Biohybrid Bovine Bone Matrix for Controlled Release of Mesenchymal Stem/Stromal Cell Lyosecretome: A Device for Bone Regeneration

SmartBone^® (SB) is a biohybrid bone substitute advantageously proposed as a class III medical device for bone regeneration in reconstructive surgeries (oral, maxillofacial, orthopedic, and oncology). In the present study, a new strategy to improve SB osteoinductivity was developed. SB scaffolds were loaded with lyosecretome, a freeze-dried formulation of mesenchymal stem cell (MSC)-secretome, containing proteins and extracellular vesicles (EVs). Lyosecretome-loaded SB scaffolds (SBlyo) were prepared using an absorption method. A burst release of proteins and EVs (38% and 50% after 30 min, respectively) was observed, and then proteins were released more slowly with respect to EVs, most likely because they more strongly adsorbed onto the SB surface. In vitro tests were conducted using adipose tissue-derived stromal vascular fraction (SVF) plated on SB or SBlyo. After 14 days, significant cell proliferation improvement was observed on SBlyo with respect to SB, where cells filled the cavities between the native trabeculae. On SB, on the other hand, the process was still present, but tissue formation was less organized at 60 days. On both scaffolds, cells differentiated into osteoblasts and were able to mineralize after 60 days. Nonetheless, SBlyo showed a higher expression of osteoblast markers and a higher quantity of newly formed trabeculae than SB alone. The quantification analysis of the newly formed mineralized tissue and the immunohistochemical studies demonstrated that SBlyo induces bone formation more effectively. This osteoinductive effect is likely due to the osteogenic factors present in the lyosecretome, such as fibronectin, alpha-2-macroglobulin, apolipoprotein A, and TGF-β.     

The Tumor Proteolytic Landscape: A Challenging Frontier in Cancer Diagnosis and Therapy

In recent decades, dysregulation of proteases and atypical proteolysis have become increasingly recognized as important hallmarks of cancer, driving community-wide efforts to explore the proteolytic landscape of oncologic disease. With more than 100 proteases currently associated with different aspects of cancer development and progression, there is a clear impetus to harness their potential in the context of oncology. Advances in the protease field have yielded technologies enabling sensitive protease detection in various settings, paving the way towards diagnostic profiling of disease-related protease activity patterns. Methods including activity-based probes and substrates, antibodies, and various nanosystems that generate reporter signals, i.e., for PET or MRI, after interaction with the target protease have shown potential for clinical translation. Nevertheless, these technologies are costly, not easily multiplexed, and require advanced imaging technologies. While the current clinical applications of protease-responsive technologies in oncologic settings are still limited, emerging technologies and protease sensors are poised to enable comprehensive exploration of the tumor proteolytic landscape as a diagnostic and therapeutic frontier. This review aims to give an overview of the most relevant classes of proteases as indicators for tumor diagnosis, current approaches to detect and monitor their activity in vivo, and associated therapeutic applications.     

Toward a better understanding of multifunctional cement-based materials: The impact of graphite nanoplatelets (GNPs)

The impact of graphite nanoplatelets (GNPs) on the physical and mechanical properties of cementitious nanocomposites was investigated. A market-available premixed mortar was modified with 0.01% by weight of cement of commercial GNPs characterized by two distinctively different aspect ratios.The rheological behavior of the GNP-modified fresh admixtures was thoroughly evaluated. Hardened cementitious nanocomposites were investigated in terms of density, microstructure (Scanning Electron Microscopy, SEM and micro–Computed Tomography, μ-CT), mechanical properties (three-point bending and compression tests), and physical properties (electrochemical impedance spectroscopy, EIS and thermal conductivity measurements). At 28 days, all GNP-modified mortars showed about 12% increased density. Mortars reinforced with high aspect ratio GNPs exhibited the highest compressive and flexural strength: about 14% and 4% improvements compared to control sample, respectively. Conversely, low aspect ratio GNPs led to cementitious nanocomposites characterized by 36% decreased electrical resistivity combined with 60% increased thermal conductivity with respect to the control sample.     

