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Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts., Working paper, comments welcome

Background Cystinuria is an inborn congenital disorder characterised by a defective cystine metabolism resulting in the formation of cystine stones. The Brand’s test, used for diagnosis, requires dangerous substances, so has been replaced with high-performance liquid chromatography with fluorimetric detection (HPLC-FL). However, this technique requires the use of complex equipment. Infrared spectroscopy, universally used for stone analysis, recently was employed to detect insoluble cystine in urine. The aim of this study is to evaluate Infrared Spectroscopy combined with chemometric analysis as screening method to identify those patients requiring confirmation by HPLC-FL analysis. Methods We examined 24 h urine specimens from 57 patients. The quantitative analysis was performed by HPLC-FL. The infrared spectroscopic urine sediment analysis was performed with an ATR accessory (ATR-FTIR). Urine is centrifuged, the supernatant is discarded, and the sediment is dried on to the ATR prism surface. Statistical analysis was performed using a custom-made software developed in MATLAB environment. Results The HPLC-FL determination showed a normal excretion of cystine in 49 samples and an abnormal excretion in the remaining 8 samples. The ATR-FTIR analysis combined with a statistical approach gives a sensitivity of 1.0 and a specificity of 0.82 were obtained. Conclusions The introduction of the ATR-FTIR technique in our clinical laboratory setting may reduce time and cost analysis for diagnosis of cystinuria.

Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers for cancer liquid biopsy and personalized medicine. Despite this potential, none of the discovered biomarkers has entered the clinical practice so far, and novel approaches for the label-free characterization of EVs are highly demanded. In this regard, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides a quick, reproducible, and informative biomolecular fingerprint of EVs. In this pilot study, we investigated, for the first time in the literature, the capability of FTIR spectroscopy to distinguish between EVs extracted from sera of cancer patients and controls based on their mid-IR spectral response. For this purpose, EV-enriched suspensions were obtained from the serum of patients diagnosed with Hepatocellular Carcinoma (HCC) of nonviral origin and noncancer subjects. Our data point out the presence of statistically significant differences in the integrated intensities of major mid-IR absorption bands, including the carbohydrate and nucleic acids band, the protein amide I and II bands, and the lipid CH stretching band. Additionally, we used Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) for the automated classification of spectral data according to the shape of specific mid-IR spectral signatures. The diagnostic performances of the proposed spectral biomarkers, alone and combined, were evaluated using multivariate logistic regression followed by a Receiving Operator Curve analysis, obtaining large Areas Under the Curve (AUC = 0.91, 95% CI 0.81–1.0). Very interestingly, our analyses suggest that the discussed spectral biomarkers can outperform the classification ability of two widely used circulating HCC markers measured on the same groups of subjects, namely alpha-fetoprotein (AFP), and protein induced by the absence of vitamin K or antagonist-II (PIVKA-II).

Exosomes (EXOs) are considered an exceptionally promising source of cancer biomarkers for personalized medicine and liquid biopsy. Despite this potential, the EXO 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. Journal Pre-proof

Background Deregulation of the tricarboxylic acid cycle (TCA) due to mutations in specific enzymes or defective aerobic metabolism is associated with tumour growth. Aconitase 2 (ACO2) participates in the TCA cycle by converting citrate to isocitrate, but no evident demonstrations of its involvement in cancer metabolism have been provided so far. Methods Biochemical assays coupled with molecular biology, in silico, and cellular tools were applied to circumstantiate the impact of ACO2 in the breast cancer cell line MCF-7 metabolism. Fluorescence lifetime imaging microscopy (FLIM) of NADH was used to corroborate the changes in bioenergetics. Results We showed that ACO2 levels are decreased in breast cancer cell lines and human tumour biopsies. We generated ACO2- overexpressing MCF-7 cells and employed comparative analyses to identify metabolic adaptations. We found that increased ACO2 expression impairs cell proliferation and commits cells to redirect pyruvate to mitochondria, which weakens Warburg-like bioenergetic features. We also demonstrated that the enhancement of oxidative metabolism was supported by mitochondrial biogenesis and FoxO1-mediated autophagy/mitophagy that sustains the increased ROS burst. Conclusions This work identifies ACO2 as a relevant gene in cancer metabolic rewiring of MCF-7 cells, promoting a different utilisation of pyruvate and revealing the potential metabolic vulnerability of ACO2-associated malignancies.

