bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling and Preliminary Clinical Validation of Home-based Menstrual Neuromodulation Therapy
Authors: Radytė Emilė , Stankevičiūtė Laura , Bernatavičius Ervinas , Cook Alexander , Rodrigues Yvinna Tamiris , Lima de Alves Silva Tatiana Camila , Albuquerque Barbosa Cabral Micussi Maria Thereza , Pegado Rodrigo 
Published: February 2024

AbstractPrimary dysmenorrhea (PD), characterised by chronic pelvic pain during menstruation, significantly impairs the quality of life for many women. This paper presents a modelling and clinical validation study of the novel Nettle™ device for at-home transcranial direct current stimulation (tDCS) for alleviating PD symptoms. Specifically, we aimed to investigate the electric field patterns induced by Nettle™ and its immediate efficacy in reducing menstrual pain and improving functionality. Finite element method (FEM) simulations, using realistic head models, assessed the electric field distribution, targeting key brain regions involved in pain processing. A single-centre triple-blinded, sham-controlled study involving 34 women was conducted to compare the effects of active and sham tDCS. Results demonstrated a clinically meaningful decline in menstrual pain symptoms in the active group, with medium effect sizes for both pain reduction (Cohen’s d=0.53) and functionality (Cohen’s d=0.47), based on Nettle™’s protocol focused on the electric field within the medial prefrontal cortex. Limitations include the use of generalised brain models and small sample size, highlighting the need for further research with comprehensive modelling and larger clinical trials to validate and understand the effects of Nettle™ as a menstrual neuromodulation therapy.Clinical RelevanceThis study underscores Nettle™’s potential as a non-invasive, cost-efficient intervention for PD, with implications for broader applications in women’s health.
Modeling reveals the strength of weak interactions in stacked ring assembly
Authors: Lagunes Leonila , Briggs Koan , Martin-Holder Paige , Xu Zaikun , Maurer Dustin , Ghabra Karim , Deeds Eric J. 
Published: February 2024

AbstractCells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones like GroEL and proteases like the proteasome, are comprised protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes like the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked ring assembly is currently lacking. Here, we developed a mathematical model of stacked trimer assembly, and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term “deadlock,” in which the system gets stuck in a state where there are many large intermediates that are not the fully-assembled structure, but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority – if not all – of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of patwhays. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines, and provides general principles for not only understanding existing machines but also for the design of novel structures that can self-assemble efficiently.Statement of SignificanceUnderstanding the assembly macromolecular machines is important for understanding a wide range of cellular processes. Here, we use mathematical models to study the assembly of stacked rings, which are a common motif in these machines. Our models revealed that these complexes can readily get “stuck” during assembly when the binding affinity between subunits is too strong. This suggests an evolutionary pressure to favor weaker interactions, and our analysis of solved structures confirmed this prediction. Our findings not only contribute to the fundamental understanding of assembly but also offer insights into the evolutionary pressures shaping the architecture of stacked rings, and have implications for both cell and synthetic biology.
Multimodal single-cell profiling reveals cancer crosstalk between macrophages and stromal cells in poor prognostic cholangiocarcinoma patients
Authors: Heij Lara , Hayat Sikander , Reichel Konrad , Maryam Sidrah , O’Rourke Colm J. , Tan Xiuxiang , van den Braber Marlous , Verhoeff Jan , Halder Maurice , Peisker Fabian , Wiltberger Georg , Bednarsch Jan , Heise Daniel , Deierl Julia Campello , Lang Sven A. , Ulmer Florian , Luedde Tom , Dahl Edgar , Jonigk Danny , Nolting Jochen , Sivakumar Shivan , Siveke Jens , Rocha Flavio G. , Baba Hideo A. , Andersen Jesper B. , Garcia Vallejo Juan J. , Kramann Rafael , Neumann Ulf 
Published: February 2024

