bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hemophagocytic lymphohistiocytosis in HIV-associated HHV8-positive multicentric castleman disease
Authors: Migaud Pascal , Pria Alessia Dalla , Hosmann Kai , Kellerher Peter , Fulgenzi Claudia Anna Maria , Stocker Hartmut , Bower Mark 
Published: December 2024

Objective:
The clinical and laboratory characteristics of HHV8-associated Multicentric Castleman Disease (MCD) in people living with HIV (PLWH) overlap with those of Hemophagocytic Lymphohistiocytosis (HLH) disease and indeed the two diagnoses may co-exist. A risk-stratified treatment approach to MCD based on Rituximab immunotherapy for mild cases and chemo-immunotherapy for severe cases has been shown to yield excellent outcomes in PLWH. In contrast, HLH disease, previously known as secondary HLH, has a dismal prognosis even when promptly treated according to guidelines.

Design:
A retrospective multicentre cohort study.

Methods:
Retrospective analysis of prospectively collected clinical and pathological data on patients with biopsy proven HIV-associated MCD at the National Centre for HIV Malignancy at Chelsea and Westminster Hospital, London between 2008 and 2024 and at the Department of Infectious Diseases at St. Joseph Hospital Berlin-Tempelhof, Germany between 2020 and 2024.

Results:
In our UK-German cohort including 113 PWLH with MCD, we confirmed that HLH disease secondary to MCD is common (30%) and we demonstrated that HLH disease in this context does not adversely influence survival or risk of MCD relapse.

Conclusion:
We suggest that a high HScore in MCD should not lead to a change in the treatment in this specific setting.
Correlations Between the Pterygomaxillary Junction and Appropriate Osteotome Placement Angles During Junction Separation in Le Fort I Osteotomy
Authors: Limthanakul Isara , Chen Gloria , Chuang Kai-Ti , Chou Pang-Yun , Chen Yu-Ray 
Published: December 2024

Purpose:
The authors evaluated pterygomaxillary junction angles across malocclusion patterns and identified the optimal technique for effective pterygomaxillary junction separation during Le Fort I osteotomy, focusing on appropriate osteotome angles and clinical applications.

Methods:
This retrospective study included 211 patients with malocclusion who had undergone Le Fort I osteotomy at Chang Gung Craniofacial Center between December 2015 and September 2023. The patients were stratified by sex and malocclusion class (class I, II, or III). Preoperative cone-beam computed tomography was performed to measure pterygomaxillary junction angles at 2 axial levels: the posterior nasal spine level and the lower border level corresponding to the maxillary tuberosity. Le Fort I osteotomy involving an angle oscillating saw, and a curved-tip Dautrey osteotome was performed, ensuring the precise placement of the osteotome and tilting of the osteotome handle during pterygomaxillary junction separation. The correlations between the pterygomaxillary junction and appropriate osteotome placement angles were investigated.

Results:
Among the 211 patients, 5%, 15%, and 80% had class I, class II, and class III malocclusion, respectively. At the posterior nasal spine level, the average angles of the pterygomaxillary junction relative to the coronal plane were 16.76, 19.16, and 17.82 degrees in class I, class II, and class III groups, respectively; the corresponding angles at the lower border level were 27.29, 27.80, and 25.91 degrees. No significant between-class or between-sex differences were observed in pterygomaxillary junction angles; however, significant differences were noted between the 2 levels.

Conclusion:
Our surgical technique was both safe and effective for pterygomaxillary junction separation.
Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features
Authors: Kim Milin , Sharma Nitin , Leonardsen Esten H. , Rutherford Saige , Selbæk Geir , Persson Karin , Steen Nils Eiel , Smeland Olav B. , Ueland Torill , Richard Geneviève , Manoli Aikaterina , Valk Sofie L. , Alnæs Dag , Beckman Christian F. , Marquand Andre F. , Andreassen Ole A. , Westlye Lars T. , Wolfers Thomas , Moberget Torgeir 
Published: December 2024

