Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity.

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 µm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

A Suite of Transgenic Driver and Reporter Mouse Lines with Enhanced Brain-Cell-Type Targeting and Functionality.

Modern genetic approaches are powerful in providing access to diverse cell types in the brain and facilitating the study of their function. Here, we report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 µm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

Oligodendrocyte precursor cells ingest axons in the mouse neocortex.

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.

Exploring the bio-insecticidal activity of Eucalyptus globulus essential oil/ß-cyclodextrin inclusion complexes: In vitro and in silico assessment against Ephestia kuehniella larvae.

Owing to their beneficial functional capabilities, essential oils were largely used. However, their low aqueous solubility, instability, and high volatility urged scientists to their encapsulation with cyclodextrins (CDs) to tackle their shortcomings. In this study, the co-precipitation method was used to prepare ß-CD/Eucalyptus globulus essential oil (EGEO) inclusion complexes (ICs). ß-CD/EGEO ICs were prepared at ratios (w:w) 1:2 and 1:4 with an encapsulation efficiency of 93 and 96%, respectively. The ICs characterization using the Fourier transform Infrared spectroscopy, differential scanning calorimetry, X-ray powder diffraction, Dynamic Light Scattering, and Laser Doppler Velocimetry confirmed the formation of ß-CD/EGEO ICs. The insecticidal activity of the free EGEO and ICs was explored and displayed that the complex ß-CD/EGEO 1:4 had the highest activity with the lowest LC50 against Ephestia kuehniella larvae (5.03 ± 1.16 mg/g) when compared to the free oil (8.38 ± 1.95 mg/g). Molecular docking simulations stipulated that the compound α-Bisabolene epoxide had the best docking score (ΔG = -7.4 Kcal/mol) against the selected insecticidal target α-amylase. Additionally, toxicity evaluation of the studied essential oil suggested that it could be safely used as a potent bioinsecticide as compared to chemical insecticides. This study reveals that the formation of ß-CD/EGEO ICs enhanced the oil activity and stability and could be a promising and safe tool to boost its application in food or pharmaceutical fields.

Systematic Review of Emotion Detection with Computer Vision and Deep Learning.

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human-computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and "Other NNs", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.

Hydroxytyrosol recovery from olive pomace: a simple process using olive mill industrial equipment and membrane technology.

In this work, pilot-scale nanofiltration was used to obtain aqueous solutions rich in hydroxytyrosol and tyrosol from olive oil by-products. A large-scale simple process involving olive mill standard machinery (blender and decanter) was used for the olive pomace pre-treatment with water. The aqueous extract was then directly fed to a nanofiltration unit and concentrated by reverse osmosis. Final concentration factors ranged between 7 and 9 for hydroxytyrosol and between 4 and 7 for tyrosol. The final aqueous solution, obtained as retentate stream of reverse osmosis, was highly concentrated in hydroxytyrosol and tyrosol and their concentrations remained stable over at least 14 months.

Avelumab Maintenance Therapy for Advanced or Metastatic Urothelial Carcinoma.

BACKGROUND: Platinum-based chemotherapy is standard-of-care first-line treatment for advanced urothelial carcinoma. However, progression-free survival and overall survival are limited by chemotherapy resistance. METHODS: In a phase 3 trial, we randomly assigned patients with unresectable locally advanced or metastatic urothelial cancer who did not have disease progression with first-line chemotherapy (four to six cycles of gemcitabine plus cisplatin or carboplatin) to receive best supportive care with or without maintenance avelumab. The primary end point was overall survival, assessed among all patients who underwent randomization (overall population) and among those with tumors positive for programmed cell death ligand 1 (PD-L1). Secondary end points included progression-free survival and safety. RESULTS: Among all 700 patients who underwent randomization, the addition of maintenance avelumab to best supportive care significantly prolonged overall survival as compared with best supportive care alone (control). Overall survival at 1 year was 71.3% in the avelumab group and 58.4% in the control group (median overall survival, 21.4 months vs. 14.3 months; hazard ratio for death, 0.69; 95% confidence interval [CI], 0.56 to 0.86; P = 0.001). Avelumab also significantly prolonged overall survival in the PD-L1-positive population; overall survival at 1 year was 79.1% in the avelumab group and 60.4% in the control group (hazard ratio, 0.56; 95% CI, 0.40 to 0.79; P<0.001). The median progression-free survival was 3.7 months in the avelumab group and 2.0 months in the control group in the overall population (hazard ratio for disease progression or death, 0.62; 95% CI, 0.52 to 0.75) and 5.7 months and 2.1 months, respectively, in the PD-L1-positive population (hazard ratio, 0.56; 95% CI, 0.43 to 0.73). The incidence of adverse events from any cause was 98.0% in the avelumab group and 77.7% in the control group; the incidence of adverse events of grade 3 or higher was 47.4% and 25.2%, respectively. CONCLUSIONS: Maintenance avelumab plus best supportive care significantly prolonged overall survival, as compared with best supportive care alone, among patients with urothelial cancer who had disease that had not progressed with first-line chemotherapy. (Funded by Pfizer and Merck [Darmstadt, Germany]; JAVELIN Bladder 100 ClinicalTrials.gov number, NCT02603432.).

