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Struggles for social inclusion necessitate efforts to enhance democratic communication. Communication practices and institutions are deeply intertwined with movements advocating for and against social justice, particularly in contemporary contexts of developing inclusion. We designed and evaluated the “Telling the Truth: How to Inform While Promoting an Inclusive Society” program. This program aimed to enhance knowledge and increase positive attitudes toward inclusion among 61 journalism students through quantitative and qualitative methods. The results indicate that participants demonstrated higher positive attitudes in the post-test, showing increased support for a more inclusive socio-economic vision and a greater inclination toward activism. Overall, these findings underscore the significance of journalism training in advancing a socially inclusive context.

In the present paper, a flight dynamics model is adopted to represent the trim and stability characteristics of a side-by-side helicopter in hovering conditions. This paper develops a numerical representation of the rotorcraft behavior and proposes a set of guidelines for trimming and linearizing the highly coupled rotor dynamics derived by the modeling approach. The trim algorithm presents two nested loops to compute a solution of the steady-state conditions averaged around one blade’s revolution. On the other hand, a 38-state-space linear representation of the helicopter and rotor dynamics is obtained to study the effects of flap, lead–lag, and inflow on the overall stability. The results are compared with an analytical framework developed to validate the rotorcraft stability and compare different modeling approaches. The analysis showed that non-uniform inflow modeling led to a coupled longitudinal inflow–phugoid mode which made the vehicle prone to dangerous instabilities. The flap and lead–lag dynamics introduced damping in the system and can be considered beneficial for rotor dynamics.

This paper focuses on the application of L1 adaptive control to the speed autopilot loop of small-scale unmanned helicopters. The L1 adaptive control technique is investigated for its potential to provide reliable speed control, particularly in handling external disturbances and model uncertainties. Although L1 adaptive control has been widely studied, its application to small-scale unmanned helicopters remains relatively unexplored. Through numerical simulations and a preliminary experimental test campaign conducted on a small rotary-wing platform, this paper contributes to validating L1 adaptive control as a promising solution for speed regulation in unmanned helicopter platforms.

In the present study, a performance analysis of three different VTOL configurations is presented within an urban air mobility context. A classical lightweight helicopter was employed as a reference configuration to design a dual-rotor side-by-side helicopter and a hexacopter drone layout. An analytical model based on general momentum and blade element theories was developed for single- and multiple-rotor configurations in horizontal and vertical flight conditions. Suitable battery pack and electric motor designs were produced to evaluate the endurance and range of the different configurations for a specific mission. This paper provides fundamental insights into the endurance and range capabilities of multiple-rotor unmanned aerial vehicles (UAVs) and a qualitative discussion on the safety and acceptability features of each configuration implemented in an advanced air mobility context. As a result, the side-by-side helicopter configuration was identified as the best solution to be introduced within urban environments, fulfilling all the performance and mission requirements.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

The cooperative transportation of a cable-suspended load by two unmanned rotorcraft is analyzed. Initially, the equations describing a system composed of three point masses and two rigid cables are derived. The model is then linearized about the hovering condition, and analytical expressions are derived to describe the eigenstructure of the open-loop system. Thanks to the specific parameterization of the problem, the different dynamic modes are outlined and discussed within an analytical framework. A novel controller is designed to enable the UAVs in the formation to perform trajectory tracking, maintain formation geometry, and stabilize payload swing simultaneously. A preliminary investigation of closed-loop stability is conducted using a linear approach. Validation is performed in a realistic simulation scenario where two drones are modeled as rigid bodies under the effect of external disturbances and rotor-generated forces and moments, as obtained by Blade Element Theory. The proposed method demonstrates relative simplicity and significantly improves the flying qualities of delivery operations while minimizing hazardous payload oscillations and reducing energy demand.

In recent years, autonomous and transportation vehicles have been evolving towards more electric or hybrid electric systems, with new challenges arising in their guidance, navigation and control (GNC) systems [...]

