Author: Agnese Callegari

Mapping individual molecular connectomes in Alzheimer’s disease published in Alzheimer’s & Dementia

Diagnostic classification. (Image from the article.)
Mapping individual molecular connectomes in Alzheimer’s disease
Zhilei Xu, Mite Mijalkov, Jiawei Sun, Yu-Wei Chang, Arianna Sala, Giovanni Volpe, Mario Severino, Mattia Veronese, Sara Garcia-Ptacek, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71310 (2026)
DOI: 10.1002/alz.71310

INTRODUCTION
Mapping individual differences is crucial to improve personalized medicine approaches in Alzheimer’s disease (AD), which is characterized by strong inter-individual variability in the accumulation patterns of tau and amyloid beta pathology.

METHODS
We assess the progression of AD across the disease continuum by building individual molecular connectomes using longitudinal positron emission tomography (PET) data.

RESULTS
We demonstrate that these connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects. Alterations in the connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional PET measures. We introduce a novel gene-specific transcription network analysis that linked individual tau and amyloid connectomes to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.

DISCUSSION
Individual molecular connectome mapping provides a novel and sensitive framework to monitor AD progression.

Highlights

  • Individual molecular connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects.
  • Alterations in individual molecular connectomes significantly increase both across the Alzheimer’s disease (AD) continuum and over time.
  • Alterations in individual molecular connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional positron emission tomography measures.
  • Susceptibilities of individual tau and amyloid connectomes to AD are linked to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.

Presentation by S. K. Mondal, online, 22 April, 2026

Optical Fiber Micro/Nano Axicon Tip: An Optical Imaging Platform
Samir K. Mondal
CSIR-CSIO, Chandigarh, India
Date: 22 April 2026
Time: 12:30
Place: Online on zoom

Optical fiber tip under structural modifications enhances light-matter interaction by focusing, collecting or modulating light in microscopic scale and combined with waveguide property, it emerges as a potential optical tool, especially for spectroscopic, endoscopic and imaging application. A chemical etching technique has been introduced to permanently modify the tip as Micro/Nano axicon, capable in generating structured beams. The optics of the axicons have been studied in detail and further used in optical imaging experiments, namely phase microscopy, photonic nanojet and nanoscopy. The seminar will highlight first-hand information about the probe and experiments addressing the above-mentioned application.

Short Bio

Dr. Mondal is Chief Scientist at CSIR-CSIO, Chandigarh. He earned his Ph.D. in Electronic Science and M.Sc. in Physics from the University of Calcutta. After postdoctoral research at the University of California, Irvine and the University of Minnesota, he joined Tyndall Research Institute, Ireland.

With over 25 years in optics and photonics, his work spans optical interconnects, photonic crystals, lasers, and fiber instrumentation. He leads research in optical fiber antennas, near-field optics, imaging, and plasmonics, aiming for sustainable photonics platforms.

He collaborates internationally and is known for pioneering micro/nano axicons on fiber tips. He has over 50 publications and serves as an editor and reviewer.

Tracking early cognitive decline in preclinical AD with brain MRI similarity published in Alzheimer’s & Dementia

Parcellation of the brain cortex. (Image from the article.)
Tracking early cognitive decline in preclinical AD with brain MRI similarity
Jiawei Sun, Blanca Zufiria-Gerbolés, Massimiliano Passaretti, Giovanni Volpe, Mite Mijalkov, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71170 (2026)
DOI: 10.1002/alz.71170

INTRODUCTION
Early detection of neuroanatomical changes in preclinical Alzheimer’s disease (AD) is critical for timely intervention. However, conventional magnetic resonance imaging (MRI) and fluid biomarkers often lack sensitivity to subtle structural alterations in early disease stages.

METHODS
To identify early brain alterations, we applied a perturbation-based brain similarity approach to cognitively normal participants from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS), stratified by amyloid status. We evaluated its predictive performance for cognition and diagnostic conversion against cortical thickness, volumetric MRI, and fluid biomarkers.

RESULTS
In both cohorts, brain similarity consistently outperformed other biomarkers across cognitive domains and amyloid groups. It also achieved superior accuracy in predicting clinical conversion and exhibited associations with cytoarchitectural organization.

DISCUSSION
These findings highlight brain similarity as a sensitive marker of early neuroanatomical disruption in AD. Its ability to detect subtle structural changes before overt atrophy underscores its potential for early disease monitoring and treatment assessment in preclinical AD trials.

Highlights

  • Brain similarity captures early brain changes in preclinical Alzheimer’s disease (AD).
  • Brain similarity outperforms conventional biomarkers such as cortical thickness, volume measures, and fluid biomarkers in predicting cognitive decline.
  • Brain similarity predicts conversion to mild cognitive impairment and AD more accurately than traditional imaging markers, and its predictive performance is further improved when combined with fluid biomarkers.
  • Brain similarity captures structural disruptions associated with cortical layer II of the cytoarchitectonic lamina of human neocortex.

Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy published in Nature Communications

The principle of differential imaging in NSM, in which we subtract the light scattered (yellow arrows indicate the scattered-light direction) by an empty nanochannel from the light scattered by the same channel with a molecule inside. A sequence of differential images of a nanochannel containing a diffusing single molecule obtained in this way is combined into a kymograph, which then contains the full molecular trajectory. (Image from the article.)
Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy
Henrik K. Moberg, Bohdan Yeroshenko, Joachim Fritzsche, David Albinsson, Barbora Spackova, Daniel Midtvedt, Giovanni Volpe, Christoph Langhammer
Nature Communications 17, 2533 (2026)
DOI: 10.1038/s41467-026-70514-z

Nanofluidic scattering microscopy characterizes single molecules in subwavelength nanofluidic channels label-free, using the interference of visible light scattered by the molecule and nanochannel. It determines a molecule’s hydrodynamic radius by tracking its diffusion trajectory and its molecular weight by analyzing its scattering intensity along that trajectory. However, using standard analysis algorithms, it is limited to characterization of proteins larger than ≈ 60 kDa. Here, we push this limit by one order of magnitude to below ≈ 6 kDa molecular weight and ≈ 1.5 nm hydrodynamic radius — as we exemplify on the peptide hormone insulin — by using ultrasmall nanofluidic channels and by analyzing the data with a hierarchical vision transformer. When we benchmark this approach against the theoretical limit set by the Cramér–Rao Lower Bound, we find that it can be approached with sufficiently long molecular trajectories. This enables quantitative label-free single-molecule microscopy for biologically relevant families of sub-10-kDa molecules, such as cytokines, chemokines and peptide hormones.

Technological Excellence Requires Human and Social Context on ArXiv

Why breakthrough research needs humanities and social sciences. (From an artwork by Jacopo Sacquegno.)
Technological Excellence Requires Human and Social Context
Karl Palmås, Mats Benner, Monica Billger, Ben Clarke, Raimund Feifel, Julia Fernandez-Rodriguez, Anna Foka, Juliette Griffié, Claes Gustafsson, Kerstin Hamilton, Johan Holmén, Kristina Lindström, Tobias Olofsson, Joana B. Pereira, Marisa Ponti, Julia Ravanis, Sviatlana Shashkova, Emma Sparr, Pontus Strimling, Fredrik Höök, Giovanni Volpe
arXiv: 2603.10653

Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation is no longer tenable. We propose a broader understanding of excellence that combines technical rigor with ethical robustness, social intelligibility, and long-term relevance. The rapid emergence of generative and agentic artificial intelligence further underscores this argument. As technological systems increasingly operate through language, interpretation, and normative alignment, expertise traditionally cultivated in the humanities and social sciences becomes integral to the design, governance, and responsible deployment of such systems. Drawing on historical examples and contemporary research practices, this article examines five interconnected domains where the humanities and social sciences, treated as integrated dimensions of research practice, can strengthen technological development: (1) ethical, legal, and social integration in agenda-setting and research design; (2) plural and reflexive foresight practices that shape technological futures; (3) graduate education as a leverage point for cross-disciplinary literacy; (4) visualization and communication as epistemic and civic practices; and (5) institutional frameworks that move beyond rigid distinctions between basic and applied research. Across these dimensions, we propose practical strategies for embedding interdisciplinary collaboration structurally rather than symbolically.

Per Hillertz joins the Soft Matter Lab

Per Hillertz. (Photo courtesy of P. Hillertz.)

We are pleased to announce that Per Hillertz (AstraZeneca) joined the Soft Matter Lab on 26 February 2026 as Adjunct Professor.

Per brings a strong scientific background in structural biology and biophysics, with broad experience across multiple therapy areas, including Oncology, CNS-pain, and respiratory diseases. In his current role as IT Site Lead in Gothenburg and Director of M&A IT at AstraZeneca, he focuses on the development and application of AI technologies within the pharmaceutical value chain.
He also plays an active role in shaping academia–industry collaboration through his involvement in several program boards at Chalmers University of Technology and the University of Gothenburg.

We warmly welcome Per to the lab and look forward to strengthening our collaboration at the interface of soft matter, life sciences, and digital innovation.