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.

Trastuzumab emtansine (T-DM1) is an anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugated to the microtubule-targeting agent emtansine (DM1). T-DM1 is an effective agent in the treatment of patients with HER2-positive breast cancer whose disease has progressed on the first-line trastuzumab containing chemotherapy. However, both primary and acquired tumour resistance limit its efficacy. Increased levels of the phosphorylated form of Translationally Controlled Tumour Protein (phospho-TCTP) have been shown to be associated with a poor clinical response to trastuzumab therapy in HER2-positive breast cancer. Here we show that phospho-TCTP is essential for correct mitosis in human mammary epithelial cells. Reduction of phospho-TCTP levels by dihydroartemisinin (DHA) causes mitotic aberration and increases microtubule density in the trastuzumab-resistant breast cancer cells HCC1954 and HCC1569. Combinatorial studies show that T-DM1 when combined with DHA is more effective in killing breast cells compared to the effect induced by any single agent. In an orthotopic breast cancer xenograft model (HCC1954), the growth of the tumour cells resumes after having achieved a complete response to T-DM1 treatment. Conversely, DHA and T-DM1 treatment induces a severe and irreversible cytotoxic effect, even after treatment interruption, thus, improving the long-term efficacy of T-DM1. These results suggest that DHA increases the effect of T-DM1 as poison for microtubules and supports the clinical development of the combination of DHA and T-DM1 for the treatment of aggressive HER2-overexpressing breast cancer.

Organisms store fatty acids in triacylglycerols in the form of lipid droplets, or hydrolyze triacylglycerols in response to energetic demands via activation of lipolytic or storage pathways. These pathways are complex sets of sequential reactions that are finely regulated in different cell types. Here we present a high spatial and temporal resolution-based method for the quantification of the turnover of fatty acids into triglycerides in live cells without introducing sample preparation artifacts. We performed confocal spectral imaging of intracellular micropolarity in cultured insulin secreting beta cells to detect micropolarity variations as they occur in time and at different pixels of microscope images. Acquired data are then analyzed in the framework of the spectral phasors technique. The method furnishes a metabolic parameter, which quantitatively assesses fatty acids - triacylglycerols turnover and the activation of lipolysis and storage pathways. Moreover, it provides a polarity profile, which represents the contribution of hyperpolar, polar and non-polar classes of lipids. These three different classes can be visualized on the image at a submicrometer resolution, revealing the spatial localization of lipids in cells under physiological and pathological settings. This new method allows for a fine-tuned, real-time visualization of the turnover of fatty acids into triglycerides in live cells with submicrometric resolution. It also detects imbalances between lipid storage and usage, which may lead to metabolic disorders within living cells and organisms.

Graphical abstract, Intracellular micropolarity is essential in several metabolic processes, as it controls membrane permeability, regulating the fluxes of molecules and energy. Here we describe a method for the determination of the micropolarity in living cells using spectral confocal microscopy. The method is based on a phasor analysis of spectrally resolved images of live cells, labelled with the solvatochromic probe Nile Red. An application is provided to extract a polarity profile from the acquired Spectral datasets, which represent the contribution of hyperpolar, polar and non-polar lipids, and to generate a micropolarity map at submicrometric spatial resolution. A metabolic parameter, representing a quantitative index of the fatty acid-triacylglycerol turnover, is also furnished. This method allows a functional profiling of cells and tissues and the detection of metabolic imbalances between lipid storage and usage. • Use of spectral resolved confocal microscopy of Nile Red labelled cells for pixel resolved determination of the membranes micropolarity. • Spectral acquisition increases the specificity and sensitivity of the detection to provide a polarity profile and a metabolic index for fatty acid-TG turnover. • Use of spectral resolved confocal microscopy of Nile Red labelled cells for pixel resolved determination of the membranes micropolarity.

