bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-modal contrastive learning of subcellular organization using DICE
Authors: Nasser Rami , Schaffer Leah V , Ideker Trey , Sharan Roded
Published: September 2024

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein–protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches.

Availability

https://github.com/raminass/protein-contrastive

Contact

raminass@gmail.com
PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks
Authors: Pietz Tobias , Gupta Sukrit , Schlaffner Christoph N , Ahmed Saima , Steen Hanno , Renard Bernhard Y , Baum Katharina
Published: September 2024

Motivation

Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

Results

To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user’s needs based on the reliability of imputed values.

Availability and implementation

The code is available at https://github.com/DILiS-lab/pepermint.
HCS—hierarchical algorithm for simulation of omics datasets
Authors: Stomma Piotr , Rudnicki Witold R
Published: September 2024

Motivation

Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducing the dataset to include only those variables that are related to the studied phenomena. Existing simulators of the omics data were mostly developed with the goal of improving the methods for generations of high-quality data, that correspond with the highest possible fidelity to the real level of molecular markers in the biological materials. The current study aims to simulate the data on a higher level of generalization. Such datasets can then be used to perform tests of the feature selection and ML algorithms on systems that have structures mimicking those of real data, but where the ground truth may be implanted by design. They can also be used to generate contrast variables with the desired correlation structure for the feature selection.

Results

We proposed the algorithm for the reconstruction of the omic dataset that, with high fidelity, preserves the correlation structure of the original data with a reduced number of parameters. It is based on the hierarchical clustering of variables and uses principal components of the clusters. It reproduces well topological descriptors of the correlation structure. The correlation structure of the principal components of the clusters then is used to obtain datasets with correlation structures similar to the original data but not correlated with the original variables.

Availability and implementation

The code and data is available at: https://github.com/p100mma/hcrs_omics.
scNODE
: generative model for temporal single cell transcriptomic data prediction
Authors: Zhang Jiaqi , Larschan Erica , Bigness Jeremy , Singh Ritambhara
Published: September 2024

Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE’s predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path.

Availability and implementation

The data and code are publicly available at https://github.com/rsinghlab/scNODE.
Learning meaningful representation of single-neuron morphology via large-scale pre-training
Authors: Fan Yimin , Li Yaxuan , Zhong Yunhua , Hong Liang , Li Lei , Li Yu
Published: September 2024

Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis.

Availability and implementation

The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.
CLigOpt: controllable ligand design through target-specific optimization
Authors: Li Yutong , Avelar Pedro Henrique da Costa , Chen Xinyue , Zhang Li , Wu Min , Tsoka Sophia
Published: September 2024

Motivation

A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design.

Results

We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds.

Availability and implementation

The source code is available via  https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Design-through-Target-Specific-Optimisation.
skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements
Authors: Zhang Xiaolei Brian , Oualline Grace , Shaw Jim , Yu Yun William
Published: September 2024

Motivation: Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated plasmids. Horizontal transfer of MGEs across bacterial species may also pose a significant threat to global health due to their capability to harbor antibiotic resistance genes. However, despite cheap and rapid whole-genome sequencing, the varied nature of MGEs makes it difficult to fully characterize them, and existing methods for detecting MGEs often do not agree on what should count. In this manuscript, we first define and argue in favor of a divergence-based characterization of mobile-genetic elements.

Results: Using that paradigm, we present skandiver, a tool designed to efficiently detect MGEs from whole-genome assemblies without the need for gene annotation or markers. skandiver determines mobile elements via genome fragmentation, average nucleotide identity (ANI), and divergence time. By building on the scalable skani software for ANI computation, skandiver can query hundreds of complete assemblies against >65 000 representative genomes in a few minutes and 19 GB memory, providing scalable and efficient method for elucidating mobile element profiles in incomplete, uncharacterized genomic sequences. For isolated and integrated large plasmids (>10 kb), skandiver’s recall was 48% and 47%, MobileElementFinder was 59% and 17%, and geNomad was 86% and 32%, respectively. For isolated large plasmids, skandiver’s recall (48%) is lower than state-of-the-art reference-based methods geNomad (86%) and MobileElementFinder (59%). However, skandiver achieves higher recall on integrated plasmids and, unlike other methods, without comparing against a curated database, making skandiver suitable for discovery of novel MGEs.

