Low back pain (LBP) in one of the most disabling symptoms affecting nearly 80% of the population worldwide. Its primary cause seems to be intervertebral disc degeneration (IDD): a chronic and progressive process characterized by loss of viable cells and extracellular matrix (ECM) breakdown within the intervertebral disc (IVD) especially in its inner region, the nucleus pulposus (NP). Over the last decades, innovative biological treatments have been investigated in order to restore the original healthy IVD environment and achieve disc regeneration. Mesenchymal stem cells (MSCs) have been widely exploited in regenerative medicine for their capacity to be easily harvested and be able to differentiate along the osteogenic, chondrogenic, and adipogenic lineages and to secrete a wide range of trophic factors that promote tissue homeostasis along with immunomodulation and anti-inflammation. Several in vitro and preclinical studies have demonstrated that MSCs are able to acquire a NP cell-like phenotype and to synthesize structural components of the ECM as well as trophic and anti-inflammatory mediators that may support resident cell activity. However, due to its unique anatomical location and function, the IVD presents distinctive features: avascularity, hypoxia, low glucose concentration, low pH, hyperosmolarity, and mechanical loading. Such conditions establish a hostile microenvironment for both resident and exogenously administered cells, which limited the efficacy of intradiscal cell therapy in diverse investigations. This review is aimed at describing the characteristics of the healthy and degenerated IVD microenvironment and how such features influence both resident cells and MSC viability and biological activity. Furthermore, we focused on how recent research has tried to overcome the obstacles coming from the IVD microenvironment by developing innovative cell therapies and functionalized bioscaffolds.

Stem cells sources for intervertebral disc regeneration

Instrumented spine procedures have been performed for decades to treat a wide variety of spinal disorders. New technologies have been employed to obtain a high degree of precision, to minimize risks of damage to neurovascular structures and to diminish harmful exposure of patients and the operative team to ionizing radiations. Robotic spine surgery comprehends 3 major categories: telesurgical robotic systems, robotic-assisted navigation (RAN) and virtual augmented reality (AR) systems, including AR and virtual reality. Telesurgical systems encompass devices that can be operated from a remote command station, allowing to perform surgery via instruments being manipulated by the robot. On the other hand, RAN technologies are characterized by the robotic guidance of surgeon-operated instruments based on real-time imaging. Virtual AR systems are able to show images directly on special visors and screens allowing the surgeon to visualize information about the patient and the procedure (i.e., anatomical landmarks, screw direction and inclination, distance from neurological and vascular structures etc.). The aim of this review is to focus on the current state of the art of robotics and AR in spine surgery and perspectives of these emerging technologies that hold promises for future applications.

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.

In March 2019, the World Health Organization (WHO) recognized the COVID-19 pandemic as a global issue. To reduce the spread of this disease, health safety pathways were implemented worldwide. These extraordinary measures changed people’s lifestyles, e.g., by being forced to isolate, and in many cases, to work remotely from home. Low back pain (LBP), the most common cause of disability worldwide, is often a symptom of COVID-19. Moreover, it is often associated with different lifestyle features (type of job, physical activity, body weight). Therefore, the purpose of this systematic review and meta-analysis was to estimate the effect of the COVID-19 lockdown on LBP intensity and prevalence compared with LBP rates before the pandemic. A systematic search was performed on Scopus, PubMed, and Cochrane Central. Overall, eight studies with 2365 patients were included in the analysis. We used the Joanna Briggs Institute (JBI) critical appraisal tool to evaluate the risk of bias: six studies (75%) were at moderate risk of bias and two studies (25%) were at low risk of bias. These studies showed an increase in both the prevalence and intensity of LBP during the COVID-19 lockdown.