bluuebunny/crossref_metadata_2025_split huggingface dataset:
Atrial fibrillation burden and clinical outcomes following BTK inhibitor initiation
Authors: Gambril John Alan , Ghazi Sanam M. , Sansoterra Stephen , Ferdousi Mussammat , Kola-Kehinde Onaopepo , Ruz Patrick , Kittai Adam S. , Rogers Kerry , Grever Michael , Bhat Seema , Wiczer Tracy , Byrd John C. , Woyach Jennifer , Addison Daniel 
Published: August 2024

AbstractBruton’s tyrosine kinase inhibitors (BTKi) have dramatic efficacy against B-cell malignancies, but link with cardiotoxicity, including atrial fibrillation (AF). Burden, severity, and implications of BTKi-related AF are unknown. Leveraging a large-cohort of consecutive B-cell malignancy patients initiated on BTKi from 2009–2020, we identified patients with extended ambulatory rhythm monitoring. The primary outcome was AF burden after BTKi-initiation. Secondary outcomes included ventricular arrhythmia burden and other arrhythmias. Observed incident-AF rates and burden with next-generation BTKi’s were compared to ibrutinib. Multivariable regression defined association between rhythm measures and major adverse cardiac events (MACE), and mortality. There were 98 BTKi-treated patients [38.8% next-generation BTKi’s, 14.3% prior-AF], with 28,224 h of monitoring. Median duration BTKi-use was 34 months. Over mean duration 12 days monitoring, 72.4% developed arrhythmias (16.3% incident-AF, 31.6% other SVTs, 14.3% ventricular tachycardia). 14.3% had high AF-burden. AF-burden was similar between ibrutinib and next-generation BTKi’s. No single antiarrhythmic-therapy prevented BTKi-related AF. However, antiarrhythmic initiation associated with reduction in arrhythmic burden (P = 0.009). In a multivariable model accounting for traditional cardiovascular risk factors, prior-AF associated with increased post-BTKi AF-burden. In follow-up, high AF burden associated with MACE (HR 3.12, P = 0.005) and mortality (HR 2.97, P = 0.007). Among BTKi-treated patients, high AF burden prognosticates future MACE and mortality risk.
Standardizing feeding strategies for preterm infants born greater than 1500 grams
Authors: Fu Ting Ting , Arhin Maame , Schulz Ashley T. , Gardiner Abigail , Chapman Stacie , Adamchak Abigail , Ward Laura P. , Kim Jae H. 
Published: August 2024

Abstract
Background
Use of standardized feeding protocols and donor breast milk (DBM) have been studied primarily in infants born <1500 g and not examined exclusively in infants born >1500 g.

Methods
In this retrospective pre-post-implementation cohort study, we evaluated a protocol for preterm infants born >1500 g that was implemented clinically to standardize feeding advancements at 30 mL/kg/day, with infants born <33 weeks eligible to receive DBM. We compared placement of peripherally inserted central catheters for parenteral nutrition, feeding tolerance, growth, and maternal milk provision in the 18 months before/after implementation. The association between DBM intake and growth was evaluated using multivariable linear regression.

Results
We identified 133 and 148 eligible infants pre/post-implementation. Frequency of peripherally inserted central catheters and rate of maternal milk provision was not statistically different. While there was no difference in median days to full enteral volume, there was a narrower distribution post-implementation (p < 0.001). Growth was similar between eras, but each 10% increase in DBM was associated with 1.0 g/d decrease in weight velocity (p < 0.001).

Conclusions
A feeding protocol for preterm infants >1500 g is associated with more consistent time to full enteral volume. Further investigation is needed to clarify DBM’s impact on growth in this population.

Impact:

Despite practice creep, no study has examined the use of standardized feeding protocols or pasteurized donor breast milk exclusively in infants >1500 g.

A feeding protocol in this population may achieve full enteral feedings more consistently.