Availability and implementation

https://github.com/YoukaiFromAccounting/skandiver
Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling
Authors: Deshpande Srijay , Georgaka Sokratia , Haley Michael , Sellers Robert , Minshull James , Nallala Jayakrupakar , Fergie Martin , Stone Nicholas , Rajpoot Nasir , Baker Syed Murtuza , Iqbal Mudassar , Couper Kevin , Roncaroli Federico , Minhas Fayyaz
Published: September 2024

Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area.

Availability and implementation

Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.
Improving dictionary-based named entity recognition with deep learning
Authors: Nastou Katerina , Koutrouli Mikaela , Pyysalo Sampo , Jensen Lars Juhl
Published: September 2024

Motivation

Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly.

Results

In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%).

Availability and implementation

All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360.
Metadata-guided feature disentanglement for functional genomics
Authors: Rakowski Alexander , Monti Remo , Huryn Viktoriia , Lemanczyk Marta , Ohler Uwe , Lippert Christoph
Published: September 2024

Summary: With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)—an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code will be made available at https://github.com/HealthML/MFD.
Guide assignment in single-cell CRISPR screens using crispat
Authors: Braunger Jana M , Velten Britta
Published: September 2024

Motivation

Pooled single-cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step in the analysis of the resulting data is the assignment of cells to gRNAs corresponding to a specific genetic intervention. However, this step is challenging due to a lack of systematic benchmarks and accessible software to apply and compare different guide assignment strategies. To address this, we here propose crispat (CRISPR guide assignment tool), a Python package to facilitate the choice of a suitable guide assignment strategy for single-cell CRISPR screens.

Results

We demonstrate the package on four single-cell CRISPR interference screens at low multiplicity of infection from two studies, where crispat identifies strong differences in the number of assigned cells, downregulation of the target genes and number of discoveries across different guide assignment strategies, highlighting the need for a suitable guide assignment strategy to obtain optimal power in single-cell CRISPR screens.

Availability and implementation

crispat is implemented in python, the source code, installation instructions and tutorials can be found at https://github.com/velten-group/crispat and it can be installed from PyPI (https://pypi.org/project/crispat/). Code to reproduce all findings in this paper is available at https://github.com/velten-group/crispat_analysis, as well as at https://zenodo.org/records/13373265.
Development and characterization of human T-cell receptor (TCR) alpha and beta clones’ library as biological standards and resources for TCR sequencing and engineering
Authors: Wei Yu-Chun , Pospiech Mateusz , Meng Yiting , Alachkar Houda
Published: September 2024

Characterization of T-cell receptors (TCRs) repertoire was revolutionized by next-generation sequencing technologies; however, standardization using biological controls to facilitate precision of current alignment and assembly tools remains a challenge. Additionally, availability of TCR libraries for off-the-shelf cloning and engineering TCR-specific T cells is a valuable resource for TCR-based immunotherapies. We established nine human TCR α and β clones that were evaluated using the 5′-rapid amplification of cDNA ends-like RNA-based TCR sequencing on the Illumina platform. TCR sequences were extracted and aligned using MiXCR, TRUST4, and CATT to validate their sensitivity and specificity and to validate library preparation methods. The correlation between actual and expected TCR ratios within libraries confirmed accuracy of the approach. Our findings established the development of biological standards and library of TCR clones to be leveraged in TCR sequencing and engineering. The remaining human TCR clones’ libraries for a more diverse biological control will be generated.
Patterns of metabolic response in patients receiving commercial CAR T-cells for relapsing/refractory aggressive B cells lymphoma
Authors: Vercellino Laetitia , Betterki Siham , Blanc Estelle , de Kerviler Eric , Cristinelli Caterina , Merlet Pascal , Thieblemont Catherine , Meignin Véronique , Di Blasi Roberta
Published: September 2024

