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Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.

The pressing need for multifunctional materials in medical settings encompasses a wide array of scenarios, necessitating specific tissue functionalities. A critical challenge is the occurrence of biofouling, particularly by contamination in surgical environments, a common cause of scaffolds impairment. Beyond the imperative to avoid infections, it is also essential to integrate scaffolds with living cells to allow for tissue regeneration, mediated by cell attachment. Here, we focus on the development of a versatile material for medical applications, driven by the diverse time-definite events after scaffold implantation. We investigate the potential of incorporating graphene oxide (GO) into polycaprolactone (PCL) and create a composite for 3D printing a scaffold with time-controlled antibacterial and anti-adhesive growth properties. Indeed, the as-produced PCL-GO scaffold displays a local hydrophobic effect, which is translated into a limitation of biological entities-attachment, including a diminished adhesion of bacteriophages and a reduction of E. coli and S. aureus adhesion of ∼81% and ∼69%, respectively. Moreover, the ability to 3D print PCL-GO scaffolds with different heights enables control over cell distribution and attachment, a feature that can be also exploited for cellular confinement, i.e., for microfluidics or wound healing applications. With time, the surface wettability increases, and the scaffold can be populated by cells. Finally, the presence of GO allows for the use of infrared light for the sterilization of scaffolds and the disruption of any bacteria cell that might adhere to the more hydrophilic surface. Overall, our results showcase the potential of PCL-GO as a versatile material for medical applications.

Surgically addressing tumors poses a challenge, requiring a tailored, multidisciplinary approach for each patient based on the unique aspects of their case. Innovative therapeutic regimens combined to reliable reconstructive methods can contribute to an extended patient's life expectancy. This study presents a detailed comparative investigation of near-infrared therapy protocols, examining the impact of non-fractionated and fractionated irradiation regimens on cancer treatment. The therapy is based on the implantation of graphene oxide/poly(lactic-co-glycolic acid) three-dimensional printed scaffolds, exploring their versatile applications in oncology by the examination of pro-inflammatory cytokine secretion, immune response, and in vitro and in vivo tumor therapy. The investigation into cell death patterns (apoptosis vs necrosis) underlines the pivotal role of protocol selection underscores the critical influence of treatment duration on cell fate, establishing a crucial parameter in therapeutic decision-making. In vivo experiments corroborated the profound impact of protocol selection on tumor response. The fractionated regimen emerged as the standout performer, achieving a substantial reduction in tumor size over time, surpassing the efficacy of the non-fractionated approach. Additionally, the fractionated regimen exhibited efficacy also in targeting tumors in proximity but not in direct contact to the scaffolds. Our results address a critical gap in current research, highlighting the absence of a standardized protocol for optimizing the outcome of photodynamic therapy. The findings underscore the importance of personalized treatment strategies in achieving optimal therapeutic efficacy for precision cancer therapy.

Respiratory tract infections represent the main cause of death from infectious diseases worldwide. SARS-CoV-2 infection (i.e. COVID-19) added to the existing global burden of respiratory tract infections, including tuberculosis. Among nanomaterials for fabric functionalization, graphene, in combination with hydrophobic molecules such as phytochemicals, represents a promising low-cost alternative to antibiotics. In this work, we used graphene and curcumin to create fabric coatings on cotton and polyester for the production of personal protective equipment resistant to infective agents. These coatings ensure the trapping of microorganisms via interaction with SARS-CoV-2 or mycobacteria surface and inhibit microbial infections.

Graphene quantum dots (GQDs) are biocompatible nanoparticles employed in biomedical field, thanks to their size and photophysical properties. GQDs have shown the capability to cross biological barriers, including the blood-brain barrier, which makes them promising agents for brain diseases therapy. It has been shown that surface-functionalized GQDs enhance membrane fluidity and intracellular uptake, exerting a synergistic effect with antitumor drugs at subtherapeutic doses. Here, we tested GQDs effects in combination with chemotherapeutic agents doxorubicin and temozolomide, on a complex 3D spheroid model of glioblastoma. We observed that the capability of GQDs to absorb and convert near-infrared light into heat is a key factor in membrane permeability enhancement on 3D model. This non-invasive therapeutic strategy named photothermal therapy (PTT), combined to chemotherapy at subtherapeutic doses, significantly increased the effect of antitumor drugs by reducing tumor growth and viability. Furthermore, the increase in membrane permeability due to GQDs-mediated PTT enhanced the release of reactive oxygen species with strong migration of the immune system towards irradiated cancer spheroids. Our data indicate that the increase in membrane permeability can enhance the efficacy of antitumor drugs at subtherapeutic doses against glioblastoma, reducing side effects, and directing immune response, ultimately improving quality of life for patients.

Graphene Oxide (GO) is the oxidized form of graphene rich in surface groups comprising carbonyl, carboxyl, hydroxyl, and epoxy residues. The solubility in water makes GO an ideal material in the biomedical field though the plethora of synthesis methods available can modify the balance of oxygen groups and, consequently, the effects on eukaryotic and prokaryotic cells. For this reason, GO materials are always characterized with spectroscopic methods such as X-ray photoelectron and Fourier-transform Infrared spectroscopy. However, these techniques have some limitations, being disruptive and not clearly indicating oxygen functionalities available to react with polymers or biological media. In this work, we exploit GO reactivity with copper ions to develop a colorimetric method for the facile evaluation of surface oxidation degree and accessibility in polymeric samples. In the presence of GO, Cu2+ is reduced to Cu1+ and can react with bicinchoninic acid and induce light absorption at 562 nm. We observed that this reaction is dependent both on concentration and oxidation degree, and can be used to estimate GO exposure in thick composite samples. This technique will be fundamental in the future for scaffold characterization in tissue engineering and all the surface science studies analyzing GO-related materials' interactions with biological entities.

During exercise with increasing intensity, the human body transforms energy with mechanisms dependent upon actual requirements. Three phases of the body’s energy utilization are recognized, characterized by different metabolic processes, and separated by two threshold points, called aerobic (AerT) and anaerobic threshold (AnT). These thresholds occur at determined values of exercise intensity(workload) and can change among individuals. They are considered indicators of exercise capacities and are useful in the personalization of physical activity plans. They are usually detected by ventilatory or metabolic variables and require expensive equipment and invasive measurements. Recently, particular attention has focused on AerT, which is a parameter especially useful in the overweight and obese population to determine the best amount of exercise intensity for weight loss and increasing physical fitness. The aim of study is to propose a new procedure to automatically identify AerT using the analysis of recurrences (RQA) relying only on Heart rate time series, acquired from a cohort of young athletes during a sub-maximal incremental exercise test (Cardiopulmonary Exercise Test, CPET) on a cycle ergometer. We found that the minima of determinism, an RQA feature calculated from the Recurrence Quantification by Epochs (RQE) approach, identify the time points where generic metabolic transitions occur. Among these transitions, a criterion based on the maximum convexity of the determinism minima allows to detect the first metabolic threshold. The ordinary least products regression analysis shows that values of the oxygen consumption VO2, heart rate (HR), and Workload correspondent to the AerT estimated by RQA are strongly correlated with the one estimated by CPET (r > 0.64). Mean percentage differences are <2% for both HR and VO2 and <11% for Workload. The Technical Error for HR at AerT is <8%; intraclass correlation coefficients values are moderate (≥0.66) for all

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits.

Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a "Personalized Metabolic Avatar" (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16-0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s-13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by 40%. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.

The Coronavirus Disease 2019 (COVID-19) pandemic has led to collaboration between nanotechnology scientists, industry stakeholders, and clinicians to develop solutions for diagnostics, prevention, and treatment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections. Nanomaterials, including carbon-based materials (CBM) such as graphene and carbon nanotubes, have been studied for their potential in viral research. CBM unique effects on microorganisms, immune interaction, and sensitivity in diagnostics have made them a promising subject of SARS-CoV-2 research. This review discusses the interaction of CBM with SARS-CoV-2 and their applicability, including CBM physical and chemical properties, the known interactions between CBM and viral components, and the proposed prevention, treatment, and diagnostics uses.

Nerve damage is a prevalent and debilitating condition with limited treatment options. Recent years have seen an increased incidence of neural damage due to factors such as aging populations and traumatic brain injuries. Addressing the urgent need for effective therapies, this study explores the controlled delivery of mesenchymal stem cells (MSCs) secretome, a complex mixture of bioactive factors, which is currently being investigated for its potential in nerve regeneration. The secretome offers significant advantages over stem cells themselves, as it can be more easily characterized and controlled, enabling precise regulation of therapeutic interventions. However, the challenge lies in delivering the secretome specifically to the target anatomical region. To overcome this limitation, we propose a novel approach utilizing near-infrared (NIR) radiation-responsive bioprinted alginate-graphene oxide (AGO) microbeads. Graphene oxide (GO) is a highly biocompatible material with unique properties, including NIR responsiveness, enabling controlled release of therapeutic agents upon NIR exposure. We hypothesized that AGO microbeads could encapsulate MSCs secretome and release it in a controlled manner using NIR radiation. To investigate our hypothesis, controlled damage was induced to hippocampal neurons, and MSCs secretome was encapsulated within AGO microbeads. Subsequently, NIR radiation was applied to trigger the release of the secretome. We compared the efficacy of MSCs secretome with that of astrocytes, which also possess nerve growth and proliferation-promoting capabilities. Our findings demonstrated that the controlled release of MSCs secretome from AGO microbeads through non-invasive NIR radiation significantly promoted the proliferation and regeneration of neurons following nerve injury. AGO microbeads offer multiple advantages over conventional delivery methods, including precise control over the timing, location, and dosage of therapeutic agents. Furthermore, th

Seminoma is the most common testicular cancer. Pituitary tumor-transforming gene 1 (PTTG1) is a securin showing oncogenic activity in several tumors. We previously demonstrated that nuclear PTTG1 promotes seminoma tumor invasion through its transcriptional activity on matrix metalloproteinase 2 (MMP-2) and E-cadherin (CDH1). We wondered if specific interactors could affect its subcellular distribution. To this aim, we investigated the PTTG1 interactome in seminoma cell lines showing different PTTG1 nuclear levels correlated with invasive properties. A proteomic approach upon PTTG1 immunoprecipitation uncovered new specific securin interactors. Western blot, confocal microscopy, cytoplasmic/nuclear fractionation, sphere-forming assay, and Atlas database interrogation were performed to validate the proteomic results and to investigate the interplay between PTTG1 and newly uncovered partners. We observed that spectrin beta-chain (SPTBN1) and PTTG1 were cofactors, with SPTBN1 anchoring the securin in the cytoplasm. SPTBN1 downregulation determined PTTG1 nuclear translocation, promoting its invasive capability. Moreover, a PTTG1 deletion mutant lacking SPTBN1 binding was strongly localized in the nucleus. The Atlas database revealed that seminomas that contained higher nuclear PTTG1 levels showed significantly lower SPTBN1 levels in comparison to non-seminomas. In human seminoma specimens, we found a strong PTTG1/SPTBN1 colocalization that decreases in areas with nuclear PTTG1 distribution. Overall, these results suggest that SPTBN1, along with PTTG1, is a potential prognostic factor useful in the clinical management of seminoma.

PurposeNearly, 40% of the causes of male infertility remain idiopathic. The only suggested treatment in idiopathic oligo- and/or asthenozoospermia in normogonadotropic patients is the FSH. In the current clinical practice, efficacy is exclusively assessable through semen analysis after 3 months of treatment. No molecular markers of treatment efficacy are appliable in clinical practice. The aim of the present work is to evaluate the combination of extracellular signal regulated kinase (ERK) 1 and 2 and prolactin inducible peptide (PIP) as potential markers of idiopathic infertility and FSH treatment efficacy.MethodsWestern blot and confocal microscopy were performed to analyze the modulation of PIP and ERK1/2 in idiopathic infertile patients (IIP) sperm cells. Taking advantage of mass spectrometry analysis, we identified these proteins unequivocally in sperm cells.Results We demonstrated a significant decrease of both PIP protein and of ERK1/2 levels in spermatozoa obtained from IIP in comparison to healthy fertile patients (HFP). Conversely, we reported a significant increase of these markers comparing infertile patients before and after 3 months of FSH treatment. Importantly, this correlated with an increase in total number of sperm and sperm motility after FSH treatment. Finally, we identified of PIP and ERK2 proteins in sperm samples by proteomic analysis.ConclusionsThe combined evaluation of ERK1/2 and PIP proteins might represent a useful molecular marker to tailor FSH treatment in the management of male normogonadotropic idiopathic infertility.

Globally, breast cancer is the most diagnosed invasive cancer among women. Current therapies (e.g., chemotherapy) show numerous limitations due to the lack of selectivity and involved side effects, which urgently asks for novel approaches with enhanced tumor-killing efficacy. We previously demonstrated that MXenes, new bioactive nanomaterials with promising photophysical properties, are capable to increase the efficiency of the targeted breast cancer photothermal therapy (PTT). In this work, we investigated the effect of few- and multi-layer Ti3C2Tx MXenes mediated-PTT on two different 3D reliable breast cancer models such as conventional and bio-printed spheroids. We performed PTT on both cancer models using a non-toxic MXene concentration of 50 µg/mL. After PTT, a significant reduction in the cell viability along with a notable increase in reactive oxygen species (ROS) was observed. Moreover, we studied the effect of PTT on the migration of macrophages and endothelial cells toward cancer regions in both 3D models. Our results indicate that PTT mediated by both few- and multi-layer MXenes significantly modulates the tumor progression through cells’ death by increasing the temperature, which holds particularly true for the bio-printed model.

