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Abstract Our nervous system has the remarkable ability to adapt our gait to accommodate changes in our body or surroundings. However, our adapted walking patterns often generalize only partially (or not at all) between different contexts. Here, we sought to understand how the nervous system generalizes adapted gait patterns from one context to another. Through a series of split-belt treadmill walking experiments, we evaluated different mechanistic hypotheses to explain the partial generalization of adapted gait patterns from split-belt treadmill to overground walking. In support of the credit assignment hypothesis, our experiments revealed the central finding that adaptation involves recalibration of two distinct forward models. Recalibration of the first model generalizes to overground walking, suggesting that the model represents the general movement dynamics of our body. On the other hand, recalibration of the second model does not generalize to overground walking, suggesting the model represents dynamics specific to treadmill walking. These findings reveal that there is a predefined portion of forward model recalibration that generalizes across context, leading to overall partial generalization of walking adaptation.

Background Survival rates for breast cancer are increasing, but many clinical needs in a larger and larger population of survivors are not properly met. During and beyond cancer treatments, patients tend to reduce their physical activity levels, gain weight and may complain about chronic pain, lymphedema, fatigue, cognitive problems, depression and anxiety, reduced social contacts and difficulties in resuming daily activities. In addition, younger women must also face fertility issues and early menopause, higher levels of distress regarding body image, sexual health, childcare and career management. Beside this, all the survivors may feel a fear for disease recurrence and many of them are not educated to identify and express their needs. Materials and Methods Tailored supportive strategies, including physical and psychological interventions, should be offered since the time of diagnosis and along the entire cancer journey and the follow up period. They should be aimed not only to the prevention and control of psychological and physical impairments (such as anxiety, stress, depression, lymphedema, genitourinary symptoms and sexual dysfunctions), but also to encourage remodulation of personal lifestyles for tertiary prevention and nurture patient’s resources that can promote a personal growth after the traumatic experience of cancer. Conclusions Toward this goal, many cancer centres worldwide including ours offer dedicated programs of psycho-oncological support, nutritional and physical activity counselling and evidence based complementary treatments (such as acupuncture, mindfulness, meditation, reflexology, herbal medicine, music therapy and more).

Abstract In his Theory of Needs, Maslow argues that in the self- actualization stage the individual would have more developed B- values, which would involve, among other characteristics, the tendency to be mostly dedicated to causes of collective interest to the detriment of individual interests. It would also be in the self- actualization stage that the environment would have an intrinsic value, an important aspect if we consider the need for developing solutions to socio-environmental problems. Would the Social Representation of the environment have a relationship with the different stages of needs proposed by Maslow in his theory? Could the self-actualization stage reveal an individual with a globalizing representation of the environment? To answer these questions, an study was conducted with 134 participants adapting and using the Environmental Motivation Profile Test instrument and the results showed a significant correlation between the stage of self- actualization and a globalizing representation of the Environment.

Most anticancer treatments act on oxidative-stress pathways by producing reactive oxygen species (ROS) to kill cancer cells, commonly resulting in consequential drug-induced systemic cytotoxicity. Physical activity (PA) has arisen as an integrative cancer therapy, having positive health effects, including in redox-homeostasis. Here, we investigated the impact of an online supervised PA program on promoter-specific DNA methylation, and corresponding gene expression/activity, in 3 antioxidants- (SOD1, SOD2, and CAT) and 3 breast cancer (BC)-related genes (BRCA1, L3MBTL1 and RASSF1A) in a population-based sample of women diagnosed with primary BC, undergoing medical treatment. We further examined mechanisms involved in methylating and demethylating pathways, predicted biological pathways and interactions of exercise-modulated molecules, and the functional relevance of modulated antioxidant markers on parameters related to aerobic capacity/endurance, physical fatigue and quality of life (QoL).PA maintained levels of SOD activity in blood plasma, and at the cellular level significantly increased SOD2 mRNA (≈+77 %), contrary to their depletion due to medical treatment. This change was inversely correlated with DNA methylation in SOD2 promoter (≈−20 %). Similarly, we found a significant effect of PA only on L3MBTL1 promoter methylation (≈−25 %), which was inversely correlated with its mRNA (≈+43 %). Finally, PA increased TET1 mRNA levels (≈+15 %) and decreased expression of DNMT3B mRNA (≈−28 %).Our results suggest that PA-modulated DNA methylation affects several signalling pathways/biological activities involved in the cellular oxidative stress response, chromatin organization/regulation, antioxidant activity and DNA/protein binding. These changes may positively impact clinical outcomes and improve the response to cancer treatment in post-surgery BC patients.

