bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heme promotes venetoclax resistance in multiple myeloma through MEK-ERK signaling and purine biosynthesis
Authors: Nair Remya , Vu An H. , Freer Abigail K. , Bhatia Karanpreet S. , Wang Dongxue , Savani Milan R. , Matulis Shannon M. , Lonial Sagar , Jaye David L. , Boise Lawrence H. , Seo Seung-Yong , Corson Timothy W. , Nooka Ajay K. , Bhatt Shruti , McBrayer Samuel K. , Gupta Vikas A. , Hu Xin , Barwick Benjamin G. , Reddi Amit R. , Shanmugam Mala 
Published: December 2024

Abstract
We previously demonstrated that reduced intrinsic electron transport chain (ETC) activity predicts and promotes sensitivity to the B-cell lymphoma 2 (BCL-2) antagonist, venetoclax (Ven), in multiple myeloma (MM). Heme, an iron-containing prosthetic group and metabolite, is fundamental to maintaining ETC activity. Interrogation of the cyclin D1 group 2 subgroup of MM from the Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass) trial (NCT01454297), which can be used as a proxy for Ven-sensitive MM (VS MM), shows reduced expression of the conserved heme biosynthesis pathway gene signature. Consistent with this, we identified that VS MM exhibits reduced heme biosynthesis and curiously elevated hemin (oxidized heme) uptake. Supplementation with hemin or protoporphyrin IX (heme lacking iron) promotes Ven resistance, whereas targeting ferrochetalase, the penultimate enzyme involved in heme biosynthesis, increases Ven sensitivity in cell lines and primary MM cells. Mechanistically, heme-mediated activation of prosurvival rapidly accelerated fibrosarcoma-rat sarcoma virus–mitogen-activated protein kinase (MEK) signaling and metabolic rewiring, increasing de novo purine synthesis, were found to contribute to heme-induced Ven resistance. Cotargeting BCL-2 and myeloid cell leukemia-1 suppresses heme-induced Ven resistance. Interrogation of the Multiple Myeloma Research Foundation CoMMpass study of patients shows increased purine and pyrimidine biosynthesis to corelate with poor progression-free survival and overall survival. Elevated heme and purine biosynthesis gene signatures were also observed in matched relapse refractory MM, underscoring the relevance of heme metabolism in therapy-refractory MM. Overall, our findings reveal, for the first time, a role for extrinsic heme, a physiologically relevant metabolite, in modulating proximity to the apoptotic threshold with translational implications for BCL-2 antagonism in MM therapy.
Inter- and intracellular mitochondrial communication: signaling hubs in aging and age-related diseases
Authors: Zhang Meng , Wei Jin , He Chang , Sui Liutao , Jiao Chucheng , Zhu Xiaoyan , Pan Xudong 
Published: December 2024

AbstractMitochondria are versatile and complex organelles that can continuously communicate and interact with the cellular milieu. Deregulated communication between mitochondria and host cells/organelles has significant consequences and is an underlying factor of many pathophysiological conditions, including the process of aging. During aging, mitochondria lose function, and mitocellular communication pathways break down; mitochondrial dysfunction interacts with mitochondrial dyscommunication, forming a vicious circle. Therefore, strategies to protect mitochondrial function and promote effective communication of mitochondria can increase healthy lifespan and longevity, which might be a new treatment paradigm for age-related disorders. In this review, we comprehensively discuss the signal transduction mechanisms of inter- and intracellular mitochondrial communication, as well as the interactions between mitochondrial communication and the hallmarks of aging. This review emphasizes the indispensable position of inter- and intracellular mitochondrial communication in the aging process of organisms, which is crucial as the cellular signaling hubs. In addition, we also specifically focus on the status of mitochondria-targeted interventions to provide potential therapeutic targets for age-related diseases.
Graphical Abstract
Informing the decision-making process for potential PMT/vPvM chemicals through the adoption of a risk-based prioritization framework: the ZeroPM approach
Authors: Gouin Todd , Bitsch Annette , van Duursen Majorie , Escher Sylvia E. , Hamers Timo 
Published: December 2024

