bluuebunny/crossref_metadata_2025_split huggingface dataset:
Piezo Proteins, Mechano Reception and Behaviour in Hydra
Authors: Alzugaray Maria Eugenia , Gavazzi Maria Victoria , Griffo Lucia , Ronderos Jorge Rafael 
Published: November 2024

Abstract

Cells are constantly exposed to mechanical forces. These stimuli must be detected and transduced to maintain homeostasis. Due that reason, mechano-sensory systems (MS) are present in all the organisms to generate appropriate responses. Piezo proteins are a recently discovered family of mechano-gated ion channels that respond to mechanical changes of the plasma membrane, allowing the influx of cations, mainly Ca2+. Piezo MS channels are widely represented in Metazoa, acting in several physiological systems. Hydra sp. is a freshwater member of the phylum Cnidaria which is assumed as the sister group of Bilateria. Despite the existence of Piezo channels in Hydra is known, their physiological roles remain unknown. In this work we delve into the physiological relevance of MS Piezo in responses associated to mechanical stimuli in Hydra sp. We analysed the effects of Jedi1 (a specific agonist of Piezo1) on the contractile behaviours, and cnidocyst discharge, and compared them with responses caused by natural stimuli. The results show that the activation of Piezo channels increases the contractile behaviour, stimulating the occurrence of contraction burst, a sudden kind retraction observed in response to touch and osmotic stress. Cnidocyst discharge was also induced by Jedi1, resembling the response caused by the contact of the prey. The effects of both Jedi1 and natural stimuli were avoided in the presence of the inhibitor of MS channels GdCl3. The bioinformatic analysis shows that the protein predicted by Hydra genome has the characteristic motifs of Piezo proteins. These results confirm the existence of Piezo channels in Hydra, unveiling their physiological roles. Because of the relevance of Piezo channels in several pathological conditions and the high level of conservation in metazoans, Hydra could provide a significative experimental model to assay biological and pharmacological issues.
Excitatory synaptic integration of three types of granule cells in the dentate gyrus
Authors: Mao Yue , Liu Ming , Sun Xiaojuan 
Published: November 2024

Abstract

Granule cells (GCs) are mainly responsible for receiving and integrating information from the entorhinal cortex and transferring it to the hippocampus to accomplish memory-related functions such as pattern separation. Owing to the heterogeneity of GCs, there are also two other subtypes, namely semilunar granule cells (SGCs) and hilar ectopic granule cells (HEGCs). In order to investigate their differences, here we examine the disparities in dendritic integration among the different subtypes of GCs. By utilizing biological experimental data, we developed detailed multi-compartment models for each type of GC. Our findings reveal that under the excitatory synaptic inputs (mediated by AMPA receptors), the dendritic integration of GCs, SGCs and HEGCs are linear, sublinear, and supralinear respectively. Furthermore, we propose that the sublinear integration observed in SGCs may be attributed to a high density of V-type potassium channels (K \(_\mathrm{V}\) ) distributed in dendrites with smaller volume and higher input resistance; while the supralinear integration seen in HEGCs may be due to a high density of T-type calcium channels (Ca \(_\mathrm{T}\) ) distributed in dendrites with larger volume and lower input resistance. Additionally, sodium channels, six types of potassium channels (K \(_\mathrm{A}\) , K \(_\mathrm{M}\) , sK \(_{\mathrm{DR}}\) , fK$_{\mathrm{DR}}$, BK, SK), and two types of calcium channels (Ca \(_\mathrm{N}\) , Ca \(_\mathrm{L}\) ) have minimal influence on their respective integration modes. We also found different integration modes exhibit varied somatic firing rates when subjected to different spatial synaptic activation sets, the HEGCs with the supralinear integration demonstrate higher somatic firing rates than the SGCs with the sublinear integration. These results provide theoretical insights into understanding the distinct roles played by these three subtypes of granule cells in memory-related functions within the dentate gyrus.
Synergistic chondroprotective effect of ER stress inhibition and autophagy induction in in-vitro model of Osteoarthritis
Authors: Goyal Himanshi , Rana Diksha , Singh Harwinder , Kaur jyotdeep 
Published: November 2024