CAR T-cells is an innovative treatment for relapsed/refractory aggressive B cell lymphomas, initially proposed as third-line therapy and beyond, now allowed as soon as second-line treatment for patients with early relapse after first-line treatment. FDG PET/CT remains the modality of choice to evaluate response to this therapeutic strategy, to detect or confirm treatment failure, and allow for salvage therapy if needed. Correct classification of patients regarding response is thus of the utmost importance. In many cases, metabolic response follows classical known patterns, and Deauville score and Lugano criteria yield accurate characterization of patient status. However, given its specific mode of action, it can result in delayed response or atypical patterns of response. We report here a few examples of response from our experience to illustrate the existence of tricky cases. These atypical cases require multidisciplinary management, with clinical, biological, imaging, and pathological work-up.
Short-term outcomes after primary total mesorectal excision (TME) versus local excision followed by completion TME for early rectal cancer: population-based propensity-matched study
Authors: van Lieshout Annabel S , Smits Lisanne J H , Sijmons Julie M L , van Dieren Susan , van Oostendorp Stefan E , Tanis Pieter J , Tuynman Jurriaan B
Published: September 2024

Background

Colorectal cancer screening programmes have led to a shift towards early-stage colorectal cancer, which, in selected cases, can be treated using local excision. However, local excision followed by completion total mesorectal excision (two-stage approach) may be associated with less favourable outcomes than primary total mesorectal excision (one-stage approach). The aim of this population study was to determine the distribution of treatment strategies for early rectal cancer in the Netherlands and to compare the short-term outcomes of primary total mesorectal excision with those of local excision followed by completion total mesorectal excision.

Methods

Short-term data for patients with cT1–2 N0xM0 rectal cancer who underwent local excision only, primary total mesorectal excision, or local excision followed by completion total mesorectal excision between 2012 and 2020 in the Netherlands were collected from the Dutch Colorectal Audit. Patients were categorized according to treatment groups and logistic regressions were performed after multiple imputation and propensity score matching. The primary outcome was the end-ostomy rate.

Results

From 2015 to 2020, the proportion for the two-stage approach increased from 22.3% to 43.9%. After matching, 1062 patients were included. The end-ostomy rate was 16.8% for the primary total mesorectal excision group versus 29.6% for the local excision followed by completion total mesorectal excision group (P < 0.001). The primary total mesorectal excision group had a higher re-intervention rate than the local excision followed by completion total mesorectal excision group (16.7% versus 11.8%; P = 0.048). No differences were observed with regard to complications, conversion, diverting ostomies, radical resections, readmissions, and death.

Conclusion

This study shows that, over time, cT1–2 rectal cancer has increasingly been treated using the two-stage approach. However, local excision followed by completion total mesorectal excision seems to be associated with an elevated end-ostomy rate. It is important that clinicians and patients are aware of this risk during shared decision-making.
Role of the serosa in intestinal anastomotic healing: insights from in-depth histological analysis of human and murine anastomoses
Authors: Weber Marie-Christin , Clees Zoé , Buck Annalisa , Fischer Adrian , Steffani Marcella , Wilhelm Dirk , Martignoni Marc , Friess Helmut , Rinkevich Yuval , Neumann Philipp-Alexander
Published: September 2024

Background

Anastomotic leakage following colorectal surgery remains a significant complication despite advances in surgical techniques. Recent findings on serosal injury repair in coelomic cavities, such as the peritoneum, challenge the current understanding of the cellular origins and mechanisms underlying intestinal anastomotic healing. Understanding the contribution of each layer of the intestinal wall during anastomotic healing is needed to find new therapeutic strategies to prevent anastomotic leakage. The aim of this experimental study was to investigate the role of the serosal layer of the intestinal wall in anastomotic healing.

Materials and methods

Comprehensive histologic analysis of human and murine anastomoses was performed to elucidate histologic changes in the different intestinal layers during anastomotic healing. In vivo staining of the extracellular matrix (ECM) in the serosal layer was performed using a fluorophore-conjugated N-hydroxysuccinimide-ester before anastomosis surgery in a murine model.

Results

Histological examination of both human and murine anastomoses revealed that closure of the serosal layer occurred first during the healing process. In vivo serosal ECM staining demonstrated that a significant portion of the newly formed ECM within the anastomosis was indeed deposited onto the serosal layer. Furthermore, mesenchymal cells within the anastomotic scar were positive for mesothelial cell markers, podoplanin and Wilms tumour protein.