AbstractA risk-based strategy is presented aimed at prioritizing chemicals screened as potential persistent, mobile and toxic (PMT) or very persistent and very mobile (vPvM) substances. Prioritization is done to strengthen the decision-making process regarding actions that might be taken against chemicals screened as potential PMT/vPvM substances. Such actions can range from acquiring additional data aimed at reducing uncertainties in toxicological effect concentrations or internal exposure concentrations to—in case of acceptable uncertainty—suggesting compounds for prevention and/or removal measures in order to limit future exposure. The prioritization strategy is developed within the ZeroPM project and applies a variety of tools, including in silico and in vitro models for exposure and toxicity hazard assessment. These tools will be applied to chemicals identified as PMT/vPvM substances, with a preliminary emphasis on substances belonging to three chemical classes, i.e. perfluorinated compounds, triazines and triazoles. Here we describe the ZeroPM approach providing a proof-of-principle illustrative example, based on data-rich substances, results from which demonstrate how prioritization can be achieved using a risk-based approach that uses data obtained from new approach methodologies (NAMs) and environmental exposure concentrations, obtained either through modelling or monitoring studies. Results are communicated using a risk-based prioritization matrix, which can be used to help to communicate prioritization needs, such as identifying data gaps or for guiding actions aimed at mitigating exposure. The precision and accuracy of the prioritization matrix is evaluated using several data-rich chemicals, which identifies perfluorooctanoic acid and perfluorooctane sulfonic acid as high priority, due to a combination of toxicity and exposure estimates, whereas atrazine and melamine are observed at lower priority. The proposed risk-based prioritization framework thus represents a complementary source of information that should help support regulatory decision-making for PMT/vPvM substances.
Subconjunctival dirofilariasis in a healthy subject: a case report
Authors: Aghajani Amirhossein , Rajabi Mohammad Taher , Rafizadeh Seyed Mohsen , Rahmanikhah Elham , Samiee Reza , Abdol Homayuni Mohammad Reza , Zand Amin 
Published: December 2024

Abstract
Purpose
To report a case of subconjunctival dirofilariasis presenting as an inflammatory mass-like lesion in a healthy individual.

Case presentation
A 20-year-old male with a recent history of wildlife exposure in a rural village presented with an inflammatory subconjunctival mass in his left eye. The lesion was surgically excised, and histopathological examination confirmed the presence of an immature nematode of Dirofilaria immitis. The patient was treated with oral ivermectin for two weeks postoperatively. After two months, the lesion had almost completely resolved, and no recurrence was observed during the 6-month follow-up.

Conclusions
Given the rarity of ocular dirofilariasis and its diverse presentations in different ocular and adnexal structures, prompt and complete excision followed by meticulous histopathological evaluation is crucial to guide appropriate management and achieve favorable outcomes.
Predicted breeding values for relative scrapie susceptibility for genotyped and ungenotyped sheep
Authors: Eiríksson Jón H. , Þórarinsdóttir Þórdís , Gautason Egill 
Published: December 2024

Abstract
Background
Scrapie is an infectious prion disease in sheep. Selective breeding for resistant genotypes of the prion protein gene (PRNP) is an effective way to prevent scrapie outbreaks. Genotyping all selection candidates in a population is expensive but existing pedigree records can help infer the probabilities of genotypes in relatives of genotyped animals.

Results
We used linear models to predict allele content for the various PRNP alleles found in Icelandic sheep and compiled the available estimates of relative scrapie susceptibility (RSS) associated with PRNP genotypes from the literature. Using the predicted allele content and the genotypic RSS we calculated estimated breeding values (EBV) for RSS. We tested the predictions on simulated data under different scenarios that varied in the proportion of genotyped sheep, genotyping strategy, pedigree recording accuracy, genotyping error rates and assumed heritability of allele content. Prediction of allele content for rare alleles was less successful than for alleles with moderate frequencies. The accuracy of allele content and RSS EBV predictions was not affected by the assumed heritability, but the dispersion of prediction was affected. In a scenario where 40% of rams were genotyped and no errors in genotyping or recorded pedigree, the accuracy of RSS EBV for ungenotyped selection candidates was 0.49. If only 20% of rams were genotyped, or rams and ewes were genotyped randomly, or there were 10% pedigree errors, or there were 2% genotyping errors, the accuracy decreased by 0.07, 0.08, 0.03 and 0.04, respectively. With empirical data, the accuracy of RSS EBV for ungenotyped sheep was 0.46–0.65.

Conclusions
A linear model for predicting allele content for the PRNP gene, combined with estimates of relative susceptibility associated with PRNP genotypes, can provide RSS EBV for scrapie resistance for ungenotyped selection candidates with accuracy up to 0.65. These RSS EBV can complement selection strategies based on PRNP genotypes, especially in populations where resistant genotypes are rare.
NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface
Authors: Conte Andrea , Gulmini Nicola , Costa Francesco , Cartura Matteo , Bröhl Felix , Patanè Francesco , Filippini Francesco 
Published: December 2024

Abstract

Background
Vaccines development in this millennium started by the milestone work on Neisseria
meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills.

