bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electroconvulsive therapy in autism spectrum disorders: an update to the literature
Authors: Wachtel Lee , Luccarelli James , Falligant John Michael , Smith Joshua Ryan 
Published: January 2025

Purpose of review
Over the last quarter century, the clinical evidence surrounding the use of electroconvulsive therapy (ECT) in individuals with autism spectrum disorder (ASD) has expanded. This review provides the most up-to-date findings on the usage of ECT in ASD and discusses these results within the historical context and direct patient care experience.

Recent findings
ECT is typically implemented for psychotropic-refractory catatonic, affective, psychotic, and combined pathology for individuals across the lifespan. Although highly stigmatized, ECT is well tolerated, efficacious, and potentially lifesaving for select individuals. A case presentation of an individual with autism as well as a summary of legal restrictions hampering his ECT access is presented.

Summary
ECT is increasingly used in individuals with ASD who present with a wide range of ECT-responsive psychopathology, as well as repetitive self-injury and late, autistic-like regression for which no cause is found. ECT is well tolerated and offers real hope for many ASD individuals with devastating, treatment-refractory conditions.
Dementia prevalence and dementia risk in Indigenous Peoples: recent findings, current developments, and future directions
Authors: Furlano Joyla A. , Okada de Oliveira Maira , Pintado-Caipa Maritza , Röhr Susanne 
Published: January 2025

Purpose of review
To highlight recent findings on the prevalence and risk and protective factors for dementia in Indigenous Peoples, who are disproportionately affected by health inequities driven by social determinants of health and historical injustices. With increasing numbers of Indigenous individuals entering older age, there is a growing need for research to better understand dementia and opportunities for prevention in Indigenous Peoples.

Recent findings
Recent studies highlight a wide range of dementia prevalence across Indigenous Peoples, with estimates varying significantly by methodology, socio-cultural context, and region with stark gaps in regional representation. Studies suggested that Indigenous Peoples show distinct profiles of dementia risk factors, including higher rates of cardiovascular disease, lower educational attainment, and socioeconomic disadvantage. However, traditional lifestyles, strong social ties, and culturally grounded practices are suggested to offer protective effects against cognitive decline.

Summary
Despite growing research attention, significant limitations remain in understanding dementia in Indigenous Peoples. The available evidence points to the need for culturally sensitive approaches in research and dementia prevention, as well as greater consideration of the social and environmental factors shaping brain health. Future research should aim to incorporate Indigenous knowledge systems and address the social determinants of dementia risk in Indigenous Peoples.
Defining the limits of plant chemical space: challenges and estimations
Authors: Hart Chloe Engler , Gadiya Yojana , Kind Tobias , Krettler Christoph A. , Gaetz Matthew , Misra Biswapriya B. , Healey David , Allen August , Colluru Viswa , Domingo-Fernández Daniel 
Published: January 2025

AbstractThe plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species have been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches—a formula prediction model, a de novo prediction model, a combination of library search andde novoprediction, and MS2 clustering—to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, conservatively estimating that the total plant chemical space likely spans millions, if not more, with the vast majority still unexplored.
Alprazolam induces anterograde amnesia for contextual fear memory and alters dorsoventral hippocampal neuronal ensembles in female mice
Authors: Kaplan Kameron , Toennies Lainey B. , Hunsberger Holly C. 
Published: January 2025

ABSTRACTBenzodiazepines (BZDs) are commonly prescribed anxiolytic drugs that act on GABAa receptors, and can result in anterograde amnesia, or the inability to form new memories. While BZDs have been used for decades, the brain regions and neuronal mechanisms responsible for this detrimental side effect are largely unknown at the systems neuroscience level. To analyze the effects of BZDs on long-term memory, activity-dependent ArcCreERT2x eYFP mice were injected with Alprazolam 30 minutes prior to a 3-shock contextual fear conditioning (CFC) procedure and encoding ensembles were tagged with eYFP. Mice were re-exposed to the same context 5 days later, and retrieval cell activation was analyzed using the immediate early gene (IEG), c-Fos, allowing us to determine which brain regions undergo changes after alprazolam injection. Additionally, we address the question of whether alprazolam induces state-dependent memory by altering the timelines of injection. We found that 1) alprazolam treated male and female mice exhibit a decrease in memory retention and, 2) alprazolam treated female and male mice show a decrease in memory retention with saline injection prior to re-exposure, 3) alprazolam treated female mice exhibit increased EYFP+ (encoding) activation in the dCA1 and enhanced engram activation in the dCA3, and 4) alprazolam treated females showed less c-Fos+ activation in the vCA1. These results suggest that alprazolam induces sex-specific ensemble activation throughout the hippocampus and will help us understand the long-term memory deficits associated with BZD use.
Sparse Sequencing permits accurate and efficient quantification of genome-wide cytosine modification levels
Authors: Loo Christian E. , Fowler Johanna M. , Zhu Heqiao , Krapp Christopher , Zhu Ruiyao , Bartolomei Marisa S. , Zhou Wanding , Kohli Rahul M. 
Published: January 2025