With appropriate fortification, donor breast milk can support adequate growth in infants born >1500 g but warrants further study.
The feedback loop between MTA1 and MTA3/TRIM21 modulates stemness of breast cancer in response to estrogen
Authors: Zhang Jingyao , Wang Yinuo , Zhang Jingjing , Wang Xin , Liu Jiaxiang , Huo Miaomiao , Hu Ting , Ma Tianyu , Zhang Die , Li Yu , Guo Chang , Yang Yunkai , Zhang Min , Yuan Baowen , Qin Hao , Teng Xu , Gao Tianyang , Hao Xinhui , Yu Hefen , Huang Wei , Xu Binghe , Wang Yan 
Published: August 2024

AbstractThe metastasis-associated protein (MTA) family plays a crucial role in the development of breast cancer, a common malignancy with a high incidence rate among women. However, the mechanism by which each member of the MTA family contributes to breast cancer progression is poorly understood. In this study, we aimed to investigate the roles of MTA1, MTA3, and tripartite motif-containing 21 (TRIM21) in the proliferation, invasion, epithelial-mesenchymal transition (EMT), and stem cell-like properties of breast cancer cells in vivo and in vitro. The molecular mechanisms of the feedback loop between MTA1 and MTA3/TRIM21 regulated by estrogen were explored using Chromatin immunoprecipitation (ChIP), luciferase reporter, immunoprecipitation (IP), and ubiquitination assays. These findings demonstrated that MTA1 acts as a driver to promote the progression of breast cancer by repressing the transcription of tumor suppressor genes, including TRIM21 and MTA3. Conversely, MTA3 inhibited MTA1 transcription and TRIM21 regulated MTA1 protein stability in breast cancer. Estrogen disrupted the balance between MTA1 and MTA3, as well as between MTA1 and TRIM21, thereby affecting stemness and the EMT processes in breast cancer. These findings suggest that MTA1 plays a vital role in stem cell fate and the hierarchical regulatory network of EMT through negative feedback loops with MTA3 or TRIM21 in response to estrogen, supporting MTA1, MTA3, and TRIM21 as potential prognostic biomarkers and MTA1 as a treatment target for future breast cancer therapies.
Dental implants significantly increase adjacent tooth loss risk due to root fracture
Authors: Afrashtehfar Kelvin I. , Kazma Jad Moriss , Yahia Islam , Jaber Aesa A. 
Published: August 2024

Abstract
Design
This retrospective cohort study aimed to investigate the risk and variables of tooth loss for teeth adjacent to dental implants compared to teeth nonadjacent to implants. The study followed the STROBE guidelines and was approved by the Institutional Review Board.

Cohort selection
The study included patients treated with dental implants at UCSF School of Dentistry between 2000 and 2020. The inclusion criteria for teeth adjacent to implants required the implant to support a fixed prosthesis and a follow-up period of at least 12 months. Nonadjacent teeth also required a follow-up period of at least 12 months. Teeth were excluded if they had a hopeless prognosis or were planned for extraction before the completion of restorative treatment.

Data analysis
Data were extracted from electronic health records, including patient demographics, dental histories, and outcomes for teeth adjacent and nonadjacent to implants. Statistical analyses, including Kaplan-Meier survival plots, log-rank tests, and multivariate logistic regression, were used to compare tooth survival and identify aetiologies of tooth loss.

Results
The study included 787 patients, with 2048 teeth adjacent and 15,637 teeth nonadjacent to implants. The 10-year cumulative survival rate was 89.2% for teeth adjacent to implants and 99.3% for nonadjacent teeth. Teeth adjacent to implants had a significantly higher risk of tooth loss (Odds Ratio [OR] 13.15). The primary etiology of tooth loss adjacent to implants was root fracture (45.2%), followed by caries (28.9%), periodontitis (24.1%), and endodontic failure (1.8%). For nonadjacent teeth, periodontitis was the leading cause of tooth loss (51.9%).