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.

BACKGROUND: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS: We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS: Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS: This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

Augmented Reality-Assisted Ultrasound Breast Biopsy.

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

Fusion Object Detection and Action Recognition to Predict Violent Action.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning.

Defect detection is a key element of quality control in today's industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.

Plain language summary of results from the JAVELIN Bladder 100 study: avelumab maintenance treatment for advanced urothelial cancer.

WHAT IS THIS SUMMARY ABOUT?: This is a plain language summary of an article originally published in The New England Journal of Medicine. It is about initial results (collected in October 2019) from the JAVELIN Bladder 100 study (a clinical trial), which looked at avelumab maintenance treatment in people with advanced urothelial cancer. Urothelial cancer is the most common type of bladder cancer. People with advanced urothelial cancer often receive chemotherapy. If this is the first treatment people with advanced disease are given, it is called first-line treatment. If the cancer stops growing or shrinks with first-line chemotherapy, people can be given different treatment to try to prevent the cancer from growing again. This is called maintenance treatment. It may help people live longer. WHAT HAPPENED IN THE JAVELIN BLADDER 100 STUDY?: In the JAVELIN Bladder 100 study, researchers wanted to find out if maintenance treatment with avelumab would help people with advanced urothelial cancer live longer. Avelumab is a type of medicine called immunotherapy. Immunotherapy helps the body's immune system fight cancer. 700 people took part in the study. To take part, they must have already been treated with first-line chemotherapy. Also, their cancer must have shrunk or not grown with this treatment. They were then treated with either avelumab maintenance treatment plus best supportive care or best supportive care alone. Best supportive care means treatments that help improve symptoms and quality of life. These treatments do not affect the cancer directly and can include medicines to relieve pain. WHAT WERE THE RESULTS?: Researchers found that people treated with avelumab maintenance treatment plus best supportive care lived, on average, 7 months longer than people who received best supportive care alone. People treated with avelumab had more side effects than those not treated with avelumab, but most were not severe. Common side effects with avelumab included persistent tiredness, itchy skin, urinary tract infection, and diarrhea. WHAT DO THE RESULTS OF THE STUDY MEAN?: Results from the JAVELIN Bladder 100 study support the use of avelumab as maintenance treatment for people with advanced urothelial cancer whose cancer has shrunk or not grown with first-line chemotherapy. ClinicalTrials.gov NCT number: NCT02603432.

Amorphous and Co-Amorphous Olanzapine Stability in Formulations Intended for Wet Granulation and Pelletization.

The preparation of amorphous and co-amorphous systems (CAMs) effectively addresses the solubility and bioavailability issues of poorly water-soluble chemical entities. However, stress conditions imposed during common pharmaceutical processing (e.g., tableting) may cause the recrystallization of the systems, warranting close stability monitoring throughout production. This work aimed at assessing the water and heat stability of amorphous olanzapine (OLZ) and OLZ-CAMs when subject to wet granulation and pelletization. Starting materials and products were characterized using calorimetry, diffractometry and spectroscopy, and their performance behavior was evaluated by dissolution testing. The results indicated that amorphous OLZ was reconverted back to a crystalline state after exposure to water and heat; conversely, OLZ-CAMs stabilized with saccharin (SAC), a sulfonic acid, did not show any significant loss of the amorphous content, confirming the higher stability of OLZ in the CAM. Besides resistance under the processing conditions of the dosage forms considered, OLZ-CAMs presented a higher solubility and dissolution rate than the respective crystalline counterpart. Furthermore, in situ co-amorphization of OLZ and SAC during granule production with high fractions of water unveils the possibility of reducing production steps and associated costs.

Avelumab expanded access program in metastatic Merkel cell carcinoma: Efficacy and safety findings from patients in Europe and the Middle East.