Results
We report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop-Razor, to recover fragments of promising vaccine candidates or Epitope Prediction for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment.

Conclusions
With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.
Genome-wide association studies unveil major genetic loci driving insecticide resistance in Anopheles funestus in four eco-geographical settings across Cameroon
Authors: Gadji Mahamat , Kengne-Ouafo Jonas A. , Tchouakui Magellan , Wondji Murielle J. , Mugenzi Leon M. J. , Hearn Jack , Boyomo Onana , Wondji Charles S. 
Published: December 2024

Abstract
Background
Insecticide resistance is jeopardising malaria control efforts in Africa. Deciphering the evolutionary dynamics of mosquito populations country-wide is essential for designing effective and sustainable national and subnational tailored strategies to accelerate malaria elimination efforts. Here, we employed genome-wide association studies through pooled template sequencing to compare four eco-geographically different populations of the major vector, Anopheles funestus, across a South North transect in Cameroon, aiming to identify genomic signatures of adaptive responses to insecticides.

Results
Our analysis revealed limited population structure within Northern and Central regions (FST<0.02), suggesting extensive gene flow, while populations from the Littoral/Coastal region exhibited more distinct genetic patterns (FST>0.049). Greater genetic differentiation was observed at known resistance-associated loci, resistance-to-pyrethroids 1 (rp1) (2R chromosome) and CYP9 (X chromosome), with varying signatures of positive selection across populations. Allelic variation between variants underscores the pervasive impact of selection pressures, with rp1 variants more prevalent in Central and Northern populations (FST>0.3), and the CYP9 associated variants more pronounced in the Littoral/Coastal region (FST =0.29). Evidence of selective sweeps was supported by negative Tajima’s D and reduced genetic diversity in all populations, particularly in Central (Elende) and Northern (Tibati) regions. Genomic variant analysis identified novel missense mutations and signatures of complex genomic alterations such as duplications, deletions, transposable element (TE) insertions, and chromosomal inversions, all associated with selective sweeps. A 4.3 kb TE insertion was fixed in all populations with Njombe Littoral/Coastal population, showing higher frequency of CYP9K1 (G454A), a known resistance allele and TE upstream compared to elsewhere.

Conclusion
Our study uncovered regional variations in insecticide resistance candidate variants, emphasizing the need for a streamlined DNA-based diagnostic assay for genomic surveillance across Africa. These findings will contribute to the development of tailored resistance management strategies crucial for addressing the dynamic challenges of malaria control in Cameroon.
Unintentional medication discrepancies at care transitions: prevalence and their impact on post-discharge emergency visits in critically ill older adults
Authors: Park Jiyoung , Kim A Jeong , Cho Eun-Jung , Cho Yoon Sook , Jun Kwanghee , Jung Yoon Sun , Lee Ju-Yeun 
Published: December 2024

Abstract
Background
Unintentional medication discrepancies during care transitions pose a significant risk for medication errors, particularly in critically ill older patients. This study aimed to investigate the prevalence of such discrepancies during care transitions and their impact on post-discharge emergency department (ED) visits in this patient population.

Methods
This retrospective cross-sectional study included patients aged 65 and older who were on chronic medications and admitted to the intensive care units of emergency departments (ED-ICUs) between 2019 and 2020. We evaluated unintentional medication discrepancies, including omissions or changes in medication type, dose, frequency, formulation, or administration route without clear clinical justification during care transition. The association between these discrepancies and post-discharge ED visits was analyzed using a multivariable Cox-proportional hazard model.

Results
Of the 339 patients analyzed, 68% encountered unintentional medication discrepancies at some point during care transitions, with prevalence of 35% at admission, 20% during transfer, and 49% at discharge. After adjusting for confounding factors, patients with unintentional medication discrepancies had a twofold higher risk of ED visits within 30 days of discharge (HR = 2.13, 95% CI = 1.06–4.30).

Conclusion
This study demonstrated a substantial prevalence of unintentional medication discrepancies among critically ill older adults during care transitions, significantly increasing the risk of ED visits within a month of discharge. The findings highlight the crucial need for systematic identification and management of medication discrepancies throughout the care transition process to enhance patient safety.
The AxBioTick study – immune gene expression signatures in human skin bitten by Borrelia-infected versus non-infected ticks
Authors: Berthén Nellie Carlströmer , Cronhjort Samuel , Nordberg Marika , Lindgren Per-Eric , Larsson Marie , Wilhelmsson Peter , Sjöwall Johanna 
Published: December 2024

Abstract
Background
Borrelia infection is caused by Borrelia burgdorferi sensu lato and transmitted by Ixodes ricinus ticks, a common tick-borne infection in Northern Europe. The establishment of Borrelia infection depends on transmission of the spirochetes, as well as the immune response generated in the skin after a bite. Here we aim to investigate the local immune response in the skin after a tick bite and assess the possible direct effects of Borrelia, by applying gene expression analysis of the immune response in skin exposed to Borrelia-infected and non-infected ticks, respectively.

