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Neurodegenerative diseases are characterized by the progressive decline of neuronal function in several brain areas, and are always associated with cognitive, psychiatric, or motor deficits due to the atrophy of certain neuronal populations.Most neurodegenerative diseases share common pathological mechanisms, such as neurotoxic protein misfolding, oxidative stress, and impairment of autophagy machinery.Amyotrophic lateral sclerosis (ALS) is one of the most common adult-onset motor neuron disorders worldwide. It is clinically characterized by the selective and progressive loss of motor neurons in the motor cortex, brain stem, and spinal cord, ultimately leading to muscle atrophy and rapidly progressive paralysis.Multiple recent studies have indicated that the amyloid precursor protein (APP) and its proteolytic fragments are not only drivers of Alzheimer’s disease (AD) but also one of the earliest signatures in ALS, preceding or anticipating neuromuscular junction instability and denervation. Indeed, altered levels of APP peptides have been found in the brain, muscles, skin, and cerebrospinal fluid of ALS patients.In this short review, we discuss the nature and extent of research evidence on the role of APP peptides in ALS, focusing on the intracellular C-terminal peptide and its regulatory motif 682YENPTY687, with the overall aim of providing new frameworks and perspectives for intervention and identifying key questions for future investigations.

Abstract Background Semaphorins (Sema) belong to a large family of repellent guidance cues instrumental in guiding axons during development. In particular, Class 3 Sema (Sema 3) is among the best characterized Sema family members and the only produced as secreted proteins in mammals, thereby exerting both autocrine and paracrine functions. Intriguingly, an increasing number of studies supports the crucial role of the Sema 3A in hippocampal and cortical neurodevelopment. This means that alterations in Sema 3A signaling might compromise hippocampal and cortical circuits and predispose to disorders such as autism and schizophrenia. Consistently, increased Sema 3A levels have been detected in brain of patients with schizophrenia and many polymorphisms in Sema 3A or in the Sema 3A receptors, Neuropilins (Npn 1 and 2) and Plexin As (Plxn As), have been associated to autism. Results Here we present data indicating that when overexpressed, Sema 3A causes human neural progenitors (NP) axonal retraction and an aberrant dendritic arborization. Similarly, Sema 3A, when overexpressed in human microglia, triggers proinflammatory processes that are highly detrimental to themselves as well as NP. Indeed, NP incubated in microglia overexpressing Sema 3A media retract axons within an hour and then start suffering and finally die. Sema 3A mediated retraction appears to be related to its binding to Npn 1 and Plxn A2 receptors, thus activating the downstream Fyn tyrosine kinase pathway that promotes the threonine-serine kinase cyclin-dependent kinase 5, CDK5, phosphorylation at the Tyr15 residue and the CDK5 processing to generate the active fragment p35. Conclusions All together this study identifies Sema 3A as a critical regulator of human NP differentiation. This may imply that an insult due to Sema 3A overexpression during the early phases of neuronal development might compromise neuronal organization and connectivity and make neurons perhaps more vulnerable to other insults across their lifespan.

Semaphorin 3A (SEMA3A) plays a crucial role in the development, differentiation, and plasticity of specific types of neurons that secrete Gonadotropin-Releasing Hormone (GnRH) and regulates the acquisition and maintenance of reproductive competence in humans and mice. Its insufficient expression has been linked to reproductive disorders in humans, which are characterized by reduced or failed sexual competence. Various mutations, polymorphisms, and alternatively spliced variants of SEMA3A have been associated with infertility. One of the common causes of infertility in women of reproductive age is diminished ovarian reserve (DOR), characterized by a reduced ovarian follicular pool. Despite its clinical significance, there are no universally accepted diagnostic criteria or therapeutic interventions for DOR. In this study, we analyzed the SEMA3A plasma levels in 77 women and investigated their potential role in influencing fertility in patients with DOR. The results revealed that the SEMA3A levels were significantly higher in patients with DOR than in healthy volunteers. Furthermore, the SEMA3A levels were increased in patients who underwent fertility treatment and had positive Beta-Human Chorionic Gonadotropin (βHCG) values (β+) after controlled ovarian stimulation (COS) compared to those who had negative βHCG values (β−). These findings may serve as the basis for future investigations into the diagnosis of infertility and emphasize new possibilities for the SEMA3A-related treatment of sexual hormonal dysfunction that leads to infertility.