Abstract Objectives Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. Methods For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. Results To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers’ emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users’ cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

The text briefly recounts the history of the meeting of Cristina Rossi, a teacher from an Italian vocational school, with Russian philologist Tatiana Kasatkina and the value of their interaction for the Italian school system.

Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS. Methods: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network’s performance was compared to that of human readers using accuracy and interrater agreement (Cohen’s Kappa). Results: The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model’s performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (κreader1 = 0.72, κreader2 = 0.78) while the readers compared to each other revealed a Cohen’s Kappa of 0.84, thus showing near-perfect agreement. Conclusions: With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network’s predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

This paper presents an innovative research project that aims to study the emotional factors influencing decision-making elicited by infomercials, a powerful sales technique that uses emotional communication to engage viewers, capture attention, and build trust. Using cutting-edge consumer neuroscience techniques, this study focuses on the identification of the variables that most impact the Call-to-Action and Purchase Intention. Forty participants were selected and divided into two groups, with each group exposed to one of two infomercials (condition A = male seller; condition B = female seller). EEG signals were recorded as well as Eye-tracking data. After the viewing, participants completed a self-report questionnaire. Results show that seller characteristics such as Performance and Trustworthiness, as well as Neurophysiological variables such as Approach-Withdrawal Index, Willingness to Pay, Attention and Engagement, significantly impact the final Call-to-Action, Purchase Intention, and infomercial Likeability responses. Moreover, eye-tracking data revealed that the more time is spent observing crucial areas of the infomercial, the more it will increase our Willingness to Pay and our interest and willingness to approach the infomercial and product. These findings highlight the importance of considering both the Seller attributes and the consumers’ Neurophysiological responses to understand and predict their behaviors in response to marketing stimuli since they all seem to play a crucial role in shaping consumers’ attitudes and purchase intentions. Overall, the study is a significant pilot in the new field of neuroselling, shedding light on crucial emotional aspects of the seller/buyer relationship and providing valuable insights for researchers and marketers.

Ada Trombetta rappresenta la studiosa più importante dell'arte medievale in Molise del secolo scorso. I suoi lavori editoriali, infatti, costituiscono il punto di partenza per l'analisi delle opere d'arte regionali ad oggi, grazie al suo occhio critico, alle sue intuizioni e all'aiuto del supporto fotografico del padre e del nonno. Ada Trombetta represents the most important scholar of medieval art in Molise in the last century. Her editorial works, in fact, provide the starting point for the analysis of regional works of art to date, thanks to her critical eye, insights and the help of her father's and grandfather's photographic support.

Abstract Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). Methods In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a–d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set. Results Of the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected. Conclusion TA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.

Background: Physical activity (PA) can play a role in lowering the risk of breast cancer (BC), but also in reducing perioperative complications and treatments related side effects, improving the quality of life and decreasing mortality in BC survivors. PA and nutritional screening are not offered to patients after cancer diagnosis as standard of care, even in high quality breast units. Methods: From February 2019 to March 2020, we performed a preoperative physical and nutritional screening in 504 consecutive BC patients waiting for surgery. The screening included an IPAQ questionnaire to evaluate the level of physical activity; nutritional screening with measurement of anthropometric parameters (weight, height, waist and hips circumference, BMI, and waist hip ratio) and evaluation of body composition using Bioelectrical Impedance Analysis (BIA). Results: The majority of patients in our series resulted physically inactive: clustering the IPAQ scores, 47% of patients proved to be physically inactive (MET score 2520). In addition, approximately half of the patients (49.01%) resulted overweight or obese, and more than half (55.2%) had a percentage of fatty tissue over the recommended cut off for adult women. Conclusions: Our data confirm that assessment of PA levels should become part of the standard preoperative evaluation of BC patients and behavioral interventions should be offered to them, in order to pre-habilitate for surgery and improve outcomes. IPAQ Questionnaire and body composition analysis could be quick and easy screening tools in order to identify which patients may need more support in being active during and after anticancer treatments.