Invited lecture by A. Callegari, A. Ciarlo, and S. K. Manikandan at the Winter school on Geometry of nonequilibrium critical phenomena, Chalmers, 22-27 Feb 2026

Active Matter: Model Systems and Experimental Tests
Agnese Callegari, Antonio Ciarlo, Sreekanth Manikandan
Dates and times:
23 Feb 14:00-15:00 (Agnese)
24 Feb 11:30-12:30 (Antonio)
24 Feb 14:00-15:00 (Sreekanth)
Place: PJ
Winter school on Geometry of nonequilibrium critical phenomena

Active matter is a broad class of systems that operate intrinsically out of equilibrium. It spans multiple length scales—from macroscopic to micro- and nanoscopic—and includes both biological and artificial realizations, often displaying rich and emerging collective behaviors. The study of active matter aims to explain and interpret these phenomena using concepts and tools from physics. As such, understanding active and non-equilibrium systems requires a combination of theoretical, computational, and experimental approaches.

In the first part of the lecture, we introduce the concept of an active particle and demonstrate how it can be embodied in a macroscopic, self-propelled toy robot (a Hexbug). Despite their simplicity, such systems reproduce characteristic—and sometimes counterintuitive—features of microscopic active matter. These experiments have a strong pedagogical value and are designed to help bridge a gap in traditional physics curricula at the primary and secondary education levels.

The second part of the lecture focuses on active matter and non-equilibrium phenomena at the microscopic scale, where advanced experimental tools are essential. Optical tweezers provide precise control over microscopic systems and access to key physical observables. We introduce their operating principles and illustrate how they can be used to construct a minimal, well-controlled experimental model for studying non-equilibrium dynamics at the single-particle level.

In the final part of the lecture, we turn to the theoretical and computational tools required to analyze active matter systems. We discuss how non-equilibrium dynamics can be quantitatively characterized directly from experimental data in a model-independent framework. This naturally leads to an introduction to machine-learning–based inference techniques, which extract dynamical and thermodynamic information from data without relying on a priori assumptions about the underlying physical model.

References:
[1] A. Barona Balda, A. Argun, A. Callegari, G. Volpe. Playing with Active Matter, Am. J. Phys. 92, 847–858 (2024). https://doi.org/10.1119/5.0125111
[2] Martins, T.T., Malavazi, A.H.A., Kamizaki, L.P. et al. Fluctuation theorems with optical tweezers: theory and practice. Eur. Phys. J. Plus 141, 71 (2026). https://doi.org/10.1140/epjp/s13360-025-07181-4
[3] Manikandan, Sreekanth K. and Ghosh, T. and Mandal, T. and Biswas, A. and Sinha, B. and Mitra, D. Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering. Phys. Rev. Res. 6, 023310 (2024). https://doi.org/10.1103/PhysRevResearch.6.023310

Photos

Antonio, presenting. (Photo by M. Orsino)
Sreekanth, presenting. (Photo by A. Ciarlo)

Anton Widengård joins the Soft Matter Lab

Anton Widengård joined the Soft Matter Lab on 9 February 2026.

Anton is a master student in Biomedical Engineering at Chalmers University of Technology.

Alongside with his classmate Max Haraldsson, Anton will be working on his Master’s Thesis at Soft Matter Lab, in collaboration with IFLAI, supervised by Jesús Pineda.

Anton’s and Max’s research focuses on evaluating and efficiently adapting pre-trained deep-learning vision models for cell segmentation and tracking. 

Max Haraldsson joins the Soft Matter Lab

Max Haraldsson joined the Soft Matter Lab on 9 February 2026.

Max is a master student in Biomedical Engineering at Chalmers University of Technology.

Together with his classmate Anton Widengård, he will be conducting his Master’s thesis at the Soft Matter Lab in collaboration with IFLAI, with Jesús Pineda as supervisor.

Max’s and Anton’s project is about evaluating and efficiently adapting pre-trained deep learning models for cell segmentation and tracking. 

Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!

Cover of the PhD thesis. (Image by F. Skärberg)
Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!
The defense took place in FB, Institutionen för fysik, Origovägen 6b, Göteborg, at 09:00.

Title: From Light to Data Using Deep Learning for Quantitative Microscopy

Abstract: Quantitative microscopy aims to measure physical properties of microscopic particles from optical images, but weak and complex signals often make this difficult. This thesis explores how computational methods, especially deep learning guided by physical understanding, can improve particle detection and characterization in microscopy.
The work introduces new approaches for locating and tracking particles, extends these ideas to three-dimensional and label-free imaging, and reviews practical analysis workflows. It further shows how combining complementary imaging techniques can enhance nanoparticle measurements and how deep learning can recover three-dimensional structural information from microscopy images.
Overall, this thesis strengthens the connection between optical measurements and quantitative particle information, expanding the potential of label-free microscopy for biological and nanoscale studies.

Thesis: https://gupea.ub.gu.se/handle/2077/90201

Supervisor: Daniel Midtvedt
Examiner: Raimund Feifel
Opponent: Arrate Munoz Barrutia
Committee: Per Augustsson, Jens Petersen, Rebecka Jörnsten
Alternate board member: Vitali Zhaunerchyk