The exploitation of insects as protein sources in the food industry has had a strong impact in recent decades for many reasons. The emphasis for this phenomenon has its primary basis on sustainability and also to the nutritional value provided. The gender of the insects, specifically Acheta domesticus, is strictly related to their nutritional value and therefore the availability of an automatic system capable of counting the number of Acheta in an insect farm based on their gender will have a strong impact on the sustainability of the farm itself. This paper presents a non-contact measurement system designed for gender counting and recognition in Acheta domesticus farms. A specific test bench was designed and realized to force the crickets to travel inside a transparent duct, across which they were framed by means of a high-resolution camera able to capture the ovipositor, the distinction element between male and female. All possible sources of uncertainty affecting the identification and counting of individuals were considered, and methods to mitigate their effect were described. The proposed method, which achieves 2.6 percent error in counting and 8.6 percent error in gender estimation, can be of significant impact in the sustainable food industry.

Far-infrared and submillimeter observations have established the fundamental role of dust-obscured star formation in the assembly of stellar mass over the past ∼12 billion years. At z = 2–4, the so-called “cosmic noon”, the bulk of star formation is enshrouded in dust, and dusty star-forming galaxies (DSFGs) contain ∼50% of the total stellar mass density. Star formation occurs in dense molecular clouds, and is regulated by a complex interplay between all the ISM components that contribute to the energy budget of a galaxy: gas, dust, cosmic rays, interstellar electromagnetic fields, gravitational field, and dark matter. Molecular gas is the actual link between star-forming gas and its complex environment: much of what we know about star formation comes from observations of molecular line emissions. They provide by far the richest information about the star formation process. However, their interpretation requires complex modeling of the astrochemical networks which regulate molecular formation and establish molecular abundances in a cloud, and a modeling of the physical conditions of the gas in which molecular energy levels become populated. This paper critically reviews the main astrochemical parameters needed to obtain predictions about molecular signals in DSFGs. Molecular lines can be very bright compared to the continuum emission, but radiative transfer models are required to properly interpret the observed brightness. We review the current knowledge and the open questions about the interstellar medium of DSFGs, outlining the key role of molecular gas as a tracer and shaper of the star formation process.

The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment.

Extracellular vesicles (EVs) are small lipid particles secreted by almost all human cells into the extracellular space. They perform the essential function of cell-to-cell communication, and their role in promoting breast cancer progression has been well demonstrated. It is known that EVs released by triple-negative and highly aggressive MDA-MB-231 breast cancer cells treated with paclitaxel, a microtubule-targeting agent (MTA), promoted chemoresistance in EV-recipient cells. Here, we studied the RNA content of EVs produced by the same MDA-MB-231 breast cancer cells treated with another MTA, eribulin mesylate. In particular, we analyzed the expression of different RNA species, including mRNAs, lncRNAs, miRNAs, snoRNAs, piRNAs and tRNA fragments by RNA-seq. Then, we performed differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and miRNA-target identification. Our findings demonstrate the possible involvement of EVs from eribulin-treated cells in the spread of chemoresistance, prompting the design of strategies that selectively target tumor EVs.

Gravitational lensing, a compelling physical phenomenon, offers a unique avenue to investigate the morphology and physical properties of distant and faint celestial objects. This paper seeks to provide a comprehensive overview of the current state of observations concerning strongly lensed Dusty Star-Forming Galaxies. Emphasis is placed on the pivotal role played by cutting-edge facilities like the James Webb Space Telescope and the Square Kilometer Array Observatory. These advanced instruments operating at the two opposite ends of the electromagnetic spectrum, in conjunction with the amplifying effect of gravitational lensing, promise significant steps in our understanding of these sources. The synergy between these observatories is poised to unlock crucial insights into the evolutionary path of high-redshift, dust-obscured systems and unravel the intricate interplay between Active Galactic Nuclei and their host galaxies.

