements in developing novel therapeutic strategies with fewer side effects. In recent years, chimeric antigen receptor (CAR)-T-cell therapy has emerged as the most promising immunotherapy for cancer that typically uses patients' T cells and genetically engineered them to target cancer cells. Although recent improvements in CAR-T-cell therapy have shown remarkable success for treating hematological malignancies, the heterogeneity in tumor antigens and the immunosuppressive nature of the tumor microenvironment (TME) limits its efficacy in solid tumors. Despite the enormous efforts that have been made to make CAR-T-cell therapy more effective and have minimal side effects for treating hematological malignancies, more research needs to be conducted regarding its use in the clinic for treating various other types of cancer. The main concern for CAR-T-cell therapy is severe toxicities due to the cytokine release syndrome, whereas the other challenges are associated with complexity and immune-suppressing TME, tumor antigen heterogeneity, the difficulty of cell trafficking, CAR-T-cell exhaustion, and reduced cytotoxicity in the tumor site. This review discussed the latest discoveries in CAR-T-cell therapy strategies and combination therapies, as well as their effectiveness in different cancers. It also encompasses ongoing clinical trials; current challenges regarding the therapeutic use of CAR-T-cell therapy, especially for solid tumors; and evolving treatment strategies to improve the therapeutic application of CAR-T-cell therapy. [ABSTRACT FROM AUTHOR]

Leukemia is a type of cancer that affects the body's blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals. [ABSTRACT FROM AUTHOR]

Cancer treatment brings about a phenomenon not fully clarified yet, termed chemobrain. Its strong negative impact on patients' well‐being makes it a trending topic in current research, interconnecting many disciplines from clinical oncology to neuroscience. Clinical and animal studies have often reported elevated concentrations of proinflammatory cytokines in various types of blood cancers. This inflammatory burst could be the background for chemotherapy‐induced cognitive deficit in patients with blood cancers. Cancer environment is a dynamic interacting system. The review puts into close relationship the inflammatory dysbalance and oxidative/nitrosative stress with disruption of the blood–brain barrier (BBB). The BBB breakdown leads to neuroinflammation, followed by neurotoxicity and neurodegeneration. High levels of intracellular reactive oxygen species (ROS) induce the progression of cancer resulting in increased mutagenesis, conversion of protooncogenes to oncogenes, and inactivation of tumor suppression genes to trigger cancer cell growth. These cell alterations may change brain functionality, as well as morphology. Multidrug chemotherapy is not without consequences to healthy tissue and could even be toxic. Specific treatment impacts brain function and morphology, functions of the immune system, and metabolism in a unique mixture. In general, a chemo‐drug's effects on cognition in cancer are not direct and/or in‐direct, usually a combination of effects is more probable. Last but not least, chemotherapy strongly impacts the immune system and could contribute to BBB disruption. This review points out inflammation as a possible mechanism of brain damage during blood cancers and discusses chemotherapy‐induced cognitive impairment. Highlights: An inflammatory burst could be the background for chemobrain in blood cancer. In acute phases, blood sera of patients contain massive levels of proinflammatory cytokines.In blood cancer, direct cytokine neurotoxicity, and neuroinflammation occur in concert with cytokine‐mediated disruption of the blood–brain barrier.A blood cancer drug exerts multifaceted effects on chemobrain: ∘Methotrexate combines brain structure damage with persisting chemotherapy‐induced neuroinflammation.∘Doxorubicin fulfills the concept of chemotherapy‐induced neuroinflammation with peripheral origin. [ABSTRACT FROM AUTHOR]

Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient's life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to compare psychological well-being, hope, and health concerns in leukemia patients before and after receiving stem cells. Methods: The statistical population of the present study consisted of leukemia patients in Tehran, and the research sample was among the patients referred to the Bone Marrow Transplant Center of Shariati Hospital in Tehran, and they were placed in two experimental and control groups (15 people in each group), which were selected by purposive sampling method. Results: To collect the data for the research, three psychological well-being questionnaires were used by Riff (2002), Schneider's Hope Scale (SHS), and Schneider's (1991) Health Concern Questionnaire (HCQ). To analyze the data in this research, according to the "pre-test-post-test design with the control group," covariance analysis was used. Conclusion: Based on the research findings, it was concluded that receiving stem cells increases hope and psychological well-being in leukemia patients and significantly reduces health concerns. [ABSTRACT FROM AUTHOR]

The microRNA (miR)-29 family has been deregulated in several types of hematologic malignancies. However, role of this family and their target genes DNMT3A (DNA methyltransferase 3A) and TET2 (Ten-Eleven Translocation 2) remains unclear. Here, we have made an attempt to determine the relative expression levels of three miRNAs and target genes in patients with newly diagnosed Multiple myeloma (MM) using quantitative real-time PCR. Moreover, the expression levels of selected miRNAs and genes and their correlations with clinical parameters were compared and analyzed. The ROC curve was used to analyze their diagnostic efficacy for MM. The expression level of hsa-miR-29b-3p was significantly higher in patients with newly diagnosed MM compared with the control group. ROC analysis showed that hsa-miR-29b-3p demonstrated a moderate diagnostic power in MM. The relative expression level of hsa-miR-29b-3p in patients with high LDH levels was markedly reduced compared to that in patients with normal and low LDH levels. DNMT3A expression level was significantly increased in patients with high LDH levels and patients with lambda light chain. Our results indicate that hsa-miR-29b-3p may be used as a potential biomarker in the diagnosis of MM. [ABSTRACT FROM AUTHOR]