Conclusions

In this experimental study, the results suggest that serosal scar formation is an important mechanism for anastomotic integrity in intestinal anastomoses. Mesothelial cells may significantly contribute to scar formation during anastomotic healing through epithelial-to-mesenchymal transition, potentially suggesting a novel therapeutic target to prevent anastomotic leakage by enhancing physiological healing processes.
The Ripple Effect: Unpacking the Impact of Early Educational Disruptions on Rural Migrant Children’s Learning in China
Authors: Wu Aihui , Wang Cuntong , Zhu Ruoyuan
Published: September 2024

This study investigates the effects of early educational disruptions on the academic achievements of rural migrant children in China. Using a life course theory framework, it examines whether early educational disruptions negatively affect children’s future cognitive abilities, academic performance and ability to cope with academic challenges, drawing on data from the 2013 to 2014 China Education Panel Survey. The analysis shows that early educational disruptions lead to a decline in the academic achievements of rural migrant children during their middle school years, affirming the assertions of life course theory. Notably, gender does not significantly modulate the influence of educational disruptions. Parental educational expectations and the management practices of schools emerge as critical moderators, with high parental expectations potentially exacerbating the adverse effects of frequent disruptions. Conversely, the experience of teachers and strict school discipline play protective roles, highlighting the importance of supportive learning environments and effective educational strategies. These findings are pivotal for social workers intervening with migrant children. They advocate for the development of comprehensive social work strategies—encompassing both curative and preventive measures—that proactively address the unique challenges faced by this population, fostering resilience and enhancing life trajectories in a context characterised by significant societal and educational transitions.
Ubiquitin-specific peptidase 49 promotes adenocarcinoma of the esophagogastric junction malignant progression via activating SHCBP1–β-catenin–GPX4 axis
Authors: Ding Yun , Liu Zhen , Dai Xiaofeng , Ruan Ruiwen , Zhong Hongguang , Wu Zhipeng , Yao Yangyang , Chen Jun , Deng Jun , Xiong Jianping
Published: September 2024

Adenocarcinoma of the esophagogastric junction (AEG) has received widespread attention because of its increasing incidence. However, the molecular mechanism underlying tumor progression remains unclear. Here, we report that the downregulation of ubiquitin-specific peptidase 49 (USP49) promotes ferroptosis in OE33 and OE19 cells, thereby inhibiting cell proliferation in vitro and in vivo, whereas the overexpression of USP49 had the opposite effect. In addition, USP49 downregulation promoted AEG cell radiotherapy sensitivity. Moreover, overexpression of Glutathione PeroXidase 4 reversed the ferroptosis and proliferation inhibition induced by USP49 knockdown. Mechanistically, USP49 deubiquitinates and stabilizes Shc SH2-domain-binding protein 1, subsequently facilitating the entry of β-catenin into the nucleus to enhance Glutathione PeroXidase 4 transcriptional expression. Finally, high USP49 expression was correlated with shorter overall survival in patients with AEG. In summary, our findings identify USP49 as a novel regulator of ferroptosis in AEG cells, indicating that USP49 may be a potential therapeutic target in AEG.
Outcomes of Adjunctive Corticosteroid Treatment in Hypoxemic Adults Hospitalized for Mycoplasma pneumoniae Pneumonia: A Retrospective Cohort Study
Authors: Hagman Karl , Nilsson Anna C , Hedenstierna Magnus , Ursing Johan
Published: September 2024

Background

Corticosteroids appears to be beneficial for severe Mycoplasma pneumoniae pneumonia in children, but data in adults are limited. This study investigated effects of adjunctive corticosteroids in hypoxemic adults with M. pneumoniae pneumonia.

Methods

Adults admitted 2013–2017 with verified M. pneumoniae pneumonia and hypoxemia (SpO2 < 93% or oxygen treatment) were included in a cohort. Treatment was defined as receipt of at least 1 glucocorticoid dose.

Primary outcome was time to regression of hypoxemia, analyzed with a multivariable Cox regression. Secondary outcomes included fever duration, length of stay, and complications.

