This review discusses the significant progress made in the development of CNT/GO-based biosensors for disease biomarker detection. It highlights the specific applications of CNT/GO-based biosensors in the detection of various disease biomarkers, including cancer, cardiovascular diseases, infectious diseases, and neurodegenerative disorders. The superior performance of these biosensors, such as their high sensitivity, low detection limits, and real-time monitoring capabilities, makes them highly promising for early disease diagnosis. Moreover, the challenges and future directions in the field of CNT/GO-based biosensors are discussed, focusing on the need for standardization, scalability, and commercialization of these biosensing platforms. In conclusion, CNT/GO-based biosensors have demonstrated immense potential in the field of disease biomarker detection, offering a promising approach towards early diagnosis. Continued research and development in this area hold great promise for advancing personalized medicine and improving patient outcomes.

Introduction: the presence of anti-CCP is an important prognostic tool for rheumatoid arthritis (RA), but its relationship with the activity of the disease and functional capacity is still being investigated. Objectives: to study the relationship between anti-CCP and the indices of disease activity, functional capacity and structural damage, by means of conventional radiography (CR) and magnetic resonance imaging (MRI), in stabilized RA. Methods: cross-sectional study of RA patients with one to 10 years of disease. The participants were subjected to clinical evaluation with anti-CCP screening. Disease activity was assessed by means of the Clinical Disease Activity Index (CDAI) and functional capacity by means of the Health Assessment Questionnaire (HAQ). CR was analyzed by the Sharp van der Heijde index (SmvH) and MRI by the Rheumatoid Arthritis Magnetic Resonance Image Scoring System (RAMRIS). Results: 56 patients were evaluated, with median (IIq) of 55 (47.5–60.0) years, 50 (89.3%) were female among whom 37 (66.1%) were positive for anti-CCP. The median (IIq) of CDAI, HAQ, SmvH and RAMRIS were 14.75 (5.42–24.97), 1.06 (0.28–1.75), 2 (0–8) and 15 (7–35), respectively. There was no association between anti-CCP and CDAI, HAQ, SmvH and RAMRIS. Conclusion: our results did not establish the association of anti-CCP with the severity of the disease. So far, we cannot corroborate the anti-CCP as a prognostic tool in RA established.

Background: Through the development of robust techniques and their comprehensive validation, cardiac magnetic resonance imaging (CMR) has developed a wide range of indications in its almost 25 years of clinical use. The recording of cardiac volumes and systolic ventricular function as well as the characterization of focal myocardial scars are now part of standard CMR imaging. Recently, the introduction of accelerated image acquisition technologies, the new imaging methods of myocardial T1 and T2 mapping and 4-D flow measurements, and the new post-processing technique of myocardial feature tracking have gained relevance. Method: This overview is based on a comprehensive literature search in the PubMed database on new CMR techniques and their clinical application. Results and conclusion: This article provides an overview of the latest technical developments in the field of CMR and their possible applications based on the most important clinical questions.

Depression is a mental disorder caused by various causes with significant and persistent depressed mood as the main clinical feature, and is the most common mental illness worldwide and in our country. The number of patients with depression worldwide was as high as 350 million in 2017, and the number of patients with depression in our country was nearly 100 million in 2019. The greatest danger of depression is self-injurious and suicidal behaviour, and this behaviour carries a high medical burden. Medication is the most costly treatment for depression in China, and while it is an effective way to treat patients with depression, it has many side effects and poor patient compliance. Non-pharmacological treatments commonly used in clinical practice include physiotherapy and psychotherapy. Physiotherapy is commonly used in non-convulsive electroconvulsive therapy, but its clinical efficacy is uncertain and it can also cause adverse effects such as heart failure and arrhythmias, which are poorly tolerated by patients. Psychotherapy is also a common non-pharmacological therapy. Cognitive therapy is a common form of psychotherapy, but the cycle of cognitive therapy is too long, the cost to the patient is high, and the patient’s cognitive ability has certain requirements. Music therapy is a combination of art and science. It is a cross-discipline that combines body, movement, dance and psychology and is a method of psychotherapy that has biological, psychological and social functions to compensate for deficiencies. Music therapy sees a fundamental connection between mind and body and emphasises that what affects the body also affects the mind. When mind-body integration is lacking, individuals will suffer from a variety of psychological disorders. Therefore, the core principles of music therapy emphasise that holistic individual health is embodied in the integration of mind and body, that body movement is expressive and communicative, and that music therapy uses body movement as a method of assessing the individual and as a means of clinical intervention.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.