Iranian Journal of Blood and Cancer
Volume:16 Issue: 4, Dec 2024

Hemophilia A is a bleeding disorder caused by a deficiency of coagulation factor VIII. Hemophilia A is an X-linked recessive disorder. Depending on the level of blood coagulation factor VIII, hemophilia severity is classified as mild (5-40%), moderate (1-5%), or severe (<1%). The absence of hemophilia A mutation studies in Indonesia makes this topic important to study.

This study detected and classified F8 gene mutations. A member of the Indonesian Hemophilia Society Association for the Special Region of Yogyakarta provided saliva for DNA testing. Long-read sequencing data were performed using the next-generation sequencing (NGS) technique via the Oxford Nanopore Technologies plc (ONT) PromethION 24 platform. The mutation was confirmed using Sanger sequencing, after amplifying intron 8 of the F8 gene with the PCR technique. The F8 gene intron 8 nucleotide sequence was aligned using the alignment tool on the Benchling website.

The results of this study showed that there was a splice donor site mutation in intron 8 of the F8 gene (c.1271+1G>A) in one patient. This mutation can cause the occurrence of cryptic splice donor sites. Cryptic splice donor site prediction was carried out using the splice donor prediction tool available on the NNSPLICE website. The appearance of cryptic splice donor sites can lead to the formation of out-of-frame proteins.

The F8 gene mutation causing hemophilia A was detected using long-read sequencing and the next-generation sequencing (NGS) technique. The type of mutation identified is a splice donor site mutation, specifically the variant c.1271+1G>A, in sample code HM13.

The field of hematology faces significant challenges in data analysis, especially in the diagnosis and prediction of diseases. Traditional methods of analysis are often time-consuming, complex, or inadequate to handle the complex nature of blood-related data. This requires the development of advanced techniques for accurate prediction and classification. Artificial Intelligence (AI)-based methods have emerged as a powerful solution that enables more efficient and accurate analysis of hematological data. This study aims to systematically review published research on the use of different artificial intelligence algorithms in the analysis of this field of data.

Using a combination of keywords related to blood data analysis and artificial intelligence, we searched medical and scientific databases to identify relevant articles. A data extraction form was developed to collect relevant information from selected studies based on predefined inclusion and exclusion criteria. The content analysis method was used to analyze the extracted data and the findings were organized in tables and figures to meet the research objectives.

After reviewing 7300 studies, 25 full-text studies were selected for final analysis based on their relevance to the research objectives. The findings showed that AI methods, especially deep learning (DL), are widely used to predict and diagnose hematological and Hematopathological diseases. Among the most common algorithms used in ML were XGBoost, which was one of the most important deep learning algorithms, as well as Convolutional Neural Networks (CNN). AI-based models had Accuracy, Specificity, and Sensitivity of 96.6%, 95%, and 96%, respectively.

This review shows that AI-based models have the potential to be significantly applied to the analysis of blood data. As artificial intelligence continues to evolve, medical professionals and researchers will have access to powerful ML-based tools to quickly and accurately diagnose.

Cancer remains a critical public health issue in India, with rising cases of breast cancer and cervical cancer. Accurate predictions and spatial analysis of cancer incidence are essential for shaping prevention strategies and targeting interventions in high-risk regions.

This study utilized a big data framework employing machine learning techniques from the SparkML library to predict cancer cases and analyze spatial distributions across Indian states from 2016 to 2021. Three machine learning models used Random Forest Regressor, Gradient Boosting Regressor, and Geographically Weighted Regression (GWR) were applied to the dataset. Spatial autocorrelation analysis used Moran’s I statistic to identify clustering patterns.

The spatial analysis revealed significant clustering of cancer cases, particularly in 2020, with a z-score of 2.23, a p-value of 0.02, and a Moran’s index of 0.15. Among the machine learning models, GWR achieved a predictive accuracy of 98% for both breast cancer and cervical cancer, while the Random Forest Regressor and Gradient Boosting Regressor achieved 95% and 97% accuracy, respectively, over the six-year period. Gradient Boosting outperformed other models in identifying key predictors and ensuring high predictive accuracy.

The findings highlight the efficacy of Gradient Boosting and GWR in predicting cancer incidence and analyzing spatial patterns. These models provide critical insights into cancer clustering and risk factors, supporting the development of targeted prevention strategies and policy interventions for high-risk regions in India. The results emphasize the utility of machine learning techniques in public health research and cancer control.