Ag and AgNO film were fabricated through sputter deposition on indium tin oxide (ITO) conductive glass. Electrochemical detection using cyclic voltammetry (CV) was carried out for low albumin levels in blood as a parameter of cancer patients. This study aims to determinate low albumin level as a biomarker cancer. Albumin adsorption on the Agfilm/ITO and AgNOfilm/ITO was also investigated to determine the level of sensitivity of both electrodes. Analysis of the CV measurements indicated that the Agfilm/ITO electrode was more sensitive compared to the AgNOfilm/ITO electrode, with a sensitivity value of 4.564 µA M-1cm-2 for the Agfilm/ITO electrode and 2.123 µA M-1cm-2 for the AgNOfilm/ITO electrode. The testing of albumin levels in blood within a range of low concentration levels between 10-1 and 10-8 g/dL indicated a detection limit of 10-9 g/mL. The selectivity of Ag electrodes was found to be very good for other interfering molecules such as urine, glucose, and lysozyme. The results of the modeling of electric field and magnetic field distribution showed that the Agfilm/ITO electrode possessed larger values than the AgNOfilm/ITO electrode. From this analysis, it can be concluded that the electrode modified with micro-sized Ag achieved more effective results than the electrode modified with AgNO. This sensor can determine low albumin level as a blood cancer biomarker. This sensor holds great promise for use in real samples in the future. [ABSTRACT FROM AUTHOR]

Blood cancer, also known as hematological malignancy, is one of the devastating types of cancer that has significantly paved its mortality mark globally. It persists as an extremely deadly cancer type and needs utmost attention owing to its negligible overall survival rate. Major challenges in the treatment of blood cancer include difficulties in early diagnosis, as well as severe side effects resulting from chemotherapy. In addition, immunotherapies and targeted therapies can be prohibitively expensive. Over the past two decades, scientists have devised a few nanoparticle-based drug delivery systems aimed at overcoming this challenge. These therapeutic strategies are engineered to augment the cellular uptake, pharmacokinetics, and effectiveness of anticancer drugs. However, there are still numerous types of nanoparticles that could potentially improve the efficacy of blood cancer treatment, while also reducing treatment costs and mitigating drug-related side effects. To the best of our knowledge, there has been limited reviews published on the use of nano-based drug delivery systems for the treatment of hematological malignancies. Therefore, we have made a concerted effort to provide a comprehensive review that draws upon recent literature and patents, with a focus on the most promising results regarding the use of nanoparticle-based approaches for the treatment of hematological malignancies. All these crucial points covered under a common title would significantly help researchers and scientists working in the area. [ABSTRACT FROM AUTHOR]

BackgroundHematological cancers impose a complex burden on individuals, affecting their physical health and mental and emotional well-being. This study evaluated the health-related Quality of Life (HRQOL) and its determinants among adults with hematological cancers in Qatar in 2023.MethodsA cross-sectional study used a validated structured questionnaire conducted among adult patients diagnosed with hematological cancers. All adult patients who attended The National Centre for Cancer Care and Research (NCCCR) in Qatar during the Data collection period (January to March 2023) and agreed to participate were included in the study.ResultsA total of 257 participants were enrolled in the study. The highest median (IQR) score of the HRQOL domain was observed in the functionating score of 90.6 (13.8), followed by the global health score of 83.3(25. 0). The median (IQR) of the symptoms burden score was 07.4(12.3). Gender significantly affects HRQOL, with males reporting better functioning and lower symptom burden than females. Employment status is positively associated with functioning scores. Regular exercise correlates with higher global health and functioning scores and lower symptom burden, while depressive symptoms are linked to poorer HRQOL outcomes. Patients experiencing cancer recurrence or active disease report lower global health and functioning scores and higher symptom burden. Treatment modalities such as chemotherapy and bone marrow transplant (BMT) timing also influence HRQOL, with recent treatment recipients showing lower global health and higher symptom burden scores. Depressive symptoms were the primary factor, lowering the global health score by 15.2%. Regarding the low functioning score predictors, female gender, depressive symptoms, and cancer recurrence emerged as significant predictors of the low functioning score. Furthermore, Regular exercise increased the functioning score by 03.4 units (p-value=0.018). Finally, Multiple linear regression analysis reinforced the significance of depressive symptoms, active disease status, and recurrence within the past five years as substantial predictors of higher symptom scores.ConclusionsThe study emphasizes the profound impact of depressive symptoms on all aspects of Health-Related Quality of Life (HRQOL), mainly affecting global health. It highlights the positive role of regular exercise in enhancing global health, functioning, and symptom burden scores.