VO2max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes whose fraction increases with the VO2max and with the emergence of neuromuscular fatigue. This analysis can be extremely valuable by providing personalized feedback about the user's fitness level improvement that can be realized by developing personalized exercise plans aimed to target a contextual increase in the dynamic regime fraction related to VO2max increase, at the expense of the dynamic regime fraction related to the emergence of fatigue. These strategies can ultimately result in the reduction in cardiovascular risk.

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

In this work, we will investigate if red blood cell (RBC) membrane fluidity, influenced by several hyperglycemia-induced pathways, could provide a complementary index of HbA1c to monitor the development of type 2 diabetes mellitus (T2DM)-related macroangiopathic complications such as Peripheral Artery Disease (PAD). The contextual liquid crystalline (LC) domain spatial organization in the membrane was analysed to investigate the phase dynamics of the transition. Twenty-seven patients with long-duration T2DM were recruited and classified in DM, including 12 non-PAD patients, and DM + PAD, including 15 patients in any stage of PAD. Mean values of RBC generalized polarization (GP), representative of membrane fluidity, together with spatial organization of LC domains were compared between the two groups; p-values < 0.05 were considered statistically significant. Although comparable for anthropometric characteristics, duration of diabetes, and HbA1c, RBC membranes of PAD patients were found to be significantly more fluid (GP: 0.501 +/- 0.026) than non-PAD patients (GP: 0.519 +/- 0.007). These alterations were shown to be triggered by changes in both LC microdomain composition and distribution. We found a decrease in Feret diameter from 0.245 +/- 0.281 mu m in DM to 0.183 +/- 0.124 mu m in DM + PAD, and an increase in circularity. Altered RBC membrane fluidity is correlated to a spatial reconfiguration of LC domains, which, by possibly altering metabolic function, are associated with the development of T2DM-related macroangiopathic complications.

Simple Summary Seminoma represents one of the most common neoplasms in Caucasian males between 15 and 40 years old. The molecular pathways underlying its clinical behavior are far from being understood yet. We previously demonstrated that nuclear Pituitary-tumor transforming-gene 1 (PTTG1), overexpressed in several neoplasms, promotes invasiveness through its transcriptional target matrix-metalloproteinase-2 (MMP2). PTTG1 sustains the migratory and invasive properties of cancer cells through the induction of the epithelial-to-mesenchymal transition (EMT). E-Cadherin (E-CAD) repression is the first step of EMT. Therefore, we investigated the role of PTTG1 in EMT in human seminoma using an in vitro and in vivo model and through Atlas database interrogation. Our data showed a PTTG1-mediated E-CAD transcriptional repression through Zinc finger E-box binding homeobox 1 (ZEB1), a master regulator of the EMT process. Our data provide insights into the molecular characterization of seminoma, promoting PTTG1 as a prognostic marker useful in human seminoma clinical management. (1) Background: PTTG1 sustains the EMT process and the invasiveness of several neoplasms. We previously showed the role of nuclear PTTG1 in promoting invasiveness, through its transcriptional target MMP2, in seminoma in vitro models. Here, we investigated the key players involved in PTTG1-mediated EMT in human seminoma. (2) Methods: Two seminoma cell lines and four human seminoma tumor specimens were used. E-Cadherin gene regulation was investigated using Western blot, real-time PCR, and luciferase assay. Immunoprecipitation, ChIP, RE-ChIP, and confocal microscopy analysis were performed to evaluate the interplay between PTTG1 and ZEB1. Matrigel invasion and spheroid formation assays were applied to functionally investigate PTTG1 involvement in the EMT of seminoma cell lines. RNA depletion and overexpression experiments were performed to verify the role of PTTG1/ZEB1 in E-Cadherin repression and seminom

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

Background: Extracellular Vesicles (EVs) are sub-micrometer lipid-bound particles released by most cell types. They are considered a promising source of cancer biomarkers for liquid biopsy and personalized medicine due to their specific molecular cargo, which provides biochemical information on the state of parent cells. Despite this potential, EVs translation process in the diagnostic practice is still at its birth, and the development of novel medical devices for their detection and characterization is highly required. Results: In this study, we demonstrate mid-infrared plasmonic nanoantenna arrays designed to detect, in the liquid and dry phase, the specific vibrational absorption signal of EVs simultaneously with the unspecific refractive index sensing signal. For this purpose, EVs are immobilized on the gold nanoantenna surface by immunocapture, allowing us to select specific EV sub-populations and get rid of contaminants. A wet sample-handling technique relying on hydrophobicity contrast enables effortless reflectance measurements with a Fourier-transform infrared (FTIR) spectro-microscope in the wavelength range between 10 and 3 mu m. In a proof-of-principle experiment carried out on EVs released from human colorectal adenocarcinoma (CRC) cells, the protein absorption bands (amide-I and amide-II between 5.9 and 6.4 mu m) increase sharply within minutes when the EV solution is introduced in the fluidic chamber, indicating sensitivity to the EV proteins. A refractive index sensing curve is simultaneously provided by our sensor in the form of the redshift of a sharp spectral edge at wavelengths around 5 mu m, where no vibrational absorption of organic molecules takes place: this permits to extract of the dynamics of EV capture by antibodies from the overall molecular layer deposition dynamics, which is typically measured by commercial surface plasmon resonance sensors. Additionally, the described metasurface is exploited to compare the spectral response of EVs d

Red blood cells (RBCs) are characterized by a remarkable elasticity, which allows them to undergo very large deformation when passing through small vessels and capillaries. This extreme deformability is altered in various clinical conditions, suggesting that the analysis of red blood cell (RBC) mechanics has potential applications in the search for non-invasive and cost-effective blood biomarkers. Here, we provide a comparative study of the mechanical response of RBCs in patients with Alzheimer's disease (AD) and healthy subjects. For this purpose, RBC viscoelastic response was investigated using atomic force microscopy (AFM) in the force spectroscopy mode. Two types of analyses were performed: (i) a conventional analysis of AFM force-distance (FD) curves, which allowed us to retrieve the apparent Young's modulus, E; and (ii) a more in-depth analysis of time-dependent relaxation curves in the framework of the standard linear solid (SLS) model, which allowed us to estimate cell viscosity and elasticity, independently. Our data demonstrate that, while conventional analysis of AFM FD curves fails in distinguishing the two groups, the mechanical parameters obtained with the SLS model show a very good classification ability. The diagnostic performance of mechanical parameters was assessed using receiving operator characteristic (ROC) curves, showing very large areas under the curves (AUC) for selected biomarkers (AUC > 0.9). Taken all together, the data presented here demonstrate that RBC mechanics are significantly altered in AD, also highlighting the key role played by viscous forces. These RBC abnormalities in AD, which include both a modified elasticity and viscosity, could be considered a potential source of plasmatic biomarkers in the field of liquid biopsy to be used in combination with more established indicators of the pathology.

