Publications

To interrogate neural circuits and crack their codes, in vivo brain activity imaging must be combined with spatiotemporally precise stimulation in three dimensions using genetic or pharmacological specificity. This challenge requires deep penetration and focusing as provided by infrared light and multiphoton excitation, and has promoted two-photon photopharmacology and optogenetics. However, three-photon brain stimulation in vivo remains to be demonstrated. We report the regulation of neuronal activity in zebrafish larvae by three-photon excitation of a photoswitchable muscarinic agonist at 50 pM, a billion-fold lower concentration than used for uncaging, and with mid-infrared light of 1560 nm, the longest reported photoswitch wavelength. Robust, physiologically relevant photoresponses allow modulating brain activity in wild-type animals with spatiotemporal and pharmacological precision. Computational calculations predict that azobenzene-based ligands have high three-photon absorption cross-section and can be used directly with pulsed infrared light. The expansion of three-photon pharmacology will deeply impact basic neurobiology and neuromodulation phototherapies.© 2023 Wiley-VCH GmbH.

JTD Keywords: absorption, azobenzene photoswitches, deep, glutamate-receptor, intravital microscopy, multiphoton excitation, muscarinic neuromodulation, photopharmacology, two-photon lithography and polymerization, 2-photon excitation, Azobenzene, Multiphoton excitation, Muscarinic neuromodulation, Photopharmacology, Photopharmacology, azobenzene, muscarinic neuromodulation, multiphoton excitation, two-photon lithography and polymerization, Two-photon lithography and polymerization

JTD Keywords: deep generative models, eatris european infrastructure for translational medicine, integrative omics, machine learning, multi-omics, personalized medicine, Deep generative models, Eatris european infrastructure for translational medicine, Integrative omics, Machine learning, Multi-omics, Personalized medicine, Protein protein interaction (ppi) network

Microfluidics represents a very promising technological solution for conducting massive biological experiments. However, the difficulty of managing the amount of information available often precludes the wide potential offered. Using machine learning, we aim to accelerate microfluidics uptake and lead to quantitative and reliable findings. In this work, we propose complementing microfluidics with machine learning (MLM) approaches to enhance the diagnostic capability of lab-on-chip devices. The introduction of data analysis methodologies within the deep learning framework corroborates the possibility of encoding cell morphology beyond the standard cell appearance. The proposed MLM platform is used in a diagnostic test for blood diseases in murine RBC samples in a dedicated microfluidics device in flow. The lack of plasticity of RBCs in Pyruvate Kinase Disease (PKD) is measured massively by recognizing the shape deformation in RBCs walking in a forest of pillars within the chip. Very high accuracy results, far over 85 %, in recognizing PKD from control RBCs either in simulated and in real experiments demonstrate the effectiveness of the platform.

JTD Keywords: Blood disease, Deep transfer learning, Deficiency, Deformability, Machine learning microfluidics, Video analysis

Background: There is a pressing need for scalable healthcare solutions and a shift in the rehabilitation paradigm from hospitals to homes to tackle the increase in stroke incidence while reducing the practical and economic burden for patients, hospitals, and society. Digital health technologies can contribute to addressing this challenge; however, little is known about their effectiveness in at-home settings. In response, we have designed the RGS@home study to investigate the effectiveness, acceptance, and cost of a deep tech solution called the Rehabilitation Gaming System (RGS). RGS is a cloud-based system for delivering Al-enhanced rehabilitation using virtual reality, motion capture, and wearables that can be used in the hospital and at home. The core principles of the brain theory-based RGS intervention are to deliver rehabilitation exercises in the form of embodied, goal-oriented, and task-specific action.; Methods: The RGS@home study is a randomized longitudinal clinical trial designed to assess whether the combination of the RGS intervention with standard care is superior to standard care alone for the functional recovery of stroke patients at the hospital and at home. The study is conducted in collaboration with hospitals in Spain, Sweden, and France and includes inpatients and outpatients at subacute and chronic stages post-stroke. The intervention duration is 3 months with assessment at baseline and after 3, 6, and 12 months. The impact of RGS is evaluated in terms of quality of life measurements, usability, and acceptance using standardized clinical scales, together with health economic analysis. So far, one-third of the patients expected to participate in the study have been recruited (N = 90, mean age 60, days after stroke >= 30 days). The trial will end in July 2023.; Discussion: We predict an improvement in the patients' recovery, high acceptance, and reduced costs due to a soft landing from the clinic to home rehabilitation. In addition, the data provided will allow us to assess whether the prescription of therapy at home can counteract deterioration and improve quality of life while also identifying new standards for online and remote assessment, diagnostics, and intervention across European hospitals.