Results

Corticosteroids were given to 31% (122/388) during hypoxemia. Median age was 44 (interquartile range [IQR] 34–57) years. Median time to start of corticosteroid treatment was 1.9 (IQR 0.6–3.6) days from admission. Median cumulative dose was equivalent to 15 (IQR 10–19) mg betamethasone. Treatment duration was 5 (IQR 3–6) days. Patients treated with corticosteroids had more severe respiratory disease, longer symptom duration, and were more often treated with fluoroquinolones.

Time to regression of hypoxemia (hazard ratio [HR] 0.92 [95% confidence interval {CI}: .72–1.19], P = .53) and length of stay (HR 0.91 [95% CI: .71–1.16], P = .44) were not significantly different between corticosteroid treated and controls. Corticosteroid treatment was associated to shorter fever duration (HR 1.44 [95% CI: 1.00–2.06], P = .046). Complications did not differ significantly between treatment groups.

Conclusions

Adjunctive corticosteroids were not associated with reduced time to regression of hypoxemia in adults with M. pneumoniae pneumonia. However, duration of fever was shorter and no increase in complications was seen.
Olfr2-positive macrophages originate from monocytes proliferate in situ and present a pro-inflammatory foamy-like phenotype
Authors: Armstrong Suthahar Sujit Silas , Nettersheim Felix Sebastian , Alimadadi Ahmad , Wang Erpei , Billitti Monica , Resto-Trujillo Natalya , Roy Payel , Hedrick Catherine C , Ley Klaus , Orecchioni Marco
Published: September 2024

Aims

Olfactory receptor 2 (Olfr2) has been identified in a minimum of 30% of vascular macrophages, and its depletion was shown to reduce atherosclerosis progression. Mononuclear phagocytes, including monocytes and macrophages within the vessel wall, are major players in atherosclerosis. Single-cell RNA sequencing studies revealed that atherosclerotic artery walls encompass several monocytes and vascular macrophages, defining at least nine distinct subsets potentially serving diverse functions in disease progression. This study investigates the functional phenotype and ontogeny of Olfr2-expressing vascular macrophages in atherosclerosis.

Methods and results

Olfr2+ macrophages rapidly increase in Apoe−/− mice’s aorta when fed a Western diet (WD). Mass cytometry showed that Olfr2+ cells are clustered within the CD64 high population and enriched for CD11c and Ccr2 markers. Olfr2+ macrophages express many pro-inflammatory cytokines, including Il1b, Il6, Il12, and Il23, and chemokines, including Ccl5, Cx3cl1, Cxcl9, and Ccl22. By extracting differentially expressed genes from bulk RNA sequencing (RNA-seq) of Olfr2+ vs. Olfr2− macrophages, we defined a signature that significantly mapped to single-cell data of plaque myeloid cells, including monocytes, subendothelial MacAir, and Trem2Gpnmb foamy macrophages. By adoptive transfer experiments, we identified that Olfr2 competent monocytes from CD45.1Apoe−/−Olfr2+/+ mice transferred into CD45.2Apoe−/−Olfr2−/− recipient mice fed WD for 12 weeks, accumulate in the atherosclerotic aorta wall already at 72 h, and differentiate in macrophages. Olfr2+ macrophages showed significantly increased BrdU incorporation compared to Olfr2− macrophages. Flow cytometry confirmed that at least 50% of aortic Olfr2+ macrophages are positive for BODIPY staining and have increased expression of both tumour necrosis factor and interleukin 6 compared to Olfr2− macrophages. Gene set enrichment analysis of the Olfr2+ macrophage signature revealed a similar enrichment pattern in human atherosclerotic plaques, particularly within foamy/TREM2hi-Mφ and monocytes.

Conclusions

In summary, we conclude that Olfr2+ macrophages in the aorta originate from monocytes and can accumulate at the early stages of disease progression. These cells can undergo differentiation into MacAir and Trem2Gpnmb foamy macrophages, exhibiting proliferative and pro-inflammatory potentials. This dynamic behaviour positions them as key influencers in shaping the myeloid landscape within the atherosclerotic plaque.
Bradycardia-induced heart failure with preserved ejection fraction successfully treated with empagliflozin and theophylline: a case report
Authors: Miric Dino , Juric Paic Marina , Borovac Josip Andelo
Published: September 2024

Background

The SGLT2 inhibitor empagliflozin has recently gained approval for treating heart failure (HF) across the entire spectrum of ejection fractions including heart failure with preserved ejection fraction (HFpEF). Bradycardia-induced HF, previously described in the literature as bradycardiomyopathy, is an uncommon cause of HFpEF.