Exosomes (EXOs) are considered an exceptionally promising source of cancer biomarkers for personalized medicine and liquid biopsy. Despite this potential, the EXOs translation process in diagnostics is still at its birth, and the development of reliable and reproducible methods for their characterization is highly demanded. Fourier Transform Infrared (FTIR) Spectroscopy is perfectly suited for this purpose, as it can provide a label-free biochemical profile of EXOs in terms of lipid, protein, and nucleic acid content. Here we evaluated the applicability of FTIR spectroscopy to the study of cancer-derived EXOs as a function of cell differentiation. For this purpose, we used N-acetyl-L-Cysteine (NAC) to induce a controlled differentiation in human colon carcinoma cells from a proliferative mesenchymal morphology to a less invasive epithelial phenotype, as measured with fluorescence and electron microscopy. EXOs derived from cells with different phenotypes showed significant variation in the relative intensity of the amide I-II and CH-stretching bands in the mid-IR range, indicating the spectroscopic lipid/protein ratio as an effective classification parameter. Additionally, we showed that different cell phenotypes are associated with a shape modification in these spectral bands that can be automatically detected by combining Principal Component Analysis (PCA) with Linear Discriminant Analysis (LDA). On the one hand, our study confirms that an FTIR analysis of EXOs allows scientists to precisely detect modifications occurring at the parental cell level; on the other hand, it unveils a set of effective spectral biomarkers able to monitoring cell changes from a mesenchymal to an epithelial phenotype, a clinically valuable piece of information considering that the epithelial-to-mesenchymal transition is a key step in the metastatic process.

Cancer spheroids are in vitro 3D models that became crucial in nanomaterials science thanks to the possibility of performing high throughput screening of nanoparticles and combined nanoparticle-drug therapies on in vitro models. However, most of the current spheroid analysis methods involve manual steps. This is a time-consuming process and is extremely liable to the variability of individual operators. For this reason, rapid, user-friendly, ready-to-use, high-throughput image analysis software is necessary. In this work, we report the INSIDIA 2.0 macro, which of-fers researchers high-throughput and high content quantitative analysis of in vitro 3D cancer cell spheroids and allows advanced parametrization of the expanding and invading cancer cellular mass. INSIDIA has been implemented to provide in-depth morphologic analysis and has been used for the analysis of the effect of graphene quantum dots photothermal therapy on glioblastoma (U87) and pancreatic cancer (PANC-1) spheroids. Thanks to INSIDIA 2.0 analysis, two types of effects have been observed: In U87 spheroids, death is accompanied by a decrease in area of the entire spheroid, with a decrease in entropy due to the generation of a high uniform density spheroid core. On the other hand, PANC-1 spheroids’ death caused by nanoparticle photothermal disruption is accompanied with an overall increase in area and entropy due to the progressive loss of integrity and increase in variability of spheroid texture. We have summarized these effects in a quantitative parameter of spheroid disruption demonstrating that INSIDIA 2.0 multiparametric analysis can be used to quantify cell death in a non-invasive, fast, and high-throughput fashion.

Amyotrophic lateral sclerosis (ALS) is characterized by the degeneration and death of motor neurons. This neurodegenerative disease leads to muscle atrophy, paralysis, and death due to respiratory failure. MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) with a length of 19 to 25 nucleotides, participating in the regulation of gene expression. Different studies have demonstrated that miRNAs deregulation is critical for the onset of a considerable number of neurodegenerative diseases, including ALS. Some studies have underlined how miRNAs are deregulated in ALS patients and for this reason, design therapies are used to correct the aberrant expression of miRNAs. With this rationale, delivery systems can be designed to target specific miRNAs. Specifically, these systems can be derived from viral vectors (viral systems) or synthetic or natural materials, including exosomes, lipids, and polymers. Between many materials used for nonviral vectors production, the two-dimensional graphene and its derivatives represent a good alternative for efficiently delivering nucleic acids. The large surface-to-volume ratio and ability to penetrate cell membranes are among the advantages of graphene. This review focuses on the specific pathogenesis of miRNAs in ALS and on graphene delivery systems designed for gene delivery to create a primer for future studies in the field.

Estimating the post-mortem interval is a fundamental, albeit challenging task in forensic sciences. To this aim, forensic practitioners need to assess post-mortem changes through a plethora of different methods, most of which are inherently qualitative, thus providing broad time intervals rather than precise determinations. This challenging problem is further complicated by the influence of environmental factors, which modify the temporal dynamics of post-mortem changes, sometimes in a rather unpredictable fashion. In this context, the search for quantitative and objective descriptors of post-mortem changes is highly demanded. In this study, we used computed tomography (CT) to assess the post-mortem anatomical modifications occurring in the time interval 0–4 days after death in the brain of four corpses. Our results show that fractal analysis of CT brain slices provides a set of quantitative descriptors able to map post-mortem changes over time throughout the whole brain. Although incapable of producing a direct estimation of the PMI, these descriptors could be used in combination with other more established methods to improve the accuracy and reliability of PMI determination.

Additive manufacturing has played a crucial role in the COVID-19 global emergency allowing for rapid production of medical devices, indispensable tools for hospitals, or personal protection equipment. However, medical devices, especially in nosocomial environments, represent high touch surfaces prone to viral infection and currently used filaments for 3D printing can't inhibit transmission of virus [1]. Graphene-family materials are capable of reinforcing mechanical, optical and thermal properties of 3D printed constructs. In particular, graphene can adsorb near-infrared light with high efficiency. Here we demonstrate that the addition of graphene nanoplatelets to PLA filaments (PLA-G) allows the creation of 3D-printed devices that can be sterilized by near-infrared light exposure at power density analog to sunlight. This method has been used to kill SARS-CoV-2 viral particles on the surface of 3D printed PLA-G by 3 min of exposure. 3D-printed PLA-G is highly biocompatible and can represent the ideal material for the production of sterilizable personal protective equipment and daily life objects intended for multiple users.

2-deoxy-2-fluorine-(18F)fluoro-D-glucose Positron Emission Tomography/Computed Tomography (18F-FDG-PET/CT) is widely used in oncology mainly for diagnosis and staging of various cancer types, including lung cancer, which is the most common cancer worldwide. Since histopathologic subtypes of lung cancer show different degree of 18F-FDG uptake, to date there are some diagnostic limits and uncertainties, hindering an 18F-FDG-PET-driven classification of histologic subtypes of lung cancers. On the other hand, since activated macrophages, neutrophils, fibroblasts and granulation tissues also show an increased 18F-FDG activity, infectious and/or inflammatory processes and post-surgical and post-radiation changes may cause false-positive results, especially for lymph-nodes assessment. Here we propose a model-free, machine-learning based algorithm for the automated classification of adenocarcinoma, the most common type of lung cancer, and other types of tumors. Input for the algorithm are dynamic acquisitions of PET data (dPET), providing for a spatially and temporally resolved characterization of the uptake kinetic. The algorithm consists in a trained Random Forest classifier which, relying contextually on several spatial and temporal features of 18F-FDG uptake, generates as an outcome probability maps allowing to distinguish adenocarcinoma from other lung histotype and to identify metastatic lymph-nodes, ultimately increasing the specificity of the technique. Its performance, evaluated on a dPET dataset of 19 patients affected by primary lung cancer, provides a probability 0.943 ± 0.090 for the detection of adenocarcinoma. The use of this algorithm will guarantee an automatic and more accurate localization and discrimination of tumors, also providing a powerful tool for detecting at which extent tumor has spread beyond a primary tumor into lymphatic system.

