CAR-T cell therapy: Efficacy in management of cancers, adverse effects, dose-limiting toxicities and long-term follow up.

Chimeric Antigen Receptor T-cell (CAR-T) therapy has emerged as a groundbreaking and highly promising approach for the management of cancer. This paper reviews the efficacy of CAR-T therapy in the treatment of various hematological malignancies, also, with a mention of its effect on solid tumors, for which they have not received FDA approval yet. Different common and uncommon side effects are also discussed in this paper, with attention to the effect of each drug separately. By reviewing the recommendations of the FDA for CAR-T therapy research, we have extensively discussed dose-limiting toxicities. This further highlights the need for precise dosing strategies, striking a balance between therapeutic benefits and potential risks. Additionally, we reviewed the long-term follow-up of patients receiving CAR-T therapy to gain valuable insights into response durability and late-onset effects.

The Contemporary Role of Hematopoietic Stem Cell Transplantation in the Management of Chronic Myeloid Leukemia: Is It the Same in All Settings?

Hematopoietic stem cell transplantation (HSCT) for chronic myeloid leukemia (CML) patients has transitioned from the standard of care to a treatment option limited to those with unsatisfactory tyrosine kinase inhibitor (TKI) responses and advanced disease stages. In recent years, the threshold for undergoing HSCT has increased. Most CML patients now have life expectancies comparable to the general population, and therefore, the goal of therapy is shifting toward achieving treatment-free remission (TFR). While TKI discontinuation trials in CML show potential for achieving TFR, relapse risk is high, affirming allogeneic HSCT as the sole curative treatment. HSCT should be incorporated into treatment algorithms from the time of diagnosis and, in some patients, evaluated as soon as possible. In this review, we will look at some of the recent advances in HSCT, as well as its indication in the era of aiming for TFR in the presence of TKIs in CML.

Male Fertility and Fatherhood in Chronic Myeloid Leukemia: Current Understanding and Future Perspectives.

Chronic myeloid leukemia (CML), while traditionally a disease of the elderly, has recently risen in incidence among younger patients. Hence, fertility concerns have emerged considering the disease process and treatments, especially with the current scarce and conflicting recommendations. This review explores the impact of CML treatments including the first-line tyrosine kinase inhibitors (TKIs) and other treatments on male fertility in chronic myeloid leukemia (CML) patients. The aim of this review was to compile the available evidence on male fertility to ultimately tailor treatment plans for male CML patients for whom fertility and future chances for conception pose a concern. The data available on the conventional and newer TKIs to address fertility concerns were reviewed, particularly the potential long- and short-term effects. Also, the possible side effects on subsequent generations were a crucial focus point of this review to reach a more comprehensive CML management approach. We found and compared the evidence on TKIs approved to treat CML. We also reported the effects of hydroxyurea, interferon, and transplantation, which are considered second-line treatments. Our findings suggest that these drugs might have an undiscovered effect on fertility. More research with larger sample sizes and longer follow-up periods is essential to solidify our understanding of these effects.

Emerging roles of activating transcription factor 2 in the development of breast cancer: a comprehensive review.

Activating transcription factor 2 (ATF2) is a member of the leucine zipper family of DNA binding proteins that are responsible for regulating various genes that play an essential role in major biological and cellular functions. Since ATF2 plays a vital role in cellular proliferation and apoptosis, it is believed that it greatly affects the development of breast cancers. However, its exact role in breast cancer is incompletely understood. It remains a subject of debate, ambiguity, and continuous research. Several studies have suggested the role of ATF2 as an oncogene, promoting cellular proliferation and worsening the outcome of cancers. In contrast, other studies have postulated that ATF2 plays a tumor suppressive role in estrogen receptor-positive breast cancer. The ambiguity surrounding its role in breast cancer is the reason why there is an influx of recent studies and research in this area. In this narrative review, we investigate several studies that have been published about the role of ATF2 in breast cancer. We also explore studies that have examined the association between ATF2 and endocrine therapy resistance. ATF2 has been suggested to modulate estrogen receptor (ER) expression and activity, potentially affecting tamoxifen sensitivity in breast cancer cells. Therefore, the role of ATF2 in DNA repair mechanisms and drug resistance has been deeply explored in this review. Additionally, there are numerous ongoing clinical trials exploring the effect of targeting ATF2 pathways and mechanisms on the outcome of breast cancers, some of which we have discussed. The studies and clinical trials that are being conducted to understand the multifaceted role of ATF2 and its signaling pathways may provide valuable insight for developing efficient targeted therapeutic solutions to enhance the outcomes of breast cancer and overcome endocrine resistance. We suggest further research to elucidate the dual roles of ATF2 in breast cancer and potential therapeutic therapies for its treatment.

Artificial intelligence in sickle disease.

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Applications of Machine Learning in Chronic Myeloid Leukemia.

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.

Applications of Artificial Intelligence in Thrombocytopenia.

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.

HPV and Recurrent Respiratory Papillomatosis: A Brief Review.

Recurrent Respiratory Papillomatosis (RRP) is a rare but severe manifestation of human papillomavirus (HPV). As our knowledge about HPV infections has expanded, it has become possible to understand the course of RRP disease and unravel plausible efficient methods to manage the disease. However, the surge in reports on HPV has not been accompanied by a similar increase in research about RRP specifically. In this paper, we review the clinical manifestation and typical presentation of the illness. In addition, the pathogenesis and progression of the disease are described. On the other hand, we discuss the types of treatments currently available and future treatment strategies. The role of vaccination in both the prevention and treatment of RRP will also be reviewed. We believe this review is essential to update the general knowledge on RRP with the latest information available to date to enhance our understanding of RRP and its management.