ArchiveSpinal cord injury (SCI) is a devastating and disabling medical condition generally caused by a traumatic event (primary injury). This initial trauma is accompanied by a set of biological mechanisms directed to ameliorate neural damage but also exacerbate initial damage (secondary injury). The alterations that occur in the spinal cord have not only local but also systemic consequences and virtually all organs and tissues of the body incur important changes after SCI, explaining the progression and detrimental consequences related to this condition. Psychoneuroimmunoendocrinology (PNIE) is a growing area of research aiming to integrate and explore the interactions among the different systems that compose the human organism, considering the mind and the body as a whole. The initial traumatic event and the consequent neurological disruption trigger immune, endocrine, and multisystem dysfunction, which in turn affect the patient's psyche and well-being. In the present review, we will explore the most important local and systemic consequences of SCI from a PNIE perspective, defining the changes occurring in each system and how all these mechanisms are interconnected. Finally, potential clinical approaches derived from this knowledge will also be collectively presented with the aim to develop integrative therapies to maximize the clinical management of these patients.
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0–20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
Rheumatoid arthritis (RA) is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability, thus adversely affecting locomotion ability and life quality. Consequently, good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA. Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging. Herein, we review the fluorescent probes developed for the detection and imaging of RA biomarkers, namely reactive oxygen/nitrogen species (hypochlorous acid, peroxynitrite, hydroxyl radical, nitroxyl), pH, and cysteine, and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.