Unraveling Autonomic Dysfunction in GBA-Related Parkinson's Disease.

Background: Patients with Parkinson's disease (PD) and GBA gene mutations (GBA-PD) develop nonmotor complications more frequently than noncarriers. However, an objective characterization of both cardiovascular and sudomotor autonomic dysfunction using extensive clinical and instrumental measures has never been provided so far. Survival is reduced in GBA-PD regardless of age and dementia, suggesting that other hitherto unrecognized factors are involved. Objectives: To provide instrumental measures of pattern and severity of autonomic dysfunction in GBA-PD and explore their correlation with other non-motor symptoms and implications for clinical practice. Methods: In this cross-sectional study, 21 GBA-PD and 24 matched PD noncarriers underwent extensive assessment of motor and non-motor features, including neuropsychological testing. Cardiovascular autonomic function was explored through a comprehensive battery of indexes, including power spectral analysis of the R-R intervals and blood pressure short-term variability during resting state and active maneuvers. Dynamic Sweat Test was used to assess post-ganglionic sudomotor dysfunction. Results: Despite minimal or absent clinical correlates, cardiovagal and sympathetic indexes, heart rate variability parameters and sudomotor postganglionic function were more severely impaired in GBA-PD than noncarriers (overcoming relatively preserved compensatory peripheral sympathetic function), suggesting more prominent cardiac sympatho-vagal demodulation, efferent baroreflex failure and peripheral sympathetic dysfunction in GBA-PD. Cardiovascular dysautonomia showed marginal correlations with cognitive impairment. Conclusions: Compared to PD noncarriers, GBA-PD display more severe instrumental autonomic abnormalities, which may be underestimated by purely clinical measures, despite their relevance on morbidity and mortality. This supports the necessity of implementing instrumental autonomic assessment in all GBA-PD, regardless of clinically overt symptoms.

Caregivers' Expectations on Possible Functional Changes following Disease-Modifying Treatment in Type II and III Spinal Muscular Atrophy: A Comparative Study.

Background: The primary aim of this study was to explore current caregivers' expectations on possible functional changes following treatment in comparison to data obtained in the pre-pharmacological era. Methods: A questionnaire, previously used in 2016, was administered to caregivers of type II and III SMA patients of age between 3 and 71 years, and to patients over the age of 13 years. The questionnaire focuses on (1) caregivers and patients expectations, (2) meaningfulness of the changes observed on the functional motor scales, and (3) their willingness to be enrolled in a clinical trial. A comparative study was performed with data obtained using the same questionnaire soon before the advent of disease-modifying therapies. Results: We administered the questionnaire to 150 caregivers. When comparing current caregiver data to those obtained in 2016, the most obvious differences were related to disease perception over the last year (stability: 16.5% in 2016 vs. 43.6% in 2022; deterioration 70.5% vs. 12.8%, and improvement: 12.9% vs. 43.6%) and expectations from clinical trials with higher expectations in 2022 compared to 2016 (p < 0.001). Forty-five of the 150 in the current study were caregivers of patients above the age of 13. In these 45 the questionnaire was also administered to the patient. No difference was found in responses between patients and their caregivers. Conclusions: Both carers and patients reported that even small changes on functional scales, similar to those reported by clinical studies and real-world data, are perceived as meaningful. Comparing the recent responses to those obtained in 2016, before pharmacological treatment was available, we found significant changes in caregivers' perception with increased expectations. These findings will provide a better understanding of the patients' expectations and facilitate discussion with regulators.

Prognostic Role of Soluble and Extracellular Vesicle-Associated PD-L1, B7-H3 and B7-H4 in Non-Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The treatment of non-small cell lung cancer (NSCLC) has changed dramatically with the advent of immune checkpoint inhibitors (ICIs). Despite encouraging results, their efficacy remains limited to a subgroup of patients. Circulating immune checkpoints in soluble (s) form and associated with extracellular vesicles (EVs) represent promising markers, especially in ICI-based therapeutic settings. We evaluated the prognostic role of PD-L1 and of two B7 family members (B7-H3, B7-H4), both soluble and EV-associated, in a cohort of advanced NSCLC patients treated with first- (n = 56) or second-line (n = 126) ICIs. In treatment-naïve patients, high baseline concentrations of sPD-L1 (>24.2 pg/mL) were linked to worse survival, whereas high levels of sB7-H3 (>0.5 ng/mL) and sB7-H4 (>63.9 pg/mL) were associated with better outcomes. EV characterization confirmed the presence of EVs positive for PD-L1 and B7-H3, while only a small portion of EVs expressed B7-H4. The comparison between biomarker levels at the baseline and in the first radiological assessment under ICI-based treatment showed a significant decrease in EV-PD-L1 and an increase in EV-B7H3 in patients in the disease response to ICIs. Our study shows that sPD-L1, sB7-H3 and sB7-H4 levels are emerging prognostic markers in patients with advanced NSCLC treated with ICIs and suggests potential EV involvement in the disease response to ICIs.

Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics.

Delta-radiomics is a branch of radiomics in which features are confronted after time or after introducing an external factor (such as treatment with chemotherapy or radiotherapy) to extrapolate prognostic data or to monitor a certain condition. Immune checkpoint inhibitors (ICIs) are currently revolutionizing the treatment of non-small cell lung cancer (NSCLC); however, there are still many issues in defining the response to therapy. Contrast-enhanced CT scans of 33 NSCLC patients treated with ICIs were analyzed; altogether, 43 lung lesions were considered. The radiomic features of the lung lesions were extracted from CT scans at baseline and at first reassessment, and their variation (delta, Δ) was calculated by means of the absolute difference and relative reduction. This variation was related to the final response of each lesion to evaluate the predictive ability of the variation itself. Twenty-seven delta features have been identified that are able to discriminate radiologic response to ICIs with statistically significant accuracy. Furthermore, the variation of nine features significantly correlates with pseudo-progression.

Microwave-Based Subsurface Characterization through a Combined Finite Element and Variable Exponent Spaces Technique.

A microwave characterization technique to inspect subsurface scenarios is proposed and numerically assessed in this paper. The approach is based on a combination of finite element electromagnetic modeling and an inversion procedure in Lebesgue spaces with variable exponents. The former allows for description of the measurement system and subsurface scenario with high accuracy, while the latter exploits the adaptive definition of exponent function to achieve improved results in the regularized solution of the inverse scattering problem. The method has been assessed with numerical simulations regarding two-layered environments with both planar and non-planar air-soil interfaces. The results show the capabilities of the method of detecting buried objects in different operative conditions.

Radiomic Detection of EGFR Mutations in NSCLC.

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non-small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A "test-retest" approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. SIGNIFICANCE: These findings demonstrate that data normalization and "test-retest" methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.

Through-the-Wall Microwave Imaging: Forward and Inverse Scattering Modeling.

The imaging of dielectric targets hidden behind a wall is addressed in this paper. An analytical solver for a fast and accurate computation of the forward scattered field by the targets is proposed, which takes into account all the interactions of the electromagnetic field with the interfaces of the wall. Furthermore, an inversion procedure able to address the full underlying non-linear inverse scattering problem is introduced. This technique exploits a regularizing scheme in Lebesgue spaces in order to reconstruct an image of the hidden targets. Preliminary numerical results are provided in order to initially assess the capabilities of the developed solvers.

Mapping the Dielectric Properties of Unknown Targets by Using a Network of Microwave Sensors: A Proof-of-Concept.

The subject of this paper is the possible use of a network of microwave sensors to achieve a map of the electromagnetic properties of unknown targets. The basic idea is to use a set of microwave sensors to illuminate a region of interest and to measure the resulting axial component of the electric field. Measurements are then processed by means of a technique based on inverse-scattering, which provides an estimate map of the dielectric values of the area under examination, allowing to discriminate among possible targets. In order to initially evaluate the feasibility of the proposed approach, numerical results in a simulated environment are preliminarily considered and discussed. Furthermore, an initial test on experimental data in a simplified configuration is also presented.

Three-dimensional electromagnetic imaging of dielectric targets by means of the multiscaling inexact-Newton method.

A novel approach for three-dimensional electromagnetic imaging is presented. This technique is a combination of an iterative multiscaling approach with an inexact-Newton method. The multiscaling procedure allows one to iteratively focus the region of interest on the detected target, whereas the inexact-Newton method provides an efficient regularized solution of the nonlinear electromagnetic inverse scattering problem for each scaling step. The proposed method is validated against numerical data with different configuration settings. A preliminary experimental validation is also reported.