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.

Real-World Effectiveness of the Varicella Vaccine among Children and Adolescents in Qatar: A Case-Control Study

BACKGROUND

Despite the availability of a highly efficacious vaccine, varicella outbreaks are still being reported globally. In this study, we evaluated the real-world effectiveness of varicella vaccination among children between the ages of 1 and 18 years old during the period 2017 to 2019 in Qatar.

METHODS

A matched case-control study was conducted that included all reported varicella-infected children who visited the primary healthcare system in Qatar from January 2017 to December 2019. The cases were children under the age of 18 years who were clinically diagnosed with varicella. The controls were of the same age, who visited the Primary Health Care Corporation (PHCC) during 2017-2019 with a skin rash where varicella infection was ruled out. The data on varicella vaccination for each participant were obtained from the electronic database in the PHCC during the study period.

RESULTS

We included 862 cases of varicella and 5454 matched controls, with a median age of 8 years (IQR 3-12); 47.4% were female and almost 50% were of Qatari nationality. The year 2019 had the highest varicella infection count with a total of 416 cases. The cases were less likely to be vaccinated against varicella, with approximately a quarter (25.6%) of cases and 36.7% of the controls having either one or two doses of the vaccine (p < 0.001). Compared to not being vaccinated, a single dose vaccination showed a 56% reduction in the odds of varicella infection [OR 0.44, 95% CI: 0.34-0.55; p < 0.000], and a two-dose vaccination showed an 86% reduction in the odds of varicella infection [OR 0.13, 95% CI: 0.06-0.29; p < 0.000].

CONCLUSION

In this multicultural setting, a two-dose varicella vaccination shows reasonable protection against varicella infection.

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.

DVT as the Initial Presentation of Multiple Myeloma: A Rare Case Report and Literature Review

Multiple myeloma patients are recognized to have a higher risk of venous thrombosis. The cause of this could be attributed to several risk factors, such as circulating prothrombotic microparticles, disease-specific variables, and alterations in coagulation and fibrinolysis factors. Recent research has revealed that these individuals also experience greater arterial thrombosis, including acute myocardial infarction and stroke. In this case report, we present the clinical profile and management of a 42-year-old patient who presented with signs and symptoms of deep venous thrombosis (DVT) and was diagnosed with multiple myeloma. The aim of this case report is to highlight a rare clinical presentation and diagnostic workup in a patient with multiple myeloma. Additionally, we discuss the possible factors provoking the development of DVT as a first presentation before treatment initiation and their possible mechanisms.

Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review

Thalassemia is an autosomal recessive genetic disorder that affects the beta or alpha subunits of the hemoglobin structure. Thalassemia is classified as a hypochromic microcytic anemia and a definitive diagnosis of thalassemia is made by genetic testing of the alpha and beta genes. Thalassemia carries similar features to the other diseases that lead to microcytic hypochromic anemia, particularly iron deficiency anemia (IDA). Therefore, distinguishing between thalassemia and other causes of microcytic anemia is important to help in the treatment of the patients. Different indices and algorithms are used based on the complete blood count (CBC) parameters to diagnose thalassemia. In this article, we review how effective artificial intelligence is in aiding in the diagnosis and classification of thalassemia.

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.

Proteome changes in autosomal recessive primary microcephaly

BACKGROUND/AIM

Autosomal recessive primary microcephaly (MCPH) is a rare and genetically heterogeneous group of disorders characterized by intellectual disability and microcephaly at birth, classically without further organ involvement. MCPH3 is caused by biallelic variants in the cyclin-dependent kinase 5 regulatory subunit-associated protein 2 gene CDK5RAP2. In the corresponding Cdk5rap2 mutant or Hertwig's anemia mouse model, congenital microcephaly as well as defects in the hematopoietic system, germ cells and eyes have been reported. The reduction in brain volume, particularly affecting gray matter, has been attributed mainly to disturbances in the proliferation and survival of early neuronal progenitors. In addition, defects in dendritic development and synaptogenesis exist that affect the excitation-inhibition balance. Here, we studied proteomic changes in cerebral cortices of Cdk5rap2 mutant mice.

MATERIAL AND METHODS

We used large-gel two-dimensional gel (2-DE) electrophoresis to separate cortical proteins. 2-DE gels were visualized by a trained observer on a light box. Spot changes were considered with respect to presence/absence, quantitative variation and altered mobility.

RESULT

We identified a reduction in more than 30 proteins that play a role in processes such as cell cytoskeleton dynamics, cell cycle progression, ciliary functions and apoptosis. These proteome changes in the MCPH3 model can be associated with various functional and morphological alterations of the developing brain.

CONCLUSION

Our results shed light on potential protein candidates for the disease-associated phenotype reported in MCPH3.