Thyroid cancer (TC) is the prevalent endocrine tumor with a rising incidence, particularly in higher-income countries, leading to an increased interest in its management and treatment. While overall, survival rates for TC are usually favorable, advanced cases, especially with metastasis and specific histotypes, pose challenges with poorer outcomes, advocating the need of systemic treatments. Targeted therapies have shown efficacy in both preclinical models and clinical trials but face issues of resistance, since they usually induce partial and transient response. These resistance phenomena are currently only partially addressed by traditional preclinical models. This review explores the limitations of traditional preclinical models and emphasizes the potential of three-dimensional (3D) models, such as transwell assays, spheroids, organoids, and organ-on-chip technology in providing a more comprehensive understanding of TC pathogenesis and treatment responses. We reviewed their use in the TC field, highlighting how they can produce new interesting insights. Finally, the advent of organ-on-chip technology is currently revolutionizing preclinical research, offering dynamic, multi-cellular systems that replicate the complexity of human organs and cancer–host interactions.

The role of tumor necrosis factor-α (TNF-α) in Alzheimer’s disease (AD) has recently become a topic of debate. TNF-α levels increase in the blood of patients with AD, and amyloid beta (Aβ) plaques contain TNF-α deposits. The therapeutic efficacy of blocking TNF-α in patients with AD remains controversial as it is mostly based on preclinical studies. Thus, whether and how TNF-α contributes to amyloidogenic processes in AD is still an open question to be addressed. We analyzed plasma TNF-α and Aβ42 levels in patients with subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD, and in healthy volunteers (HLT). In addition, we performed correlation analysis to evaluate whether changes in plasma TNF-α levels correlate with cognitive decline, Aβ42 levels, age, and BMI, which are all factors considered to contribute to or predispose individuals to AD. We found that TNF-α and Aβ42 plasma levels were higher in patients with AD than in HLT individuals. High TNF-α levels were also observed in patients with SCI, in whom TNF-α and Aβ42 levels were negatively correlated. Notably, TNF-α did not affect the amyloidogenic pathway in human microglial cultures exposed to 48 h of incubation, although it did trigger neuroinflammatory processes. These results imply that high TNF-α levels are more likely to be a clinical condition linked to AD than are direct contributors. Nonetheless, elevated levels of TNF-α in early-stage patients, like those with SCI and MCI, may provide a distinguishing feature for identifying clinical profiles that are at risk of having a poorer outcome in AD and could benefit from tailored therapies.

Non-pharmacological approaches, including exercise programs, have been proposed to improve cognitive function and behavioral symptoms, such as depression, agitation, or aggression, in the management of patients with Alzheimer's disease (AD). Indeed, physical inactivity is one of the main modifiable risk factors in patients with AD, as well as in the development of cardiovascular diseases and related pathologies.Although Nordic Walking (NW), a particular type of aerobic exercise, is known to benefit the health of aging populations, there is little evidence that patients with AD may benefit from this non-pharmacological treatment. In this context, we performed a pilot study in 30 patients with mild/moderate AD to evaluate whether NW influences different cognitive domains, including executive functions, visual-spatial abilities, and verbal episodic memory. To this aim, 15 patients (Control group, CG) underwent reality orientation therapy, music therapy, motor, proprioceptive and postural rehabilitation, and 15 patients (experimental group, EG) in addition to the activities performed by the CG also had the NW with a frequency of twice a week. Neuropsychological assessments and evaluations of daily activities and quality of life were performed at baseline and after 24 weeks. Twenty-two patients, including 13 in the CG and nine in the EG completed the activity program after 24 weeks. The EG showed a significant improvement in the Frontal Assessment Battery, Rey's auditory Verbal Learning Test Delayed Recall, Raven's Colored Progressive Matrices, and completion time for the Stroop Word-Color Interference test, compared to the CG. NW was able to improve cognitive domains like visual-spatial reasoning abilities, verbal episodic memory, selective attention, and processing speed in AD patients. These results, if confirmed by further studies with a larger number of patients and a longer training period, may prospect NW as a safe and likely useful strategy to slow down cognitive impairment in mild/moderate AD.