Heparin-induced thrombocytopenia (HIT) is a serious immunological adverse drug reaction that rarely occurs in patients receiving heparin. The heparin-induced platelet activation (HIPA) test, a gold-standard assay for HIT, is time-consuming, challenging, and produces qualitative results. We aimed to compare the performance properties of a flow cytometry-based functional assay for HIT diagnosis with HIPA assay.

This research was carried out on HIT-suspected patients referred to the Iranian Blood Transfusion Organization between 2021 and 2023. After clinical evaluation and 4Ts scores calculation, anti-PF4 screening and HIPA test were conducted. Thirty HIPA-positive and 30 HIPA-negative samples were selected. Subsequently, a flow cytometry-based functional assay, Emo-Test HIT confirm, was performed, and the sensitivity and specificity for HIT diagnosis were measured.

Among the 30 samples with negative HIPA results, one was positive with the Emo-test HIT Confirm® assay, and the remaining were negative. Among 30 positive HIPA samples, the result of one sample was inconclusive, two samples were negative with flowcytometry Emo-test and the others were positive. The sensitivity and specificity of this flow cytometry-based functional assay were 90% (95% CI: 79.3-100) and 96.6% (95% CI:90.2-100). The negative predictive value and positive predictive value were 93.5% and 96.4% respectively.

Flow cytometry-based functional assay has a good sensitivity and specificity for HIT diagnosis confirmation, indicating that it may be a promising approach in the clinical setting.

The present study was conducted to investigate the biochemical and morphological changes in the blood coagulation system caused by chronic cadmium intoxication and the anticoagulant activity of the amino acid complex (γ-aminobutyric acid, β-alanine, glutamine, ethanolamine-O-sulphate). Previous studies have investigated the effect of the amino acid complex (AAc) on blood glucose levels in animals with experimental alloxan diabetes. The use of this complex demonstrated the ability to suppress the hyperglycaemic effect of alloxan, while also exhibiting anticoagulant activity.

The experiments were carried out on non-linear white male rats divided into 3 groups: control rats, rats receiving cadmium sulphate and rats with cadmium intoxication injected with AAc. Biochemical (recalcification, prothrombin time, international sensitivity index, thrombin time, activated partial thromboplastin time, fibrinogen level, calcium level) and histological (Haematoxylin & Eosin and Giemsa staining) methods were used in the studies.
Results and

Chronic cadmium intoxication leads to alterations in several blood coagulation parameters, suggesting a predisposition to hypercoagulation. However, administration of AAc reduces blood coagulation. Blood samples from poisoned rats showed the presence of red blood cells and leukocytes with morphological changes, including the presence of numerous platelets in clusters or groups. Conversely, when AAc was administered to cadmium poisoned rats, erythrocytes and neutrophils showed morphologically normal characteristics.  The results obtained confirm the anticoagulant activity of AAc, which may be used in the future for the treatment of various thrombotic conditions.

 To study the alteration in coagulation parameters such as activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), D-dimer and fibrinogen among patients with benign and malignant breast lesions when compared to normal controls.

The present study was a prospective cross-sectional study conducted among 50 cases diagnosed with benign and malignant breast cancer and comparing them with 20 age-matched controls. Coagulation parameters such as PT, TT, APTT, fibrinogen, and D-dimer were collected and compared between the malignant cases, benign cases, and age-matched control groups based on clinical and demographic details, the status of Progesterone receptor, Estrogen receptor, Her2 Neu, side and grading of cancer.

Significant difference was reported with regard to mean age (p=0.0103) between malignant and benign tumor group (p=0.0103). Mean fibrinogen (p<0.001), thrombin time (p=0.007), and D-dimer (p<0.001) between the three groups also showed a statistically significant difference.

Of the biological parameters assessed in the present study; thrombin time, D-dimer and fibrinogen levels show significant differences in patients with benign and malignant breast diseases. This may serve as a biological marker to evaluate the beginning of Cancer-associated venous thrombosis (CAT) in breast cancer patients.

Pancreatic cancer is one of the most aggressive and lethal malignancies, with limited treatment options and a poor prognosis. Recent research has highlighted the potential role of metformin, a widely used antidiabetic drug, in modulating cancer risk and progression. This review aims to explore the current evidence on the impact of metformin on pancreatic cancer outcomes and its potential mechanisms of action.