ABSTRACT5-methylcytosine (5mC) and 5-hydroxymethycytosine (5hmC) play crucial roles in epigenetic gene regulation, with dynamic levels in development and disease. While high-depth, base-resolution whole genome studies offer the most detailed view of epigenetic landscapes for individual samples, many open questions are answered by surveying changes in 5mC/5hmC levels genome-wide or at specific genomic elements across larger cohorts of samples. Nonetheless, current global quantification methods, including mass spectrometry and immunochemistry, are typically limited in accessibility, throughput, or accuracy. Here, using computational down-sampling of deeply sequenced data, we first demonstrate that sequencing base-resolution libraries to shallow depth can be sufficient for highly accurate quantification. Sparse sampling (<0.24% of the genome) can precisely measure genomic 5mC/5hmC levels (error <5%), even when levels are low (<0.3%). Using a combined chemical/enzymatic workflow, we then validate that Sparse-Sequencing (Sparse-Seq) shows high accuracy and less variability than mass spectrometry, while distinctively preserving genomic context. Applying Sparse-Seq to track developing mouse brains serially revealed an earlier emergence of 5hmCpG compared to 5mCpH and uncovered previously overlooked, genomic feature-specific differences in epigenetic dynamics. This work establishes a rigorous foundation for employing Sparse-Seq as a highly accessible approach for 5mC/5hmC quantification, allowing for economical and high-throughput analysis of epigenetic landscapes.
Robustness of Selection and Timing Inference under Model Variation in Population Genetics
Authors: Escabi Javier , Hormoz Sahand 
Published: January 2025

1AbstractIn population genetics, accurately inferring the selection coefficient and the time of onset of advantageous mutations from genetic data is fundamental for understanding evolutionary processes. Here, we investigate how mismatches between the true evolutionary process and the inference model—specifically in the reproductive variance (σ2) and the number of generations (L)—affect the posterior distributions of the selection coefficient and the time of onset. Using the Kolmogorov forward and backward equations, we model the stochastic dynamics of gene frequencies under selection and drift. We show that while the posterior distribution of the selection coefficient remains unaffected by changes inσ2andL, this invariance does not apply to the time of onset. By framing the problem as a first passage time issue, we derive explicit expressions for the offsets in the posterior mean and variance of the time of onset that result from incorrect assumptions aboutσ2andL. Our analysis reveals that these offsets are related to the mean and variance of the first passage time required for the allele frequency to reach a certain threshold, starting from an initial frequency determined by the model parameters. Under the assumption of a uniform prior for the time of onset, we find that the offset in the inferred mean is given by the difference in the effective generation duration (Δ = 1/σ2) between the true process and the inference model. We validate our theoretical findings through simulations, demonstrating that the empirical offsets closely match our predictions. Furthermore, we generalize our results to accommodate non-uniform prior distributions, such as exponential priors, and provide numerical methods for calculating offsets under arbitrary priors. Stochastic fluctuations due to genetic drift, which are influenced by the reproductive variance and generational structure, can introduce significant biases in the posterior distribution of time of onset of advantageous mutations. By quantifying these biases, our framework enables more accurate adjustments to inferences drawn from genetic data, thereby enhancing our understanding of evolutionary dynamics and improving the reliability of population genetic analyses.
PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images
Authors: Pennie Rachel Louise , Mason David , Rakovic Kai , Ballantyne Fiona , Powley Ian R , Georgakopoulou Anastasia , Bird Thomas G , Officer-Jones Leah , Le Quesne John 
Published: January 2025

AbstractMultiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their interpretation presents a considerable analytical challenge.Cell segmentation from images is a key bottleneck and a major focus of research activity in artificial intelligence. Most current methods depend initially on the use of a nuclear counterstain to identify nuclear boundaries, which is a relatively straightforward task. The cellular boundary is then assigned either by expansion of the nuclear outline, or by the use of membrane or cytoplasm-specific stains to delineate cell boundaries, or by some combination of the two. The task is critical, as inaccurate segmentation leads to information loss and data contamination from neighbouring cells. Increasingly sophisticated methods are being developed to address these issues, but each has its own shortcomings. We present an alternative method which is inspired by the fact that the assignation of a cellular phenotype ‘by eye’ does not depend upon the accurate identification of cell boundaries. We present an easy-to-use deep learning-based cellular phenotyping method which leverages this human capacity to assign phenotypes without segmenting the entire cell, and which can accurately phenotype cells based on nuclear segmentation alone.Using human ground truth annotations of entire cellular regions, we developed a classifier leveraging the U-Net architecture within a commercially available deep learning image analysis platform, but the principle is transferrable to any deep-learning framework. Crucially, training requires only a single example of each compartmental stain (nuclear/cytoplasmic/membranous). The resulting algorithm assigns class identities to cells with nuclear labelling alone, without the need for whole cell expansion. The method is highly novel, broadly generalisable, and comparable in accuracy to intensity-based phenotyping methods, bridging the gap between inaccurate cellular segmentation and accurate phenotype generation.
Three-dimensional patient-derived models of glioblastoma retain intra-tumoral heterogeneity
Authors: Moore Zachery , Storey Claire , Brown Daniel V. , Valkovic Adam , Spiteri Montana , Pignatelli Jasmine F. , Oliver Shannon J. , Fakhri Alana , Drummond Katharine J. , Malinowski Seth , Ligon Keith L. , Sieber Oliver M. , Whittle James R. , Freytag Saskia , Best Sarah A. 
Published: January 2025

AbstractThe intra- and inter-tumoral heterogeneity of glioblastoma represents a significant therapeutic challenge, as well as difficulty in generating reliable models forin vitrostudies. Historical 2D adherent cell lines do not recapitulate this complexity, whereas both patient-derived neurospheres (PDN) and organoids (PDO) demonstrate intra-tumoral heterogeneity. Here, we quantify the tumor cell composition from matched models established from the same primary tumor using a series of multi-omic interrogations. We find that both patient-derived models recapitulate the genomic, epigenomic and tumor cell heterogeneity of the primary tissue. Furthermore, single-nuclei RNA sequencing revealed a subset of organoids containing small numbers of non-malignant cells from neuron and immune cell lineages. Harnessing the intra-tumoral heterogeneity of PDN models, we reveal the impact of temozolomide chemotherapy on individual cell states, altering composition of tumors over time in response to treatment. Our data confirms that both patient-derived models recapitulate patient intra-tumoral heterogeneity providing a platform for tumor cell state refined therapeutic studies.Key PointsGeneration of matched patient-derived neurosphere and organoid models from resected GBM tissueNeurosphere models exhibit greater proliferative signaturesBoth patient-derived models recapitulate genomic and epigenomic features of the primary tissueSingle-nuclei RNA sequencing reveals both models recapitulate intra-tumoral heterogeneity of the primary tissueNeurosphere models enable interrogation of therapeutic responses in the context of heterogeneityImportance of studyPatient derived models can be powerful tools when they faithfully recapitulate the tumor tissue from which they are derived. In glioblastoma, patient derived neurospheres (PDN) and organoids (PDO) have both been used in studies, however the differences between these models and the recapitulation of patient heterogeneity remain to be fully characterized. To address this, we performed multi-omic profiling of PDN and PDO models generated from the same tumor tissue. We find that that across a range of data modalities, both model systems exhibit a high level of resemblance to tissue, and critically, maintain heterogeneity and tumor cell composition. The importance of modeling heterogeneity was demonstrated in PDN models, where temozolomide treatment specifically alters the abundance of MES-like and AC-like tumor cells. Our findings demonstrate that neurosphere and organoid models effectively preserve cellular heterogeneity, genomic alterations, methylation signatures and transcriptomic features, both highly suitable to model glioblastoma’s complex cellular landscape.
A Technical Evaluation of Plasma Proteomics Technologies
Authors: Beimers William F. , Overmyer Katherine A. , Sinitcyn Pavel , Lancaster Noah M. , Quarmby Scott T. , Coon Joshua J. 
Published: January 2025