Incidence rates of Merkel cell carcinoma (MCC), an uncommon skin cancer with an aggressive disease course, have increased in recent decades. Limited treatment options are available for patients with metastatic MCC (mMCC). Avelumab, an anti-programmed cell death-ligand 1 monoclonal antibody, became the first approved treatment for mMCC after the results of the phase 2 JAVELIN Merkel 200 study. Prior to its regulatory approval, an expanded access program (EAP) enabled compassionate use of avelumab in patients with mMCC. Here we report findings from patients enrolled in the EAP in Europe and the Middle East. Efficacy and safety data were provided at the discretion of treating physicians. Between March 2, 2016, and December 22, 2018, 403 requests for avelumab were received from 21 countries, and avelumab was supplied to 335 patients. Most patients (96.7%) received avelumab as second-line or later treatment. In 150 patients for whom response data were available, the objective response rate was 48.0%, and in responding patients, median duration of treatment was 7.4 months (range, 1.0-41.7 months). The most common treatment-related adverse events were infusion-related reaction (2.4%) and pyrexia (2.1%), and no new safety signals were observed. Overall, results from European and Middle Eastern patients enrolled in this EAP confirm the efficacy and safety of avelumab treatment observed in previous studies in patients with mMCC.

BestFLR Trial: Liver Regeneration at CT before Major Hepatectomies for Liver Cancer-A Randomized Controlled Trial Comparing Portal Vein Embolization with N-Butyl-Cyanoacrylate Plus Iodized Oil versus Polyvinyl Alcohol Particles Plus Coils.

Background In patients with liver cancer, portal vein embolization (PVE) is recommended to promote liver growth before major hepatectomies. However, the optimal embolization strategy has not been established. Purpose To compare liver regeneration as seen at CT in participants with liver cancer, before major hepatectomies, with N-butyl-cyanoacrylate (NBCA) plus iodized oil versus standard polyvinyl alcohol (PVA) particles plus coils, for PVE. Materials and Methods In this single-center, prospective, randomized controlled trial (Best Future Liver Remnant, or BestFLR, trial; International Standard Randomized Controlled Trial Number 16062796), PVE with NBCA plus iodized oil was compared with standard PVE with PVA particles plus coils in participants with liver cancer. Participant recruitment started in November 2017 and ended in March 2020. Participants were randomly assigned to undergo PVE with PVA particles plus coils or PVE with NBCA plus iodized oil. The primary end point was liver growth assessed with CT 14 days and 28 days after PVE. Secondary outcomes included posthepatectomy liver failure, surgical complications, and length of intensive care treatment and hospital stay. The Mann-Whitney U test was used to compare continuous outcomes according to PVE material, whereas the Χ2 test or Fisher exact test was used for categoric variables. Results Sixty participants (mean age, 61 years ± 11 [standard deviation]; 32 men) were assigned to the PVA particles plus coils group (n = 30) or to the NBCA plus iodized oil group (n = 30). Interim analysis revealed faster and superior liver hypertrophy for the NBCA plus iodized oil group versus the PVA particles plus coils group 14 days and 28 days after PVE (absolute hypertrophy of 46% vs 30% [P < .001] and 57% vs 37% [P < .001], respectively). Liver growth for the proposed hepatectomy was achieved in 87% of participants (26 of 30) in the NBCA plus iodized oil group versus 53% of participants (16 of 30) in the PVA particles plus coils group (P = .008) 14 days after PVE. Liver failure occurred in 13% of participants (three of 24) in the NBCA plus iodized oil group and in 27% of participants (six of 22) in the PVA particles plus coils group (P = .27). Conclusion Portal vein embolization with N-butyl-cyanoacrylate plus iodized oil produced greater and faster liver growth as seen at CT in participants with liver cancer, compared with portal vein embolization with polyvinyl alcohol particles plus coils, allowing for earlier surgical intervention. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Arellano in this issue.

Systemic Lupus Erythematosus Disease Activity Score (SLE-DAS) enables accurate and user-friendly definitions of clinical remission and categories of disease activity.

