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The application of artificial intelligence in esophageal tumor pathology highlights the ongoing and significant transformative impact AI has in medicine. This article provides a comprehensive overview of AI technologies' current use in diagnosing and treating esophageal squamous cell carcinoma (ESCC). Through our research, we identified and meticulously analyzed 33 relevant academic papers and studies that contribute to this field. These papers encompass a broad spectrum of AI applications related to the management of esophageal squamous cell carcinoma. AI integration and deployment in both the diagnosis process and therapeutic interventions for tumors offer highly promising prospects, for example, in endoscopic procedures AI algorithm can process endoscopic images in real-time to identify abnormalities that may be missed by the human eye, it can highlight subtle changes such as color variations, small growths or tissue anomalies, which might go unnoticed due to fatigue, distraction or human limitations in perceiving fine details. AI technologies enhance medical practice precision and effectiveness by significantly reducing the likelihood of human errors and offering practical, innovative solutions. Consequently, AI confluence with medical practice not only increases diagnosis accuracy but also improves the overall efficiency of treating esophageal squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

In recent years, a series of recommendations have been issued regarding the administration of drugs because of awareness of the serious side effects associated with certain classes of drugs, especially in vulnerable patients. Taking into account the obligation of the continuous improvement of professionals in the medical fields and the fact that we are in the midst of a "malpractice accusations pandemic", through this work, we propose to carry out a "radiography" of the scientific literature regarding adverse effects that may occur as a result of the interaction of drugs with the physiopathological particularities of patients. The literature reports various cases regarding different classes of drugs administration associated with adverse effects in the elderly people, such as fluoroquinolones, which can cause torsade de pointes or tendinopathy, or diuretics, which can cause hypokalemia followed by torsade de pointes and cardiorespiratory arrest. Also, children are more prone to the development of adverse reactions due to their physiological particularities, while for pregnant women, some drugs can interfere with the normal development of the fetus, and for psychiatric patients, the use of neuroleptics can cause agranulocytosis. Considering the physiopathological particularities of each patient, the drug doses must be adjusted or even completely removed from the treatment scheme, thus requiring the mandatory active participation both of clinician pharmacists and specialists in the activity of medical-pharmaceutical analysis laboratories within the structure of hospitals. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Adverse drug reactions represent a major public health problem, both from an economic point of view and, mainly, from the point of view of the induced pathology (iatrogenic diseases), being difficult to differentiate from other pathological conditions or even from the treated disease. Thus, these aspects prevent the use of the first-choice drugs needed for a particular treatment, in different therapeutic classes: beta-lactam antibiotics; sulfonamides; macrolide antibiotics; quinolones; non-steroidal anti-inflammatories; corticosteroids; Angiotensin converting enzyme (ACE) inhibitors; general anesthetics; biological drugs; antiepileptic drugs etc. On the other hand, adverse drug reactions represent a major problem for both clinical practice and preclinical research, in order to develop new drugs. Hypersensitivity reactions mainly refer to the adverse effects that can be harmful, disturbing, and sometimes fatal, that appear under the conditions of a normal immune system, including allergies and autoimmune reactions, both triggered by an immunological-allergic mechanism. The main purpose of this paper is to review the main classes of drugs involved in inducing hypersensitivity reactions. [ABSTRACT FROM AUTHOR]

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