AbstractMental and neurological conditions have been linked to structural brain variations. However, aside from dementia, the value of brain structural characteristics derived from brain scans for prediction is relatively low. One reason for this limitation is the clinical and biological heterogeneity inherent to such conditions. Recent studies have implicated aberrations in the cerebellum – a relatively understudied brain region – in these clinical conditions. Here, we used machine learning to test the value of individual deviations from normative cerebellar development across the lifespan (based on trained data from >27k participants) for prediction of autism spectrum disorder (ASD) (n=317), bipolar disorder (BD) (n=238), schizophrenia (SZ) (n=195), mild cognitive impairment (MCI) (n=122), and Alzheimer’s disease (AD) (n=116). We applied several atlases and derived median, variance, and percentages of extreme deviations within each region of interest. Our results show that lobular and voxel-wise cerebellar data can be used to discriminate healthy controls from ASD and SZ with moderate accuracy (the area under the receiver operating characteristic curves ranged from 0.56 to 0.64), The strongest contributions to these predictive models were from posterior regions of the cerebellum, which are more strongly linked to higher cognitive functions than to motor control.
Identification of Circular RNAs associated with Ageing of the Dorsolateral Prefrontal Cortex across the Adult Lifespan
Authors: Amjadi-Moheb Fatemeh , Gobhidharan Sumangali , Mohan Adith , Sachdev Perminder S. , Thalamuthu Anbupalam , Mather Karen A 
Published: December 2024

AbstractBackgroundCircular RNAs (circRNAs) are emerging as crucial regulators of biological processes and have been implicated in age-related diseases. Few studies have explored age-related circRNA expression in the human brain across the adult lifespan. This study aims to identify age-related differentially expressed circRNAs in human post-mortem dorsolateral prefrontal cortex (DLPFC) samples, a region critically involved in cognition that exhibits early signs of age-related changes.MethodsTotal RNA sequencing was conducted on a discovery cohort of 67 postmortem DLPFC samples, from individuals with no neurological disease diagnosis at the time of death (35-103 years). CircRNAs were identified using CIRCexplorer2, with 11,907 circRNAs available for analyses. Linear regression was used to analyse the relationships between circRNA expression and age at death. Replication of the results was performed in an independent neurologically healthy dataset from the CommonMind Consortium (n=321, age at death: 35-91 years). Co-expression network analysis was performed to identify modules of highly co-expressed circRNAs associated with age. Potential microRNA and RNA-binding protein target sites were predicted.ResultsIn the discovery dataset, 37 circRNAs were age-associated (FDR <0.05). Seven out of the 37 were successfully replicated. The host genes of replicated age-associated circRNAs are implicated in synapse regulation. Co-expression analysis revealed two circRNA modules significantly correlated with age. We identified 484 microRNA and 99 RNA-binding protein target sites on the replicated circRNAs.ConclusionSeven age-associated circRNAs were identified as important candidates for involvement in post-transcriptional regulatory networks in the DLPFC. Future studies should aim to elucidate their functional roles in brain ageing.
scSurv: a deep generative model for single-cell survival analysis
Authors: Mizukoshi Chikara , Kojima Yasuhiro , Hayashi Shuto , Abe Ko , Kasugai Daisuke , Shimamura Teppei 
Published: December 2024

ABSTRACTSingle-cell omics analysis has unveiled the heterogeneity of various cell types within tumors. However, no methodology currently reveals how this heterogeneity influences cancer patient survival at single-cell resolution. Here, we introduce scSurv, combining a Cox proportional hazards model with a deep generative model of single-cell transcriptome, to estimate individual cellular contributions to clinical outcomes. The accuracy of scSurv was validated using both simulated and real datasets. This method identifies cells associated with favorable or adverse prognoses and extracts genes correlated with their contribution levels. In melanoma, scSurv reproduces known prognostic macrophage classifications and facilitates hazard mapping through spatial transcriptomics in renal cell carcinoma. We also identified genes consistently associated with prognosis across multiple cancers and demonstrated the applicability of this method to infectious diseases. scSurv is a novel framework for quantifying the heterogeneity of individual cellular effects on clinical outcomes.
MatriCom: a scRNA-Seq data mining tool to infer ECM-ECM and cell-ECM communication systems
Authors: Lamba Rijuta , Paguntalan Asia M. , Petrov Petar B. , Naba Alexandra , Izzi Valerio 
Published: December 2024

