Is Melanoma Progression Affected by Thyroid Diseases?

Clinical and epidemiological evidence indicate a relationship between thyroid diseases and melanoma. In particular, the hypothyroidism condition appears to promote melanoma spread, which suggests a protective role of thyroid hormones against disease progression. In addition, experimental data suggest that, in addition to thyroid hormones, other hormonal players of the hypothalamic–pituitary–thyroid (HPT) axis, namely the thyrotropin releasing hormone and the thyrotropin, are likely to affect melanoma cells behavior. This information warrants further clinical and experimental studies in order to build a precise pattern of action of the HPT hormones on melanoma cells. An improved knowledge of the involved molecular mechanism(s) could lead to a better and possibly personalized clinical management of these patients.     

The New Microtubule-Targeting Agent SIX2G Induces Immunogenic Cell Death in Multiple Myeloma

Microtubule-targeting agents (MTAs) are effective drugs for cancer treatment. A novel diaryl [1,2]oxazole class of compounds binding the colchicine site was synthesized as cis-restricted-combretastatin-A-4-analogue and then chemically modified to have improved solubility and a wider therapeutic index as compared to vinca alkaloids and taxanes. On these bases, a new class of tricyclic compounds, containing the [1,2]oxazole ring and an isoindole moiety, has been synthetized, among which SIX2G emerged as improved MTA. Several findings highlighted the ability of some chemotherapeutics to induce immunogenic cell death (ICD), which is defined by the cell surface translocation of Calreticulin (CALR) via dissociation of the PP1/GADD34 complex. In this regard, we computationally predicted the ability of SIX2G to induce CALR exposure by interacting with the PP1 RVxF domain. We then assessed both the potential cytotoxic and immunogenic activity of SIX2G on in vitro models of multiple myeloma (MM), which is an incurable hematological malignancy characterized by an immunosuppressive milieu. We found that the treatment with SIX2G inhibited cell viability by inducing G2/M phase cell cycle arrest and apoptosis. Moreover, we observed the increase of hallmarks of ICD such as CALR exposure, ATP release and phospho-eIF2α protein level. Through co-culture experiments with immune cells, we demonstrated the increase of (i) CD86 maturation marker on dendritic cells, (ii) CD69 activation marker on cytotoxic T cells, and (iii) phagocytosis of tumor cells following treatment with SIX2G, confirming the onset of an immunogenic cascade. In conclusion, our findings provide a framework for further development of SIX2G as a new potential anti-MM agent.     

Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.     

Clinical Significance of Molecular Subtypes in Western Advanced Gastric Cancer: A Real-World Multicenter Experience

In recent years, the molecular subtyping of gastric cancer has led to the identification of novel clinically relevant biomarkers as well as promising therapeutic targets. In parallel, the advent of checkpoint inhibitors has expanded treatment options beyond conventional chemotherapy. Compelling evidence has shown unprecedented efficacy results for anti-PD1-based therapies in the molecular subgroups of dMMR/MSI-h, EBV+ and PD-L1 CPS+ patients, to the point that these are granted approval for gastric cancer adenocarcinoma (AGC) in several countries. Despite this, cytotoxic chemotherapy remains the only treatment choice for the considerable proportion of biomarkers-negative patients. In this context, little is known about the association between subtypes-defining biomarkers (HER2, MMR/MSI, PD-L1, and EBV) and the efficacy of standard chemotherapy in non-Asian AGC. Here, we aimed to investigate the prevalence, the clinic-pathologic features, and the impact on treatment outcome of clinical molecular subtypes in a new-diagnosed Western cohort of AGC.     

FT-NIR spectroscopy for the quality characterization of apricots (Prunus armeniaca L.)

Abstract: The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm⁻¹. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R² values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended. Practical Application : The results of the study could be used as a tool for the assessment of the ripening stage during the harvest and the quality during the postharvest storage of apricot fruits.     

Power Efficient SDR Implementation of IEEE 802.11a/p Physical Layer

Software defined physical layer modems can be considered the new trend in the field of communications. Differently from dedicated hardware, software can be easily modified to implement a large variety of standards on the same platform. The use of software can significantly reduce development costs, but generally comes at the price of an increase in silicon area and power consumption. For different reasons, this price is something that is not always convenient or even possible to pay, as in the case of low-cost ICs implementing a single waveform, or even multi-mode modems embedding legacy IPs already available in hardware. In particular, power consumption overhead can be prohibitive for mobile terminals or in general for battery-powered devices. The very first challenge for a computing fabric to be competitive is to find and implement the right trade-off between flexibility and performance. This was the guideline for the design of the Block Processing Engine (BPE), a template architecture conceived for power-efficient baseband processing. The BPE core feature is a mixed-grain instruction set balancing general-purpose fine-grain instructions with more specific coarse-grain instructions wrapping custom hardware modules. To further limit the power consumption, the BPE also implements instruction-pipelining, variable-size SIMD and multi-task support. To prove the efficiency of such an approach, a dual-mode IEEE 802.11a/p receiver has been implemented.     

Influence of the Tunnel Shape on Shotcrete Lining Stresses

Tunnel excavation is frequently carried out in rock masses by the drill and blast method and the final shape of the tunnel boundary can be irregular due to overbreaks. In order to investigate the effects of overbreaks a study of the effect of tunnel boundary irregularity has been carried out. This is done developing a computational tool able to take into account fuzzy variables (i.e., thickness of the beams of the bedded spring approach used for the model). The obtained results show that irregularity effects should be considered when a shotcrete lining is used as the final tunnel lining (for the case where the tunneling procedure does not permit a smooth surface to be obtained). This is crucial to obtain a durable lining.     

A spatial relation-based framework to perform visual information extraction

The Spatial Relation Query (SRQ) tool is a graphical software environment, supported by a SQL-like language, which enables users to perform information extraction driven by the visual appearance and the spatial arrangement of the information. The tool has been initially customised to work on specific application domains, like web pages and geospatial data. In this paper, we present the theoretical formalisation of the visual information extraction (VIE) task and accordingly the redesign of the SRQ tool, which is now a full-featured, general-purpose information extraction system. Moreover, we show a new application of the VIE framework to the analysis and visual information extraction from PDF files.     

Calibration and validation of a solar thermal system model in Modelica

Recent advancements in the domain of modeling physical processes offer opportunities to use equation based modeling environments, such as Modelica, for the simulation of building heating, ventilation, and air-conditioning (HVAC) systems. The current work demonstrates Modelica capabilities in a case study of real building solar thermal system simulation. The simulated system is part of an innovative ENERGYbase building, designed according to the so called Passivhaus standard. Model calibration and validation procedure is developed to include optimization based parametric adjustments of component models using the monitoring data during a single week. The calibrated system adequately reproduces half a year of real system operation. Future work will concentrate on application of the developed calibration and validation methodology in the whole year overall building energy simulation.