In this paper, we investigate the benefits of teaming up data from the radio to the far-infrared (FIR) regime for the characterization of dusty star-forming galaxies (DSFGs). These galaxies are thought to be the star-forming progenitors of local massive quiescent galaxies and to play a pivotal role in the reconstruction of the cosmic star formation rate density up to high redshift. Due to their dust-enshrouded nature, DSFGs are often invisible in the near-infrared/optical/UV bands. Therefore, they necessitate observations at longer wavelengths, primarily the FIR band, where dust emission occurs, and the radio band, which is not affected by dust absorption. Combining data from these two spectral windows makes it possible to characterize even the dustiest objects, enabling the retrieval of information about their age, dust temperature, and star-formation status, and facilitates the differentiation between various galaxy populations that evolve throughout cosmic history. Despite the detection of faint radio sources being a challenging task, this study demonstrates that an effective strategy to build statistically relevant samples of DSFGs would be reaching deep sensitivities in the radio band, even restricted to smaller areas, and then combining these radio observations with FIR/submm data. Additionally, this paper quantifies the improvement in the spectral energy distribution (SED) reconstruction of DSFGs by incorporating ALMA band measurements, in particular, in its upgraded status thanks to the anticipated Wideband Sensitivity Upgrade.

A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.

Our study aimed to identify clusters of hospitalized older COVID-19 patients according to their main comorbidities and routine laboratory parameters to evaluate their association with in-hospital mortality. We performed an observational study on 485 hospitalized older COVID-19 adults (aged 80+ years). Patients were aggregated in clusters by a K-medians cluster analysis. The primary outcome was in-hospital mortality. Medical history and laboratory parameters were collected on admission. Frailty, defined by the Clinical Frailty Scale (CFS), referred to the two weeks before hospitalization and was used as a covariate. The median age was 87 (83–91) years, with a female prevalence (59.2%). Three different clusters were identified: cluster 1 (337), cluster 2 (118), and cluster 3 (30). In-hospital mortality was 28.5%, increasing from cluster 1 to cluster 3: cluster 1 = 21.1%, cluster 2 = 40.7%, and cluster 3 = 63.3% (p < 0.001). The risk for in-hospital mortality was higher in clusters 2 [HR 1.96 (95% CI: 1.28–3.01)] and 3 [HR 2.87 (95% CI: 1.62–5.07)] compared to cluster 1, even after adjusting for age, sex, and frailty. Patients in cluster 3 were older and had a higher prevalence of atrial fibrillation, higher admission NT-proBNP and C-reactive protein levels, higher prevalence of concurrent bacterial infections, and lower estimated glomerular filtration rates. The addition of CFS significantly improved the predictive ability of the clusters for in-hospital mortality. Our cluster analysis on older COVID-19 patients provides a characterization of those subjects at higher risk for in-hospital mortality, highlighting the role played by cardio-renal impairment, higher inflammation markers, and frailty, often simultaneously present in the same patient.

Accurately assessing the geometric features of curvilinear structures on images is of paramount importance in many vision-based measurement systems targeting technological fields such as quality control, defect analysis, biomedical, aerial, and satellite imaging. This paper aims at laying the basis for the development of fully automated vision-based measurement systems targeting the measurement of elements that can be treated as curvilinear structures in the resulting image, such as cracks in concrete elements. In particular, the goal is to overcome the limitation of exploiting the well-known Steger’s ridge detection algorithm in these applications because of the manual identification of the input parameters characterizing the algorithm, which are preventing its extensive use in the measurement field. This paper proposes an approach to make the selection phase of these input parameters fully automated. The metrological performance of the proposed approach is discussed. The method is demonstrated on both synthesized and experimental data.

