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Pancreatic cancer, most frequently as ductal adenocarcinoma (PDAC), is the third leading cause of cancer death. Clear-cell primary adenocarcinoma of the pancreas (CCCP) is a rare, aggressive, still poorly characterized subtype of PDAC. We report here a case of a 65-year-old male presenting with pancreatic neoplasia. A histochemical examination of the tumor showed large cells with clear and abundant intracytoplasmic vacuoles. The clear-cell foamy appearance was not related to the hyperproduction of mucins. Ultrastructural characterization with transmission electron microscopy revealed the massive presence of mitochondria in the clear-cell cytoplasm. The mitochondria showed disordered cristae and various degrees of loss of structural integrity. Immunohistochemistry staining for NADH dehydrogenase [ubiquinone] 1 alpha subcomplex, 4-like 2 (NDUFA4L2) proved specifically negative for the clear-cell tumor. Our ultrastructural and molecular data indicate that the clear-cell nature in CCCP is linked to the accumulation of disrupted mitochondria. We propose that this may impact on the origin and progression of this PDAC subtype.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel ® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges., Competing Interests: The authors declare no conflicts of interest.

Selective laser melting (SLM) is the most widely used laser powder-bed fusion (L-PBF) technology for the additive manufacturing (AM) of parts from metallic powders. The surface quality of the SLM parts is highly dependent on many factors and process parameters. These factors include the powder grain size, the layer thickness, and the building angle. This paper conducted an experimental analysis of the effects of SLM process parameters on the surface quality of CuCrZr cubic specimens. Thanks to its excellent thermal and mechanical properties, CrCrZr has become one of the most widely used materials in SLM technology. The specimens have been produced with different combinations of layer thickness, laser patterns, building angles, and scanning speed, keeping the energy density constant. The results show how different combinations of parameters affect the surface quality macroscopically (i.e., layer thickness, building angle, and scanning speed); in contrast, other parameters (i.e., laser pattern) do not seem to have any contributions. By varying these parameters within typical ranges of the AM machine used, variations in surface quality can be achieved from 10.4 µm up to 40.8 µm. These results represent an important basis for developing research activities that will further focus on implementing a mathematical/experimental model to help designers optimize the surface quality during the AM pre-processing phase.

Background and Objective: Because of the three-dimensional distribution of morphological features of human vertebrae and the whole spine, in recent years, to make more precise diagnoses and to design optimized surgical procedures, new protocols have been proposed based on analysing their three-dimensional (3D) models. In the related literature, processes of segmentation and morphological features recognition are essentially performed by a skilled operator that selects the interesting areas. So, being affected by the preparation and experience of the operator, this produces an evaluation that is poorly reproducible and repeatable for the uncertainties of a typical manual measurement process., Methods: To overcome this limitation, in this paper a new automatic method is proposed for feature segmentation and recognition of human vertebrae. The proposed computer-based method, starting from 3D high density discretized models of thoracic and lumbar vertebrae, automatically performs both the semantic and geometric segmentation of their morphological features. The segmentation and recognition rules codify some important definitions used in the traditional manual method, considering all the vertebra morphology information that is invariant inter-subject., Results: The automatic method proposed here is verified by analysing many real vertebrae, both acquired using a 3D scanner and coming from Computerized Tomography (CT) scans. The obtained results are critically discussed and compared with the traditional manual methods for vertebra analysis. The method has proven to be robust and reliable in the segmentation and recognition of morphological features of vertebrae. Furthermore, the proposed automatic method avoids the blurring of quantitative parameters get from vertebrae, resulting from poor repeatability and reproducibility of manual methods used in the state-of-the-art., Conclusions: Starting from the automatic segmentation and recognition here proposed, it is possible to automatically calculate the parameters of thoracic or lumbar vertebrae used in archaeology, medicine, or biomechanics or define their new ones., Competing Interests: Declaration of Competing Interest The authors have nothing to declare. The authors have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript., (Copyright © 2021 Elsevier B.V. All rights reserved.)

From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.

The construction of the artificial emissary of Fucino Lake is one of the most ambitious engineering buildings of antiquity. It was the longest tunnel ever made until the 19th century and, due to the depth of the adduction inlet, it required a monumental and complex incile , which, for functionality, cannot be compared to other ancient emissaries. The Roman emissary and its " incile " (Latin name of the inlet structure) were almost completely destroyed in the 19th century, when Fucino Lake was finally dried. Today, only few auxiliary structures such as wells, tunnels, and winzes remain of this ancient work. As evidence of the ancient incile remains a description made by those who also destroyed it and some drawings made by travelers who, on various occasions, visited the site. This paper presents a virtual reconstruction of the Roman incile , obtained both through the philological study of the known documentation, interpreting iconographic sources that represent the last evidence of this structure, and through the survey on the territory. The main purpose is to understand its technical functionalities, the original structures, and its evolution during the time, taking into account the evolution of the Fucino Lake water levels, technological issues, and finally offering its visual reconstruction.

This paper proposes a new automatic approach to determine the accurate measure of human teeth. The aim of the proposed computer based method is to reduce inaccuracy of measurement with respect to traditional approaches. Starting from a 3D model of the teeth which is obtained from 3D scanning, the method algorithmically evaluates the most important dimensional features detectable in central incisors. For this purpose, specific rules are put forward and implemented in original software with a view to identifying repere points, from which to detect dimensional features both unambiguously and accurately. The automatic method which is proposed here is verified by means of the analysis of real teeth and is then compared with the current state-of-the-art methods for teeth measurement., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In this paper a new automatic approach to determine the accurate measure of human vertebrae is proposed. The aim is to speed up the measurement process and to reduce the uncertainties that typically affect the measurement carried out by traditional approaches. The proposed method uses a 3D model of the vertebra obtained from CT-scans or 3D scanning, from which some characteristic dimensions are detected. For this purpose, specific rules to identify morphological features, from which to detect dimensional features unambiguously and accurately, are put forward and implemented in original software. The automatic method which is here proposed is verified by analysing real vertebrae and is then compared with the state-of-the-art methods for vertebra measurement., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

The methods for symmetry line detection presented in the literature are typically suited to analyse symmetric upright postures, both standing and seated. The proposed method focuses on the symmetry line detection in subjects assuming asymmetric postures in which this line falls far outside the sagittal plane. The proposed approach evaluates the symmetry line by means of an autoregressive process in order to determine the set of planes suited to slice the back coherently with its geometric spatial configuration. The method is analysed assuming the cutaneous marking as reference and it is compared with a previous one, also developed by these authors. Results are analysed and critically discussed.