Phasor identifier: A cloud-based analysis of phasor-FLIM data on Python notebooks.

This paper introduces an innovative approach utilizing Google Colaboratory for the versatile analysis of phasor fluorescence lifetime imaging microscopy (FLIM) data collected from various samples (e.g., cuvette, cells, tissues) and in various input file formats. In fact, phasor-FLIM widespread adoption has been hampered by complex instrumentation and data analysis requirements. We mean to make advanced FLIM analysis more accessible to researchers through a cloud-based solution that 1) harnesses robust computational resources, 2) eliminates hardware limitations, and 3) supports both CPU and GPU processing. We envision a paradigm shift in FLIM data accessibility and potential, aligning with the evolving field of artificial intelligence-driven FLIM analysis. This approach simplifies FLIM data handling and opens doors for diverse applications, from studying cellular metabolism to investigating drug encapsulation, benefiting researchers across multiple domains. The comparative analysis of freely distributed FLIM tools highlights the unique advantages of this approach in terms of adaptability, scalability, and open-source nature.

Phasor-FLIM-guided unraveling of ATRA supramolecular organization in liposomal nanoformulations.

Here we use fluorescence lifetime imaging microscopy (FLIM) to study the supramolecular organization of nanoencapsulated liposomal all-trans retinoic acid (ATRA), exploiting ATRA's intrinsic fluorescence as a source of signal and phasor transformation as a fit-free analytical approach to lifetime data. Our non-invasive method is suitable for checking for the presence of a fraction of ATRA molecules interacting with liposomal membranes. The results are validated by independent small-angle X-ray scattering (SAXS) and nano-differential scanning calorimetry (NanoDSC) measurements, probing ATRA's putative position on the membrane and effect on membrane organization. Besides the insights on the specific case-study proposed, the present results confirm the effectiveness of Phasor-FLIM analysis in elucidating the nanoscale supramolecular organization of fluorescent drugs in pharmaceutical formulations. This underscores the importance of leveraging advanced imaging techniques to deepen our understanding and optimize drugs' performance in delivery applications.

Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy.

We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.

Fluorescence Lifetime Nanoscopy of Liposomal Irinotecan Onivyde: From Manufacturing to Intracellular Processing.

Onivyde was approved by the Food and Drug Administration (FDA) in 2015 for the treatment of solid tumors, including metastatic pancreatic cancer. It is designed to encapsulate irinotecan at high concentration, increase its blood-circulation lifetime, and deliver it to cells where it is enzymatically converted into SN-38, a metabolite with 100- to 1000-fold higher anticancer activity. Despite a rewarding clinical path, little is known about the physical state of encapsulated irinotecan within Onivyde and how this synthetic identity changes throughout the process from manufacturing to intracellular processing. Herein, we exploit irinotecan intrinsic fluorescence and fluorescence lifetime imaging microscopy (FLIM) to selectively probe the supramolecular organization of the drug. FLIM analysis on the manufacturer's formulation reveals the presence of two coexisting physical states within Onivyde liposomes: (i) gelated/precipitated irinotecan and (ii) liposome-membrane-associated irinotecan, the presence of which is not inferable from the manufacturer's indications. FLIM in combination with high-performance liquid chromatography (HPLC) and a membrane-impermeable dynamic quencher of irinotecan reveals rapid (within minutes) and complete chemical dissolution of the gelated/precipitated phase upon Onivyde dilution in standard cell-culturing medium with extensive leakage of the prodrug from liposomes. Indeed, confocal imaging and cell-proliferation assays show that encapsulated and nonencapsulated irinotecan formulations are similar in terms of cell-uptake mechanism and cell-division inhibition. Finally, 2-channel FLIM analysis discriminates the signature of irinotecan from that of its red-shifted SN-38 metabolite, demonstrating the appearance of the latter as a result of Onivyde intracellular processing. The findings presented in this study offer fresh insights into the synthetic identity of Onivyde and its transformation from production to in vitro administration. Moreover, these results serve as another validation of the effectiveness of FLIM analysis in elucidating the supramolecular organization of encapsulated fluorescent drugs. This research underscores the importance of leveraging advanced imaging techniques to deepen our understanding of drug formulations and optimize their performance in delivery applications.

Unveiling nanoscale optical signatures of cytokine-induced ß-cell dysfunction.