Red blood cells (RBCs) are characterized by a remarkable elasticity, which allows them to undergo very large deformation when passing through small vessels and capillaries. This extreme deformability is altered in various clinical conditions, suggesting that the analysis of red blood cell (RBC) mechanics has potential applications in the search for non-invasive and cost-effective blood biomarkers. Here, we provide a comparative study of the mechanical response of RBCs in patients with Alzheimer’s disease (AD) and healthy subjects. For this purpose, RBC viscoelastic response was investigated using atomic force microscopy (AFM) in the force spectroscopy mode. Two types of analyses were performed: (i) a conventional analysis of AFM force–distance (FD) curves, which allowed us to retrieve the apparent Young’s modulus, E; and (ii) a more in-depth analysis of time-dependent relaxation curves in the framework of the standard linear solid (SLS) model, which allowed us to estimate cell viscosity and elasticity, independently. Our data demonstrate that, while conventional analysis of AFM FD curves fails in distinguishing the two groups, the mechanical parameters obtained with the SLS model show a very good classification ability. The diagnostic performance of mechanical parameters was assessed using receiving operator characteristic (ROC) curves, showing very large areas under the curves (AUC) for selected biomarkers (AUC > 0.9). Taken all together, the data presented here demonstrate that RBC mechanics are significantly altered in AD, also highlighting the key role played by viscous forces. These RBC abnormalities in AD, which include both a modified elasticity and viscosity, could be considered a potential source of plasmatic biomarkers in the field of liquid biopsy to be used in combination with more established indicators of the pathology.

Purpose This study aimed to assess the feasibility of a rapid diffusion tensor imaging (DTI) for evaluation of the female urinary sphincter function based on differentiation between rest and muscle contraction. Methods Magnetic resonance imaging (MRI) of the lower pelvis was performed at 3 Tesla in 10 healthy female volunteers (21–36 years; body mass index, 20.8±3.6 kg/m2) between June and July 2019. High-resolution T1- and T2-weighted images were acquired for anatomical reference, and following DTI performed in 4 experiment phases: twice during rest (denoted rest-1, rest-2) and contraction (contraction-1, contraction-2). Manual segmentation of the urinary sphincter and the levator ani muscles were performed by 2 independent readers. Mean diffusivity (MD) and fractional anisotropy (FA) values derived from DTI volumes were compared in search for significant differences between the experiment phases. Interreader agreement was assessed by intraclass correlation coefficient (ICC). Results Kruskal-Wallis test showed significant differences between MD values among all the experiment phases, by both independent readers (1st: X2 [3,76]=17.16, P

It has been proposed that motor adaptation and subsequent savings (or faster relearning) of an adapted movement pattern are mediated by cognitive processes. Here, we evaluated the pattern of cognitive-motor interference that emerges when young and late middle-aged adults perform an executive working memory task during locomotor adaptation. We also asked if this interferes with savings of a newly learned walking pattern, as has been suggested by a study of reaching adaptation. We studied split-belt treadmill adaptation and savings in young (21 ± 2 y/o) and late middle-aged (56 ± 6 y/o) adults with or without a secondary 2-back task during adaptation. We found that young adults showed similar performance on the 2-back task during baseline and adaptation, suggesting no effect of the dual-task on cognitive performance; however, dual-tasking interfered with adaptation over the first few steps. Conversely, dual-tasking caused a decrement in cognitive performance in late middle-aged adults with no effect on adaptation. To determine if this effect was specific to adaptation, we also evaluated dual-task interference in late middle-aged adults that dual-tasked while walking in a complex environment that did not induce motor adaptation. This group exhibited less cognitive-motor interference than late middle-aged adults who dual-tasked during adaptation. Savings was unaffected by dual-tasking in both young and late middle-aged adults, which may indicate different underlying mechanisms for savings of reaching and walking. Collectively, our findings reveal an age-dependent effect of cognitive-motor interference during dual-task locomotor adaptation and no effect of dual-tasking on savings, regardless of age. Young adults maintain cognitive performance and show a mild decrement in locomotor adaptation, while late middle-aged adults adapt locomotion at the expense of cognitive performance.