Conclusions
The study found that teeth adjacent to dental implants had a significantly higher risk of tooth loss, primarily due to root fractures. The findings suggest that dental implants may act as an iatrogenic factor, increasing the risk of complications for adjacent teeth. Conservative management of natural dentition should be prioritized, with emphasis on stringent periodontal surveillance and effective home care. Future research should focus on prospective studies to further explore these associations and improve clinical outcomes.
α-Synuclein oligomers form by secondary nucleation
Authors: Xu Catherine K. , Meisl Georg , Andrzejewska Ewa A. , Krainer Georg , Dear Alexander J. , Castellana-Cruz Marta , Turi Soma , Edu Irina A. , Vivacqua Giorgio , Jacquat Raphaël P. B. , Arter William E. , Spillantini Maria Grazia , Vendruscolo Michele , Linse Sara , Knowles Tuomas P. J. 
Published: August 2024

AbstractOligomeric species arising during the aggregation of α-synuclein are implicated as a major source of toxicity in Parkinson’s disease, and thus a major potential drug target. However, both their mechanism of formation and role in aggregation are largely unresolved. Here we show that, at physiological pH and in the absence of lipid membranes, α-synuclein aggregates form by secondary nucleation, rather than simple primary nucleation, and that this process is enhanced by agitation. Moreover, using a combination of single molecule and bulk level techniques, we identify secondary nucleation on the surfaces of existing fibrils, rather than formation directly from monomers, as the dominant source of oligomers. Our results highlight secondary nucleation as not only the key source of oligomers, but also the main mechanism of aggregate formation, and show that these processes take place under conditions which recapitulate the neutral pH and ionic strength of the cytosol.
Temporal regularities shape perceptual decisions and striatal dopamine signals
Authors: Fritsche Matthias , Majumdar Antara , Strickland Lauren , Liebana Garcia Samuel , Bogacz Rafal , Lak Armin 
Published: August 2024

AbstractPerceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates.
Aging insights from heterochronic parabiosis models
Authors: Lagunas-Rangel Francisco Alejandro 
Published: August 2024

AbstractHeterochronic parabiosis consists of surgically connecting the circulatory systems of a young and an old animal. This technique serves as a model to study circulating factors that accelerate aging in young organisms exposed to old blood or induce rejuvenation in old organisms exposed to young blood. Despite the promising results, the exact cellular and molecular mechanisms remain unclear, so this study aims to explore and elucidate them in more detail.
A systematic exploration of unexploited genes for oxidative stress in Parkinson’s disease
Authors: Suzuki Takayuki , Bono Hidemasa 
Published: August 2024

AbstractHuman disease-associated gene data are accessible through databases, including the Open Targets Platform, DisGeNET, miRTex, RNADisease, and PubChem. However, missing data entries in such databases are anticipated because of curational errors, biases, and text-mining failures. Additionally, the extensive research on human diseases has led to challenges in registering comprehensive data. The lack of essential data in databases hinders knowledge sharing and should be addressed. Therefore, we propose an analysis pipeline to explore missing entries of unexploited genes in the human disease-associated gene databases. Using this pipeline for genes in Parkinson’s disease with oxidative stress revealed two unexploited genes: nuclear protein 1 (NUPR1) and ubiquitin-like with PHD and ring finger domains 2 (UHRF2). This methodology enhances the identification of underrepresented disease-associated genes, facilitating easier access to potential human disease-related functional genes. This study aims to identify unexploited genes for further research and does not include independent experimental validation.
Intradermal vaccination with a phytoglycogen nanoparticle and STING agonist induces cytotoxic T lymphocyte-mediated antitumor immunity
Authors: Hernandez-Franco Juan F. , Jan Imran M. , Elzey Bennett D. , HogenEsch Harm 
Published: August 2024