Single-use plastics used for food packaging have increased significantly environmental pollution. There is a global need to improve the sustainability of our packaging solutions by designing smart biodegradable polymers. In this work, we demonstrate a completely green, facile two-steps route to produce an antibacterial Poly(vinyl alcohol) (PVA) film for packaging by using N-Acetylcysteine (NAC). PVA is a biopolymer with good compatibility, processability, and is degradable in water. NAC is known in medicine and nutrition for its antioxidant and antibacterial effects. Here we show how PVA films enriched with NAC (PVA-NAC) are mechanical resistant and at the same time exhibit strong UV light shielding. NAC is released from films and inhibits E. coli and S. aureus microorganisms growth. The developed PVA-NAC films are soluble in water and can be considered as a promising substitute for polluting plastic packaging.

Exosomes (EXOs) are nano-sized vesicles secreted by most cell types. They are abundant in bio-fluids and harbor specific molecular constituents from their parental cells. Due to these characteristics, EXOs have a great potential in cancer diagnostics for liquid biopsy and personalized medicine. Despite this unique potential, EXOs are not yet widely applied in clinical settings, with two main factors hindering their translational process in diagnostics. Firstly, conventional extraction methods are time-consuming, require large sample volumes and expensive equipment, and often do not provide high-purity samples. Secondly, characterization methods have some limitations, because they are often qualitative, need extensive labeling or complex sampling procedures that can induce artifacts. In this context, novel label-free approaches are rapidly emerging, and are holding potential to revolutionize EXO diagnostics. These methods include the use of nanodevices for EXO purification, and vibrational spectroscopies, scattering, and nanoindentation for characterization. In this progress report, we summarize recent key advances in label-free techniques for EXO purification and characterization. We point out that these methods contribute to reducing costs and processing times, provide complementary information compared to the conventional characterization techniques, and enhance flexibility, thus favoring the discovery of novel and unexplored EXO-based biomarkers. In this process, the impact of nanotechnology is systematically highlighted, showing how the effectiveness of these techniques can be enhanced using nanomaterials, such as plasmonic nanoparticles and nanostructured surfaces, which enable the exploitation of advanced physical phenomena occurring at the nanoscale level.

Using postmortem CT (PMCT), changes in the volume of the lateral cerebral ventricles (LCVs) and modifications of the radiodensity of cerebrospinal fluid (CSF) have been examined to identify a possible relationship between these changes and the time of death. Subsequent periodical CT scans termed “sequential scans” for ten corpses at known time of death were obtained, and a 3D segmentation of the entire LCV was carried out to measure its volume and radiodensity over time from ~ 5.5- h up to 273-h postmortem. A linear decrease of the LCV volume for all the cases was observed in the investigated time range, together with an overall logarithmic increase of radiodensity. Although a larger sampling should be performed to improve the result reliability, our finding suggests that the postmortem variation of CSF radiodensity can be a potentially useful tool in determining postmortem interval, a finding that is worthy of further investigation.

Recent advancements in bidimensional nanoparticles production such as graphene (G) and graphene oxide (GO) have the potential to meet the need for highly functional personal protective equipment (PPE) against SARS-CoV-2 infection. The ability of G and GO to interact with microorganisms provides an opportunity to develop engineered textiles for use in PPE and limit the spread of COVID-19. PPE in current use in high-risk settings for COVID transmission provides only a physical barrier that decreases infection likelihood and does not inactivate the virus. Here, we show that virus pre-incubation with soluble GO inhibits SARS-CoV-2 infection of VERO cells. Furthermore, when G/GO-functionalized polyurethane or cotton was in contact SARS-CoV-2, the infectivity of the fabric was nearly completely inhibited. The findings presented here constitute an important innovative nanomaterial-based strategy to significantly increase PPE efficacy in protection against the SARS-CoV-2 virus that may implement water filtration, air purification, and diagnostics methods.

Purpose: Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner. Methods: In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon–Mann–Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness. Results: Three features were selected: maximum fractal dimension with IB = 0–50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0–50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively. Conclusions: The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.

Free fatty acids are essential structural components of the cell, and their intracellular distribution and effects on membrane organelles have crucial roles in regulating the metabolism, development, and cell cycle of most cell types. Here we engineered novel fluorescent, polarity-sensitive fatty acid derivatives, with the fatty acid aliphatic chain of increasing length (from 12 to 18 carbons). As in the laurdan probe, the lipophilic acyl tail is connected to the environmentally sensitive dimethylaminonaphthalene moiety. The fluorescence lifetime imaging analysis allowed us to monitor the intracellular distribution of the free fatty acids within the cell, and to simultaneously examine how the fluidity and the microviscosity of the membrane environment influence their localization. Each of these probes can thus be used to investigate the membrane fluidity regulation of the correspondent fatty acid intracellular distribution. We observed that, in PC-12 cells, fluorescent sensitive fatty acid derivatives with increased chain length compartmentalize more preferentially in the fluid regions, characterized by a low microviscosity. Moreover, fatty acid derivatives with the longest chain compartmentalize in lipid droplets and lysosomes with characteristic lifetimes, thus making these probes a promising tool for monitoring lipophagy and related events.