Thanks to the latest technological developments LiDAR (Light Detection And Ranging) sensors are no longer an exclusive feature of manned airborne platforms but they are close to becoming a commercial solution in the UAS (Uncrewed Aerial Systems) domain. The release on the market of the Zenmuse L1 by DJI (Dà-Jiāng Innovations) is a step further in this direction, thanks also to a substantial work of enhancement made by the Chinese company not only on the hardware side, but also on the software one. The research presented in this work is focused on the use of the L1 LiDAR for the 3D survey of built heritage, analysing the results of different tests to highlight first considerations on its performances and the point cloud quality. Considering its recent release, this sensor is still yet to be thoroughly analysed and validated and its performances to be assessed. LiDAR data has been acquired on a selected test site, documented also with traditional Terrestrial Laser Scanner (TLS) and UAS photogrammetry. The latter techniques (supported also by a topographic survey) will thus be exploited to generate the ground reference and to assess the quality and accuracy of the L1 dataset.

In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation.

Numerical simulations of volcanic processes play a fundamental role in understanding the dynamics of magma storage, ascent, and eruption. The recent extraordinary progress in computer performance and improvements in numerical modeling techniques allow simulating multiphase systems in mechanical and thermodynamical disequilibrium. Nonetheless, the growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques. In this work we present MagmaFOAM, a library based on the open-source computational fluid dynamics software OpenFOAM that incorporates models for solving the dynamics of multiphase, multicomponent magmatic systems. Retaining the modular structure of OpenFOAM, MagmaFOAM allows runtime selection of the solution technique depending on the physics of the specific process and sets a solid framework for in-house and community model development, testing, and comparison. MagmaFOAM models thermomechanical nonequilibrium phase coupling and phase change, and it implements state-of-the-art multiple volatile saturation models and constitutive equations with composition-dependent and space–time local computation of thermodynamic and transport properties. Code testing is performed using different multiphase modeling approaches for processes relevant to magmatic systems: Rayleigh–Taylor instability for buoyancy-driven magmatic processes, multiphase shock tube simulations propaedeutical to conduit dynamics studies, and bubble growth and breakage in basaltic melts. Benchmark simulations illustrate the capabilities and potential of MagmaFOAM to account for the variety of nonlinear physical and thermodynamical processes characterizing the dynamics of volcanic systems.

In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients' treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure. [ABSTRACT FROM AUTHOR]

In the original article [...]

Currently, the differentiated thyroid cancer (DTC) management is shifted toward a tailored approach based on the estimated risks of recurrence and disease-specific mortality. While the current recommendations on the management of metastatic and progressive DTC are clear and unambiguous, the management of slowly progressive or indeterminate disease varies according to different centers and different physicians. In this context, active surveillance (AS) becomes the main tool for clinicians, allowing them to plan a personalized therapeutic strategy, based on the risk of an unfavorable prognosis, and to avoid unnecessary treatment. This review analyzes the main possible scenarios in treated DTC patients who could take advantage of AS.

The role of the post-translational modifications in tissue regeneration is still not clearly understood. Here, the authors show that many nuclear proteins change S-nitrosylation state in the regenerating zebrafish tailfin, highlighting the importance of Kdm1a S-nitrosylation in the repair process.

Abstract Background Adverse childhood experiences (ACEs), social–emotional impairments (SEIs), and neurodevelopmental disorders (NDs) are frequent in psychiatric disorders, including substance-use disorders. We aimed to determine the prevalence of ACE, SEI, or ND in individuals with cannabis-use disorder (CUD). We compared individuals with preCUD-onset ACE, SEI, or ND to those without. Methods We crosssectionally studied 323 inpatients or outpatients with a history of past or current CUD, aged 12–35 years (mean age 22.94 ± 4.79), 64.5% of whom were male. The sample was divided into two groups: the non-premorbid (N = 52) and the premorbid ACE/SEI/ND group (N = 271). Within the premorbid group, further subgroups were based on ACEs, SEI, and NDs. We also analyzed other substance use and psychiatric symptoms/diagnoses based on the non-premorbid-premorbid dichotomy in the CUD sample. Results Pre-CUD ACE-SEI-ND had higher prevalence of bipolar, schizoaffective, borderline personality, and attention-deficit/hyperactivity disorders, and a history of agitation, hallucinations, and self-injury. The ACE group had higher rates of agitation, depression, delusions, hallucinations, eating disorders, and use of cocaine, amphetamines, and hallucinogens than the SEI or ND. Patients in the premorbid group initiated cannabis use at an earlier age, experienced the first comorbid psychiatric episode earlier, and were hospitalized earlier than those in the non- premorbid ACE-SEI-ND group. Conclusions PreCUD-onset ACE, SEI, or ND conditions in individuals with CUDare linked to earlier onset of comorbid mental illness. Furthermore, ACEs contribute to significant and potentially severe clinical symptoms, as well as the use of substances other than cannabis.