All living systems are maintained by a constant flux of metabolic energy and, among the different reactions, the process of lipids storage and lipolysis is of fundamental importance. Current research has focused on the investigation of lipid droplets (LD) as a powerful biomarker for the early detection of metabolic and neurological disorders. Efforts in this field aim at increasing selectivity for LD detection by exploiting existing or newly synthesized probes. However, LD constitute only the final product of a complex series of reactions during which fatty acids are transformed into triglycerides and cholesterol is transformed in cholesteryl esters. These final products can be accumulated in intracellular organelles or deposits other than LD. A complete spatial mapping of the intracellular sites of triglycerides and cholesteryl esters formation and storage is, therefore, crucial to highlight any potential metabolic imbalance, thus predicting and counteracting its progression. Here, we present a machine learning assisted, polarity-driven segmentation which enables to localize and quantify triglycerides and cholesteryl esters biosynthesis sites in all intracellular organelles, thus allowing to monitor in real-time the overall process of the turnover of these non-polar lipids in living cells. This technique is applied to normal and differentiated PC12 cells to test how the level of activation of biosynthetic pathways changes in response to the differentiation process.

Classification of the category of diabetes is extremely important for clinicians to diagnose and select the correct treatment plan. Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins, as well as impairment in cholesterol homeostasis, can alter lipid density, packing, and interactions of Red blood cells (RBC) plasma membranes in type 1 and type 2 diabetes, thus varying their membrane micropolarity. This can be estimated, at a submicrometric scale, by determining the membrane relative permittivity, which is the factor by which the electric field between the charges is decreased relative to vacuum. Here, we employed a membrane micropolarity sensitive probe to monitor variations in red blood cells of healthy subjects (n=16) and patients affected by type 1 (T1DM, n=10) and type 2 diabetes mellitus (T2DM, n=24) to provide a cost-effective and supplementary indicator for diabetes classification. We find a less polar membrane microenvironment in T2DM patients, and a more polar membrane microenvironment in T1DM patients compared to control healthy patients. The differences in micropolarity are statistically significant among the three groups (p, Graphical abstract Image 1, Highlights • Dynamic flux of cholesterol is differentially altered in T1DM and T2DM. • Red blood cell senses the dynamic flux of lipids by changing its micropolarity. • Laurdan can measure micropolarity in red blood cells membranes. • Differences in micropolarity between the three groups are statistically significant. • Red blood cell Micropolarity is an innovative assay for diabetes classification.

Membrane fluidity, a broad term adopted to describe the thermodynamic phase state of biological membranes, can be altered by local pressure variations caused by ultrasound exposure. The alterations in lipid spatial configuration and dynamics can modify their interactions with membrane proteins and activate signal transduction pathways, thus regulating several cellular functions. Here fluidity maps of murine fibroblast cells are generated at a sub-micrometric scale during ultrasound stimulation with an intensity and frequency typical of medical applications. Ultrasound induces a phase separation characterized by two-step kinetics leading to a time-dependent decrease in fluidity. First, nucleation of liquid crystallin domains with an average dimension of ∼1 μm occurs. Then, these domains condense into larger clusters with an average dimension of ∼1.5 μm. The induced phase separation could be an important driving force critical for the cellular response connecting the ultrasound-induced mechanical stress and signal transduction.

During the transition period, high-yielding dairy cows suffer from negative energy balance, intense lipomobilization and impaired lipid metabolism; this metabolic condition can lead to overburdened triglycerides accumulation in the liver, known as liver lipidosis, which has been associated to impaired fertility in dairy cows. The mechanisms of this impairment can be in principle correlated with the presence and the extent of ectopic fat depots. However, current methods for evaluating fat accumulation in liver and in the reproductive tract suffer from low resolution, sensitivity, and specificity. Confocal microscopes are equipped with Gallium arsenide phosphide detectors, thus enabling the acquisition of intense signals from tissue biopsies. This method could differentiate whether fat deposition occurred without requiring sample sectioning. Here, we examined with this technique liver, uterine and ovarian samples of heifers and regularly slaughtered repeat breeder and overconditioned dairy cows, to quantify lipid droplets and depots at a submicrometer scale with high specificity. With the aid of this technique, we found lipid depots in uterine and ovarian specimens. Moreover, we found that the size and number of depots increased with the degree of liver lipidosis. Further studies are needed to elucidate the relationship between the severity and extent of these deposits and the fertility of lactating dairy cows. Since tissues other than liver display different characteristic lipid droplet distributions, this technique can be potentially employed to shed new light on the pathogenesis of lipidosis and to assess new risk factors for infertility.