Background: A high level of glycosylated haemoglobin (HbA1c), which is a nonenzymatic glycosylation product, is correlated with an increased risk of developing microangiopathic complications in Diabetes Mellitus (DM). Erythrocyte membrane fluidity could provide a complementary index to monitor the development of complications since it is influenced by several hyperglycaemia-induced pathways and other independent risk factors. Materials and methods: 15 healthy controls and 33 patients with long-duration (≥20 years) type 1 Diabetes Mellitus (T1DM) were recruited. Diabetic subjects were classified into two groups: T1DM, constituted by 14 nonretinopathic patients, and T1DM + RD, constituted by 19 patients in any stage of diabetic retinopathy. Red blood cells (RBC) were incubated with the fluorescent Laurdan probe and median values of Generalized Polarization (GP), representative of membrane fluidity, were compared between the two groups. Baseline characteristics among groups have been compared with Student's t test or ANOVA. Values of P <.05 were considered statistically significant. Results: All the participants were comparable for age, Body Mass Index (BMI), creatinine and lipid profile. The duration of diabetes was similar for T1DM (34.4 ± 7.8 years) and T1DM + RD (32.8 ± 7.5 years) subjects as well as values of HbA1c: (55.6 ± 8.1) mmol/mol for T1DM and (61.2 ± 11.0) mmol/mol for T1DM + RD, respectively. Erythrocyte plasmatic membranes of RD patients were found to be more fluid (GP: 0.40 ± 0.04) than non-RD patients (GP: 0.43 ± 0.03) with a statistically significant difference (P =.035). Conclusions: Altered erythrocyte membrane fluidity may therefore represent a marker of retinopathy in T1DM patients as a result of post-translational modifications of multifactorial aetiology (nonenzymatic glycosylation of proteins, generation of reactive oxygen speci

Autofluorescence microscopy is a promising label-free approach to characterize NADH and FAD metabolites in live cells, with potential applications in clinical practice. Although spectrally resolved lifetime imaging techniques can acquire multiparametric information about the biophysical and biochemical state of the metabolites, these data are evaluated at the whole-cell level, thus providing only limited insights in the activation of metabolic networks at the microscale. To overcome this issue, here we introduce an artificial intelligence-based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows to detect and classify, through an unsupervised learning approach, metabolic clusters, which are regions having almost uniform metabolic properties. This method contextually detects the cellular mitochondrial turnover and the metabolic activation state of intracellular compartments at the pixel level, described by two functions: the cytosolic activation state (CAF) and the mitochondrial activation state (MAF). This method was applied to investigate metabolic changes elicited in the breast cancer cell line MCF-7 by specific inhibitors of glycolysis and electron transport chain, and by the deregulation of a specific mitochondrial enzyme (ACO2) leading to defective aerobic metabolism associated with tumor growth. In this model, mitochondrial fraction undergoes to a 13% increase upon ACO2 overexpression and the MAF function changes abruptly by altering the metabolic state of about the 25% of the mitochondrial pixels.

Solvatochromic probes undergo an emission shift when the hydration level of the membrane environment increases and are commonly used to distinguish between solid-ordered and liquid-disordered phases in artificial membrane bilayers. This emission shift is currently limited in unraveling the broad spectrum of membrane phases of natural cell membranes and their spatial organization. Spectrally resolved fluorescence lifetime imaging can provide pixel-resolved multiparametric information about the biophysical state of the membranes, like membrane hydration, microviscosity and the partition coefficient of the probe. Here, we introduce a clustering based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows us to classify through an unsupervised learning approach multiple membrane phases with sub-micrometric resolution. This method extends the spectrum of detectable membrane phases allowing to dissect and characterize up to six different phases, and to study real-time phase transitions in cultured cells and tissues undergoing different treatments. We applied this method to investigate membrane remodeling induced by high glucose on PC-12 neuronal cells, associated with the development of diabetic neuropathy. Due to its wide applicability, this method provides a new paradigm in the analysis of environmentally sensitive fluorescent probes.

Reconstruction of bone defects with the efficient restoration of tissue morphology and functionality is a critical medical challenge. Progress in 3D printing technologies offers new opportunities for precise bone architecture reproduction during scaffolds construction. At the same time, biomaterials are being designed and produced to achieve the desired functional, mechanical and supportive features and to guide cell behavior in vivo. Latest progress in biomaterials research makes the family of bidimensional carbon materials in the spotlight for their excellent bone regeneration and mechanical properties. Graphene, graphene oxide and reduced graphene oxide can also be 3D printed to obtain patient-tailored scaffolds with controlled feature sizes in the mesoscopic range. In this review, we describe technologies developed for graphene 3D printing focusing on the osteogenic properties of graphene scaffolds. The multiple benefits achievable by a proper design of the graphene surface as well as the concerns on their biocompatibility are also highlighted. We believe that this overview of the latest progress and trends will provide the basis for the future improvement of implantable graphene bone grafts.

Solvatochromic probes undergo an emission shift when the hydration level of the membrane environment increases and are commonly used to distinguish between solid-ordered and liquid-disordered phases in artificial membrane bilayers. This emission shift is currently limited in unraveling the broad spectrum of membrane phases of natural cell membranes and their spatial organization. Spectrally resolved fluorescence lifetime imaging can provide pixel-resolved multiparametric information about the biophysical state of the membranes, like membrane hydration, microviscosity and the partition coefficient of the probe. Here, we introduce a clustering based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows us to classify through an unsupervised learning approach multiple membrane phases with sub-micrometric resolution. This method extends the spectrum of detectable membrane phases allowing to dissect and characterize up to six different phases, and to study real-time phase transitions in cultured cells and tissues undergoing different treatments. We applied this method to investigate membrane remodeling induced by high glucose on PC-12 neuronal cells, associated with the development of diabetic neuropathy. Due to its wide applicability, this method provides a new paradigm in the analysis of environmentally sensitive fluorescent probes.

Global pandemic management represents a serious issue for health systems. In some cases, repurposing of existing medications might help find compounds that have the unexpected potential to combat microorganisms. In the same way, changing cell–drug interaction by nanotechnology could represent an innovative strategy to fight infectious diseases. Tuberculosis (TB) remains one of the most alarming worldwide infectious diseases and there is an urgent need for new drugs and treatments, particularly for the emergence and spread of drug-resistant Mycobacterium tuberculosis (Mtb) strains. New nanotechnologies based on carbon nanomaterials are now being considered to improve anti-TB treatments, and graphene oxide (GO) showed interesting properties as an anti-TB drug. GO, which preferentially accumulates in the lungs and is degraded by macrophagic peroxidases, can trap Mycobacterium smegmatis and Mtb in a dose-dependent manner, reducing the entry of bacilli into macrophages. In this paper, combinations of isoniazid (INH), amikacin (AMK) and linezolid (LZD) and GO anti-mycobacterial properties were evaluated against Mtb H37Rv by using a checkerboard assay or an in vitro infection model. Different GO effects have been observed when incubated with INH, AMK or LZD. Whereas the INH and AMK anti-mycobacterial activities were blocked by GO co-administration, the LZD bactericidal effect increased in combination with GO. GO-LZD significantly reduced extracellular mycobacteria during infection and was able to kill internalized bacilli. GO-LZD co-administration is potentially a new promising anti-TB treatment at the forefront in fighting emerging antibiotic-resistant Mtb strains where LZD administration is suggested. This innovative pharmacological approach may lead to reduced treatment periods and decreased adverse effects. More importantly, we demonstrate how nanomaterials–drugs combinations can represent a possible strategy to quickly design drugs for pandemics treatment.