AbstractBackground and aimsCholangiocarcinoma (CCA) is a deadly disease, and this cancer entity is characterized by an abundant stroma. The tumor microenvironment (TME) plays an important role in aggressive behavior and poor response to therapeutics; however, underlying pathways are unknown.MethodsTo fill this gap, we used multiplexed immunohistochemistry, high-dimensional phenotyping, and transcriptomics to analyze human CCA samples and identify cell cluster crosstalk in patients with a poor prognosis.ResultsOur findings confirmed the presence of Tregs and the lack of effector memory cells in the tumor. New findings are the spatiality of the effector memory cells being more present in the peripheral tissue, for some reason these immune cells fail to reach the tumor niche. We revealed cancer crosstalk with macrophages and stromal cells and identified responsible genes in the poor prognosis group. Amongst the responsible ligand pairs are GAS6-AXL belonging to the TAM family. We then identified VCAN-TLR2 to be present and influencing the ECM in a way to supports immune exhaustion. Last, EGFR-TGF-β is expressed in macrophages and this finding is important in Tregs induction and blocking cytotoxic T cell function.ConclusionsThe multiple mechanisms leading to the exclusion of relevant immune cells needed for an anti-cancer response and mechanisms leading to active immune suppression are part of complex cell-cell crosstalk. This study provides a deeper insight into the immune exhausted phenotype in CCA.
Deep learning-based proteomics enables accurate classification of bulk and single-cell samples
Authors: Krull Karl K. , Kühn Arlene , Höhn Julia , Brinker Titus J. , Krijgsveld Jeroen 
Published: February 2024

AbstractProteins are the main drivers of cell function and disease, making their analysis a powerful technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and even the sensitivity to access single cells, however limitations in throughput restrict its application, e.g. not allowing classification of samples according to biological or clinical status in large sample cohorts. Therefore, we developed a deep learning-based approach for the analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we designed an architecture referred to as Proformer, and show that it is superior to convolutional neural network-driven architectures, is explainable, and demonstrates robustness towards batch-effects. Based on its tabular approach, we highlight the integration of all four dimensions of proteomic measurements (retention time, mass-to-charge, intensity and ion mobility), and demonstrate enhanced sample discrimination involving a treatment with IFN-γ, despite its subtle effect on the cell’s proteome. In addition, the Proformer is not restricted to proteomic depth, and can classify cells by cell type and their differentiation status even using single-cell proteomic data. Collectively, this work presents a novel deep learning-based model for rapid classification of proteomic data, with important future implications to enhance patient stratification, early detection and single-cell analysis.
Defining the conformational states that enable transglutaminase 2 to promote cancer cell survival versus cell death
Authors: Aplin Cody , Zielinski Kara A. , Pabit Suzette , Ogunribido Deborah , Katt William P. , Pollack Lois , Cerione Richard A. , Milano Shawn K. 
Published: February 2024

AbstractTransglutaminase 2 (TG2) is a GTP-binding/protein-crosslinking enzyme that has been investigated as a therapeutic target for Celiac disease, neurological disorders, and aggressive cancers. TG2 has been suggested to adopt two conformational states that regulate its functions: a GTP-bound, closed conformation, and a calcium-bound, crosslinking-active open conformation. TG2 mutants that constitutively adopt an open conformation are cytotoxic to cancer cells. Thus, small molecules that maintain the open conformation of TG2 could offer a new therapeutic strategy. Here, we investigate TG2, using static and time-resolved small-angle X-ray scattering (SAXS) and single-particle cryoelectron microscopy (cryo-EM), to determine the conformational states responsible for conferring its biological effects. We also describe a newly developed TG2 inhibitor, LM11, that potently kills glioblastoma cells and use SAXS to investigate how LM11 affects the conformational states of TG2. Using SAXS and cryo-EM, we show that guanine nucleotide-bound TG2 adopts a monomeric closed conformation while calcium-bound TG2 assumes an open conformational state that can form higher order oligomers. SAXS analysis also suggests how a TG2 mutant that constitutively adopts the open state binds nucleotides through an alternative mechanism to wildtype TG2. Furthermore, we use time-resolved SAXS to show that LM11 increases the ability of calcium to drive TG2 to an open conformation, which is not reversible by guanine nucleotides and is cytotoxic to cancer cells. Taken together, our findings demonstrate that the conformational dynamics of TG2 are more complex than previously suggested and highlight how conformational stabilization of TG2 by LM11 maintains TG2 in a cytotoxic conformational state.Significance StatementThe multi-functional protein transglutaminase 2 (TG2) undergoes large conformational changes in response to nucleotide and calcium binding, resulting in diverse cellular effects that can differentially promote either cancer cell survival or cell death. Previous biochemical and structural characterizations have revealed that TG2 primarily adopts two conformational states, a closed nucleotide-bound conformation, and an open calcium-bound conformation. In this study, we use advanced structural methods to describe the conformational changes associated with TG2 activation and inhibition and define the mechanism by which small molecule inhibitors maintain TG2 in a structural state that kill cancer cells.
Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect
Authors: Williams Heather E. , Shehata Ahmed W. , Cheng Kodi Y. , Hebert Jacqueline S. , Pilarski Patrick M. 
Published: February 2024