The COVID-19 pandemic triggered new working modalities, typically aimed at flexibility. However, the COVID-related restrictions caused adverse effects such as unemployment, precariousness, and social anxiety. Effects on working conditions differ depending on the socio-demographic features of those affected (e.g., gender, social status, economic situation, ethnicity). Scholars agree that people who were disadvantaged before the pandemic—the so-called minority power groups, e.g., women, young people, and immigrants—suffered the most from its effects. This literature review systematizes the main findings of studies on one of these minority power groups, namely women.

Plant-derived polyphenols exhibit beneficial effects on physiological and pathological processes, including cancer and neurodegenerative disorders, mainly because of their antioxidant activity. Apples are highly enriched in these compounds, mainly in their peel. The Tuscia Red (TR) apple variety exhibits the peculiar characteristic of depositing high quantities of polyphenols in the pulp, the edible part of the fruit. Since polyphenols, as any natural product, cannot be considered a panacea per se, in this paper, we propose to assess the biological effects of TR flesh extracts, in comparison with two commercial varieties, in a model system, the insect Drosophila melanogaster, largely recognized as a reliable system to test the in vivo effects of natural and synthetic compounds. We performed a comparative, qualitative and quantitative analysis of the polyphenol compositions of the three cultivars and found that TR flesh shows the highest content of polyphenols, and markedly, anthocyanins. Then, we focused on their effects on a panel of physiological, morphometrical, cellular and behavioral phenotypes in wild-type D. melanogaster. We found that all the apple polyphenol extracts showed dose-dependent effects on most of the phenotypes we considered. Remarkably, all the varieties induced a strong relenting of the cell division rate.

Background: Breast cancer (BC) survivors have physical and psychological needs that require convincing responses by health care providers. The quality of life issue and clinical unmet needs are among the main reasons pushing a number of patients toward “natural” therapies that are often misleading and alternative to mainstream cancer care. Integrative Oncology (IO) tries to respond to many of those needs, by combining lifestyle counseling, body-mind activities, and complementary evidence-informed therapies with anticancer standard treatments. Methods: In our model at Fondazione Policlinico Gemelli (FPG), every woman diagnosed with a BC waiting for surgery or candidate to neoadjuvant chemotherapy undergoes a preliminary psycho-oncological distress evaluation and a brief lifestyle interview. Anthropometric measurements, body composition analysis, and individual levels of physical activity are recorded. Patients are given evidence based recommendations about the advisable diet and physical activity in a prehabilitation setting. A physician provides patients with information about integrative care plans to treat symptoms related to the disease or its treatments. Therapeutic approaches include acupuncture, mindfulness-based protocols, qigong, massage therapy, and classes of music/art therapy. Results: Between September 2018 and February 2020, the Center for Integrative Oncology at FPG has carried out 1249 lifestyle counseling sessions, 1780 acupuncture treatments, 1340 physiotherapy sessions, 3261 psycho-oncological consultations, 218 herbal medicine counseling sessions. Moreover, 90 BC patients completed the mindfulness based stress reduction (MBSR) protocol and 970 patients participated in qigong, art therapy, and music therapy classes. Conclusions: Our integrative approach aims to achieve a person-centered medicine by improving symptoms management, adherence to oncological protocols, and eventually overall quality of life.

