Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

The prognostic significance of lymphocyte doubling time (LDT) in chronic lymphocytic leukemia (CLL) was identified when the biology of the disease was poorly understood and therapy was not effective. We assessed the clinical and biological significance of LDT in 848 CLL patients in a real-life setting and the context of new biomarkers and effective therapy. A short LDT (≤ 12 months) was enriched for adverse biomarkers. Patients with a rapid LDT did need therapy shortly after diagnosis (median 23 months vs. not reached ; p<0.001) and had a poorer overall survival (median 95 months vs. not reached p <0.001). LDT, IGHV mutational status, Beta-2 microglobulin, and Rai clinical stage were independent predictors for time to first treatment in the whole series and in Binet stage A patients. No correlation was observed between LDT and response to chemoimmununotherapy. However, a short LDT along with age ≥65 years, high-risk FISH (del(17p), del(11q)), unmutated IGHV, increased Beta-2 microglobulin, and TP53 mutations predicted short survival. Moreover, the prognostic significance of LDT was independent of the CLL-IPI and the Barcelona/Brno prognostic model.LDT remains an important outcome marker in the modern CLL era and should be incorporated into the clinical assessment and stratification of CLL patients.

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices.

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