AbstractA critical aspect of cancer vaccine development is the formulation with effective adjuvants. This study evaluated whether combining a cationic plant-derived nanoparticle adjuvant (Nano-11) with the clinically tested STING agonist ADU-S100 (MIW815) could stimulate anticancer immunity by intradermal vaccination. Nano-11 combined with ADU-S100 (NanoST) synergistically activated antigen-presenting cells, facilitating protein antigen cross-presentation in vitro and in vivo. Intradermal vaccination using ovalbumin (OVA) as a tumor antigen and combined with Nano-11 or NanoST prevented the development of murine B16-OVA melanoma and E.G7-OVA lymphoma tumors. The antitumor immunity was abolished by CD8+ T cell depletion but not by CD4+ T cell depletion. Therapeutic vaccination with NanoST increased mouse survival by inhibiting B16-OVA tumor growth, and this effect was further enhanced by PD-1 checkpoint blockade. Our study provides a strong rationale for developing NanoST as an adjuvant for intradermal vaccination and next-generation preventative and therapeutic cancer vaccines by STING-targeted activation.
EDARA: An ERA5-based Dataset for Atmospheric River Analysis
Authors: Mo Ruping 
Published: August 2024

AbstractAtmospheric Rivers (ARs) are long and narrow bands of strong horizontal water vapour transport concentrated in the lower troposphere. ARs play an important role in producing some high-impact weather events such as extreme precipitation and flooding, damaging winds, and temperature anomalies. To facilitate various studies on the short- and long-term variability of ARs and their impacts, I compiled a multi-decade global dataset containing 12 relevant meteorological variables for AR analysis. These variables were derived from the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis version 5 (ERA5). They are available at 6-hour intervals from 1940 to present. Also included in the dataset is an interactive web browser-based graphical tool for visualising the AR evolution on regional (North America) and global scales. This ERA5-based Dataset for Atmospheric River Analysis (EDARA) may serve as a valuable resource for many AR-related research and applications.
CS-PHOC: weekly census counts of Southern Ocean phocids at Cape Shirreff, Livingston Island
Authors: Woodman Samuel M. , Borras-Chavez Renato , Goebel Michael E. , Torres Daniel , Aguayo Anelio , Krause Douglas J. 
Published: August 2024

AbstractRapid climatic warming of the Antarctic Peninsula is driving regional population declines and distribution shifts of predators and prey. Affected species include Antarctic ice seals and the southern elephant seal, all of which rely on the peninsula region for critical stages of their life cycle. However, data collection is difficult in this remote region, and therefore long-term time series with which to identify and investigate population trends in these species are rare. We present the Cape Shirreff Phocid Census (CS-PHOC) dataset: weekly counts of phocids (crabeater, leopard, southern elephant, and Weddell seals) hauled out at Cape Shirreff, Livingston Island, during most austral summers since 1997. Data from these censuses were cleaned and aggregated, resulting in robust and comparable count data from 284 censuses across 23 field seasons. The CS-PHOC dataset, which is publicly available through the SCAR Biodiversity Portal, will be updated yearly to provide important information about Southern Ocean phocids in the Antarctic Peninsula.
A global coastal permeability dataset (CoPerm 1.0)
Authors: Moosdorf Nils , Tschaikowski Jarrid , Kretschmer Daniel , Reinecke Robert 
Published: August 2024

AbstractThe permeability of aquifers strongly influences groundwater flow characteristics. Worldwide, coastal groundwater is often the primary freshwater source for coastal communities and ecosystems but is also particularly vulnerable to abstraction since saltwater intrusion may threaten its quality. Thus, understanding coastal permeability is crucial to the sustainable use of coastal groundwater. Here, we present the first global dataset of coastal permeability (CoPerm 1.0), which provides data on coasts’ landward, shoreline, and seaward permeability. CoPerm accounts for shoreline characteristics such as cliffs and beaches and contains information on four million segments representing more than two million kilometers of global coastline. Rocky Shores are the most abundant shoreline class, followed by mangroves, beaches, and muddy coasts. Permeability differs between the immediate shoreline (median permeability: 10−12.3 m2), the seaward (median: 10−13.3 m2), and the landward (median: 10−13 m2) sides of the coast. CoPerm provides input data for global coastal groundwater assessments and regional studies of submarine groundwater discharge or saltwater intrusion that can radiate into ecological and economic studies.
Neural encoding of linguistic speech cues is unaffected by cognitive decline, but decreases with increasing hearing impairment
Authors: Bolt Elena , Giroud Nathalie 
Published: August 2024