Background: The aim of this study is to demonstrate the feasibility of automatic classification of Ki-67 histological immunostainings in patients with squamous cell carcinoma of the vulva using a deep convolutional neural network (dCNN). Material and methods: For evaluation of the dCNN, we used 55 well characterized squamous cell carcinomas of the vulva in a tissue microarray (TMA) format in this retrospective study. The tumor specimens were classified in 3 different categories C1 (0–2%), C2 (2–20%) and C3 (>20%), representing the relation of the number of KI-67 positive tumor cells to all cancer cells on the TMA spot. Representative areas of the spots were manually labeled by extracting images of 351 × 280 pixels. A dCNN with 13 convolutional layers was used for the evaluation. Two independent pathologists classified 45 labeled images in order to compare the dCNN's results to human readouts. Results: Using a small labeled dataset with 1020 images with equal distribution among classes, the dCNN reached an accuracy of 90.9% (93%) for the training (validation) data. Applying a larger dataset with additional 1017 labeled images resulted in an accuracy of 96.1% (91.4%) for the training (validation) dataset. For the human readout, there were no significant differences between the pathologists and the dCNN in Ki-67 classification results. Conclusion: The dCNN is capable of a standardized classification of Ki-67 staining in vulva carcinoma; therefore, it may be suitable for quality control and standardization in the assessment of tumor grading.

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s-1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

Background: MR diffusion weighted imaging (DWI) may provide important information regarding the pathophysiology of parenchymal abdominal organs. The purpose of our study was to investigate the stability of imaging biomarkers of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in abdominal parenchymal organs regarding two body hydration states. Methods: Ten healthy volunteers twice underwent DWI of abdominal organs using a double-refocused spin-echo echo-planar imaging sequences with 11 different b-values (ranging from 0 to 1,500 s/mm2): after 4 h of fluid deprivation; 45 min following 1000 ml of water intake. Four different diffusion models were evaluated and compared: standard DWI, DKI with mono-exponential fitting, multistep algorithm with variable b-value threshold for IVIM, combined IVIM-Kurtosis; in four abdominal organs: kidneys, liver, spleen and psoas muscle. Results: Diffusion parameters from all four models remained similar for the renal parenchyma before and after the water challenge. Significant differences were found for the liver, spleen, and psoas muscle. The largest effects were seen for: the liver parenchyma after the water challenge by means of IVIM model's true diffusion (p < 0.02); the spleen, for IVIM's perfusion fraction (p < 0.03), the psoas muscle for the ADC value (p < 0.02). Conclusions: Herein, we showed that diffusion parameters of the kidney remain remarkably stable regarding the hydration status. This may be attributed to the kidney-specific compensatory mechanisms. For the liver, spleen and psoas muscle the diffusion parameters were sensitive to changes of the hydration. This phenomenon needs to be considered when evaluating diffusion data of these organs.

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling. The icons were sorted into three categories: “no opacities” (BI-RADS 1), “probably benign opacities” (BI-RADS 2/3) and “suspicious opacities” (BI-RADS 4/5). A dCNN was trained (70% of data), validated (20%) and finally tested (10%). A sliding window approach was applied to create colored probability maps for visual impression. Diagnostic performance of the dCNN was compared to human readout by experienced radiologists on a “real-world” dataset. The accuracies of the models on the test dataset ranged between 73.8% and 89.8%. Compared to human readout, our dCNN achieved a higher specificity (100%, 95% CI: 85.4–100%; reader 1: 86.2%, 95% CI: 67.4–95.5%; reader 2: 79.3%, 95% CI: 59.7–91.3%), and the sensitivity (84.0%, 95% CI: 63.9–95.5%) was lower than that of human readers (reader 1:88.0%, 95% CI: 67.4–95.4%; reader 2:88.0%, 95% CI: 67.7–96.8%). In conclusion, a dCNN can be used for the automatic detection as well as the standardized and observer-independent classification of soft tissue opacities in mammograms independent of the presence of microcalcifications. Human decision making in accordance with the BI-RADS classification can be mimicked by artificial intelligence.

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria. Subsequently four different dCNN models (differences in optimizer and spatial resolution) were trained (70% of data), validated (20%) and tested on a “real-world” dataset (10%). Moreover, dCNN accuracy was compared to a human readout. The overall performance of the model with lowest resolution of input data was highest, reaching an accuracy on the “real-world” dataset of 85.8%. The intra-class correlation of the dCNN and the two readers was almost perfect (0.92) and kappa values between both readers and the dCNN were substantial (0.71–0.76). Moreover, the diagnostic performance between the readers and the dCNN showed very good correspondence with an AUC of 0.89. Artificial Intelligence in the form of a dCNN can be used for standardized, observer-independent and reliable classification of parenchymal density in a BCT examination.

