First Ex Vivo Animal Study of a Biological Heart Valve Prosthesis Sensorized with Intravalvular Impedance

IntraValvular Impedance (IVI) sensing is an innovative concept for monitoring heart valve prostheses after implant. We recently demonstrated IVI sensing feasible in vitro for biological heart valves (BHVs). In this study, for the first time, we investigate ex vivo the IVI sensing applied to a BHV when it is surrounded by biological tissue, similar to a real implant condition. A commercial model of BHV was sensorized with three miniaturized electrodes embedded in the commissures of the valve leaflets and connected to an external impedance measurement unit. To perform ex vivo animal tests, the sensorized BHV was implanted in the aortic position of an explanted porcine heart, which was connected to a cardiac BioSimulator platform. The IVI signal was recorded in different dynamic cardiac conditions reproduced with the BioSimulator, varying the cardiac cycle rate and the stroke volume. For each condition, the maximum percent variation in the IVI signal was evaluated and compared. The IVI signal was also processed to calculate its first derivative (dIVI/dt), which should reflect the rate of the valve leaflets opening/closing. The results demonstrated that the IVI signal is well detectable when the sensorized BHV is surrounded by biological tissue, maintaining the similar increasing/decreasing trend that was found during in vitro experiments. The signal can also be informative on the rate of valve opening/closing, as indicated by the changes in dIVI/dt in different dynamic cardiac conditions

A novel non-invasive device for the assessment of central venous pressure in hospital, office and home

Background: Venous congestion can be quantified by central venous pressure (CVP) and its monitoring is crucial to understand and follow the hemodynamic status of patients with cardio-respiratory diseases. The standard technique for CVP measurement is invasive, requiring the insertion of a catheter into a jugular vein, with potential complications. On the other hand, the current non-invasive methods, mainly based on ultrasounds, remain operator-dependent and are unsuitable for use in the home environment. In this paper, we will introduce a novel, non-invasive device for the hospital, office and home assessment of CVP. Methods: After describing the measurement concept, we will report a preliminary experimental study enrolling 5 voluntary healthy subjects to evaluate the VenCoM measurements’ repeatability, and the system’s capability in measuring small elicited venous pressure variations (2 mmHg), as well as an induced venous hypertension within a pathological range (12÷20 mmHg). Results: The experimental measurements showed a repeatability of ±1mmHg. The VenCoM device was able to reliably detect the elicited venous pressure variations and the simulated congestive status. Discussion and Conclusion: The proposed non-invasive VenCoM device is able to provide a fast and repeatable CVP estimate, having a wide spectrum of potential clinical applications, including the monitoring of venous congestion in heart failure patients and in subjects with renal and hepatic dysfunction, as well as pulmonary hypertension (PH) that can be extended to pneumonia COVID-19 patients even after recovery. The device needs to be tested further on a large sample size of both healthy and pathological subjects, to systematically validate its reliability and impact in clinical setting

Comparison of Bone Segmentation Software over Different Anatomical Parts

Three-dimensional bone shape reconstruction is a fundamental step for any subject-specific musculo-skeletal model. Typically, medical images are processed to reconstruct bone surfaces via slice-by-slice contour identification. Freeware software packages are available, but commercial ones must be used for the necessary certification in clinics. The commercial software packages also imply expensive hardware and demanding training, but offer valuable tools. The aim of the present work is to report the performance of five commercial software packages (Mimics®, Amira™, D2P™, Simpleware™, and Segment 3D Print™), particularly the time to import and to create the model, the number of triangles of the mesh, and the STL file size. DICOM files of three different computed tomography scans from five different human anatomical areas were utilized for bone shape reconstruction by using each of these packages. The same operator and the same hosting hardware were used for these analyses. The computational time was found to be different between the packages analyzed, probably because of the pre-processing implied in this operation. The longer “time-to-import” observed in one software is likely due to the volume rendering during uploading. A similar number of triangles per megabyte (approximately 20 thousand) was observed for the five commercial packages. The present work showed the good performance of these software packages, with the main features being better than those analyzed previously in freeware packages

