The phenomenon of antimicrobial resistance (AMR) is a critical global health challenge, with prospects indicating its potential to become the leading cause of death worldwide in the coming years. Individuals with pre-existing conditions, such as neoplastic disease undergoing chemotherapy, those on immunosuppressive therapy, and individuals with rare diseases like cystic fibrosis (CF), face heightened challenges due to AMR. CF is a rare disease caused by a deficiency in the synthesis of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) channel protein, resulting in multi-organ clinical symptoms, particularly in the respiratory system. PwCF experience recurrent pulmonary exacerbations triggered by bacterial or viral infections, making them particularly vulnerable to the impact of AMR. This review delves into the complex relationship between AMR and climate dynamics, focusing on the unique challenges faced by individuals with CF. It discusses the methods employed to measure AMR, its global impact on antibiotic resistance, and the specific microbial communities present in the CF airway. The review also explores the intricacies of antimicrobial resistance within the context of cystic fibrosis, emphasizing the urgent need for research in this field.

The progress of artificial intelligence (AI), particularly its core algorithms—machine learning (ML) and deep learning (DL)—has been significant in the medical field, impacting both scientific research and clinical practice. These algorithms are now capable of analyzing ultrasound images, processing them, and providing outcomes, such as determining the benignity or malignancy of thyroid nodules. This integration into ultrasound machines is referred to as computer-aided diagnosis (CAD). The use of such software extends beyond ultrasound to include cytopathological and molecular assessments, enhancing the estimation of malignancy risk. AI’s considerable potential in cancer diagnosis and prevention is evident. This article provides an overview of AI models based on ML and DL algorithms used in thyroid diagnostics. Recent studies demonstrate their effectiveness and diagnostic role in ultrasound, pathology, and molecular fields. Notable advancements include content-based image retrieval (CBIR), enhanced saliency CBIR (SE-CBIR), Restore-Generative Adversarial Networks (GANs), and Vision Transformers (ViTs). These new algorithms show remarkable results, indicating their potential as diagnostic and prognostic tools for thyroid pathology. The future trend points to these AI systems becoming the preferred choice for thyroid diagnostics.

Background: Inflammatory microenvironment is an essential component of all tumors, including thyroid cancer. Autoimmune thyroid diseases are often associated with thyroid cancer. CD25, expressed in Treg cells and B cells, has been found to be associated with autoimmune thyroid diseases and the NFkB pathway is critical to tumor formation, regulating immune-related genes, and pro-inflammatory cytokine. Methods: Protein expression of CD25 and NFkB and its phosphorylated form was analyzed by immunohistochemistry in 80 patients with thyroid cancer (10 cases of cancers with Hashimoto’s thyroiditis and 70 cases without). Results: CD25 was mainly detected in the nucleus of the inflammatory cells such as in the thyrocytes and neoplastic cells. Protein staining was detected in the T-lymphocytes of the outermost zone of the lymphoid follicles. Moreover, in all cancer alterations, there were a higher level of p-NFkB than in the surrounding tissues. Again, p-NFkB staining was evident in neoplastic cells but not evident in inflammatory cells. Conclusions: Strong inflammatory infiltrate in the tumor microenvironment is correlated with an invasive phenotype. CD25 and p-NFkB levels were statistically significantly overexpressed in cancer cells.

Patients with COVID-19 have coagulation and platelet disorders, with platelet alterations and thrombocytopenia representing negative prognostic parameters associated with severe forms of the disease and increased lethality. Methods: The aim of this study was to study the expression of platelet glycoprotein IIIa (CD61), playing a critical role in platelet aggregation, together with TRL-2 as a marker of innate immune activation. Results: A total of 25 patients were investigated, with the majority (24/25, 96%) having co-morbidities and dying from a fatal form of SARS-CoV-2(+) infection (COVID-19+), with 13 men and 12 females ranging in age from 45 to 80 years. When compared to a control group of SARS-CoV-2 (−) negative lungs (COVID-19−), TLR-2 expression was up-regulated in a subset of patients with deadly COVID-19 fatal lung illness. The proportion of Spike-1 (+) patients found by PCR and ISH correlates to the proportion of Spike-S1-positive cases as detected by digital pathology examination. Furthermore, CD61 expression was considerably higher in the lungs of deceased patients. In conclusion, we demonstrate that innate immune prolonged hyperactivation is related to platelet/megakaryocyte over-expression in the lung. Conclusions: Microthrombosis in deadly COVID-19+ lung disease is associated with an increase in the number of CD61+ platelets and megakaryocytes in the pulmonary interstitium, as well as their functional activation; this phenomenon is associated with increased expression of innate immunity TLR2+ cells, which binds the SARS-CoV-2 E protein, and significantly with the persistence of the Spike-S1 viral sequence.

While the frequency of papillary thyroid carcinoma (PTC) has increased in recent decades, both due to improvements in diagnostic procedures and a real, effective percentage increase in cases, the frequency of medullary thyroid carcinoma (MTC), however, has remained almost unchanged, representing 3–5% of thyroid cancer cases. Our experience relates to the observation of cases with the synchronous presence of PTC and MTC, also in chronic autoimmune thyroiditis, and this led us to carry out a brief review of the literature on the subject, with the aim above all of identifying the most correct postoperative therapeutic process.

Thyroid nodule evaluation relies heavily on ultrasound imaging, yet it suffers from significant inter-operator variability. To address this, we present a preliminary validation of the Synergy-Net platform, an AI-driven Computer-Aided Diagnosis (CAD) system designed to standardize acquisition and improve diagnostic accuracy. The system integrates a U-Net architecture for anatomical segmentation and a ResNet-50 classifier for lesion characterization within a Human-in-the-Loop (HITL) workflow. The study enrolled 110 patients (71 benign, 39 malignant) undergoing surgery. Performance was evaluated against histopathological ground truth. The system achieved an Accuracy of 90.35% (95% CI: 88.2–92.5%), Sensitivity of 90.64% (95% CI: 87.9–93.4%), and an AUC of 0.90. Furthermore, the framework introduces a multimodal approach, performing late fusion of imaging features with genomic profiles (TruSight One panel). While current results validate the 2D diagnostic pipeline, the discussion outlines the transition to the ANTHEM framework, incorporating future 3D volumetric analysis and digital pathology integration. These findings suggest that AI-assisted standardization can significantly enhance diagnostic precision, though multi-center validation remains necessary.