Communities in urban contexts and firms in corporate offices have recently started to implement collaborative spaces. Several authors from different disciplines are currently advancing knowledge in this realm. Systematising this diverse knowledge base helps to advance our understanding of this novel phenomenon. To this end, the present work reviews 29 papers focusing on collaborative spaces. We analyse these papers in terms of contents, research methods, fields of study, authors’ background, and impact on the academic community. Grounding on this analysis, we outline new relevant research questions and opportunities for future investigations.

PURPOSE:We aimed to perform intraindividual comparison of computed tomography (CT) parameters, image quality, and radiation exposure between standard CT angiography (CTA) and high-pitch dual source (DS)-CTA, in subjects undergoing serial CTA of thoracoabdominal aorta.METHODS:Eighteen subjects with thoracoabdominal CTA by standard technique and high-pitch DS-CTA technique within 6 months of each other were retrieved for intraindividual comparison of image quality in thoracic and abdominal aorta. Quantitative analysis was performed by comparison of mean aortic attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Qualitative analysis was performed by visual assessment of motion artifacts and diagnostic confidence. Radiation exposure was quantified by effective dose. Image quality was apportioned to radiation exposure by means of figure of merit.RESULTS:Mean aortic attenuation and noise were higher in high-pitch DS-CTA of thoracoabdominal aorta, whereas SNR and CNR were similar in thoracic aorta and significantly lower in high-pitch DS-CTA of abdominal aorta (P = 0.024 and P = 0.016). High-pitch DS-CTA was significantly better in the first segment of thoracic aorta. Effective dose was reduced by 72% in high-pitch DS-CTA.CONCLUSION:High-pitch DS-CTA without electrocardiography-gating is an effective technique for imaging aorta with very low radiation exposure and with significant reduction of motion artifacts in ascending aorta; however, the overall quality of high-pitch DS-CTA in abdominal aorta is lower than standard CTA.

Intravoxel incoherent motion (IVIM) analysis of diffusion imaging data provides biomarkers of true passive water diffusion and perfusion properties. A new IVIM algorithm with variable adjustment of the b-value threshold separating diffusion and perfusion effects was applied for cerebral tissue characterization in healthy volunteers, computation of test-retest reliability, correlation with arterial spin labeling, and assessment of applicability in a small cohort of patients with malignant intracranial masses. The main results of this study are threefold: (i) accounting for regional differences in the separation of the perfusion and the diffusion components improves the reliability of the model parameters; (ii) if differences in the b-value threshold are not accounted for, a significant tissue-dependent systematic bias of the IVIM parameters occurs; (iii) accounting for voxel-wise differences in the b-value threshold improves the correlation with CBF measurements in healthy volunteers and patients. The proposed algorithm provides a robust characterization of regional micro-vascularization and cellularity without a priori assumptions on tissue diffusion properties. The glioblastoma multiforme with its inherently high variability of tumor vascularization and tumor cell density may benefit from a non-invasive clinical characterization of diffusion and perfusion properties.

This study aimed to monitor the course of liver regeneration by multiparametric magnetic-resonance imaging (MRI) after partial liver resection characterizing Small-for-Size Syndrome (SFSS), which frequently leads to fatal post-hepatectomy liver failure (PLF). Twenty-two C57BL/6 mice underwent either conventional 70% partial hepatectomy (cPH), extended 86% partial hepatectomy (ePH) or SHAM operation. Subsequent MRI scans on days 0, 1, 2, 3, 5 and 7 in a 4.7T MR Scanner quantified longitudinal and transverse relaxation times, apparent diffusion coefficient (ADC) and the magnetization-transfer ratio (MTR) of the regenerating liver parenchyma. Histological examination was performed by hematoxylin-eosin staining. After hepatectomy, an increase of T1 time was detected being larger for ePH on day 1: 18% for cPH vs. 40% for ePH and on day 2: 24% for cPH vs. 49% for ePH. An increase in T2 time, again greater in ePH was most pronounced on day 5: 21% for cPH vs. 41% for ePH. ADC and MTR showed a decrease on day 1: 21% for ePH vs. 13% for cPH for ADC, 15% for ePH vs. 11% for cPH for MTR. Subsequently, all MR parameters converged towards initial values in surviving animals. Dying PLF animals exhibited the strongest increase of T1 relaxation time and most prominent decreases of ADC and MTR. The retrieved MRI biomarkers indicate SFSS and potentially developing PLF at an early stage, likely reflecting cellular hypertrophy with extended water content and concomitantly diluted cellular components as features of liver regeneration, appearing more intense in ePH as compared to cPH.