Classification performance for covid patient prognosis from automatic ai segmentation—a single-center study

Background: COVID assessment can be performed using the recently developed individual risk score (prediction of severe respiratory failure in hospitalized patients with SARS-COV2 infection, PREDI-CO score) based on High Resolution Computed Tomography. In this study, we evaluated the possibility of automatizing this estimation using semi-supervised AI-based Radiomics, leveraging the possibility of performing non-supervised segmentation of ground-glass areas. Methods: We collected 92 from patients treated in the IRCCS Sant’Orsola-Malpighi Policlinic and public databases; each lung was segmented using a pre-trained AI method; ground-glass opacity was identified using a novel, non-supervised approach; radiomic measurements were collected and used to predict clinically relevant scores, with particular focus on mortality and the PREDI-CO score. We compared the prediction obtained through different machine learning approaches. Results: All the methods obtained a well-balanced accuracy (70%) on the PREDI-CO score but did not obtain satisfying results on other clinical characteristics due to unbalance between the classes. Conclusions: Semi-supervised segmentation, implemented using a combination of non-supervised segmentation and feature extraction, seems to be a viable approach for patient stratification and could be leveraged to train more complex models. This would be useful in a high-demand situation similar to the current pandemic to support gold-standard segmentation for AI training

3D Patient-Specific Virtual Models for Presurgical Planning in Patients with Recto-Sigmoid Endometriosis Nodules: A Pilot Study

Background and Objective: In recent years, 3D printing has been used to support surgical planning or to guide intraoperative procedures in various surgical specialties. An improvement in surgical planning for recto-sigmoid endometriosis (RSE) excision might reduce the high complication rate related to this challenging surgery. The aim of this study was to build novel presurgical 3D models of RSE nodules from magnetic resonance imaging (MRI) and compare them with intraoperative findings. Materials and Methods: A single-center, observational, prospective, cohort, pilot study was performed by enrolling consecutive symptomatic women scheduled for minimally invasive surgery for RSE between November 2019 and June 2020 at our institution. Preoperative MRI were used for building 3D models of RSE nodules and surrounding pelvic organs. 3D models were examined during multi-disciplinary preoperative planning, focusing especially on three domains: degree of bowel stenosis, nodule's circumferential extension, and bowel angulation induced by the RSE nodule. After surgery, the surgeon was asked to subjectively evaluate the correlation of the 3D model with the intra-operative findings and to express his evaluation as "no correlation", "low correlation", or "high correlation" referring to the three described domains. Results: seven women were enrolled and 3D anatomical virtual models of RSE nodules and surrounding pelvic organs were generated. In all cases, surgeons reported a subjective "high correlation" with the surgical findings. Conclusion: Presurgical 3D models could be a feasible and useful tool to support surgical planning in women with recto-sigmoidal endometriotic involvement, appearing closely related to intraoperative findings

Nusinersen safety and effects on motor function in adult spinal muscular atrophy type 2 and 3.

ABSTRACT Objective To retrospectively investigate safety and efficacy of nusinersen in a large cohort of adult Italian patients with spinal muscular atrophy (SMA). Methods Inclusion criteria were: (1) clinical and molecular diagnosis of SMA2 or SMA3; (2) nusinersen treatment started in adult age (>18 years); (3) clinical data available at least at baseline (T0-beginning of treatment) and 6 months (T6). Results We included 116 patients (13 SMA2 and 103 SMA3) with median age at first administration of 34 years (range 18–72). The Hammersmith Functional Rating Scale Expanded (HFMSE) in patients with SMA3 increased significantly from baseline to T6 (median change +1 point, p<0.0001), T10 (+2, p<0.0001) and T14 (+3, p<0.0001). HFMSE changes were independently significant in SMA3 sitter and walker subgroups. The Revised Upper Limb Module (RULM) in SMA3 significantly improved between T0 and T14 (median +0.5, p=0.012), with most of the benefit observed in sitters (+2, p=0.018). Conversely, patients with SMA2 had no significant changes of median HFMSE and RULM between T0 and the following time points, although a trend for improvement of RULM was observed in those with some residual baseline function. The rate of patients showing clinically meaningful improvements (as defined during clinical trials) increased from 53% to 69% from T6 to T14. Conclusions Our data provide further evidence of nusinersen safety and efficacy in adult SMA2 and SMA3, with the latter appearing to be cumulative over time. In patients with extremely advanced disease, effects on residual motor function are less clear