OBJECTIVES: There is an unmet need for accurate and user-friendly definitions of systemic lupus erythematosus (SLE) disease activity and remission. We aimed to derive and validate the SLE Disease Activity Score (SLE-DAS) definitions for disease activity categories and clinical remission state. METHODS: Derivation was conducted at Padova Lupus Clinic (Italy). Validation was prospectively performed at Cochin Lupus Clinic (France) and by post hoc analysis of BLISS-76 trial. At each clinic, an expert classified patients in three categories: remission, mild or moderate/severe activity. The SLE-DAS cut-offs were derived using the receiver operating characteristic curve analysis in Padova cohort; its performance was assessed against expert classification in Cochin cohort and British Isles Lupus Assessment Group (BILAG) index in BLISS-76. Gold standard for clinical remission state was the fulfilment of Definition Of Remission In SLE. A Boolean and an index-based definitions of remission were sustained by chi-square automatic interaction detection algorithm. An SLE-DAS online calculator was developed and tested. RESULTS: We included 1190 patients with SLE: 221 in the derivation cohort and 969 in the validation cohorts (150 from Cochin; 819 from BLISS-76). Derived cut-offs were: remission, SLE-DAS ≤2.08; mild activity, 2.087.64. Regarding validation in Cochin cohort, sensitivity and specificity are above 90%, 82% and 95% for remission, mild and moderate/severe activity, respectively. The SLE-DAS Boolean-based and index-based remission showed sensitivity of 100% and specificity above 97%. CONCLUSION: The SLE-DAS is an accurate and easy-to-use tool for defining SLE clinical remission state and disease activity categories, validated against expert assessment and BILAG.

Avelumab treatment in Italian patients with metastatic Merkel cell carcinoma: experience from an expanded access program.

BACKGROUND: The incidence of Merkel cell carcinoma (MCC), a rare form of skin cancer with a poor prognosis, has increased in Italy in recent decades. Avelumab, an anti-programmed death ligand 1 monoclonal antibody, is approved for the treatment of metastatic MCC (mMCC) based on the results of the phase 2 JAVELIN Merkel 200 trial. The global avelumab expanded access program (EAP) was designed to provide compassionate use of avelumab prior to approval for patients with mMCC who had limited treatment options. We report findings from a subgroup of Italian patients enrolled in the avelumab EAP. METHODS: Eligible patients had mMCC and progressive disease following ≥ 1 prior line of chemotherapy or were ineligible for chemotherapy or clinical trial participation. Patients received avelumab 10 mg/kg intravenously every 2 weeks. Treating physicians were provided with an initial 3-month supply of avelumab; resupply was permitted if the patient achieved a complete response, partial response, stable disease, or other clinical benefit per physician assessment. Safety and efficacy data for the EAP were reported at the treating physician's discretion. RESULTS: Between April 1, 2016, and September 14, 2018, 109 requests for avelumab were received from Italy, and 102 were approved. All but 1 of the approved patients had received ≥ 1 prior line of therapy. At data cutoff (March 22, 2019), 95 patients had been supplied with avelumab and response data were available for 55 patients. The objective response rate in response-evaluable patients was 29.1%, including 6 patients (10.9%) who achieved a complete response and 10 patients (18.2%) who achieved a partial response; in the total population supplied with avelumab (n = 95), the proportion who had an objective response was 16.8%. The median duration of treatment in responding patients was 9.7 months (range, 3.5-41.7 months). The most frequently reported treatment-related adverse events were infusion-related reaction (single preferred term; n = 3 [3.2%]) and pyrexia (n = 2 [2.1%]). CONCLUSIONS: Results from Italian patients enrolled in the avelumab EAP are consistent with the findings of the JAVELIN Merkel 200 trial and confirm the efficacy and safety of avelumab treatment in this population.

SAR.IoT: Secured Augmented Reality for IoT Devices Management.

Currently, solutions based on the Internet of Things (IoT) concept are increasingly being adopted in several fields, namely, industry, agriculture, and home automation. The costs associated with this type of equipment is reasonably small, as IoT devices usually do not have output peripherals to display information about their status (e.g., a screen or a printer), although they may have informative LEDs, which is sometimes insufficient. For most IoT devices, the price of a minimalist display, to output and display the device's running status (i.e., what the device is doing), might cost much more than the actual IoT device. Occasionally, it might become necessary to visualize the IoT device output, making it necessary to find solutions to show the hardware output information in real time, without requiring extra equipment, only what the administrator usually has with them. In order to solve the above, a technological solution that allows for the visualization of IoT device information in actual time, using augmented reality and a simple smartphone, was developed and analyzed. In addition, the system created integrates a security layer, at the level of AR, to secure the shown data from unwanted eyes. The results of the tests carried out allowed us to validate the operation of the solution when accessing the information of the IoT devices, verify the operation of the security layer in AR, analyze the interaction between smartphones, the platform, and the devices, and check which AR markers are most optimized for this use case. This work results in a secure augmented reality solution, which can be used with a simple smartphone, to monitor/manage IoT devices in industrial, laboratory or research environments.