PURPOSEWe aimed to assess the correlation between pulmonary hemorrhage and pneumothorax in computed tomography (CT)-guided transthoracic fine needle aspiration (TTFNA), particularly its possible value as protection against the development of pneumotorax.MATERIALS AND METHODSWe reviewed the CT images of 538 patients (364 males and 174 females, mean age 70 years, range 36–90 years) who underwent CT-guided TTFNA of pulmonary nodules between January 2008 and September 2013. The following CT findings were assessed: pulmonary hemorrhage (type 1, along the needle track; type 2, perilesional; low-grade, ≤6 mm; high-grade, >6 mm), pneumothorax, distance between the target nodule and the pleural surface, and emphysema.RESULTSPneumothorax occurred in 154 cases (28.6%) and pulmonary hemorrhage occurred in 144 cases (26.8%). The incidence of pneumothorax was lower in patients showing type 1 and high-grade pulmonary hemorrhage pattern. The incidence of pneumothorax in biopsies ≥30 mm from pleural surface was 26% (12/46) in cases showing this pattern, while it was 71.4% (30/42) when this pattern was not seen. Similarly, the incidence of pneumothorax in biopsies

The imaging techniques in patients treated for lung cancer may be challenging to interpret. Radiologists are often asked to evaluate computed tomography (CT) scans after surgery, and this interpretation requires an understanding of both the timing and type of the surgical procedure. However, follow-up strategies are still not well defined. The assessment of tumor response to chemoradiotherapy relies on a tight integration of CT and clinical findings. Positron emission tomography-computed tomography (PET-CT) with fluorodeoxyglucose may help to exclude tumor recurrence when the sole CT scan is equivocal. More efforts are needed to validate the tools for volumetric tumor measurement in routine practice and to demonstrate their superiority compared to the Response Evaluation Criteria in Solid Tumors (RECIST). Familiarity with the assessment of lung cancer perfusion is also important because of the increasing use of cytostatic therapy. In this review, we outlined the imaging assessment of tumor recurrence after surgery and the role of CT, magnetic resonance imaging, and PET-CT in the follow-up after chemotherapy, radiotherapy, and radiofrequency ablation.

SCOPE. Patients with tracheostomy tube after major cardiac surgery undergoing Cardiac Rehabilitation Program often present swallowing disorders that need a specific evaluation. This study aimed at validating the usefulness of a swallowing screen protocol in order to obtain an early assessment of dysphagia and to prevent aspiration, malnutrition and dehydration risks. MATERIALS AND METHODS. The protocol has been applied to 38 consecutive patients with tracheostomy tube after cardiac surgery between September 2007 and December 2009. The average age of patients was 73 ± 6 years; the average value of left ventricular ejection fraction was 41 ± 4%. The protocol included a water swallowing test and a specific swallowing test with blue dye. During tests, the presence of signs of swallowing dysfunction was evaluated and oxygen saturation levels were monitored. RESULTS. Out of 38 patients, 2 did not show any swallowing deficits; 18 showed deficits in the water swallowing test even though they presented a preserved swallowing function during specific swallowing test. All these 20 patients (53%), before discharge, restarted an oral feeding and obtained an adequate body mass index and effective coughing, so they were soon decannulated. Twelve patients (31%) showed deficits during the water swallowing test, confirmed by the specific swallowing test with blue dye: the tracheostomy tube was not removed and a specific program of swallowing rehabilitation was performed. Before discharge all patients restarted an oral feeding, recovered an effective coughing and were decannulated. Six patients (16%), because of persistent dysphagia, underwent percutaneous endoscopic gastrostomy. None of these 38 patients developed ab ingestis pneumonia, dehydration or malnutrition. CONCLUSIONS. An early assessment of swallowing in patients with tracheostomy tube after cardiac surgery allows the selection of patients with higher aspiration risk, preventing possible severe complications.