AbstractPlasma proteomic technologies are rapidly evolving and of critical importance to the field of biomedical research. Here we report a technical evaluation of six notable plasma proteomic technologies – unenriched (Neat), Acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4,500), while Olink detected ∼2,600 proteins. Other MS-based methods ranged from ∼500-2,200. Neat, Mag-Net, Seer, and Olink had strong reproducibility, while PreOmics and Acid showed higher variability. All MS methods showed good linearity with spiked-in C-Reactive Protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer had more than double the number of quantifiable proteins (4,800) for both LOD and LOQ than the next best method. Olink was comparable to Neat and Mag-Net for LOD, but worse for LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a non-small cell lung cancer (NSCLC) cohort.
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration
Authors: Valverde Alexander , Solano Luis 
Published: January 2025

AbstractIn Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition. To address this issue, we developed a convolutional neural network (CNN) specifically for seashell identification. We built a dataset from scratch, consisting of approximately 19000 images from the Pacific and Caribbean coasts. Using this dataset, the model achieved a classification accuracy exceeding 85%.The model has been integrated into a user-friendly application, which has classified over 36,000 seashells to date, delivering real-time results within 3 seconds per image. To further enhance the system’s accuracy, an anomaly detection mechanism was incorporated to filter out irrelevant or anomalous inputs, ensuring only valid seashell images are processed.Abstract Figure
Tissular glucocorticoid reactivating enzyme 11β-HSD1 drives pathogenic myofibroblast differentiation in chronic kidney disease
Authors: Arnoux Grégoire , Legouis David , Tihy Matthieu , Yvon Raphaël , Liaudet Nicolas , Moser Seraina O. , Poinot Hélène , Sassi Ali , Lempke Olga M. , Fernandez Marylise , Monnay Isabelle , Chassot Alexandra , Bourquin Carole , Moll Solange , Somm Emmanuel , Rutkowski Joseph , Wenger Roland H. , Naesens Maarten , Odermatt Alex , Khodo Stellor Nlandu , Pommier Aurélien , Feraille Eric 
Published: January 2025

AbstractChronic kidney disease (CKD) is a growing public health crisis, affecting over 10% of the global population and significantly increasing mortality and morbidity. Irrespective of its underlying cause, tubulointerstitial fibrosis (TIF) is a hallmark of CKD progression, with myofibroblasts being the primary effectors of renal fibrosis.Here, we show that 11beta-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a critical driver of pathogenic myofibroblast differentiation and fibrosis in CKD. Using genetic deletion and pharmacological inhibition of 11β-HSD1 in mouse models, we demonstrate a marked reduction in TIF severity and improved renal function, linked to the suppression of a regulatory myofibroblast (Reg-MF) subpopulation.Single-cell and spatial transcriptomics data reveal that 11β-HSD1 is essential for the activation and expansion of Reg-MFs, which is conserved across species and predicts worse outcomes in CKD patients and kidney allograft recipients.These findings establish a direct link between 11β-HSD1 activity and renal fibrogenesis, highlighting its role during the transition from pericytes to pathogenic Reg-MFs. Our results support 11β-HSD1 inhibition as a promising therapeutic strategy to mitigate CKD progression, offering both mechanistic insights and translational potential for improving patient outcomes.
Endogenous gene tagging with FnCas9 to track and sort neural lineages from 3D cortical organoids
Authors: Kumar Manoj , Torras Berta Terre , Guillemot François , Chakraborty Debojyoti 
Published: January 2025

AbstractNeural lineage tracing, or molecular dissection of lineage-specific brain cell types, is used in many labs to learn how neurons grow and mature. However, these studies depend on the growth and characterization of pure cultures, which takes a long time because of biochemical or fluorescence-based isolation through cell surface markers that overlap. These lineage-specific cells, however, can be efficiently sorted using endogenously expressed, fluorescently labeled marker genes. The labeled cell lines can be used not only to differentiate and purify different types of neurons but also to study the long-term development of neural lineages in two- and three-dimensional development models. In this study, we used an orthogonal Cas protein to generate human embryonic stem cell (hESC) lines with genetically labeled fluorescent barcodes for discrete neural lineages. We use these lines to successfully demonstrate spatial and temporal tracing of DCX-positive neuroblasts and immature neuronal cells within 2D neural cultures and 3D cortical organoids derived from human embryonic stem cells. This allowed the purification of endogenously tagged live neural cells from heterogeneous cortical organoids across multiple stages of development.