Background: Internal Target Volume (ITV) is one of the most common strategies to passively manage tumour motion in Radiotherapy (RT). The reliability of this approach is based on the assumption that the tumour motion estimated during pre-treatment 4D Computed Tomography (CT) acquisition is representative of the motion during the whole RT treatment. With the introduction of Magnetic Resonance-guided RT (MRgRT), it has become possible to monitor tumour motion during the treatment and verify this assumption. Aim of this study was to investigate the reliability of the ITV approach with respect to the treatment fraction time (TFT) in abdominal and thoracic lesions. Methods: A total of 12 thoracic and 15 abdominal lesions was analysed. Before treatment, a 10-phase 4DCT was acquired and ITV margins were estimated considering the envelope of the lesion contoured on the different 4DCT phases. All patients underwent MRgRT treatment in free-breathing, monitoring the tumour position on a sagittal plane with 4 frames per second (sec). ITV margins were projected on the tumour trajectory and the percentage of treatment time in which the tumour was inside the ITV (%TT) was measured to varying of TFT. The ITV approach was considered moderately reliable when %TT ≥ 90% and strongly reliable when %TT ≥ 95%. Additional ITV margins required to achieve %TT ≥ 95% were also calculated. Results: In the analysed cohort of patients, ITV strategy can be considered strongly reliable only for lung lesions with TFT ≤ 7 min (min). The ITV strategy can be considered only moderately reliable for abdominal lesions, and additional margins are required to obtain %TT ≥ 95%. Considering a TFT ≤ 4 min, additional margins of 2 mm in cranio-caudal (CC) and 1 mm in antero-posterior (AP) are suggested for pancreatic lesions, 3 mm in CC and 2 mm in AP for renal and liver ones. Conclusions: On the basis of the analysed cases, the ITV approach appears to be reliable in the thorax, while it results more challengin

Glioblastoma is the most aggressive and lethal brain cancer. Current treatments involve surgical resection, radiotherapy and chemotherapy. However, the life expectancy of patients with this disease remains short and chemotherapy leads to severe adverse effects. Furthermore, the presence of the blood-brain barrier (BBB) makes it difficult for drugs to effectively reach the brain. A promising strategy lies in the use of graphene quantum dots (GQDs), which are light-responsive graphene nanoparticles that have shown the capability of crossing the BBB. Here we investigate the effect of GQDs on U87 human glioblastoma cells and primary cortical neurons. Non-functionalized GQDs (NF-GQDs) demonstrated high biocompatibility, while dimethylformamide-functionalized GQDs (DMF-GQDs) showed a toxic effect on both cell lines. The combination of GQDs and the chemotherapeutic agent doxorubicin (Dox) was tested. GQDs exerted a synergistic increase in the efficacy of chemotherapy treatment, specifically on U87 cells. The mechanism underlying this synergy was investigated, and it was found that GQDs can alter membrane permeability in a manner dependent on the surface chemistry, facilitating the uptake of Dox inside U87 cells, but not on cortical neurons. Therefore, experimental evidence indicates that GQDs could be used in a combined therapy against brain cancer, strongly increasing the efficacy of chemotherapy and, at the same time, reducing its dose requirement along with its side effects, thereby improving the life quality of patients.

Optical neural networks process information at the speed of light and are energetically efficient. Photonic artificial intelligence allows speech recognition, image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and biological materials may realize optical neural networks with thousands of nodes trained only at the input and at the readout. May we use living matter for machine learning? Here, we employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. The tumor spheroid act as a computational reservoir. The ROM detects cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism. The ROM is a sensitive noninvasive smart probe for cytotoxicity assay and enables real-time investigation of tumor dynamics. We hence design and demonstrate a novel bio-hardware for optical computing and the study of light/complex matter interaction.

Purpose: Tumor control probability (TCP)-based early regression index (ERITCP) is a radiobiological parameter that showed promising results in predicting pathologic complete response (pCR) on T2-weighted 1.5 T magnetic resonance (MR) images of patients with locally advanced rectal cancer. This study aims to validate the ERITCP in the context of low-tesla MR-guided radiation therapy, using images acquired with different magnetic field strength (0.35 T) and image contrast (T2/T1). Furthermore, the optimal timing for pCR prediction was estimated, calculating the ERI index at different biologically effective dose (BED) levels. Methods and Materials: Fifty-two patients with locally advanced rectal cancer treated with neoadjuvant chemoradiation therapy were enrolled in this multi-institutional retrospective study. For each patient, a 0.35 T T2/T1-weighted MR image was acquired during simulation and on each treatment day. Gross tumor volume was contoured according to International Commission on Radiation Units Report 83 guidelines. According to the original definition, ERITCP was calculated considering the residual tumor volume at BED = 25 Gy. ERI was also calculated in correspondence with several BED levels: 13, 21, 32, 40, 46, 54, 59, and 67. The predictive performance of the different ERI indices were evaluated in terms of receiver operating characteristic curve. The robustness of ERITCP with respect to the interobserver variability was also evaluated considering 2 operators and calculating the intraclass correlation index. Results: Fourteen patients showed pCR. ERITCP correctly 47 of 52 cases (accuracy = 90%), showing good results in terms of sensitivity (86%), specificity (92%), negative predictive value (95%), and positive predictive value (80%). The analysis at different BED levels shows that the best predictive performance is obtained when this parameter is calculated at BED = 25 Gy (area under the curve = 0.93). ERITCP results are robust with respect to interobser

In physiological conditions, red blood cells (RBCs) are capable of dramatic deformations when passing through the microvasculature. This extreme deformability is closely related to the RBC biconcave shape, to the fluidic nature of the haemoglobin and the cell membrane structure, primarily consisting of a phospholipid bilayer with an underlying two-dimensional spectrin network. In many pathological and inflammatory conditions, the shape and the extreme deformability of erythrocytes appear to be significantly altered. These findings have stimulated intense research towards the search and validation of novel erythrocyte-based mechanical biomarkers, useful for disease diagnosis and therapy monitoring. In this study, we investigated with Atomic Force Microscopy (AFM) the mechanical properties of erythrocytes obtained from a 68 years old cirrhotic man diagnosed with spur cell anaemia and cold agglutinated disease, before and after liver transplantation. Mechanical changes are compared with ultrastructural alterations as studied by scanning electron microscopy and discussed according to confocal fluorescence microscopy results, showing possible alterations induced by the cirrhotic environment at the level of the RBCs cytoskeletal organisation and lipidic composition. Taken together, the results here presented show that liver transplantation not only contributes to restoring the proper RBC morphology, but it also induces recovery of the physiological viscous behaviour of cells, further stressing the relevance of viscous and dissipative forces in determining the RBC biomechanical response.