The β-hexachlorocyclohexane (β-HCH) is a very stable and accumulable isomer of Lindane, a well known organochlorine pesticide. The HCHs were banned in all developed countries but to date high concern still exists for environment, animal and human health due to contaminated sites. In this study, several in vitro tests [cell viability (XTT), trypan blue exclusion (TBE), lactate dehydrogenase release (LDH) and bromodeoxyuridine (BrdU) incorporation assays] were performed to investigate the toxic effects of exposure to β-HCH (from 0.1 to 1000 μM) on bovine peripheral blood mononuclear cells (PBMCs). All the trials were performed incubating PBMCs for 2 and 7 days. At high concentrations (i.e. 1000 μM), the β-HCH approximately halved the number of living cells regardless the exposure time, significantly decreased the cell viability assessed by the XTT assay, and compromised the proliferation potential of PBMCs. At lower β-HCH exposure levels (0.1 to 100 μM), particularly after 7 days of exposure, a progressive decrease of cell viability has been observed. These adverse effects were significant at concentrations observed in the blood of cattle reared in polluted areas. The LDH results suggest that β-HCH does not clearly affect the integrity of the cell membrane in the range of exposure levels tested. All in all, these findings warn about the risk posed by the long-term exposure to β-HCH of farm animals reared in rural areas polluted by β-HCH. Further research is needed to deepen our knowledge about the mechanisms through which β-HCH affects the PBMCs functionality.

Amazonian forest conversion into agricultural and livestock areas is considered one of the activities that contribute most to the emission of greenhouse gases, including methane. Biogenic methane production is mainly performed by methanogenic Archaea, which underscores the importance of understanding the drivers shaping microbial communities involved in the methane cycling and changes in methane metabolism. Here, we aimed to investigate the composition and structure of bacterial and archaeal communities in tropical soils in response to land-use changes, emphasizing the methanogenic communities. We collected soil samples from primary forest, pasture, and secondary forest of the Amazonian region and used a strategy based on the enrichment of the methanogenic community with three different methanogenic substrates followed by measurements of methane emission, quantification of mcrA gene copies by qPCR, and total 16 S rRNA gene sequencing (metataxonomics). We observed variations in the structure of bacterial and archaeal communities of soils under different uses. The richness of methanogenic communities was higher in pasture than forest soils and this richness remained during the incubation period, and as a consequence, the enrichment induced earlier methane emission in pastures-derived samples. Furthermore, pastures enrichments exhibited methanogenic archaea networks more complex than primary and secondary forests. In conclusion, pastures harbor a richer and more responsive methanogenic community than forest samples, suggesting that conversion of forest areas to pasture may boost methane emission.

Levels of 237 pesticides were assessed in 1063 fruit and vegetable samples from 12 São Paulo markets spanning the period May 2015 to December 2022. The QuEChERS method was employed for extraction, followed by GC-MS/MS and LC-MS/MS analysis. Findings indicated that 30% of the samples contained residues below the Maximum Residue Limits (MRLs), while 6% exceeded these. Additionally, 23% exhibited excessive residues for their respective crops and 40% had no detectable residues. Health risk evaluation focused on tomatoes, cabbage and oranges, revealing exposure within 0.002–0.9% of the Acceptable Daily Intake (ADI), indicating no chronic risks. However, pyraclostrobin in orange presented a potential acute risk for adults (112%). These results underscore the necessity for continuous monitoring of pesticide residues in fruits and vegetables to safeguard consumer health, especially considering the significant levels of consumption. [ABSTRACT FROM AUTHOR]