ABSTRACTThe ECM is a complex and dynamic meshwork of proteins that forms the framework of all multicellular organisms. Protein interactions within the ECM are critical to building and remodeling the ECM meshwork, while interactions between ECM proteins and cell surface receptors are essential for the initiation of signal transduction and the orchestration of cellular behaviors. Here, we report the development of MatriCom, a web application (https://matrinet.shinyapps.io/matricom) and a companion R package (https://github.com/Izzilab/MatriCom), devised to mine scRNA-Seq datasets and infer communications between ECM components and between different cell populations and the ECM. To impute interactions from expression data, MatriCom relies on a unique database, MatriComDB, that includes over 25,000 curated interactions involving matrisome components, with data on 80% of the ∼1,000 genes that compose the mammalian matrisome. MatriCom offers the option to query open-access datasets sourced from large sequencing efforts (Tabula Sapiens, The Human Protein Atlas, HuBMAP) or to process user-generated datasets. MatriCom is also tailored to account for the specific rules governing ECM protein interactions and offers options to customize the output through stringency filters. We illustrate the usability of MatriCom with the example of the human kidney matrisome communication network. Last, we demonstrate how the integration of 46 scRNA-Seq datasets led to the identification of both ubiquitous and tissue-specific ECM communication patterns. We envision that MatriCom will become a powerful resource to elucidate the roles of different cell populations in ECM-ECM and cell-ECM interactions and their dysregulations in the context of diseases such as cancer or fibrosis.ONE SENTENCE SUMMARYMatriCom is a web application devised to mine scRNA sequencing datasets to infer ECM-ECM and cell-ECM communication systems in the context of the diverse cell populations that constitute any tissue or organ.
Characterization and identification of enzyme-producing microflora isolated from the gut of freshwater Prawn,MacrobrachiumSpp
Authors: Nimbalkar Dhanraj N , Nalavade Virdhaval M. , Chavan Jaykumar J. 
Published: December 2024

AbstractIsolation of extracellular enzyme degrading aerobic bacteria in the digestive tracts of fresh water prawn was carried out. Gut bacteria were isolated on carboxymethyl cellulose agar plates, starch agar media plates, gelatine peptone agar media plates. The isolated strain was qualitatively screen based on their extracellular enzyme on selective media. The strain was characterised based on morphological, physiological and biochemical characterization identified Bacillus species. Bacillus species was isolated by colony characterization carried out by using Gelatin agar Media, carboxymethyl cellulose media and congo red CMC media and starch agar media for different enzyms. Isolates are capable of hydrolysing proteins and carbohydrates indicating their importance in fish nutrition.
Depletion of aneuploid cells is shaped by cell-to-cell interactions
Authors: Fusari Elena , Muzzopappa Mariana , Gracia Juliette , Milán Marco 
Published: December 2024

SummaryAneuploidy, the major cause of miscarriages in humans, is pervasive in early embryos but robustly dampened during development, allowing for healthy births. Later in life, aneuploidy correlates with pathological conditions including cancer. Identification of the mechanisms underlying the elimination of aneuploid cells is relevant in development and disease. Here we generated cells carrying molecularly defined segmental monosomies and trisomies and characterized their immediate impact on cellular behavior. Our data reveal signs of out-competition of cells carrying monosomies in genomic regions devoid of previously known haploinsufficient genes. Dose-dependent effects of single genes or a discrete number of genes contribute to the observed cellular behaviors. By simultaneously inducing cells carrying monosomies and trisomies of the same genomic location, we present evidence that segmental trisomies potentiate or alleviate the negative effects of the monosomy on growth, thus revealing a key role of cell interactions in defining thein vivoelimination of aneuploid cells.HighlightsSegmental monosomies cause growth impairment and are out-competed.Growth impairment relies on dose-dependent effects of single genes or a discrete number of genes. Segmental trisomies of up to 1500 genes do not have a major impact on proliferation and survival. Cell competition is modulated by the presence of cells trisomic for the same genomic region.eTOC BlurbAneuploidy has a negative impact on the growth and proliferation of all animal cells analyzed so far. Fusari et al. unravel a role of cell interactions in defining thein vivoelimination of aneuploid cells through cell competition.
CoPPIs Algorithm: A Tool to Unravel Protein Cooperative Strategies in Pathophysiological Conditions
Authors: Lomagno Andrea , Yusuf Ishak , Tosadori Gabriele , Bonanomi Dario , Mauri Pierluigi , Di Silvestre Dario 
Published: December 2024