Pro-inflammatory cytokines contribute to ß-cell failure in both Type-1 and Type-2 Diabetes. Data collected so far allowed to dissect the genomic, transcriptomic, proteomic and biochemical landscape underlying cytokine-induced ß-cell progression through dysfunction. Yet, no report thus far complemented such molecular information with the direct optical nanoscopy of the ß-cell subcellular environment. Here we tackle this issue in Insulinoma 1E (INS-1E) ß-cells by label-free fluorescence lifetime imaging microscopy (FLIM) and fluorescence-based super resolution imaging by expansion microscopy (ExM). It is found that 24-h exposure to IL-1ß and IFN-γ is associated with a neat modification of the FLIM signature of cell autofluorescence due to the increase of either enzyme-bound NAD(P)H molecules and of oxidized lipid species. At the same time, ExM-based direct imaging unveils neat alteration of mitochondrial morphology (i.e. ~ 80% increase of mitochondrial circularity), marked degranulation (i.e. ~ 40% loss of insulin granules, with mis-localization of the surviving pool), appearance of F-actin-positive membrane blebs and an hitherto unknown extensive fragmentation of the microtubules network (e.g. ~ 37% reduction in the number of branches). Reported observations provide an optical-microscopy framework to interpret the amount of molecular information collected so far on ß-cell dysfunction and pave the way to future ex-vivo and in-vivo investigations.

A systematic review on artificial intelligence techniques for detecting thyroid diseases.

The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

Phenylethanoid glycosides as a possible COVID-19 protease inhibitor: a virtual screening approach.

From the beginning of pandemic, more than 240 million people have been infected with a death rate higher than 2%. Indeed, the current exit strategy involving the spreading of vaccines must be combined with progress in effective treatment development. This scenario is sadly supported by the vaccine's immune activation time and the inequalities in the global immunization schedule. Bringing the crises under control means providing the world population with accessible and impactful new therapeutics. We screened a natural product library that contains a unique collection of 2370 natural products into the binding site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). According to the docking score and to the interaction at the active site, three phenylethanoid glycosides (forsythiaside A, isoacteoside, and verbascoside) were selected. In order to provide better insight into the atomistic interaction and test the impact of the three selected compounds at the binding site, we resorted to a half microsecond-long molecular dynamics simulation. As a result, we are showing that forsythiaside A is the most stable molecule and it is likely to possess the highest inhibitory effect against SARS-CoV-2 Mpro. Phenylethanoid glycosides also have been reported to have both protease and kinase activity. This kinase inhibitory activity is very beneficial in fighting viruses inside the body as kinases are required for viral entry, metabolism, and/or reproduction. The dual activity (kinase/protease) of phenylethanoid glycosides makes them very promising anit-COVID-19 agents.

Deep neural networks ensemble to detect COVID-19 from CT scans.

Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities.

Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition.

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.

Controlling ionic conductivity through transprotein electropores in human aquaporin 4: a non-equilibrium molecular-dynamics study.

Electroporation is a matter of intensive ongoing research interest, and a much-neglected topic in trans-membrane proteins, particularly in view of such promising potential applications in medicine and biotechnology. In particular, selected such novel and exciting applications are predicated on controlling ionic conductivity through electro-pores. Here, we scrutinise the mechanisms of ions' electric conductivity, by means of structural rearrangements, through quasi-stable electro-pores through human-AQP4 as a well-representative prototype of trans-membrane ionic conduction, achieving exquisite control over ionic permeability manipulated by the application of intense static electric fields.

Transprotein-Electropore Characterization: A Molecular Dynamics Investigation on Human AQP4.

Electroporation characterization is a topic of intensive interest probed by extensive ongoing research efforts. Usually, these studies are carried out on lipid-bilayer electroporation. Surprisingly, the possibility of water-channel electropore formation across transmembrane proteins themselves, particularly in view of such a promising application, has not yet been elucidated. The present work examines the geometrical and kinetic aspects of electropores and their stability in such a protein milieux (as opposed through the phospholipid membranes) in depth, by means of scrutiny of such a process in human-AQP4 as a well-representative prototype. The residues forming the electropore's walls, organized in loops, reveal the formation mechanism by their dipole alignment and translational response in response to applied axial electric fields in nonequilibrium molecular dynamics simulation. The magnitude of sustaining electric fields (keeping a stable electropore open) were determined. This suggests that transmembrane proteins could play a central role in electroporation applications, e.g., in medicine and biotechnology.

Human aquaporin 4 gating dynamics under axially oriented electric-field impulses: A non-equilibrium molecular-dynamics study.

Human aquaporin 4 has been studied using non-equilibrium molecular dynamics simulations in the absence and presence of pulses of external electric fields. The pulses were 100 ns in duration and 0.005-0.015 V/Å in intensity acting along the pores' axes. Water diffusivity and the dipolar response of various residues of interest within the pores have been studied. Results show relatively little change in levels of water permeability per se within aquaporin channels during axially oriented field impulses, although care must be taken with regard to statistical certainty. However, the spatial variation of water permeability vis-à-vis electric-field intensity within the milieu of the channels, as revealed by heterogeneity in diffusivity-map gradients, indicates the possibility of somewhat enhanced diffusivity, owing to several residues being affected substantially by external fields, particularly for HIS 201 and 95 and ILE 93. This has the effect of increasing slightly intra-pore water diffusivity in the "pore-mouths" locale, albeit rendering it more spatially uniform overall vis-à-vis zero-field conditions (via manipulation of the selectivity filter).