This paper aims to describe the outcomes of the Main10ance project, which focused on developing an integrated HBIM-GIS platform to support the maintenance and conservation plans for the historic built heritage. The pilot case is the UNESCO complex of the Sacri Monti, located in northern Italy and Switzerland, which consists of groups of chapels and architectural artifacts holding significant historical and cultural value. Given their importance, specific maintenance plans involving multiple stakeholders and specialists are required. This study focuses on creating a unified system that semantically and spatially describes the architectural elements of the case study and the surrounding context and indoor assets. After a 3D integrated metric survey and the related data processing, parametric 3D models were created in a BIM environment, and a spatial database was developed to incorporate both geometric and alphanumeric entities. The database enables interoperability among different actors and domains, gathering heritage-related information crucial for restoration and conservation purposes. Additionally, the custom 4MAIN10ANCE web platform was developed with different levels of details (LODs), enabling the retrieval of both 2D and 3D data and establishing connections between the BIM models of the chapels and associated information.

La creciente disponibilidad de datos tridimensionales (3D), como nubes de puntos, provenientes de la detección de la luz y distancia (LiDAR), sistemas de mapeado móvil (MMS) o vehículos aéreos no tripulados (UAV), brinda la oportunidad de generar rápidamente modelos 3D para apoyar las actividades de restauración, conservación y salvaguardia del patrimonio cultural (CH). El llamado proceso de escaneado-a-BIM puede, de hecho, beneficiarse de dichos datos, y ellos mismos pueden ser una fuente para futuros análisis o actividades sobre el patrimonio arqueológico y el construido. Hay varias formas de explotar este tipo de datos, como el modelado de información de edificios históricos (HBIM), la creación de mallas, la rasterización, la clasificación y la segmentación semántica. Este último, referido a las nubes de puntos, es un tema de máxima actualidad no solo en el dominio del PC sino también en otros campos como la navegación autónoma, la medicina o el comercio minorista. Precisamente en estos sectores, la tarea de la segmentación semántica se ha explotado y desarrollado principalmente con técnicas de inteligencia artificial. En particular, los algoritmos de aprendizaje automático (AA) y su subconjunto de aprendizaje profundo (AP) se aplican cada vez más y han establecido un sólido estado de la técnica en la última media década. Sin embargo, las aplicaciones de las técnicas de AP en las nubes de puntos tradicionales son todavía escasas; por tanto, nos proponemos abordar este marco dentro del ámbito del patrimonio construido. Partiendo de algunas pruebas anteriores con la Red Neural Convolucional de Gráfico Dinámico (DGCNN), en esta contribución se presta atención a: i) la investigación de modelos afinados, utilizados como técnica de aprendizaje por transferencia, ii) la combinación de clasificadores externos, como Random Forest (RF), con la red neuronal artificial, y iii) la evaluación de los resultados de aumentación de datos para el conjunto de datos específico del dominio ArCH. Finalmente, después de tener en cuenta las principales ventajas y criticidades, se hace una consideración sobre la posibilidad de beneficiarse de esta metodología también a expertos no programadores o del campo.

Cellular and humoral immunity are both required for SARS-CoV-2 infection recovery and vaccine efficacy. The factors affecting mRNA vaccination-induced immune responses, in healthy and fragile subjects, are still under investigation. Thus, we monitored the vaccine-induced cellular and humoral immunity in healthy subjects and cancer patients after vaccination to define whether a different antibody titer reflected similar rates of cellular immune responses and if cancer has an impact on vaccination efficacy. We found that higher titers of antibodies were associated with a higher probability of positive cellular immunity and that this greater immune response was correlated with an increased number of vaccination side effects. Moreover, active T-cell immunity after vaccination was associated with reduced antibody decay. The vaccine-induced cellular immunity appeared more likely in healthy subjects rather than in cancer patients. Lastly, after boosting, we observed a cellular immune conversion in 20% of subjects, and a strong correlation between pre- and post-boosting IFN-γ levels, while antibody levels did not display a similar association. Finally, our data suggested that integrating humoral and cellular immune responses could allow the identification of SARS-CoV-2 vaccine responders and that T-cell responses seem more stable over time compared to antibodies, especially in cancer patients.