A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Web of Science to identify studies investigating the relationship between metformin use and pancreatic cancer. Inclusion criteria encompassed clinical trials, cohort studies, and laboratory research published in the last two decades. Data extraction focused on patient outcomes, metformin dosage, study design, and proposed mechanisms of action.

The review indicates that metformin use is associated with improved overall survival and reduced cancer incidence in patients with pancreatic cancer, particularly among those with concurrent diabetes. Mechanistic studies suggest that metformin exerts its anticancer effects through the inhibition of the mTOR pathway, reduction of insulin-like growth factors, and induction of autophagy. However, the results are heterogeneous, and several studies highlight the need for further clinical trials to establish causality and optimal therapeutic regimens.

While current evidence supports a potentially beneficial role of metformin in pancreatic cancer management, the findings are preliminary and require further validation through randomized controlled trials. The heterogeneity of the studies underscores the necessity for standardized protocols to assess the efficacy and safety of metformin as an adjunct therapy in pancreatic cancer treatment.

Gallbladder cancer (GBC) is among the utmost pervasive form of biliary tract cancers and remains relatively under-researched. Its prognosis is generally poor, with survival rates varying based on diagnostic stage, from 20% to 65%. The hallmarks of cancer, such as proliferation of cells, migration of cells, invasion, process of programmed cell death, radio/chemosensitivity, and cancer stem cell phenotype, are all influenced by miRNAs, which have been found to be essential actuators in the process of gene expression. This review is an attempt to reveal the molecular pathways influenced by miRNAs that could be targeted for therapeutic purposes in gallbladder cancer (GBC) and also emphasizes the need for precision medicine to target potent pathways, utilizing not only inhibiting receptor or antibody but also investigating miRNAs as a potential treatment strategy.

Cancer is estimated to overtake cardiovascular diseases and take the top spot as the leading and most important cause of mortality globally in the near future. Given the importance of early diagnosis to reduce mortality, many efforts have been made to discover a theranostic system for simultaneous cancer diagnosis and treatment. So far, the use of nanotechnology has greatly contributed to the improvement and development of these systems. Meanwhile, dendrimer nanoparticles have attracted considerable attention in medical research due to their unique properties. Poly(amidoamine) (PAMAM) dendrimers have become the primary category of dendrimers and have been widely studied for their possible application in cancer treatments. These nanoparticles have features including interior cavities and peripheral functional groups that allow the encapsulation of diverse medications or diagnostic agents. As a result, these particles can function as efficient nanocarriers and vectors for medical applications. This capability allows for the resolution of the obstacles presented by the tumor microenvironment. The prospective use of multifunctional PAMAM holds promise in enabling thorough monitoring of different stages of treated cancer tissue, hence providing substantial support in the early detection and prediction of tumor response. The primary focus of this study will be to investigate the most recent developments of PAMAM dendrimers in the field of cancer theranostics. The employment of NPs in anticancer medicine administration for radiation treatment, chemotherapy, and diagnostic imaging is underscored due to its significant potential.

Head and neck cancer patients are more at high risk for malnutrition before, during, and after the cancer treatment procedures due to the proximity of key anatomical structures that are essential for mastication and deglutition. A multidisciplinary approach beginning with preliminary nutritional screening, comprehensive assessment, and nutritional supportive care is mandatory for all head and neck cancer patients. Such interventions not only improve quality of life but also increase the survival rate of the head and neck cancer patients. This updated narrative review focused on the recent updates of the various steps involved in the nutritional management of head and neck cancer patients, like pre-treatment nutritional care, screening and assessment, and nutritional interventions during and after cancer therapy with updates on nanoformulations of nutraceuticals. We reviewed all published literature between 2014 and 2024 about nutrition in head and neck cancer patients from major databases such as Embase, Web of Science, and PubMed. In addition to the nutritional parameters that should be considered during the nutritional assessment of patients with head and neck cancer, this review underscores the therapeutic efficacy of nutraceuticals in treating this disease. This narrative review added a note on recent updates on the use of a combination of novel nanoformulated nutraceuticals with chemotherapeutic agents, which were known for the improved drug delivery, such as targeting the neoplastic cells and thus preventing adverse effects.