Light helicopters are used for a variety of applications, attracting users from private and public market segments because of their agility and convenient storage capabilities. However, most light helicopters on the market today are designed and manufactured with technologies dating back to the 1980s, with safety issues to be addressed by advanced design methods, more powerful engines, and innovative solutions. In this regard, the DISRUPT (Development of an innovative and safe ultralight, two-seater turbine helicopter) project, led by Curti Aerospace Division (Italy) and co-funded by the EU H2020 program, is a state-of-the-art concept for a novel ultralight helicopter equipped with a ballistic parachute. In order to validate the first parachute ejection in a safe scenario, a dronization process was selected as a viable solution to be performed in collaboration with the University of Bologna. In the present paper, the steps followed to transform the helicopter into an unmanned vehicle are detailed according to the model-based design approach, with particular focus on mathematical modeling, control system design, and experimental validation. Obtained results demonstrate the feasibility of using a civil helicopter first as a remotely-piloted vehicle and then as a highly-automated personal transportation system in the framework of smart and sustainable air mobility.

Bachground In this study, we hypothesize that mindfulness-based interventions (MBIs) may improve well-being and the related outcomes in Alzheimer’s dementia patients (AD-P) at an early stage. MBIs consist of the practice of consciously observing the psychic contents in the present moment (thoughts, sensations, feelings, and other events). This attention allows one to become aware of the psychic contents and integrate them, thus favoring the quality of life and an increase in the mood of practitioners. Methods The randomized controlled study enrolled 22 AD-P at an early stage (age ≥ 60 years) treated with MBIs and 22 patients without treatment (six months of MBI training). Tests (T0–T1 six months): Mini-Mental State Examination (MMPI); Spiritual Well-Being (SWB); Beck Depression Inventory (BDI); SF36. Test-Caregiver: Everyday Cognition scales (ECOG). Results AD-P with mindfulness: Improvement of ECOG (p = 0.026), quality of life (p < 0.001), spiritual well-being (p < 0.001); decrease in depression BDI (p < 0.001). The MMSE remains unchanged. The control group of untreated patients showed a significant worsening in all these dimensions. Conclusions MBI training is effective in increasing quality of life and preventing worsening in patients with early-stage Alzheimer’s dementia.

Professional swimming coaches make use of videos to evaluate their athletes’ performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer’s body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively.

We present an analysis of the performance of a broadband secondary radiation source based on a high-gradient laser-plasma wakefield electron accelerator. In more detail, we report studies of compact and ultra-short X-ray generation via betatron oscillations in plasma channels. For the specific working point examined in this paper, determined by the needs of other experiments ongoing at the facility, at ∼0.02 Hz operation rate, we have found ≲106 photons emitted per shot (with a fluctuation of 50%) in the soft X-rays, corresponding to a critical energy of ∼0.8 keV (with a fluctuation of 40%). The source will be implemented for experiments in time-domain spectroscopy, e.g., biological specimens, and for other applications oriented to medical physics.

Pancreatic ductal adenocarcinoma (PDAC) has high metastatic potential. The “genometastasis” theory proposes that the blood of some cancer patients contains elements able to transform healthy cells by transferring oncogenes. Since findings on genometastasis in PDAC are still scarce, we sought supporting evidence by treating non-tumour HEK293T and hTERT-HPNE human cell lines with sera of PDAC patients. Here, we showed that HEK293T cells have undergone malignant transformation, increased the migration and invasion abilities, and acquired a partial chemoresistance, whereas hTERT-HPNE cells were almost refractory to transformation by patients’ sera. Next-generation sequencing showed that transformed HEK293T cells gained and lost several genomic regions, harbouring genes involved in many cancer-associated processes. Our results support the genometastasis theory, but further studies are needed for the identification of the circulating transforming elements. Such elements could also be useful biomarkers in liquid biopsy assays.

Free Electron Lasers (FELs) are certainly among the most interesting devices, belonging to the realm of coherent radiation sources [...]

