Low-energy Pulsed Electromagnetic Field Therapy Reduces Pain in Fibromyalgia: A Randomized Single-blind Controlled Pilot Study.

Objectives: Fibromyalgia symptoms have a significant impact on the quality of life and respond poorly to medications. It has been hypothesized that the use of low-energy pulsed electromagnetic field (PEMF) induces neuroprotective effects that may interfere with pain perception. We explored the efficacy of PEMF in patients affected by fibromyalgia. Methods: Twenty-one females (median age 59 years, interquartile range [IQR] 16.5) affected by fibromyalgia were randomized to receive pulsed electromagnetic field-triple energy pain treatment (PEMF-TEPT) or placebo at T0 and at 4 weeks and 8 weeks. Fibromyalgia impact questionnaire (FIQ), widespread pain index (WPI), visual analog score (VAS) pain, symptom severity (SS) scale, and short form 36 (SF-36) health survey questionnaire have been evaluated. Results: Patients in the PEMF-TEPT group had a significantly higher reduction of WPI compared to placebo (mean difference -12.90 ± standard deviation [SD] 5.32 vs. -1.91 ± 4.55, difference in difference [DD] of -10.99; P < 0.001), of SS score (-4.10 ± 4.85 vs. -2.00 ± 2.32; DD = -2.1; P < 0.05), of VAS pain (-48 ± 30.75 vs. -16.82 ± 23.69; DD = -31.18; P < 0.01). They also reported a higher improvement of FIQ and SF-36, albeit not reaching statistical significance. Conclusion: In our pilot controlled study, PEMF-TEPT appeared to be safe and improved fibromyalgia symptoms.

An infrastructure for precision medicine through analysis of big data.

BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed. RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses. CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

Biexponential and diffusional kurtosis imaging, and generalised diffusion-tensor imaging (GDTI) with rank-4 tensors: a study in a group of healthy subjects.

OBJECT: Clinical diffusion imaging is based on two assumptions of limited validity: that the radial projections of the diffusion propagator are Gaussian, and that a single directional diffusivity maximum exists in each voxel. The former can be removed using the biexponential and diffusional kurtosis models, the latter using generalised diffusion-tensor imaging. This study provides normative data for these three models. MATERIALS AND METHODS: Eighteen healthy subjects were imaged. Maps of the biexponential parameters D (fast), D (slow) and f (slow), of D and K from the diffusional kurtosis model, and of diffusivity D' were obtained. Maps of generalised anisotropy (GA) and scaled entropy(SE) were also generated, for second and fourth rank tensors. Normative values were obtained for 26 regions. RESULTS: In grey versus white matter, D (slow) and D' were higher and D (fast), f (slow) and K were lower. With respect to maps of D', anatomical contrast was stronger in maps of D (slow) and K. Elevating tensor rank increased SE, generally more significantly than GA, in: anterior limb of internal capsule, corpus callosum, deep frontal and subcortical white matter, along superior longitudinal fasciculus and cingulum. CONCLUSION: The values reported herein can be used for reference in future studies and in clinical settings.