JTD Keywords: deep tech, e-health, home treatment, motor recovery, randomized clinical trial, stroke, upper extremities, virtual reality, Deep tech, E-health, Functional recovery, Home treatment, Motor recovery, Randomized clinical trial, Stroke, Upper extremities, Virtual reality, Wearables

© 2020 Elsevier Ltd Protein phase transitions are particularly amenable for cell signalling as these highly cooperative processes allow cells to make binary decisions in response to relatively small intracellular changes. The different processes of condensate formation and the distinct material properties of the resulting condensates provide a dictionary to modulate a range of decisions on cell fate. We argue that, on the one hand, the reversibility of liquid demixing offers a chance to arrest cell growth under specific circumstances. On the other hand, the transition to amyloids is better suited for terminal decisions such as those leading to apoptosis and necrosis. Here, we review recent examples of both scenarios, highlighting how mutations in signalling proteins affect the formation of biomolecular condensates with drastic effects on cell survival.

JTD Keywords: amyloid, cell death, deep mutagenesis, llps, rna-binding proteins, Amyloid, Cell death, Deep mutagenesis, Llps, Rna-binding proteins

Plaques of the amyloid beta (A beta) peptide are a pathological hallmark of Alzheimer's disease (AD), the most common form of dementia. Mutations in A beta also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of A beta. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in A beta, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in A beta.

JTD Keywords: aggregation, kinetics, oligomers, onset, rates, state, Aggregation, Alzheimer's, Amyloid, Computational biology, Deep mutagenesis, Genetics, Genomics, Kinetics, Nucleation, Oligomers, Onset, Precursor protein, Rates, S. cerevisiae, State, Systems biology

The purpose of this systematic study was to investigate the effects of specific substrates and potential conditions applied while tailoring the morphology and chemical composition of nanostructured Co films. In particular, Co electrodeposition in sustainable choline chloride-urea deep eutectic solvent was assessed, using glassy carbon and two metals widely employed in electrocatalysis and biocompatible purposes, Pt and Au, as substrates for modification with Co. Various in situ electrochemical techniques were combined with a broad range of ex-situ characterization and chemical-composition techniques for a detailed analysis of the prepared Co films. Among the results, nanostructured Co films with high extended active surface areas and variable composition of oxo and hydroxyl species could be tuned by simply modulating the applied potential limits, and without using additives or surfactant agents. The study highlights the effectiveness of using deep eutectic solvent as suitable electrolyte for surface modification by controlled deposition of nanostructured Co films with further application in electrocatalysis.

JTD Keywords: Cobalt electrodeposition, Deep eutectic solvent, First growth stages, Substrate influence

The first steps of nickel electrodeposition in a deep eutectic solvent (DES) are analyzed in detail. Several substrates from glassy carbon to Pt(111) were investigated pointing out the surface sensitivity of the nucleation and growth mechanism. For that, cyclic voltammetry and chronoamperometry, in combination with scanning electron microscopy (SEM), were employed. X-ray diffraction (XRD) and atomic force microscopy (AFM) were used to more deeply analyze the Ni deposition on Pt substrates. In a 0.1 M NiCl2 + DES solution (at 70 °C), the nickel deposition on glassy carbon takes place within the potential limits of the electrode in the blank solution. Although, the electrochemical window of Pt|DES is considerably shorter than on glassy carbon|DES, it was still sufficient for the nickel deposition. On the Pt electrode, the negative potential limit was enlarged while the nickel deposit grew, likely because of the lower catalytic activity of the nickel toward the reduction of the DES. At lower overpotentials, different hydrogenated Ni structures were favored, most likely because of the DES co-reduction on the Pt substrate. Nanometric metallic nickel grains of rounded shape were obtained on any substrate, as evidenced by the FE-SEM. Passivation phenomena, related to the formation of Ni oxide and Ni hydroxylated species, were observed at high applied overpotentials. At low deposited charge, on Pt(111) the AFM measurements showed the formation of rounded nanometric particles of Ni, which rearranged and formed small triangular arrays at sufficiently low applied overpotential. This particle pattern was induced by the (111) orientation and related to surface sensitivity of the nickel deposition in DES. The present work provides deep insights into the Ni electrodeposition mechanism in the selected deep eutectic solvent.

JTD Keywords: AFM, Deep eutectic solvent, Glassy carbon, Nanostructures, Nickel electrodeposition, Platinum electrode, Pt(111), SEM, Surface sensitive

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.

JTD Keywords: Computer-assisted surgery, Deep networks, Force estimation, Visual deformation