Non-myogenic mesenchymal cells contribute to muscle degeneration in facioscapulohumeral muscular dystrophy patients

Muscle-resident non-myogenic mesenchymal cells play key roles that drive successful tissue regeneration within the skeletal muscle stem cell niche. These cells have recently emerged as remarkable therapeutic targets for neuromuscular disorders, although to date they have been poorly investigated in facioscapulohumeral muscular dystrophy (FSHD). In this study, we characterised the non-myogenic mesenchymal stromal cell population in FSHD patients’ muscles with signs of disease activity, identified by muscle magnetic resonance imaging (MRI), and compared them with those obtained from apparently normal muscles of FSHD patients and from muscles of healthy, age-matched controls. Our results showed that patient-derived cells displayed a distinctive expression pattern of mesenchymal markers, along with an impaired capacity to differentiate towards mature adipocytes in vitro, compared with control cells. We also demonstrated a significant expansion of non-myogenic mesenchymal cells (identified as CD201- or PDGFRA-expressing cells) in FSHD muscles with signs of disease activity, which correlated with the extent of intramuscular fibrosis. In addition, the accumulation of non-myogenic mesenchymal cells was higher in FSHD muscles that deteriorate more rapidly. Our results prompt a direct association between an accumulation, as well as an altered differentiation, of non-myogenic mesenchymal cells with muscle degeneration in FSHD patients. Elucidating the mechanisms and cellular interactions that are altered in the affected muscles of FSHD patients could be instrumental to clarify disease pathogenesis and identifying reliable novel therapeutic targets

Differential response to pallidal deep brain stimulation among monogenic dystonias: systematic review and meta-analysis

ObjectiveGenetic subtypes of dystonia may respond differentially to deep brain stimulation of the globus pallidus pars interna (GPi DBS). We sought to compare GPi DBS outcomes among the most common monogenic dystonias.MethodsThis systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology guidelines. We searched PubMed for studies on genetically confirmed monogenic dystonia treated with GPi DBS documenting pre-surgical and post-surgical assessments using the Burke-Fahn-Marsden Dystonia Rating Scale Motor Score (BFMMS) and Burke-Fahn-Marsden Disability Score (BFMDS). We performed (i) meta-analysis for each gene mutation; (ii) weighted ordinary linear regression analyses to compare BFMMS and BFMDS outcomes between DYT-TOR1A and other monogenic dystonias, adjusting for age and disease duration and (iii) weighted linear regression analysis to estimate the effect of age, sex and disease duration on GPi DBS outcomes. Results were summarised with mean change and 95% CI.ResultsDYT-TOR1A (68%, 38.4 points; p<0.001), DYT-THAP1 (37% 14.5 points; p<0.001) and NBIA/DYT-PANK2 (27%, 21.4 points; p<0.001) improved in BFMMS; only DYT-TOR1A improved in BFMDS (69%, 9.7 points; p<0.001). Improvement in DYT-TOR1A was significantly greater than in DYT-THAP1 (BFMMS -31%), NBIA/DYT-PANK2 (BFMMS -35%; BFMDS -53%) and CHOR/DYT-ADCY5 (BFMMS -36%; BFMDS -42%). Worse motor outcomes were associated with longer dystonia duration and older age at dystonia onset in DYT-TOR1A, longer dystonia duration in DYT/PARK-TAF1 and younger age at dystonia onset in DYT-SGCE.ConclusionsGPi DBS outcomes vary across monogenic dystonias. These data serve to inform patient selection and prognostic counselling