Atopic dermatitis is a heterogeneous disease, in which the pathogenesis is associated with mutations in genes encoding epidermal structural proteins, barrier enzymes, and their inhibitors; the role of genes regulating innate and adaptive immune responses and environmental factors inducing the disease is also noted. Recent studies point to the key role of epigenetic changes in the development of the disease. Epigenetic modifications are mainly mediated by DNA methylation, histone acetylation, and the action of specific non-coding RNAs. It has been documented that the profile of epigenetic changes in patients with atopic dermatitis (AD) differs from that observed in healthy people. This applies to the genes affecting the regulation of immune response and inflammatory processes, e.g., both affecting Th1 bias and promoting Th2 responses and the genes of innate immunity, as well as those encoding the structural proteins of the epidermis. Understanding of the epigenetic alterations is therefore pivotal to both create new molecular classifications of atopic dermatitis and to enable the development of personalized treatment strategies.

We demonstrated that not whole Mycobacterium tuberculosis but its particular antigens, hsp70 _Mtb , hsp65 _Mtb , and hsp16 _Mtb , are present in lymph node tissues of patients with sarcoidosis (SA). hsp16 _Mtb occurs in the early stage of SA, whereas hsp70 _Mtb occurs in stage II of SA. hsp65 _Mtb is highly expressed in the capillary vessels in lymph node tissues in patients with SA.

Ecto-5′-nucleotidase (CD73) is a membrane-bound enzyme, which catalyzes the conversion of adenosine monophosphate to adenosine. CD73 has been postulated to play an important role in carcinogenesis, as adenosine promotes tumor progression and CD73-expressing cancer cell lines are more aggressive. However, other studies have shown that activated adenosine receptors may also inhibit cell proliferation. This study investigated the clinical significance of CD73 expression in breast cancer. The study group included 136 consecutive stage I-III breast cancer patients treated between 2001 and 2008 at 2 institutions. CD73 expression was examined by immunohistochemistry (IHC) on tissue microarrays, using antihuman mouse monoclonal antibody. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. CD73 staining was expressed as the score calculated by multiplying the staining intensity (0=negative, 1=weak, 2=intermediate, 3=strong) and percentage of positive cells (0% to 100%). The median score among all samples was 100. Positive CD73 staining (defined as score equal or higher than 100) occurred in 74% of the cases. No correlation was found between CD73 expression and grading, tumor size, lymph node status, histologic type, estrogen receptor, or progesterone receptor status. Positive CD73 expression strongly correlated with longer disease-free survival (hazard ratio=0.26; 95% confidence interval, 0.1-0.66; P=0.0044) and overall survival (hazard ratio =0.24; 95% confidence interval, 0.07-0.85; P=0.027). Multivariate analysis for disease-free survival revealed correlation with tumor size and CD73 status. Elevated CD73 expression in breast cancer can predict a good prognosis. However, the actual role of CD73 in cancerogenesis remains unclear and requires further analysis.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients—surgeons, medical oncologists, and radiation oncologists—on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.