Case summary

Herein, we describe a case of a young, 32-year-old woman with no prior medical history who was referred to the hospital due to progressive fatigue and exercise intolerance. She exhibited junctional bradycardia and sinus node dysfunction on electrocardiographic examination, was hypotensive, and had significantly elevated NT-proBNP levels at admission. Transthoracic echocardiographic examination (TTE) revealed preserved systolic function of the left ventricle with segmental abnormalities of contractility and reduced global longitudinal strain, indicative of HFpEF. Cardiac magnetic resonance imaging showed hypertrabeculations, suggesting noncompaction cardiomyopathy (NCCM), even though the definitive diagnostic criteria for NCCM were not met. The patient reported no recent episodes of fever and no chest pain. A comprehensive panel for cardiotropic viruses and Lyme disease were negative while infiltrative diseases such as sarcoidosis were clinically ruled out. Coronary angiography excluded coronary artery disease. Due to profound hypotension and bradycardia, we prescribed empagliflozin and theophylline. At the subsequent follow-up visit within 1 month, the patient reported that she was asymptomatic, with restored sinus rhythm, and complete normalization of NT-proBNP values.

Discussion

Bradycardia-induced HFpEF is a rare entity that can limit the use of most cardiovascular pharmacotherapies but can be successfully treated with empagliflozin and theophylline as demonstrated in our case.
Prognostic value of Borg scale following 6-min walk test in hospitalized older patients with heart failure
Authors: Saito Hiroshi , Maeda Daichi , Kagiyama Nobuyuki , Sunayama Tsutomu , Dotare Taishi , Fujimoto Yudai , Nakade Taisuke , Jujo Kentaro , Saito Kazuya , Kamiya Kentaro , Ogasahara Yuki , Maekawa Emi , Konishi Masaaki , Kitai Takeshi , Iwata Kentaro , Wada Hiroshi , Kasai Takatoshi , Nagamatsu Hirofumi , Momomura Shin-ichi , Matsue Yuya
Published: September 2024

Aims

The 6-min walk test (6MWT) is a widely accepted tool for evaluating exercise tolerance and physical capacity, and the 6-min walk distance (6MWD) is an established prognostic factor in patients with heart failure (HF). However, the prognostic implications of post-6MWT dyspnoea remain unknown. We aimed to investigate the prognostic value of Borg scores after the 6MWT in patients with HF.

Methods and results

Patients hospitalized for HF who underwent the 6MWT before discharge were included. Post-test dyspnoea was assessed using the Borg scale. Patients were stratified into low and high Borg score groups based on the median Borg score. The primary outcome was 2-year mortality. Among 1185 patients analysed, the median Borg score was 12. The 6MWD was significantly shorter in the high Borg score group than in the low Borg score group. The 2-year mortality rate was 20.2%. In the Kaplan–Meier analysis, the high Borg score group demonstrated an association with 2-year mortality, which remained significant even after adjustment for conventional risk factors, including the 6MWD. Furthermore, the Borg scale provided significant net reclassification improvement to the conventional risk model incorporating 6MWD.

Conclusion

In hospitalized patients with HF, post-6MWT Borg scores were associated with 2-year mortality independent of the 6MWD, providing incremental prognostic value to the 6MWD. Even if patients are able to walk long distances for 6 min, it is essential to closely observe dyspnoea immediately thereafter.
Autonomic modulation impacts conduction velocity dynamics and wavefront propagation in the left atrium
Authors: Honarbakhsh Shohreh , Roney Caroline , Horrach Caterina Vidal , Lambiase Pier D , Hunter Ross J
Published: September 2024

Aims

Atrial fibrosis and autonomic remodelling are proposed pathophysiological mechanisms in atrial fibrillation (AF). Their impact on conduction velocity (CV) dynamics and wavefront propagation was evaluated.