AbstractWe present here the Co-expressed Protein-Protein Interactions (CoPPIs) algorithm. In addition to minimizing correlation-causality imbalance and contextualizing PPIs to the investigated systems, it combines PPIs and protein co-expression networks to identify differentially correlated functional modules. To test CoPPIs, we processed a set of proteomic profiles from different brain areas of controls and subjects affected by idiopathic Parkinson’s disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in PD pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weighted PPI networks provided molecular clues consistent with the PD pathophysiology. Of note, like quantification, the CoPPIs algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.
The evolution of weaponry and aggressive behaviour in field crickets
Authors: Judge Kevin A. , Smith Briana E. , Ohlmann Shawna L. , Steckler Deanna K. , Kellington Alexandria T. , Cade William H. 
Published: December 2024

AbstractWeapons are among the most extravagant sexually selected traits known, yet the evolution of weapon diversity remains understudied. We used field crickets (Orthoptera, Gryllinae) to test two hypotheses explaining interspecific diversity in weaponry. We raised eight species ofGryllusfield crickets under common garden conditions and staged interactions between conspecific males. We measured body size and weapon shape (relative head and mouthpart size) to determine weapon allometry in both males and females, quantified the intensity of male-male aggression for each species, analyzed the effects of both body size and weapon shape on contest outcome, and tested comparative relationships between morphology and behaviour using phylogenetic least squares regression. We found that larger males won more contests than smaller males in seven of eight species, and weapon shape predicted contest success in only one species. Contrary to the fighting advantage hypothesis, body size was not related to aggressiveness across species, but weaponry was. Additionally, the most aggressive species had the most elaborate weaponry, contrary to the weapon-signal continuum hypothesis. Our results highlight the complexity of weaponry evolution in a group of organisms that has been a model system for the observation and study of aggression for approximately 1000 years.
Exploring Bioelectricity with Ace2N-mNeon during Zebrafish Embryogenesis
Authors: Wu ZhenZhen , Silva Rui Oliveira , Houssein Ruya , Trujillo Fabiola Marques , Gotti Jordan , Ganapathy Srividya , Gao Zhenyu , Brinks Daan 
Published: December 2024

AbstractBioelectricity is a fundamental biophysical phenomenon present in all cells, playing a crucial role in embryogenesis by regulating processes such as neuronal signaling, pattern formation, and cancer suppression. Precise monitoring of bioelectric signals and their dynamic changes throughout development is vital for advancing our understanding of higher organisms. However, the lack of suitable techniques for mapping bioelectric signals during early development has greatly limited our ability to interpret these mechanisms. To address this challenge, we developed an Ace2N-mNeon expression library in zebrafish, which exhibits membrane localization from 4 hours post-fertilization to at least 5 days post- fertilization and broad expression across multiple cell types throughout development. We validated the use of this library for studying bioelectric changes via voltage imaging to record signals in neurons and cardiomyocytes at different development stages. Through this approach, we found evidence of synchronized neuronal activity during early embryogenesis and observed faster voltage dynamics in cardiomyocytes as development progressed. Our results show that the Ace2N-mNeon library is a valuable tool for developmental bioelectric studies supporting advanced techniques such as voltage imaging and fluorescence lifetime imaging (FLIM). These methods enable non-invasive, dynamic monitoring of bioelectric signals across diverse cell types throughout development, significantly surpassing the capabilities of current electrophysiological techniques.
Phylogeography and genetic structure of the steppe marmot (Marmota bobakMüller, 1776)
Authors: Brandler Oleg , Tambovtseva Valentina , Tukhbatullin Andrey , Rumyantsev Vadim , Grachov Alexey , Kapustina Svetlana 
Published: December 2024