Abstract

Osteoarthritis (OA) is characterized by the deterioration of the articular cartilage in the joints, which activates the synthesis of pro-inflammatory cytokines, activating chondrocytes to release matrix metalloproteins (MMPs), thus exacerbating cartilage degradation. Risk factors for OA include age, gender, genetics, obesity, joint injuries, physical inactivity, metabolic disorders, etc. However, treatment options are still limited, with total joint replacement being the primary option for late-stage OA. Excessive and prolonged ER stress triggers apoptosis, whereas mild ER stress causes the induction of autophagy, offering the protection against apoptosis. However, the intricate interplay between ER stress, autophagy, and apoptosis in OA remains incompletely understood. So, in this study, we tried to elucidate the synergistic chondroprotective effect of ER stress inhibition and autophagy activation in ameliorating osteoarthritis. Our RT-qPCR results showed the significant upregulation of GRP78, PERK, ATF4, and CHOP genes of the UPR pathway along with the decrease in the expression of BECLIN 1, LC3, and ATG5 genes of autophagy in an IL-1β induced in vitro model of osteoarthritis. In line with these results, PERK inhibitor GSK2606414 caused significant downregulation of the PERK arm of the UPR pathway and rapamycin resulting in induction of autophagy in this in vitro OA model. Moreover, the combined usage of both the PERK arm inhibitor, GSK2606414, and autophagy activator, rapamycin, in IL-1β induced OA model significantly reduced the gene expression of COL1A1, RUNX2, and MMP13, and increased COL2A1 expression, which suggests prevention of OA progression. In summary, the study suggests the synergistic chondroprotective effect of PERK arm inhibition and autophagy activation in a mTOR dependent manner, which can be a promising strategy to ameliorate osteoarthritis progression.
Spatial interaction mapping of PD-1/PD-L1 in Head and Neck Cancer reveals the role of Macrophage-Tumour Barriers associated with immunotherapy response
Authors: Naei Vahid Yaghoubi , Tubelleza Rafael , Monkman James , Sadeghirad Habib , Donovan Meg L , Blick Tony , Wicher Agata , Bodbin Sara , Stad Robert , Basu Subham , Cooper Caroline , Barnett Catherine , O'Byrne Ken , Ladwa Rahul , Warkiani Majid Ebrahimi , Hughes Brett GM , Kulasinghe Arutha 
Published: November 2024

Abstract

Background
Mucosal head and neck squamous cell carcinoma (HNSCC) is often diagnosed at an advanced stage, where the prognosis is poor due to the high rates of recurrence and metastasis [1]. With approximately one million new cases projected in 2024, worldwide mortality of HNSCC is estimated to reach 50% of detected cases the same year [2]. Patients with early-stage tumours showed a 50–60% five-year survival rate in the US. Immune checkpoint inhibitors (ICIs) have shown promising results in prolonging survival in a subset of patients with recurrent or metastatic disease. However, challenges remain, particularly the limited efficacy of PD-1/PD-L1 blockade therapies. PD-L1 protein expression has been shown to be limited in its predictive power for ICI therapies. Emerging evidence shows that intricate characterisation of the tumour microenvironment (TME) is fundamental to understand interacting cells.
Methods
In this study, we sought to apply a more nuanced approach to understanding cellular interactions by mapping PD-1/PD-L1 interactions across whole-slide HNSCC tissue samples collected prior to ICI therapy. We used a combination of spatial proteomics (Akoya Biosciences) and an in situ proximity ligation assay (isPLA, Navinci Diagnostics) to visualise PD-1/PD-L1 interactions across cell types and cellular neighbourhoods within the tumour TME.
Results
Our findings indicate the existence of isPLA+ PD-1/PD-L1 interactions between macrophages/CD3 T cell-enriched neighbourhoods and tumour cells at the tumour-stroma boundaries in ICI-resistant tumours. The presence of these dense macrophage-tumour layers, which are either absent or dispersed in responders, indicates a barrier that may restrict immune cell infiltration and promote immune escape mechanisms. In contrast, responders had abundant B and T cell aggregates, predominantly around the tumour edges linked to enhanced immune responses to ICI therapy and better clinical outcomes.
Conclusion
This study highlights the utility of isPLA in detecting distinct tumour-immune interactions within the TME, offering new cellular interaction metrics for stratifying and optimising immunotherapy strategies.
Unlocking precision in hydraulic engineering: machine learning insights into labyrinth sluice gate discharge coefficients
Authors: Hashem Thaer , Harith Iman Kattoof , Alrubaye Noor Hassan , Mohammed Ahmed Y. , Hussien Mohammed L. 
Published: November 2024

