Safe chemotherapy for treating early breast cancer in G6PD-deficient patients

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EGFR-Mutated Non-Small Cell Lung Cancer and Resistance to Immunotherapy: Role of the Tumor Microenvironment

Lung cancer is a leading cause of cancer-related deaths worldwide. About 10-30% of patients with non-small cell lung cancer (NSCLC) harbor mutations of the EGFR gene. The Tumor Microenvironment (TME) of patients with NSCLC harboring EGFR mutations displays peculiar characteristics and may modulate the antitumor immune response. EGFR activation increases PD-L1 expression in tumor cells, inducing T cell apoptosis and immune escape. EGFR-Tyrosine Kinase Inhibitors (TKIs) strengthen MHC class I and II antigen presentation in response to IFN-gamma, boost CD8+ T-cells levels and DCs, eliminate FOXP3+ Tregs, inhibit macrophage polarization into the M2 phenotype, and decrease PD-L1 expression in cancer cells. Thus, targeted therapy blocks specific signaling pathways, whereas immunotherapy stimulates the immune system to attack tumor cells evading immune surveillance. A combination of TKIs and immunotherapy may have suboptimal synergistic effects. However, data are controversial because activated EGFR signaling allows NSCLC cells to use multiple strategies to create an immunosuppressive TME, including recruitment of Tumor-Associated Macrophages and Tregs and the production of inhibitory cytokines and metabolites. Therefore, these mechanisms should be characterized and targeted by a combined pharmacological approach that also concerns disease stage, cancer-related inflammation with related systemic symptoms, and the general status of the patients to overcome the single-drug resistance development

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios)

Effect of Cancer-Related Cachexia and Associated Changes in Nutritional Status, Inflammatory Status, and Muscle Mass on Immunotherapy Efficacy and Survival in Patients with Advanced Non-Small Cell Lung Cancer

Immune checkpoint inhibitor (ICI)-based immunotherapy has significantly improved the survival of patients with advanced non-small cell lung cancer (NSCLC); however, a significant percentage of patients do not benefit from this approach, and predictive biomarkers are needed. Increasing evidence demonstrates that cachexia, a complex syndrome driven by cancer-related chronic inflammation often encountered in patients with NSCLC, may impair the immune response and ICI efficacy. Herein, we carried out a prospective study aimed at evaluating the prognostic and predictive role of cachexia with the related changes in nutritional, metabolic, and inflammatory parameters (assessed by the multidimensional miniCASCO tool) on the survival and clinical response (i.e., disease control rate) to ICI-based immunotherapy in patients with advanced NSCLC. We included 74 consecutive patients. Upon multivariate regression analysis, we found a negative association between IL-6 levels (odds ratio (OR) = 0.9036; 95%CI = 0.8408–0.9711; p = 0.0025) and the miniCASCO score (OR = 0.9768; 95%CI = 0.9102–0.9999; p = 0.0310) with the clinical response. As for survival outcomes, multivariate COX regression analysis found that IL-6 levels and miniCASCO-based cachexia severity significantly affected PFS (hazard ratio (HR) = 1.0388; 95%CI = 1.0230–1.0548; p < 0.001 and HR = 1.2587; 95%CI = 1.0850–1.4602; p = 0.0024, respectively) and OS (HR = 1.0404; 95%CI = 1.0221–1.0589; p < 0.0001 and HR = 2.3834; 95%CI = 1.1504–4.9378; p = 0.0194, respectively). A comparison of the survival curves by Kaplan–Meier analysis showed a significantly lower OS in patients with cachexia versus those without cachexia (p = 0.0323), as well as higher miniCASCO-based cachexia severity (p = 0.0428), an mGPS of 2 versus those with a lower mGPS (p = 0.0074), and higher IL-6 levels (>6 ng/mL) versus those with lower IL-6 levels (≤6 ng/mL) (p = 0.0120). In conclusion, our study supports the evidence that cachexia, with its related changes in inflammatory, body composition, and nutritional parameters, is a key prognostic and predictive factor for ICIs. Further larger studies are needed to confirm these findings and to explore the potential benefit of counteracting cachexia to improve immunotherapy efficacy

Design and validation of an e-textile-based wearable system for remote health monitoring

The paper presents a new e-textile-based system, named SWEET Shirt, for the remote monitoring of biomedical signals. The system includes a textile sensing shirt, an electronic unit for data transmission, a custom-made Android application for real-time signal visualisation and a software desktop for advanced digital signal processing. The device allows for the acquisition of electrocardiographic, bicep electromyographic and trunk acceleration signals. The sensors, electrodes, and bus structures are all integrated within the textile garment, without any discomfort for users. A wide-ranging set of algorithms for signal processing were also developed for use within the system, allowing clinicians to rapidly obtain a complete and schematic overview of a patient's clinical status. The aim of this work was to present the design and development of the device and to provide a validation analysis of the electrocardiographic measurement and digital processing. The results demonstrate that the information contained in the signals recorded by the novel system is comparable to that obtained via a standard medical device commonly used in clinical environments. Similarly encouraging results were obtained in the comparison of the variables derived from the signal processing.</p