In the recent past, hyper-spectral imaging has found widespread application in forensic science, performing both geometric characterization of biological traces and trace classification by exploiting their spectral emission. Methods proposed in the literature for blood stain analysis have been shown to be effectively limited to collaborative surfaces. This proves to be restrictive in real-case scenarios. The problem of the substrate material and color is then still an open issue for blood stain analysis. This paper presents a novel method for blood spectra correction when contaminated by the influence of the substrate, exploiting a neural network-based approach. Blood stains hyper-spectral images deposited on 12 different substrates for 12 days at regular intervals were acquired via a hyper-spectral camera. The data collected were used to train and test the developed neural network model. Starting from the spectra of a blood stain deposited in a generic substrate, the algorithm at first recognizes whether it is blood or not, then allows to obtain the spectra that the same blood stain, at the same time, would have on a reference white substrate with a mean absolute percentage error of 1.11%. Uncertainty analysis has also been performed by comparing the ground truth reflectance spectra with the predicted ones by the neural model.

Background and Aims: Cardiac natriuretic peptides (NPs) exert several metabolic effects, including some on lipid metabolism. Higher NPs levels are likely to be associated with a favorable lipid profile. In in vitro studies, NPs have been found to modulate low-density lipoprotein receptor (LDLR) trafficking by preventing proprotein convertase subtilisin/kexin type 9 (PCSK9) overexpression. The aim of our study is to investigate a possible association between plasma levels of PCSK9 and N-terminal pro B-type natriuretic peptide (NT-proBNP) in vivo. Methods: We performed a cross-sectional study on 160 consecutive older male and female patients hospitalized for medical conditions. Patients taking lipid-lowering drugs and patients with an admission diagnosis of acute heart failure were excluded. Fasting blood samples were collected after clinical stabilization of the acute illness, the day before discharge. Results: The mean age was 87.8 ± 6.4 years with a female prevalence (62.5%). The median NT-proBNP was 2340 (814–5397) pg/mL. The mean plasma PCSK9 was 275.2 ± 113.2 ng/mL. We found an inverse correlation between plasma PCSK9 and NT-proBNP (r = −0.280; p = 0.001). This association was confirmed after taking into account NT-proBNP tertiles (plasma PCSK9 levels: 317.4 ± 123.6 ng/mL in the first tertile, 283.3 ± 101.8 ng/mL in the second tertile, 231.3 ± 99.0 ng/mL in the third tertile, p = 0.001) and even after an adjustment for confounding factors (beta = −0.361, p = 0.001 for ln(NT-proBNP); beta = −0.330, p = 0.001 for NT-proBNP tertiles). The strength of the correlation between plasma PCSK9 and NT-proBNP was likely greater in patients affected by type 2 diabetes mellitus (r = −0.483; p = 0.006) and in male patients (r = −0.431, p = 0.001). Conclusion: The inverse association found between PCSK9 and NT-proBNP plasma levels in our real-life clinical study supports the hypothesis that NPs may play a role in cholesterol metabolism, possibly through an inhibitory action on circulating PCSK9 concentrations, thus increasing the availability of LDLR.

Water-level monitoring systems are fundamental for flood warnings, disaster risk assessment and the periodical analysis of the state of reservoirs. Many advantages can be obtained by performing such investigations without the need for field measurements. In this paper, a specific method for the evaluation of the water level was developed using photogrammetry that is derived from images that were recorded by unmanned aerial vehicles (UAVs). A dense point cloud was retrieved and the plane that better fits the river water surface was found by the use of the random sample consensus (RANSAC) method. A reference point of a known altitude within the image was then exploited in order to compute the distance between it and the fitted plane, in order to monitor the altitude of the free surface of the river. This paper further aims to perform a critical analysis of the sensitivity of these photogrammetric techniques for river water level determination, starting from the effects that are highlighted by the state of the art, such as random noise that is related to the image data quality, reflections and process parameters. In this work, the influences of the plane depth and number of iterations have been investigated, showing that in correspondence to the optimal plane depth (0.5 m) the error is not affected by the number of iterations.