AbstractAlthough state-of-the-art myoelectric prostheses offer persons with upper limb amputation extensive movement capabilities, users have not been afforded a reliable means to control common movements required in daily living. Many proposed prosthesis controllers use pattern recognition, a method that learns and recognizes patterns of electromyographic (EMG) signals produced by the user’s residual limb muscles to predict and execute device movements. Such control becomes unreliable in high limb positions—a problem known as the limb position effect. Pattern recognition often uses a classification algorithm; simple to implement, but limits user-initiated control to only one device movement at a time, at a single speed. To combat position-related control deficiencies and classification controller constraints, we developed and tested two recurrent convolutional neural network (RCNN) pattern recognition-based solutions: (1) an RCNN classification controller that uses EMG plus positional inertial measurement unit (IMU) signals to offer one-speed, sequential movement control; and (2) an RCNN regression controller that uses the same data capture technique to offer simultaneous control of multiple movements and device movement velocity. We assessed both RCNN controllers by comparing them to a commonly used linear discriminant analysis classification controller (LDA-Baseline). Participants without upper limb impairment were recruited to perform multipositional tasks while wearing a simulated prosthesis. Both RCNN classification and regression controllers showed improved functional task performance over LDA-Baseline, in 11 and 38 out of 115 metrics, respectively. This work contributes an RCNN regression-based controller that provides accurate, simultaneous, and proportional movements to EMG-based technologies including prostheses, exoskeletons, and even muscle-activated video games.
SIRT7 regulates NUCKS1 chromatin binding to elicit metabolic and inflammatory gene expression in senescence and liver aging
Authors: Tran Khoa , Gilbert Michael , Vazquez Berta N. , Ianni Alessandro , Garcia Benjamin A. , Vaquero Alejandro , Berger Shelley 
Published: February 2024

SUMMARYSirtuins, a class of highly conserved histone/protein deacetylases, are heavily implicated in senescence and aging. The regulation of sirtuin proteins is tightly controlled both transcriptionally and translationally and via localization within the cell. While Sirtiun proteins are implicated with aging, how their levels are regulated during aging across cell types and eliciting tissue specific age-related cellular changes is unclear. Here, we demonstrate that SIRT7 is targeted for degradation during senescence and liver aging. To uncover the significance of SIRT7 loss, we performed proteomics analysis and identified a new SIRT7 interactor, the HMG box protein NUCKS1. We found that the NUCKS1 transcription factor is recruited onto chromatin during senescence and this is mediated by SIRT7 loss. Further, depletion of NUCKS1 delayed senescence upon DNA damage leading to reduction of inflammatory gene expression. Examination of NUCKS1 transcriptional regulation during senescence revealed gene targets of transcription factors NFKB1, RELA, and CEBPβ. Consistently, in both Sirt7 KO mouse liver and in naturally aged livers, Nucks1 was recruited to chromatin. Further, Nucks1 was bound at promoters and enhancers of age-related genes, including transcription factor Rela, and, moreover, these bound sites had increased accessibility during aging. Overall, our results uncover NUCKS1 as a novel interactor of SIRT7, and show that loss of SIRT7 during senescence and liver aging promotes NUCKS1 chromatin binding to regulate metabolic and inflammatory genes.
Lack of the IFN-γ signal leads to lethalOrientia tsutsugamushiinfection in mice with skin eschar lesions
Authors: Liang Yuejin , Wang Hui , Sun Keer , Sun Jiaren , Soong Lynn 
Published: February 2024