AbstractThe multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of cognitive decline in older adults, taking into account the effects of hearing. Participants without ($$n = 25$$

n
=
25

) and with ($$n = 19$$

n
=
19

) early signs of cognitive decline listened to an audiobook while their electroencephalography responses were recorded. Using the mTRF framework, we modeled the relationship between speech input and neural response via different acoustic, segmented and linguistic encoding models and examined the response functions in terms of encoding accuracy, signal power, peak amplitudes and latencies. Our results showed no significant effect of cognitive decline or hearing ability on the neural encoding of acoustic and linguistic speech features. However, we found a significant interaction between hearing ability and the word-level segmentation model, suggesting that hearing impairment specifically affects encoding accuracy for this model, while other features were not affected by hearing ability. These results suggest that while speech processing markers remain unaffected by cognitive decline and hearing loss per se, neural encoding of word-level segmented speech features in older adults is affected by hearing loss but not by cognitive decline. This study emphasises the effectiveness of mTRF analysis in studying the neural encoding of speech and argues for an extension of research to investigate its clinical impact on hearing loss and cognition.
Epidemic modeling for misinformation spread in digital networks through a social intelligence approach
Authors: Govindankutty Sreeraag , Gopalan Shynu Padinjappurath 
Published: August 2024

AbstractOnline digital networks, including social networks, have significantly impacted individuals’ personal and professional lives. Aside from exchanging news and topics of interest, digital networks play an essential role in the diffusion of information, which frequently significantly impacts worldwide societies. In this paper, we present a new mathematical epidemic model for digital networks that considers the sentiment of solitary misinformation in the networks and characteristics of human intelligence that play an important role in judging and spreading misinformation inside the networks. Our mathematical analysis has proved the existence and validity of the system in a real-time environment. Considering the real-world data, our simulation predicts how the misinformation could spread among different global communities and when an intervention mechanism should have to be carried out by the policyholders. Our simulation using the model proves that effective intervention mechanisms by isolating the fake news can effectively control the spread of misinformation among larger populations. The model can analyze the emotional and social intelligence of groups frequently subjected to disinformation and disseminating fake news.
TEMPRO: nanobody melting temperature estimation model using protein embeddings
Authors: Alvarez Jerome Anthony E. , Dean Scott N. 
Published: August 2024

AbstractSingle-domain antibodies (sdAbs) or nanobodies have received widespread attention due to their small size (~ 15 kDa) and diverse applications in bio-derived therapeutics. As many modern biotechnology breakthroughs are applied to antibody engineering and design, nanobody thermostability or melting temperature (Tm) is crucial for their successful utilization. In this study, we present TEMPRO which is a predictive modeling approach for estimating the Tm of nanobodies using computational methods. Our methodology integrates various nanobody biophysical features to include Evolutionary Scale Modeling (ESM) embeddings, NetSurfP3 structural predictions, pLDDT scores per sdAb region from AlphaFold2, and each sequence’s physicochemical characteristics. This approach is validated with our combined dataset containing 567 unique sequences with corresponding experimental Tm values from a manually curated internal data and a recently published nanobody database, NbThermo. Our results indicate the efficacy of protein embeddings in reliably predicting the Tm of sdAbs with mean absolute error (MAE) of 4.03 °C and root mean squared error (RMSE) of 5.66 °C, thus offering a valuable tool for the optimization of nanobodies for various biomedical and therapeutic applications. Moreover, we have validated the models’ performance using experimentally determined Tms from nanobodies not found in NbThermo. This predictive model not only enhances nanobody thermostability prediction, but also provides a useful perspective of using embeddings as a tool for facilitating a broader applicability of downstream protein analyses.