Extracellular vesicles (EVs) are secreted from almost all human cells and mediate intercellular communication by transferring heterogeneous molecules (i.e., DNA, RNAs, proteins, and lipids). In this way, EVs participate in various biological processes, including immune responses. Viruses can hijack EV biogenesis systems for their dissemination, while EVs from infected cells can transfer viral proteins to uninfected cells and to immune cells in order to mask the infection or to trigger a response. Several studies have highlighted the role of native or engineered EVs in the induction of B cell and CD8(+) T cell reactions against viral proteins, strongly suggesting these antigen-presenting EVs as a novel strategy for vaccine design, including the emerging COVID-19. EV-based vaccines overcome some limitations of conventional vaccines and introduce novel unique characteristics useful in vaccine design, including higher bio-safety and efficiency as antigen-presenting systems and as adjuvants. Here, we review the state-of-the-art for antiviral EV-based vaccines, including the ongoing projects of some biotech companies in the development of EV-based vaccines for SARS-CoV-2. Finally, we discuss the limits for further development of this promising class of therapeutic agents.

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Ciliopathies are a group of multi-organ diseases caused by the disruption of the primary cilium. This event leads to a variety of kidney disorders, including nephronophthisis, renal cystic dysplasia, and renal cell carcinoma (RCC). Primary cilium contributes to the regulation of the cell cycle and protein homeostasis, that is, the balance between protein synthesis and degradation by acting on the ubiquitin-proteasome system, autophagy, and mTOR signaling. Many proteins are involved in renal ciliopathies. In particular, fibrocystin (PKHD1) is involved in autosomal recessive polycystic kidney disease (ARPKD), while polycystin-1 (PKD1) and polycystin-2 (PKD2) are implicated in autosomal dominant polycystic kidney disease (ADPKD). Moreover, primary cilia are associated with essential signaling pathways, such as Hedgehog, Wnt, and Platelet-Derived Growth Factor (PDGF). In this review, we focused on the ciliopathies associated with kidney diseases, exploring genes and signaling pathways associated with primary cilium and the potential role of cilia as therapeutic targets in renal disorders.

Myxosporean parasites of the genus Kudoa are fish parasites of great economic importance, as some species can affect the fish fillet quality by producing macroscopic cysts or generating post mortem myoliquefaction, commonly referred to as ‘soft flesh’. Kudoa mirabilis is a ‘soft flesh’-inducing species originally described based on morphology in the musculature of Trichiurus lepturus from the Indian Ocean. An integrative morphological and genetic characterization of K. mirabilis from the type host caught off the coast of Tanzania is here provided. The spores were stellate with four unequal polar capsules, showing similarities to Kudoa thyrsites. For comparative and validation purpose, K. mirabilis was compared morphologically and genetically with K. thyrsites reference isolates, including new obtained samples from the type host Thyrsites atun caught in the SE Atlantic Ocean. Morphological analyses of spores revealed key diagnostic characters clearly distinguishing the two Kudoa species. Phylogenetic analyses based on SSU and LSU rRNA genes demonstrated that K. mirabilis is a distinct and valid species, representing a sister group to a K. thyrsites subclade that comprises several isolates from Japan and one single isolate from South Africa. This finding raises questions about the true diversity likely hidden in the K. thyrsites complex.

We report on the development of radiation and electron sources based on laser-plasma acceleration for biomedical and nuclear applications, using both the table top TW laser at ILIL and the 220 TW FLAME laser system at LNF. We use the ILIL laser to produce wakefield electrons in a self-focusing dominated regime in a mm scale gas-jet to generate large, uniform beams of MeV electrons for electron radiography and radiobiology applications. This acceleration regime is described in detail and key parameters are given to establish reproducible and reliable operation of this source. We use the FLAME laser to drive laser-plasma acceleration in a cm-scale gas target to obtain stable production of >100 MeV range electrons to drive a Thomson scattering ɣ-ray source for nuclear applications.