ABSTRACTThe steppe marmotMarmota bobakis a key species in the steppe ecosystems of Eurasia and an important object of economic and conservation practice. Its wide distribution is separated by many ecological and geographical barriers. The studied morphological variability is characterized by clinal variation of characters and indistinct differences. The subspecies system, including three subspecies, has no clear spatial boundaries and is controversial. The genetic variability ofM. bobakhas not been extensively studied. We first investigated the genetic variability ofM. bobakthroughout its range using three traditional molecular markers of the mitochondrial genomeCOI, cytb, and a control region of mtDNA. We found two phylogroups in the genetic structure of this species, which are geographically arranged in an unexpected way. Also, the character of intraspecific genetic variability does not agree with the accepted subspecies division; individuals from terra typica of all three described subspecies belong to the same phyletic lineage. Further studies are needed to develop the subspecies system ofM. bobak.
A complex adaptive systems model of labor reciprocity and normative reasoning in swidden agriculture
Authors: Tverskoi Denis , Scaggs Shane A. , Downey Sean S. 
Published: December 2024

AbstractSwidden (aka “slash-and-burn”) agriculture is a prototypical coupled human and natural system and understanding the social, cultural, and environmental factors shaping the trajectories of swidden forests is essential to mitigate climate change and ensure equitable collaboration between scientists, planners, and Indigenous communities. Despite this, mathematical models integrating its social and ecological dynamics are rare. Here, we use complex adaptive systems theory to develop a model where individuals rely on labor exchange driven by reciprocity, and on normative reasoning that can lead to sanctions. Our results identify three emergent regimes: low-intensity swidden, sustainable high-intensity swidden that maximizes ecosystem services and harvest returns, and deforestation. We show that sustainable high-intensity swidden evolves if labor reciprocity and normative reasoning are balanced: helping behavior should be significantly conditioned by normative reasoning to prevent over-harvesting, while reciprocity is necessary to prevent excessive sanctioning. We find that the sustainable high-intensity swidden regime is robust to changes in group size, is resilient to environmental shocks, can evolve under various models of forest ecology, and is most productive for both forests and farmers when the balance of labor reciprocity and normative reasoning results in an intermediate scale of forest disturbance. Overall, we illuminate the importance of Indigenous social norms and customary practices related to swidden labor in maintaining sustainable and intensive swidden agriculture.
Deep Learning-Driven Subtype Classification of Colorectal Cancer Using Histopathology Images for Personalized Medicine
Authors: Le Phan Nghia Trong , Mavuso Mqondisi Fortune , Thien Dao Phuc Nguyen , Anh Nguyen Huy Ngoc , Thuy Mai Tien , Thi Quang Kiep , Le Thang Truong 
Published: December 2024

SummaryPrecise and timely diagnosis of colorectal cancer (CRC) is vital for enhancing patient outcomes. Histopathological examination of tissue samples remains the gold standard for CRC diagnosis, but it is a time-consuming and subjective approach tending to inter-observer variability. This study explores the use of deep learning, particularly ResNet architectures, for the automated classification of CRC using histopathology images. Our research focuses on evaluating different ResNet models (ResNet-18, ResNet-34, ResNet-50) to extract relevant visible characteristics. Additionally, we use Grad-CAM heatmaps to understand the model’s areas of focus, ensuring alignment with established diagnostic criteria. To address limited data availability, we examine data augmentation techniques to improve the model’s adaptability. Our analysis indicates that ResNet-34 strikes a balance between model complexity and performance, demonstrating notable accuracies of 91.10%, 99.11%, and 100.00% for overall, top-2, and top-3 accuracy classifications, respectively, outperforming both shallower (ResNet-18) and deeper (ResNet-50) models. This indicates that a moderate depth is practical in capturing features of CRC images. Our findings have significant importance for the development of an interpretable AI-assisted diagnostic tool for CRC, with the potential to improve the efficiency and accuracy of pathologists. This approach aims to automate image analysis, offer insights into model decisions, and ultimately improve diagnostic consistency and patient care in oncology.