Interrelated Mechanism by Which the Methide Quinone Celastrol,Obtained from the Roots of Tripterygium wilfordii,Inhibits Main Protease 3CLpro of COVID-19 and Acts as Superoxide Radical Scavenger

We describe the potential anti coronavirus disease 2019 (COVID-19) action of the methide quinone inhibitor, celastrol. The related methide quinone dexamethasone is, so far, among COVID-19 medications perhaps the most effective drug for patients with severe symptoms. We observe a parallel redox biology behavior between the antioxidant action of celastrol when scavenging the superoxide radical, and the adduct formation of celastrol with the main COVID-19 protease. The related molecular mechanism is envisioned using molecular mechanics and dynamics calculations. It proposes a covalent bond between the S(Cys145) amino acid thiolate and the celastrol A ring, assisted by proton transfers by His164 and His41 amino acids, and a π interaction from Met49 to the celastrol B ring. Specifically, celastrol possesses two moieties that are able to independently scavenge the superoxide radical: the carboxylic framework located at ring E, and the methide-quinone ring A. The latter captures the superoxide electron, releasing molecular oxygen, and is the feature of interest that correlates with the mechanism of COVID-19 inhibition. This unusual scavenging of the superoxide radical is described using density functional theory (DFT) methods, and is supported experimentally by cyclic voltammetry and X-ray diffraction.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.

     

Gastric Adenocarcinomas and Signet-Ring Cell Carcinoma: Unraveling Gastric Cancer Complexity through Microbiome Analysis—Deepening Heterogeneity for a Personalized Therapy

Gastric cancer (GC) is the fifth most prevalent cancer worldwide and the third leading cause of global cancer mortality. With the advances of the omic studies, a heterogeneous GC landscape has been revealed, with significant molecular diversity. Given the multifaceted nature of GC, identification of different patient subsets with prognostic and/or predictive outcomes is a key aspect to allow tailoring of specific treatments. Recently, the involvement of the microbiota in gastric carcinogenesis has been described. To deepen this aspect, we compared microbiota composition in signet-ring cell carcinoma (SRCC) and adenocarcinoma (ADC), two distinct GC subtypes. To this purpose, 10 ADC and 10 SRCC and their paired non-tumor (PNT) counterparts were evaluated for microbiota composition through 16S rRNA analysis. Weighted and unweighted UniFrac and Bray–Curtis dissimilarity showed significant community-level separation between ADC and SRCC. Through the LEfSe (linear discriminant analysis coupled with effect size) tool, we identified potential microbial biomarkers associated with GC subtypes. In particular, SRCCs were significantly enriched in the phyla Fusobacteria, Bacteroidetes, Patescibacteria, whereas in the ADC type, Proteobacteria and Acidobacteria phyla were found. Overall, our data add new insights into GC heterogeneity and may contribute to deepening the GC classification.     

意大利如何在古丝绸之路上再铸辉煌（英文）

古"丝绸之路"使中国的丝绸取代了地中海地区的丝绸,现如今,同样的"丝绸之路"仍旧以不同的形式存在着,意大利目前仍从中国购买几乎所有的丝绸原材料。外贸对于意大利丝绸业依旧是最重要的,出口以丝织物为主,意大利丝绸产品2016年的出口总值达到8.73亿欧元,较2015年略有增长,但由于价格压力和激烈的竞争,丝绸用量显著减少。不仅仅是意大利,当今世界的丝绸行业正承受着缺乏明确制度性战略的痛苦,针对生态可持续性、公平贸易和质量改善三方面制订丝绸战略迫在眉睫。随着经济走势继续加快,一些精准的政策也需出台,以保护丝绸本身和丝绸经营者。     

The Brazilian academic genealogy: evidence of advisor–advisee relationships through quantitative analysis

Scientometrics - Science can be examined from several standpoints, such as through a bibliometric analysis of the scientific output of researchers, research groups or institutions. However, there...