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Introduction: Although both aging and menopause influence cardiovascular autonomic control, the effect of menopause per se remains unclear. The current study was undertaken to test the hypothesis that post-menopausal women (PMW) have a blunted cardiovascular autonomic adjustment to active standing compared to pre-menopausal women. Thus, we compared the heart rate variability (HRV) indexes from supine (SUP) to orthostatic (ORT) positions among young women (YW), young men (YM), older men (OM), and PMW. Methods: The participants rested for 10 min in SUP and then stood up and remained for 5 min in ORT. ECG was continuously recorded, and R-R time series of about 300 beats were analyzed using linear (spectral analysis) and non-linear (symbolic analysis) methods. The variation from SUP to ORT was calculated (Δ = ORT-SUP) for each HRV index. Results: In SUP, no difference was found for any HRV index among groups. However, Δ0V% and ΔLFn (cardiac sympathetic modulation) were reduced in PWM compared to all groups (OM, YW, and YM), while Δ2UV% and ΔHFn (cardiac vagal modulation) were reduced in PMW than the younger group (YW and YM). No differences were found among the male groups (OM and YM). Discussion: In light of our results, the cardiac autonomic dynamic response to orthostatic stress is blunted in post-menopausal women compared to younger women and older men, a finding that might be influenced not only by aging.

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Background: Actionable driver mutations account for 40-50% of NSCLC cases, and their identification clearly affects treatment choices and outcomes. Conversely, non-actionable mutations are genetic alterations that do not currently have established treatment implications. Among co-occurring alterations, the identification of concurrent actionable genomic alterations is a rare event, potentially impacting prognosis and treatment outcomes. Methods: We retrospectively evaluated the prevalence and patterns of concurrent driver genomic alterations in a large series of NSCLCs to investigate their association with clinicopathological characteristics, to assess the prognosis of patients whose tumor harbors concurrent alterations in the genes of interest and to explore their potential therapeutic implications. Results: Co-occurring driver alterations were identified in 26 out of 1520 patients with at least one gene alteration (1.7%). Within these cases, the incidence of concurrent actionable gene alterations was 39% (0.7% of the overall cohort). Among compound actionable gene mutations, EGFR was the most frequently involved gene (70%). The most frequent association was EGFR mutations with ROS1 rearrangement. Front-line targeted treatments were the preferred approach in patients with compound actionable mutations, with dismal median PFS observed (6 months). Conclusions: Advances in genomic profiling technologies are facilitating the identification of concurrent mutations. In patients with concurrent actionable gene alterations, integrated molecular and clinical data should be used to guide treatment decisions, always considering rebiopsy at the moment of disease progression.

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Various next-generation ALK TKIs are available as first-line options for ALK-positive NSCLC, with alectinib and lorlatinib being commonly preferred. However, no direct comparison between them has been conducted, making it impossible to pick a winner. We performed an analytic, 'non-comparative' assessment of the two phase 3 pivotal clinical trials showing superiority of alectinib (ALEX) and lorlatinib (CROWN) in comparison to crizotinib. Overall, the two studies were very similar in the study design and patient characteristics, with the exception of the selection and evaluation of brain metastases. PFS hazard ratios numerically favored lorlatinib, both according to the investigator and to BICR. Notably, the 3-year PFS rate was numerically higher with lorlatinib (64%) than with alectinib (46.4%). Despite similar response rates and overall intracranial response, the rate of complete intracranial response was higher with lorlatinib, with a cumulative incidence risk of CNS disease progression at 12 months of 9.4% with alectinib and 2.8% with lorlatinib. The peculiar toxicities of lorlatinib were related to lipidic profile alterations, peripheral oedema and cognitive effects, with no impact on cardiovascular risk nor impairment in quality of life versus crizotinib. Furthermore, the rate of permanent treatment discontinuation due to adverse events was numerically higher with alectinib (26%) than with lorlatinib (7%). In conclusion, despite the immature OS data for both drugs, the efficacy of lorlatinib appears higher than alectinib while maintaining a manageable toxicity profile.

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Small cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma accounting for 15% of lung cancers with dismal survival outcomes. Minimal changes in therapy and prognosis have occurred in SCLC for the past four decades. Recent progress in the treatment of extensive-stage disease (ES-SCLC) has been marked by incorporating immune checkpoint inhibitors (ICIs) into platinum-based chemotherapy, leading to modest improvements. Moreover, few second-line-and-beyond treatment options are currently available. The main limitation for the molecular study of SCLC has been the scarcity of samples, because only very early diseases are treated with surgery and biopsies are not performed when the disease progresses. Despite all these difficulties, in recent years we have come to understand that SCLC is not a homogeneous disease. At the molecular level, in addition to the universal loss of retinoblastoma (RB) and TP53 genes, a recent large molecular study has identified other mutations that could serve as targets for therapy development or patient selection. In recent years, there has also been the identification of new genetic subtypes which have shown us how intertumor heterogeneity exists. Moreover, SCLC can also develop intratumoral heterogeneity linked mainly to the concept of cellular plasticity, mostly due to the development of resistance to therapies. The aim of this review is to quickly present the current standard of care of ES-SCLC, to focus on the molecular landscapes and subtypes of SCLC, subsequently present the most promising therapeutic strategies under investigation, and finally recap the future directions of ongoing clinical trials for this aggressive disease which still remains a challenge.

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ADHD is a neurodevelopmental disorder largely diffused among children and adolescents. The current method of diagnosis is based on agreed clinical literature such as DSM-5, by identifying and evaluating signs of hyperactivity and inattention. Multiple reviews have assessed that EEG is not sufficiently reliable for the diagnosis of ADHD. Theta-Beta Ratio is now the sole EEG parameter considered for analysis, although it is not robust enough to be utilized as a confirmatory technique for diagnosis. In this setting, new objective approaches for reliably classifying neurotypical and ADHD subjects are required. As a result, we suggest a new methodology based on Functional Data Analysis, a statistical class of methods for dealing with curves and functions. The initial stage in our method is to separate frequency bands from the EEG signal using a wavelet decomposition. We next compute the Power Spectral Densities of each of these bands and represent them as mathematical functions via spline interpolation. Finally, the relevance of the collected features is assessed using the Permutation ANOVA test. Using this method, we can detect different patterns in the PSDs of the groups and identify statistically significant features, confirming prior findings in the literature. We validate the features using classification techniques such as Bagging trees, Random Forest, and AdaBoost. The latter reaches the highest accuracy score of 76.65%, confirming the relevance of the extracted features.

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Mental calculations involve various areas of the brain. The frontal, parietal and temporal lobes of the left hemisphere have a principal role in the completion of this typology of tasks. Their level of activation varies based on the mathematical competence and attentiveness of the subject under examination and the perceived difficulty of the task. Recent literature often investigates patterns of cerebral activity through fMRI, which is an expensive technique. In this scenario, EEGs represent a more straightforward and cheaper way to collect information regarding brain activity. In this work, we propose an EEG based method to detect differences in the cerebral activation level of people characterized by different abilities in carrying out the same arithmetical task. Our approach consists in the extraction of the activation level of a given region starting from the EEG acquired during resting state and during the completion of a subtraction task. We then analyze these data through Functional Data Analysis, a statistical technique that allows operating on biomedical signals as if they were functions. The application of this technique allowed for the detection of distinct cerebral patterns among the two groups and, more specifically, highlighted the presence of higher levels of activation in the parietal lobe in the population characterized by a lower performance.

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