Methods and results

Local activation times (LATs), voltage, and geometry data were obtained from patients undergoing ablation for persistent AF. LATs were obtained at three pacing intervals (PIs) in sinus rhythm (SR). LATs were used to determine CV dynamics and their relationship to local voltage amplitude. The impact of autonomic modulation- pharmacologically and with ganglionated plexi (GP) stimulation, on CV dynamics, wavefront propagation, and pivot points (change in wavefront propagation of ≥90°) was determined in SR. Fifty-four patients were included. Voltage impacted CV dynamics whereby at non-low voltage zones (LVZs) (≥0.5 mV) the CV restitution curves are steeper [0.03 ± 0.03 m/s ΔCV PI 600–400 ms (PI1), 0.54 ± 0.09 m/s ΔCV PI 400–250 ms (PI2)], broader at LVZ (0.2–0.49 mV) (0.17 ± 0.09 m/s ΔCV PI1, 0.25 ± 0.11 m/s ΔCV PI2), and flat at very LVZ (<0.2 mV) (0.03 ± 0.01 m/s ΔCV PI1, 0.04 ± 0.02 m/s ΔCV PI2). Atropine did not change CV dynamics, while isoprenaline and GP stimulation resulted in greater CV slowing with rate. Isoprenaline (2.7 ± 1.1 increase/patient) and GP stimulation (2.8 ± 1.3 increase/patient) promoted CV heterogeneity, i.e. rate-dependent CV (RDCV) slowing sites. Most pivot points co-located to RDCV slowing sites (80.2%). Isoprenaline (1.3 ± 1.1 pivot increase/patient) and GP stimulation (1.5 ± 1.1 increase/patient) also enhanced the number of pivot points identified.

Conclusion

Atrial CV dynamics is affected by fibrosis burden and influenced by autonomic modulation which enhances CV heterogeneity and distribution of pivot points. This study provides further insight into the impact of autonomic remodelling in AF.
Accuracy of Whitebark Pine and Limber Pine Identification by Forest Inventory and Analysis Field Crews
Authors: Williams Shayla R , Steed James E , Morrone Jeremy , Goeking Sara A , Lavin Matt , Dodson Erich Kyle , Simons Rachel E
Published: September 2024

Accurate identification of whitebark and limber pine has become increasingly important following the 2022 listing of whitebark pine as a threatened species under the Endangered Species Act. However, morphological similarities make identification of the two species difficult where ranges overlap. Using a genetic test that differentiates whitebark and limber pine, we compared field identification by Forest Inventory and Analysis field crews with genetic identification for needle samples from 371 trees. Field identifications were 100% correct for the 76 samples collected from outside regions of species’ range overlap. A total of 83% of the field identifications were correct in regions of range overlap (89% for large trees, 88% for saplings, and 78% for seedlings). Field-identified samples were correct 60% of the time for limber pine and >99% for whitebark pine. Random forests analysis revealed that identification accuracy is influenced by crew experience, large (≥ 12.7cm diameter) limber or whitebark pines recorded by field crews on the plot, elevation, Julian day of sample collection, and habitat type. We found that whitebark pine has likely been underestimated, and limber pine overestimated, within their overlapping ranges. We provide insights on improving accuracy of future monitoring where these species overlap.

Study Implications: Accurate identification of whitebark pine is critical for monitoring this threatened species, yet distinguishing whitebark from limber pine can be difficult. Genetic analysis determined accuracy of field identification by Forest Inventory and Analysis (FIA) crews was 83% where the species’ ranges overlap. Virtually all individuals identified as whitebark pine were genetically confirmed to be whitebark pine, although nearly 40% of individuals identified as limber pine were actually whitebark pine. Thus, previous data underestimated whitebark and overestimated limber pine abundance in the species’ range overlap. These results quantify reliability of FIA data for whitebark pine assessments and identify areas for improvement.
The Gongora gibba genome assembly provides new insights into the evolution of floral scent in male euglossine bee–pollinated orchids
Authors: Guizar Amador Maria Fernanda , Darragh Kathy , Liu Jasen W , Dean Cheryl , Bogarín Diego , Pérez-Escobar Oscar A , Serracín Zuleika , Pupulin Franco , Ramírez Santiago R
Published: September 2024