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Funding Information: The authors thank the researchers of the Motion Sickness Laboratory of the Reykjavik University (Iceland) and Engg. Teresa Pirozzi and Federica Cirillo for their technical support. Work by LD and FE in part supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)—A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Publisher Copyright: © 2023 by the authors.Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.Peer reviewe

Comparing the prognostic value of stress myocardial perfusion imaging by conventional and cadmium-zinc telluride single-photon emission computed tomography through a machine learning approach

We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall

CDX-2 expression correlates with clinical outcomes in MSI-H metastatic colorectal cancer patients receiving immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) showed efficacy in metastatic colorectal cancer (mCRC) with mismatch-repair deficiency or high microsatellite instability (dMMR-MSI-H). Unfortunately, a patient's subgroup did not benefit from immunotherapy. Caudal-related homeobox transcription factor 2 (CDX-2) would seem to influence immunotherapy's sensitivity, promoting the chemokine (C-X-C motif) ligand 14 (CXCL14) expression. Therefore, we investigated CDX-2 role as a prognostic-predictive marker in patients with mCRC MSI-H. We retrospectively collected data from 14 MSI-H mCRC patients treated with ICIs between 2019 and 2021. The primary endpoint was the 12-month progression-free-survival (PFS) rate. The secondary endpoints were overall survival (OS), PFS, objective response rate (ORR), and disease control rate (DCR). The PFS rate at 12&nbsp;months was 81% in CDX-2 positive patients vs 0% in CDX-2 negative patients (p = 0.0011). The median PFS was not reached (NR) in the CDX-2 positive group versus 2.07&nbsp;months (95%CI 2.07-10.8) in CDX-2 negative patients (p = 0.0011). Median OS was NR in CDX-2-positive patients versus 2.17&nbsp;months (95% Confidence Interval [CI] 2.17-18.7) in CDX2-negative patients (p = 0.026). All CDX-2-positive patients achieved a disease response, one of them a complete response. Among CDX-2-negative patients, one achieved stable disease, while the other progressed rapidly (ORR: 100% vs 0%, p = 0.0005; DCR: 100% vs 50%, p = 0.02). Twelve patients received 1st-line pembrolizumab (11 CDX-2 positive and 1 CDX-2 negative) not reaching median PFS, while two patients (1 CDX-2 positive and 1 CDX-2 negative) received 3rd-line pembrolizumab reaching a median PFS of 10.8&nbsp;months (95% CI, 10.8-12.1; p = 0.036). Although our study reports results on a small population, the prognostic role of CDX-2 in CRC seems confirmed and could drive a promising predictive role in defining the population more sensitive to immunotherapy treatment. Modulating the CDX-2/CXCL14 axis in CDX-2-negative patients could help overcome primary resistance to immunotherapy

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Publisher Copyright: © 2022, The Author(s).Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.Peer reviewe

Smoking status during first-line immunotherapy and chemotherapy in NSCLC patients: A case–control matched analysis from a large multicenter study

Background: Improved outcome in tobacco smoking patients with non-small cell lung cancer (NSCLC) following immunotherapy has previously been reported. However, little is known regarding this association during first-line immunotherapy in patients with high PD-L1 expression. In this study we compared clinical outcomes according to the smoking status of two large multicenter cohorts. Methods: We compared clinical outcomes according to the smoking status (never smokers vs. current/former smokers) of two retrospective multicenter cohorts of metastatic NSCLC patients, treated with first-line pembrolizumab and platinum-based chemotherapy. Results: A total of 962 NSCLC patients with PD-L1 expression ≥50% who received first-line pembrolizumab and 462 NSCLC patients who received first-line platinum-based chemotherapy were included in the study. Never smokers were confirmed to have a significantly higher risk of disease progression (hazard ratio [HR] = 1.49 [95% CI: 1.15–1.92], p = 0.0022) and death (HR = 1.38 [95% CI: 1.02–1.87], p = 0.0348) within the pembrolizumab cohort. On the contrary, a nonsignificant trend towards a reduced risk of disease progression (HR = 0.74 [95% CI: 0.52–1.05], p = 0.1003) and death (HR = 0.67 [95% CI: 0.45–1.01], p = 0.0593) were reported for never smokers within the chemotherapy cohort. After a random case–control matching, 424 patients from both cohorts were paired. Within the matched pembrolizumab cohort, never smokers had a significantly shorter progression-free survival (PFS) (HR = 1.68 [95% CI: 1.17–2.40], p = 0.0045) and a nonsignificant trend towards a shortened overall survival (OS) (HR = 1.32 [95% CI: 0.84–2.07], p = 0.2205). On the contrary, never smokers had a significantly longer PFS (HR = 0.68 [95% CI: 0.49–0.95], p = 0.0255) and OS (HR = 0.66 [95% CI: 0.45–0.97], p = 0,0356) compared to current/former smoker patients within the matched chemotherapy cohort. On pooled multivariable analysis, the interaction term between smoking status and treatment modality was concordantly statistically significant with respect to ORR (p = 0.0074), PFS (p = 0.0001) and OS (p = 0.0020), confirming the significantly different impact of smoking status across the two cohorts. Conclusions: Among metastatic NSCLC patients with PD-L1 expression ≥50% receiving first-line pembrolizumab, current/former smokers experienced improved PFS and OS. On the contrary, worse outcomes were reported among current/former smokers receiving first-line chemotherapy