AbstractScrub typhus is an acute febrile disease due toOrientia tsutsugamushi(Ot) infection and can be life-threatening with organ failure, hemorrhage, and fatality. Yet, little is known as to how the host reacts toOtbacteria at early stages of infection; no reports have addressed the functional roles of type I versus type II interferon (IFN) responses in scrub typhus. In this study, we used comprehensive intradermal (i.d.) inoculation models and two clinically predominantOtstrains (Karp and Gilliam) to uncover early immune events. Karp infection induced sequential expression ofIfnbandIfngin inflamed skin and draining lymph nodes at days 1 and 3 post-infection. Using doubleIfnar1-/-Ifngr1-/-andStat1-/-mice, we found that deficiency in IFN/STAT1 signaling resulted in lethal infection with profound pathology and skin eschar lesions, that resembled to human scrub typhus. Further analyses demonstrated that deficiency in IFN-γ, but not IFN-I, resulted in impaired NK cell and macrophage activation and uncontrolled bacterial growth and dissemination, leading to metabolic dysregulation, excessive inflammatory cell infiltration, and exacerbated tissue damage. NK cells were found to be the major cellular source of early IFN-γ, contributing to the initialOtcontrol. In vitro studies with dendritic cell cultures revealed a superior antibacterial effect offered by IFN-γ than IFN-β. Comparative in vivo studies with Karp- and Gilliam-infection revealed a crucial role of IFN-γ signaling in protection against progression of eschar lesions andOtinfection lethality. Additionally, our i.d. mouse models of lethal infection with eschar lesions are promising tools for immunological study and vaccine development for scrub typhus.SummaryScrub typhus can lead to severe complications and even fatality if not treated properly; however, the early host immune responses toOtbacterium infection remain unclear. This study focused on the functional roles of IFNs in i.d. inoculation mouse models of scrub typhus. We found that mice lacking IFN receptors were highly susceptible toOtinfection, which resulted in severe pathology and skin eschar lesions that resembled to human scrub typhus. Further investigation revealed that the lack of IFN-γ, but not IFN-I, resulted in dysregulated innate immune responses, leading to uncontrolled bacterial burdens and tissue damage. Using IFN-γ reporter mice and neutralizing antibody treatment, we confirmed that NK cells were the major source of early IFN-γ, and thus played a key role in controllingOtdissemination. Moreover, our comparative studies with twoOtstrains revealed bacterium strain- and dose-dependent eschar formation and disease severity. In conclusion, our study highlights the crucial role of IFN-γ signaling in ensuring host protection againstOtinfection. Our mouse models resemble skin eschar lesions and lethal infections observed in human disease, offering potential for future immunological studies on scrub typhus.
Proliferation of SH-SY5Y neuroblastoma cells on confined spaces
Authors: Kalwarczyk Ewelina , Lukasiak Agnieszka , Woznica Damian , Switlik Weronika , Anchimowicz Julia , Zielonka Piotr , Jakiela Slawomir 
Published: February 2024

BackgroundMicrofluidics offers precise drug delivery and continuous monitoring of cell functions, which is crucial for studying the effects of toxins and drugs. Ensuring proper cell growth in these space-constrained systems is essential for obtaining consistent results comparable to standard Petri dishes.New methodWe investigated the proliferation of SH-SY5Y cells on circular polycarbonate chambers with varying surface areas. SH-SY5Y cells were chosen for their relevance in neurodegenerative disease research.ResultsOur study demonstrates a correlation between the chamber surface area and SH-SY5Y cell growth rates. Cells cultured in chambers larger than 10 mm in diameter exhibited growth comparable to standard 60-mm dishes. In contrast, smaller chambers significantly impeded growth, even at identical seeding densities. Similar patterns were observed for HeLaGFP cells, while 16HBE14σcells proliferated efficiently regardless of chamber size. Additionally, SH-SY5Y cells were studied in a 12-mm diameter sealed chamber to assess growth under restricted gas exchange conditions.Comparison with existing methodsOur findings underscore the limitations of small chamber sizes in microfluidic systems for SH-SY5Y cells, an issue not typically addressed by conventional methods.ConclusionsSH-SY5Y cell growth is highly sensitive to spatial constraints, with markedly reduced proliferation in chambers smaller than 10 mm. This highlights the need to carefully consider chamber size in microfluidic experiments to achieve cell growth rates comparable to standard culture dishes. The study also shows that while SH-SY5Y and HeLaGFP cells are affected by chamber size, 16HBE14σcells are not. These insights are vital for designing effective microfluidic systems for bioengineering research.
Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect Detection
Authors: Dhinagar Nikhil J. , Thomopoulos Sophia I. , Laltoo Emily , Thompson Paul M. 
Published: February 2024

AbstractGenerative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. Recent research has begun to extend these models to medical and specifically neuroimaging data. Typical neuroimaging tasks such as diagnostic classification and predictive modeling often rely on deep learning approaches based on convolutional neural networks (CNNs) and vision transformers (ViTs), with additional steps to help in interpreting the results. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer’s disease (AD) effects on the brain’s anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources, testing different model sizes, effects of pretraining, training duration, and latent diffusion models. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real training scans) -and boosted its performance by over 3% when tested on real MRI scans. Further, we used implicit classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.