We report the case of a 38 year-old Caucasian man enrolled in a study aimed at investigating the physical properties of red blood cells (RBCs) using advanced microscopy techniques, including Atomic Force Microscopy (AFM). At the time of his first enrolment in the study, he had normal Fasting Plasma Glucose (FPG) values, a BMI of 24.1, and no other symptoms of diabetes, including fatigue, high triglycerides, low HDL cholesterol, and altered inflammatory and corpuscular RBC indices. The subject reported no family history of diabetes, obesity, and cardiovascular diseases. Despite his apparently healthy conditions, the biomechanics of his RBCs was altered, showing increased values of stiffness and viscosity. More than 1 year after the mechanical measurements, the subject was admitted to the Operational Unit of Diabetology of the Policlinico Gemelli Hospital with high blood glucose and glycosylated hemoglobin (HbA1c) levels and diagnosed with type 1 diabetes (T1DM). Here, we show these data, and we discuss the hypothesis that RBC mechanical properties could be sensitive to changes occurring during the pre-diabetic phase of T1DM.

Cadaveric rigidity—also referred to as rigor mortis—is a valuable source of information for estimating the time of death, which is a fundamental and challenging task in forensic sciences. Despite its relevance, assessing the level of cadaveric rigidity still relies on qualitative and often subjective observations, and the development of a more quantitative approach is highly demanded. In this context, ultrasound shear wave elastography (US SWE) appears to be a particularly well-suited technique for grading cadaveric rigidity, as it allows non-invasive quantification of muscle stiffness in terms of Young’s modulus (E), which is a widely used parameter in tissue biomechanics. In this pilot study, we measured, for the first time in the literature, changes in the mechanical response of muscular tissues from 0 to 60 h post-mortem (hpm) using SWE, with the aim of investigating its applicability to forensic practice. For this purpose, 26 corpses were included in the study, and the muscle mechanical response was measured at random times in the 0–60 hpm range. Despite the preliminary nature of this study, our data indicate a promising role of SWE in the quantitative determination of cadaveric rigidity, which is still currently based on qualitative and semiquantitative methods. A more in-depth study is required to confirm SWE applicability in this field in order to overcome some of the inherent limitations of the present work, such as the rather low number of cases and the non-systematic approach of the measurements.

Background: A high level of glycosylated haemoglobin (HbA1c), which is a nonenzymatic glycosylation product, is correlated with an increased risk of developing microangiopathic complications in Diabetes Mellitus (DM). Erythrocyte membrane fluidity could provide a complementary index to monitor the development of complications since it is influenced by several hyperglycaemia-induced pathways and other independent risk factors. Materials and methods: 15 healthy controls and 33 patients with long-duration (≥20 years) type 1 Diabetes Mellitus (T1DM) were recruited. Diabetic subjects were classified into two groups: T1DM, constituted by 14 nonretinopathic patients, and T1DM + RD, constituted by 19 patients in any stage of diabetic retinopathy. Red blood cells (RBC) were incubated with the fluorescent Laurdan probe and median values of Generalized Polarization (GP), representative of membrane fluidity, were compared between the two groups. Baseline characteristics among groups have been compared with Student's t test or ANOVA. Values of P <.05 were considered statistically significant. Results: All the participants were comparable for age, Body Mass Index (BMI), creatinine and lipid profile. The duration of diabetes was similar for T1DM (34.4 ± 7.8 years) and T1DM + RD (32.8 ± 7.5 years) subjects as well as values of HbA1c: (55.6 ± 8.1) mmol/mol for T1DM and (61.2 ± 11.0) mmol/mol for T1DM + RD, respectively. Erythrocyte plasmatic membranes of RD patients were found to be more fluid (GP: 0.40 ± 0.04) than non-RD patients (GP: 0.43 ± 0.03) with a statistically significant difference (P =.035). Conclusions: Altered erythrocyte membrane fluidity may therefore represent a marker of retinopathy in T1DM patients as a result of post-translational modifications of multifactorial aetiology (nonenzymatic glycosylation of proteins, generation of reactive oxygen speci

Exosomes possess great potential as cancer biomarkers in personalized medicine due to their easy accessibility and capability of representing their parental cells. To boost the translational process of exosomes in diagnostics, the development of novel and effective strategies for their label-free and automated characterization is highly desirable. In this context, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides direct access to specific biomolecular bands that give compositional information on exosomes in terms of their protein, lipid and genetic content. Here, we used FTIR spectroscopy in the mid-Infrared (mid-IR) range to study exosomes released from human colorectal adenocarcinoma HT-29 cancer cells cultured in different media. To this purpose, cells were studied in well-fed condition of growth, with 10% of exosome-depleted FBS (EVd-FBS), and under serum starvation with 0.5% EVd-FBS. Our data show the presence of statistically significant differences in the shape of the Amide I and II bands in the two conditions. Based on these differences, we showed the possibility to automatically classify cancer cell-derived exosomes using Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA); we tested the effectiveness of the classifier with a cross-validation approach, obtaining very high accuracy, precision, and recall. Aside from classification purposes, our FTIR data provide hints on the underlying cellular mechanisms responsible for the compositional differences in exosomes, suggesting a possible role of starvation-induced autophagy.

The present paper concerns a new description of changing in metabolism during incremental exercises test that permit an individually tailored program of exercises for obese subjects. We analyzed heart rate variability from RR interval time series (tachogram) with an alternative approach, the recurrence quantification analysis, that allows a description of a time series in terms of its dynamic structure and is able to identify the phase transitions. A transition in cardiac signal dynamics was detected and it perfectly reflects the aerobic threshold, as identified by gas exchange during an incremental exercise test, revealing the coupling from the respiratory system toward the heart. Moreover, our analysis shows that, in the recurrence plot of RR interval, it is possible to identify a specific pattern that allows to identify phase transitions between different dynamic regimes. The perfect match of the occurrence of the phase transitions with changes observed in the VO2 consumption, the gold standard approach to estimate thresholds, strongly supports the possibility of using our analysis of RR interval to detect metabolic threshold. In conclusion, we propose a novel nonlinear data analysis method that allows for an easy and personalized detection of thresholds both from professional and even from low-cost wearable devices, without the need of expensive gas analyzers.

Reconstruction of bone defects with the efficient restoration of tissue morphology and functionality is a critical medical challenge. Progress in 3D printing technologies offers new opportunities for precise bone architecture reproduction during scaffolds construction. At the same time, biomaterials are being designed and produced to achieve the desired functional, mechanical and supportive features and to guide cell behavior in vivo. Latest progress in biomaterials research makes the family of bidimensional carbon materials in the spotlight for their excellent bone regeneration and mechanical properties. Graphene, graphene oxide and reduced graphene oxide can also be 3D printed to obtain patient-tailored scaffolds with controlled feature sizes in the mesoscopic range. In this review, we describe technologies developed for graphene 3D printing focusing on the osteogenic properties of graphene scaffolds. The multiple benefits achievable by a proper design of the graphene surface as well as the concerns on their biocompatibility are also highlighted. We believe that this overview of the latest progress and trends will provide the basis for the future improvement of implantable graphene bone grafts.