Methods
Skin biopsies from the study participants were taken 7–10 days after the tick-bite. The ticks and skin biopsies were analysed by real-time PCR for Borrelia spp. and other tick-borne pathogens. Dermal transcriptome profiles derived from RNA sequencing with focus on immune system regulation were created. In addition, we performed enrichment analysis of dermal transcriptome profiles with focus on immune system regulation.

Results
Skin biopsies exposed to a Borrelia-positive tick induced an overall higher expression of immune-related genes. Cytokines involved in the regulation of T-cell and macrophage activation, pro-inflammatory regulators and Toll-like receptor 2, 3 and 7 involved in pathogen recognition were upregulated in skin exposed to Borrelia, although Borrelia DNA was not detected in the biopsies.

Conclusion
The evidence of upregulation of genes in Borrelia exposed skin suggests an influence on the immune system of ticks and spirochetes. Characterization of Borrelia-associated gene expression signatures in the skin could contribute to future diagnostics and increase our understanding of the development of various manifestations of Borrelia infection.
Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection
Authors: Rathika S. , Mahendran K. , Sudarsan H. , Ananth S. Vijay 
Published: December 2024

AbstractUltrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting abnormalities. Real-time imaging, low cost, non-invasiveness, and accessibility make US imaging indispensable in clinical practice. However, acquiring fetal US planes with correct fetal anatomical features is a difficult and time-consuming task, even for experienced sonographers. Medical imaging using AI shows promise for addressing current challenges. In response to this challenge, a Deep Learning (DL)-based automated categorization method for maternal fetal US planes are introduced to enhance detection efficiency and diagnosis accuracy. This paper presents a hybrid optimization technique for feature selection and introduces a novel Radial Basis Function Neural Network (RBFNN) for reliable maternal fetal US plane classification. A large dataset of maternal–fetal screening US images was collected from publicly available sources and categorized into six groups: the four fetal anatomical planes, the mother's cervix, and an additional category. Feature extraction is performed using Gray-Level Co-occurrence Matrix (GLCM), and optimization methods such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) approach are utilized to select the most relevant features. The optimized features from each algorithm are then input into both conventional and proposed DL models. Experimental results indicate that the proposed approach surpasses conventional DL models in performance. Furthermore, the proposed model is evaluated against previously published models, showcasing its superior classification accuracy. In conclusion, our proposed approach provides a solid foundation for automating the classification of fetal US planes, leveraging optimization and DL techniques to enhance prenatal diagnosis and care.
Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images
Authors: Yoo Jay J. , Namdar Khashayar , Khalvati Farzad 
Published: December 2024

Abstract
Purpose
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which necessitates significant time and resources. We aim to develop a pipeline that can be trained using readily accessible binary image-level classification labels, to effectively segment regions of interest without requiring ground truth annotations.

Methods
This work proposes the use of a deep superpixel generation model and a deep superpixel clustering model trained simultaneously to output weakly supervised brain tumor segmentations. The superpixel generation model’s output is selected and clustered together by the superpixel clustering model. Additionally, we train a classifier using binary image-level labels (i.e., labels indicating whether an image contains a tumor), which is used to guide the training by localizing undersegmented seeds as a loss term. The proposed simultaneous use of superpixel generation and clustering models, and the guided localization approach allow for the output weakly supervised tumor segmentations to capture contextual information that is propagated to both models during training, resulting in superpixels that specifically contour the tumors. We evaluate the performance of the pipeline using Dice coefficient and 95% Hausdorff distance (HD95) and compare the performance to state-of-the-art baselines. These baselines include the state-of-the-art weakly supervised segmentation method using both seeds and superpixels (CAM-S), and the Segment Anything Model (SAM).

Results
We used 2D slices of magnetic resonance brain scans from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset and labels indicating the presence of tumors to train and evaluate the pipeline. On an external test cohort from the BraTS 2023 dataset, our method achieved a mean Dice coefficient of 0.745 and a mean HD95 of 20.8, outperforming all baselines, including CAM-S and SAM, which resulted in mean Dice coefficients of 0.646 and 0.641, and mean HD95 of 21.2 and 27.3, respectively.

Conclusion
The proposed combination of deep superpixel generation, deep superpixel clustering, and the incorporation of undersegmented seeds as a loss term improves weakly supervised segmentation.