Orchidaceae is one of the most prominent flowering plant families, with many species exhibiting highly specialized reproductive and ecological adaptations. An estimated 10% of orchid species in the American tropics are pollinated by scent-collecting male euglossine bees; however, to date, there are no published genomes of species within this pollination syndrome. In this study, we present the first draft genome of an epiphytic orchid from the genus Gongora, a representative of the male euglossine bee–pollinated subtribe Stanhopeinae. The 1.83-Gb de novo genome with a scaffold N50 of 1.7 Mb was assembled using short- and long-read sequencing and chromosome capture (Hi-C) information. Over 17,000 genes were annotated, and 82.95% of the genome was identified as repetitive content. Furthermore, we identified and manually annotated 26 terpene synthase genes linked to floral scent biosynthesis and performed a phylogenetic analysis with other published orchid terpene synthase genes. The Gongora gibba genome assembly will serve as the foundation for future research to understand the genetic basis of floral scent biosynthesis and diversification in orchids.
Finding the Balance to Quiet the Striving: The Difference Between Successful Aging and Wise Aging
Authors: Glück Judith , Jäger Luisa , Auer-Spath Irina , Harbig Imke Alenka
Published: September 2024

This paper draws on wisdom and lifespan development research to propose a conception of “wise aging,” which may become particularly relevant in very old age as people’s capacities for successful aging decline. We propose that 3 types of balance distinguish wise aging from successful aging. First, wisdom balances one’s own interest with the greater good, emphasizing self-transcendence and compassion. Second, wisdom balances control striving with acceptance of uncontrollability. Wise aging involves a realistic awareness of one’s decreasing levels of control and one’s interconnectedness to and dependence on other people. Third, wisdom acknowledges, regulates, and balances positive and negative affect. Wise aging involves the ability to appreciate and relish the joys of life, but also to accept and embrace more negative emotions and to support others going through different times.
A new mouse model for PRPH2 pattern dystrophy exhibits functional compensation prior and subsequent to retinal degeneration
Authors: Cavanaugh Breyanna L , Milstein Michelle L , Boucher R Casey , Tan Sharon X , Hanna Mario W , Seidel Adam , Frederiksen Rikard , Saunders Thomas L , Sampath Alapakkam P , Mitton Kenneth P , Zhang Dao-Qi , Goldberg Andrew F X
Published: September 2024

Mutations in PRPH2 are a relatively common cause of sight-robbing inherited retinal degenerations (IRDs). Peripherin-2 (PRPH2) is a photoreceptor-specific tetraspanin protein that structures the disk rim membranes of rod and cone outer segment (OS) organelles, and is required for OS morphogenesis. PRPH2 is noteworthy for its broad spectrum of disease phenotypes; both inter- and intra-familial heterogeneity have been widely observed and this variability in disease expression and penetrance confounds efforts to understand genotype–phenotype correlations and pathophysiology. Here we report the generation and initial characterization of a gene-edited animal model for PRPH2 disease associated with a nonsense mutation (c.1095:C>A, p.Y285X), which is predicted to truncate the peripherin-2 C-terminal domain. Young (P21) Prph2Y285X/WT mice developed near-normal photoreceptor numbers; however, OS membrane architecture was disrupted, OS protein levels were reduced, and in vivo and ex vivo electroretinography (ERG) analyses found that rod and cone photoreceptor function were each severely reduced. Interestingly, ERG studies also revealed that rod-mediated downstream signaling (b-waves) were functionally compensated in the young animals. This resiliency in retinal function was retained at P90, by which time substantial IRD-related photoreceptor loss had occurred. Altogether, the current studies validate a new mouse model for investigating PRPH2 disease pathophysiology, and demonstrate that rod and cone photoreceptor function and structure are each directly and substantially impaired by the Y285X mutation. They also reveal that Prph2 mutations can induce a functional compensation that resembles homeostatic plasticity, which can stabilize rod-derived signaling, and potentially dampen retinal dysfunction during some PRPH2-associated IRDs.