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.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

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.

Major spices crops such as black pepper (Piper nigrum L.), cardamom (Elettaria cardamomum Maton.) and turmeric (Curcuma longa L.) production in India, is sustained losses due to several reasons. Among them, one of the major constraints are nematode infesting diseases, which causes significant yield losses and affecting their productivity. The major nematode pests infesting these crops include burrowing nematode Radopholus similis; root knot nematode, Meloidogyne incognita and M. javanica on black pepper. Whereas, lesion nematode, Pratylenchus sp., M. incognita and R. similis infesting cardamom and turmeric crops. Black pepper is susceptible to a number of diseases of which slow decline caused by R. similis and M. incognita or Phytophthora capsici either alone and in combination and root knot disease caused by Meloidogyne spp. are the major ones. Root knot disease caused by Meloidogyne spp. is major constraints in the successful cultivation and production in cardamom. Turmeric is susceptible to a number of diseases such as brown rot disease is caused by Fusarium sp. and lesion nematode, Pratylenchus sp. and root knot disease caused by M. incognita. Adoption of integrated pest management schedules is important in these crops since excessive use of pesticides could lead to pesticide residues in the produce affecting human health and also causing other ecological hazards.

In the last several decades, cardiovascular diseases (CVDs) have emerged as a major hazard to human life and health. Conventional formulations for the treatment of CVD are available, but they are far from ideal because of poor water solubility, limited biological activity, non-targeting, and drug resistance. With the advancement of nanotechnology, a novel drug delivery approach for the treatment of CVDs has emerged: nano-drug delivery systems (NDDSs). NDDSs have shown significant advantages in tackling the difficulties listed above. Cytotoxicity is a difficulty with the use of non-destructive DNA sequences. NDDS categories and targeted tactics were outlined, as well as current research advancements in the diagnosis and treatment of CVDs. It’s possible that gene therapy might be included into nano-carriers in the delivery of cardiovascular medications in the future. In addition, the evaluation addressed the drug’s safety.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.