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of leukemia at any stage is essential. The current traditional approaches rely mainly on field experts’ knowledge, which is time consuming. A lengthy testing interval combined with inadequate comprehension could harm a person’s health. In this situation, an automated leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic leukemia from blood smear pictures, a new strategy based on traditional image analysis techniques with machine learning techniques and a composite learning approach were constructed in this experiment. The diagnostic process is separated into two parts: detection and identification. The traditional image analysis approach was utilized to identify leukemia cells from smear images. Finally, four widely recognized machine learning algorithms were used to identify the specific type of acute leukemia. It was discovered that Support Vector Machine (SVM) provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with this model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.

Abstract Background Low levels of cancer awareness may contribute to delays in seeking medical help and subsequent delays in diagnosis. For blood cancer this may be a particularly prominent problem due to the high prevalence of undifferentiated symptoms such as bodily pain, weakness, nausea and weight loss, resulting in low symptom awareness. The delay is exacerbated by the dismissal of similar symptoms which are often interpreted as mild disease, resulting in multiple consultations prior to diagnosis. This study describes the development of a Cancer Awareness Measure for Blood Cancer (Blood CAM) and presents results from a population-representative survey using the measure. Methods A rapid systematic review identified constructs relevant to blood cancer. Items were taken from previous awareness measures and other literature and reviewed by expert groups including health care professionals and patients. Cognitive interviews were conducted with ten members of the public to check comprehension and clarity. A total sample of 434 participants completed the survey at Time 1 and n = 302 at Time 2 (two weeks later). Results Internal reliability was high across the different constructs included in the questionnaire (> 0.70) and test-retest reliability was moderate to good (0.49–0.79). The most commonly recognised blood cancer symptoms were unexplained weight loss (68.9%) and unexplained bleeding (64.9%) and the least commonly recognised symptoms were night sweats (31.3%) breathlessness and rash/itchy skin (both 44%). In terms of symptom experience, fatigue was the most commonly reported symptom (26.7%) followed by night sweats (25.4%). Exploratory factor analysis of barriers to presenting at primary care revealed three distinct categories of barriers; emotional, external/practical and service/healthcare professional related. Service and emotional barriers were most common. Conclusions We developed a valid and reliable tool to assess blood cancer awareness and showed variable awareness of blood cancer symptoms which can help target public health campaigns. We also incorporated additional measures (e.g. confidence to re-consult, ability to understand symptoms) that could be used to tailor public messaging for blood cancer and for other harder to suspect and diagnose cancers.

Abstract Background DNA methylation changes, frequent early events in cancer, can modulate the binding of transcription factors. RE1-silencing transcription factor (REST) plays a fundamental role in regulating the expression of neuronal genes, and in particular their silencing in non-neuronal tissues, by inducing chromatin modifications, including DNA methylation changes, not only in the proximity of its binding sites but also in the flanking regions. REST has been found aberrantly expressed in brain cancer and other cancer types. In this work, we investigated DNA methylation alterations at REST binding sites and their flanking regions in a brain cancer (pilocytic astrocytoma), two gastrointestinal tumours (colorectal cancer and biliary tract cancer) and a blood cancer (chronic lymphocytic leukemia). Results Differential methylation analyses focused on REST binding sites and their flanking regions were conducted between tumour and normal samples from our experimental datasets analysed by Illumina microarrays and the identified alterations were validated using publicly available datasets. We discovered distinct DNA methylation patterns between pilocytic astrocytoma and the other cancer types in agreement with the opposite oncogenic and tumour suppressive role of REST in glioma and non-brain tumours. Conclusions Our results suggest that these DNA methylation alterations in cancer may be associated with REST dysfunction opening the enthusiastic possibility to develop novel therapeutic interventions based on the modulation of this master regulator in order to restore the aberrant methylation of its target regions into a normal status.

In this article, a nano-composite Ag/BaTiO3/MoS2 multilayer coating-based surface plasmon resonance (SPR) sensor with high sensitivity and figure of merit for the detection of cancer cells is analyzed. The proposed SPR biomedical sensor structure is built on the angular interrogation approach to utilizing the refractive index component, attenuated total reflection (ATR) analysis is employed to identify different kinds of cancer cells. The resonance angle varied as the refractive index (RI) of healthy and cancerous cells changed. Which ranged from 1.368 to 1.392 for increasing concentration in cervical (HeLa cells) and blood (Jurkat cells), RI 1.376 to 1.390 changes as the concentration of cancerous cells increases in healthy cells. The maximum estimated sensitivities for cervical (HeLa) and blood (Jurkat) cancer cells based on numerical data were 271.25 deg/RIU and 290.714 deg/RIU, respectively. The proposed BK7/Ag/BaTiO3/ MoS2 has the ability to identify cancerous and healthy cells with the greatest overall sensitivity, as demonstrated by layered structures. Finally, the numerical results analyzed in this manuscript revealed a high degree of sensitivity and FoM compared to those of previous research studies.