Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Feature Extraction”, “Segmentation”, “Computer Vision”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Low Back Pain”, “Lumbar”. Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen–Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems’ autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

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Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.

Non-technical summary The oligopeptide transporter PepT1 is a protein found in the membrane of the cells of the intestinal walls, and represents the main route through which proteic nutrients are absorbed by the organism. Along the polypeptidic chain of this protein, two oppositely charged amino acids, an arginine in position 282 and an aspartate in position 341 of the sequence, have been hypothesised to form a barrier in the absorption pathway. In this paper we show that appropriate mutations of these amino acids change the properties of PepT1 in a way that confirms that these parts of the protein indeed act as an electrostatic gate in the transport process. The identification of the structural basis of the functional mechanism of this transporter is important because, in addition to its role in nutrient uptake, PepT1 represents a major pathway for the absorption of several therapeutic drugs.Abstract The effects of mutations in the charge pair residues Arg282 and Asp341 of the rabbit oligopeptide transporter PepT1 have been studied using electrophysiology in mRNA-injected Xenopus oocytes. Substitution of Arg282 with neutral or negatively charged residues produced a shift towards more positive potentials in the characteristics of charge movement with respect to the wild-type form. Conversely replacement of Asp341 with Arg reduced both pre-steady-state and transport currents and produced a negative shift of the charge movement properties. Both kinds of currents remained pH-sensitive in the mutants. All functional mutants were correctly localized on the cell membrane. Removal of the positive charge of Arg282 produced transporters able to generate conspicuous outward currents whose reversal potential was affected by external pH and by substrate concentration. This suggests that the mutants still translocate protons and substrate as a complex. Charged substrates were accepted by the mutants with the same potency order as the wild-type. The results support the idea that Arg282 and Asp341 play the role of electrostatic gates in the PepT1 transport cycle.

Although connexin36 (Cx36) has been studied in several tissues, it is notable that no data are available on Cx36 expression in the carotid body and the intestine. The present study was undertaken to evaluate using immunohistochemistry, PCR and Western blotting procedures, whether Cx36 was expressed in the mouse carotid body and in the intestine at ileum and colon level. In the carotid body, Cx36 was detected as diffuse punctate immunostaining and as protein by Western blotting and mRNA by RT-PCR. Cx36 punctate immunostaining was also evident in the intestine with localization restricted to the myenteric plexus of both the ileum and the colon, and this detection was also confirmed by Western blotting and RT-PCR. All the data obtained were validated using Cx36 knockout mice. Taken together the present data on localization of Cx36 gap-junctions in two tissues of neural crest-derived neuroendocrine organs may provide an anatomical basis for future functional investigations.