ABSTRACT

This study investigates the discharge coefficient (Cd) of labyrinth sluice gates, a modern gate design with complex flow characteristics. To accurately estimate Cd, regression techniques (linear regression and stepwise polynomial regression) and machine learning methods (gene expression programming (GEP), decision table, KStar, and M5Prime) were employed. A dataset of 187 experimental results, incorporating dimensionless variables of internal angle (θ), cycle number (N), and water depth contraction ratio (H/G), was used to train and evaluate the models. The results demonstrate the superiority of GEP in predicting Cd, achieving a coefficient of determination (R2) of 97.07% and a mean absolute percentage error of 2.87%. To assess the relative importance of each variable, a sensitivity analysis was conducted. The results revealed that the H/G has the most significant impact on Cd, followed by the internal head angle (θ). The cycle number (N) was found to have a relatively insignificant effect. These findings offer valuable insights into the design and operation of labyrinth sluice gates, contributing to improved water resource management and flood control.
Cryptosporidium
from sheep herds around water tributaries of the Malakand region of Pakistan
Authors: Ali Umar , Khan Naimat Ullah , Ullah Kamran , Usman Tahir , Khattak Irfan , Karanis Panagiotis 
Published: November 2024

ABSTRACT
This study was designed to determine the prevalence of Cryptosporidium infections in sheep herds around tributaries in Pakistan and to evaluate the influence of potential risk factors. In total, 448 samples from 26 sheep herds were screened microscopically and then confirmed through polymerase chain reaction (PCR) amplification of the 18S SSU rRNA gene. Our result revealed that, out of 448 samples, 107 were found positive by microscopy and 77 positive by PCR, with prevalence rates of 23.88% and 17.18% in different breeds of sheep. A high statistical difference exists between the p-value (p &lt;0.05) when comparing diarrheic and non-diarrheic sheep, breeds, ages, genders, and seasons. The highest infection rate was found in the Australian breed (8.25%); season-wise, the highest prevalence was recorded in summer. Furthermore, sheep-secreting diarrheal faces were 41% young sheep and lambs. This reveals that young animals are more susceptible to infection than adults, and cryptosporidial infection decreases with increasing age of the animal. This report is the first on the prevalence of Cryptosporidium in sheep sampled near the tributaries in Pakistan. The implications of this study's findings are clear; immediate action is necessary to prevent further spread and potentially devasting harm.
A Cross-talk between Nanomedicines and Cardiac Complications: Comprehensive View
Authors: Jawaid Shagufta , Joshi Yogesh , Neelofar Nauroz , Khursheed Khuzamah , Shams Samya , Chaudhary Mansi , Arora Mitali , Mahajan Karan , Anwar Firoz 
Published: November 2024

Background:
Cardiovascular Diseases (CVDs) are the leading cause of global morbidity and mortality,
necessitating innovative approaches for both therapeutics and diagnostics. Nanoscience has emerged as a
promising frontier in addressing the complexities of CVDs.

Objective:
This study aims to explorethe interaction of CVDs and Nanomedicine (NMs), focusing on applications
in therapeutics and diagnostics.

Observations:
In the realm of therapeutics, nanosized drug delivery systems exhibit unique advantages, such
as enhanced drug bioavailability, targeted delivery, and controlled release. NMs platform, including liposomes,
nanoparticles, and carriers, allows the precise drug targeting to the affected cardiovascular tissues with
minimum adverse effects and maximum therapeutic efficacy. Moreover, nanomaterial (NM) enables the integration
of multifunctional components, such as therapeutic agents and target ligands, into a single system for
comprehensive CVD management. Diagnostic fronts of NMs offer innovative solutions for early detection and
monitoring of CVDs. Nanoparticles and nanosensors enable highly sensitive and specific detection of Cardiac
biomarkers, providing valuable insights into a disease state, its progression, therapeutic outputs, etc. Further,
nano-based technology via imaging modalities offers high high-resolution imaging, aiding in the vascularization
of cardiovascular structures and abnormalities. Nanotechnology-based imaging modalities offer high-resolution
imaging and aid in the visualization of cardiovascular structures and abnormalities.

Conclusion:
The cross-talk of CVDs and NMs holds tremendous potential for revolutionizing cardiovascular
healthcare by providing targeted and efficient therapeutic interventions, as well as sensitive and early detection
for the improvement of patient health if integrated with Artificial Intelligence (AI).
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
Authors: Gao Yanjun , Li Ruizhe , Croxford Emma , Caskey John , Patterson Brian W , Churpek Matthew , Miller Timothy , Dligach Dmitriy , Afshar Majid 
Published: November 2024

Background
Electronic health records (EHRs) and routine documentation practices play a vital role in patients’ daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives can overwhelm health care providers, increasing the risk of diagnostic inaccuracies. While large language models (LLMs) have showcased their potential in diverse language tasks, their application in health care must prioritize the minimization of diagnostic errors and the prevention of patient harm. Integrating knowledge graphs (KGs) into LLMs offers a promising approach because structured knowledge from KGs could enhance LLMs’ diagnostic reasoning by providing contextually relevant medical information.

Objective
This study introduces DR.KNOWS (Diagnostic Reasoning Knowledge Graph System), a model that integrates Unified Medical Language System–based KGs with LLMs to improve diagnostic predictions from EHR data by retrieving contextually relevant paths aligned with patient-specific information.

Methods
DR.KNOWS combines a stack graph isomorphism network for node embedding with an attention-based path ranker to identify and rank knowledge paths relevant to a patient’s clinical context. We evaluated DR.KNOWS on 2 real-world EHR datasets from different geographic locations, comparing its performance to baseline models, including QuickUMLS and standard LLMs (Text-to-Text Transfer Transformer and ChatGPT). To assess diagnostic reasoning quality, we designed and implemented a human evaluation framework grounded in clinical safety metrics.

Results
DR.KNOWS demonstrated notable improvements over baseline models, showing higher accuracy in extracting diagnostic concepts and enhanced diagnostic prediction metrics. Prompt-based fine-tuning of Text-to-Text Transfer Transformer with DR.KNOWS knowledge paths achieved the highest ROUGE-L (Recall-Oriented Understudy for Gisting Evaluation–Longest Common Subsequence) and concept unique identifier F1-scores, highlighting the benefits of KG integration. Human evaluators found the diagnostic rationales of DR.KNOWS to be aligned strongly with correct clinical reasoning, indicating improved abstraction and reasoning. Recognized limitations include potential biases within the KG data, which we addressed by emphasizing case-specific path selection and proposing future bias-mitigation strategies.

Conclusions
DR.KNOWS offers a robust approach for enhancing diagnostic accuracy and reasoning by integrating structured KG knowledge into LLM-based clinical workflows. Although further work is required to address KG biases and extend generalizability, DR.KNOWS represents progress toward trustworthy artificial intelligence–driven clinical decision support, with a human evaluation framework focused on diagnostic safety and alignment with clinical standards.
An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study
Authors: Thomas Julia , Lucht Antonia , Segler Jacob , Wundrack Richard , Miché Marcel , Lieb Roselind , Kuchinke Lars , Meinlschmidt Gunther 
Published: November 2024

Background
Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text.

Objective
This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features.

Methods
We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model.

Results
The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language.

Conclusions
Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.
Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI–Based Mixed Methods Study
Authors: Bland Tyler 
Published: November 2024

Abstract

Background
Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.

Objective
This study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).

Methods
We transformed a standard clinical case into an engaging, interactive multimedia experience called “Shattered Slippers.” This CCN used various genAI tools for content creation: GPT-4 for developing the storyline, Leonardo.ai and Stable Diffusion for generating images, Eleven Labs for creating audio narrations, and Suno for composing a theme song. The CCN integrated narrative styles and pop culture references to enhance student engagement. It was applied in teaching first-year medical students about immune system pharmacology. Student responses were assessed through the Situational Interest Survey for Multimedia and examination performance. The target audience comprised first-year medical students (n=40), with 18 responding to the Situational Interest Survey for Multimedia survey (n=18).

Results
The study revealed a marked preference for the genAI-enhanced CCNs over traditional teaching methods. Key findings include the majority of surveyed students preferring the CCN over traditional clinical cases (14/18), as well as high average scores for triggered situational interest (mean 4.58, SD 0.53), maintained interest (mean 4.40, SD 0.53), maintained-feeling interest (mean 4.38, SD 0.51), and maintained-value interest (mean 4.42, SD 0.54). Students achieved an average score of 88% on examination questions related to the CCN material, indicating successful learning and retention. Qualitative feedback highlighted increased engagement, improved recall, and appreciation for the narrative style and pop culture references.

Conclusions
This study demonstrates the potential of using a multimodal genAI-driven approach to create CCNs in medical education. The “Shattered Slippers” case effectively enhanced student engagement and promoted knowledge retention in complex pharmacological topics. This innovative method suggests a novel direction for curriculum development that could improve learning outcomes and student satisfaction in medical education. Future research should explore the long-term retention of knowledge and the applicability of learned material in clinical settings, as well as the potential for broader implementation of this approach across various medical education contexts.