bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association of quantitative PSMA PET parameters with pathologic ISUP grade: an international multicenter analysis
Authors: Soeterik Timo F. W. , Heetman Joris G. , Hermsen Rick , Wever Lieke , Lavalaye Jules , Vinken Maarten , Bahler Clinton D. , Yong Courtney , Tann Mark , Kesch Claudia , Seifert Robert , Telli Tugce , Chiu Peter Ka-Fung , Wu Kwan Kit , Zattoni Fabio , Evangelista Laura , Segalla Emma , Barone Antonio , Ceci Francesco , Rajwa Pawel , Marra Giancarlo , Mazzone Elio , Van Basten Jean-Paul A. , Van Melick Harm H. E. , Van den Bergh Roderick C. N. , Gandaglia Giorgio ,   on behalf of the EAU-YAU Prostate Cancer Working Party
Published: August 2024

Abstract
Purpose
To assess if PSMA PET quantitative parameters are associated with pathologic ISUP grade group (GG) and upgrading/downgrading.

Methods
PCa patients undergoing radical prostatectomy with or without pelvic lymph node dissection staged with preoperative PSMA PET at seven referral centres worldwide were evaluated. PSMA PET parameters which included SUVmax, PSMAvolume, and total PSMA accumulation (PSMAtotal) were collected. Multivariable logistic regression evaluated the association between PSMA PET quantified parameters and surgical ISUP GG. Decision-tree analysis was performed to identify discriminative thresholds for all three parameters related to the five ISUP GGs The ROC-derived AUC was used to determine whether the inclusion of PSMA quantified parameters improved the ability of multivariable models to predict ISUP GG ≥ 4.

Results
A total of 605 patients were included. Overall, 2%, 37%, 37%, 10% and 13% patients had pathologic ISUP GG1, 2, 3, 4, and 5, respectively. At multivariable analyses, all three parameters SUVmax, PSMAvolume and PSMAtotal were associated with GG ≥ 4 at surgical pathology after accounting for PSA and clinical T stage based on DRE, hospital and radioligand (all p < 0.05). Addition of all three parameters significantly improved the discrimination of clinical models in predicting GG ≥ 4 from 68% (95%CI 63 – 74) to 74% (95%CI 69 – 79) for SUVmax, 72% (95%CI 67 – 76) for PSMAvolume, 74% (70 – 79) for PSMAtotal and 75% (95%CI 71 – 80) when all parameters were included (all p < 0.05). Decision-tree analysis resulted in thresholds that discriminate between GG (SUVmax 0–6.5, 6.5–15, 15–28, > 28, PSMAvol 0–2, 2–9, 9–20 and > 20 and PSMAtotal 0–12, 12–98 and > 98). PSMAvolume was significantly associated with GG upgrading (OR 1.03 95%CI 1.01 – 1.05). In patients with biopsy GG1-3, PSMAvolume ≥ 2 was significantly associated with higher odds for upgrading to ISUP GG ≥ 4, compared to PSMAvolume < 2 (OR 6.36, 95%CI 1.47 – 27.6).

Conclusion
Quantitative PSMA PET parameters are associated with surgical ISUP GG and upgrading. We propose clinically relevant thresholds of these parameters which can improve in PCa risk stratification in daily clinical practice.
Diagnosing drivers of tropical precipitation biases in coupled climate model simulations
Authors: Respati Muhamad Reyhan , Dommenget Dietmar , Segura Hans , Stassen Christian 
Published: August 2024

AbstractIn this study, we analyse the large-scale biases in tropical precipitation climatology of two different types of coupled ocean–atmosphere general circulation models (GCMs): the coupled model intercomparison project (CMIP) models and ICON-Sapphire, a global storm-resolving model. We employ the simple globally resolved energy balance (GREB) diagnostic precipitation model to evaluate four drivers of the precipitation biases in the simulations: surface specific humidity, surface relative humidity, tropospheric mean and variability in the vertical motion. The tropical precipitation biases in the CMIP and ICON-Sapphire simulations are surprisingly similar in their patterns and also in the elements forcing them. The results of our analysis using the GREB model show that the precipitation biases result from both biases in the sensitivity to the four forcing fields and biases in the simulated forcing fields themselves. The most significant bias for both, the CMIP and ICON-Sapphire simulations, is a too high sensitivity to the mean vertical circulation ($${\omega }_{\text{mean}}$$

ω
mean

) and bias in the $${\omega }_{\text{mean}}$$

ω
mean

climatology itself. This also holds for specific long-standing biases, such as the double intertropical convergence zone problem. Meanwhile, biases in the climatology of specific and relative humidity play only a minor role but contribute to an overall small increase in precipitation in CMIP models that may be related to the “drizzling” bias. These results can give insights to the modelling community regarding model development, and illustrate that the GREB diagnostic precipitation model applied is a good tool for evaluating the drivers of large-scale tropical precipitation.
Explaining recovery from coma with multimodal neuroimaging
Authors: Pozeg Polona , Jöhr Jane , Prior John O. , Diserens Karin , Dunet Vincent 
Published: August 2024

AbstractThe aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients’ neurological recovery after coma. 32 patients (18–76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient’s hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = − 0.74, p < 0.001, 95% CI: − 0.46, − 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.
IMPDH2 suppression impedes cell proliferation by instigating cell cycle arrest and stimulates apoptosis in pediatric hepatoblastoma
Authors: Li Linman , Wu Yichi , Huang Hong-ting , Yong June-kong , Lv Zicheng , Zhou Yi , Xiang Xuelin , Zhao Jie , Xi Zhifeng , Feng Hao , Xia Qiang 
Published: August 2024

Abstract
Background
Hepatoblastoma (HB) is the most common pediatric liver tumor, presenting significant therapeutic challenges due to its high rates of recurrence and metastasis. While Inosine Monophosphate Dehydrogenase 2(IMPDH2) has been associated with cancer progression, its specific role and clinical implications in HB have not been fully elucidated.

Methods
This study utilized Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and Tissue Microarray (TMA) for validation. Following this, IMPDH2 was suppressed, and a series of in vitro assays were conducted. Flow cytometry was employed to assess apoptosis and cell cycle arrest. Additionally, the study explored the synergistic therapeutic effects of mycophenolate mofetil (MMF) and doxorubicin (DOX) on HB cell lines.

Results
The study identified a marked overexpression of IMPDH2 in HB tissues, which was strongly correlated with reduced Overall Survival (OS) and Event-Free Survival (EFS). IMPDH2 upregulation was also found to be associated with key clinical-pathological features, including pre-chemotherapy alpha-fetoprotein (AFP) levels, presence of preoperative metastasis, and the pre-treatment extent of tumor (PRETEXT) staging system. Knockdown of IMPDH2 significantly inhibited HB cell proliferation and tumorigenicity, inducing cell cycle arrest at the G0/G1 phase. Notably, the combination of MMF, identified as a specific IMPDH2 inhibitor, with DOX, substantially enhanced the therapeutic response.

Conclusion
The overexpression of IMPDH2 was closely linked to adverse outcomes in HB patients and appeared to accelerate cell cycle progression. These findings suggest that IMPDH2 may serve as a valuable prognostic indicator and a potential therapeutic target for HB.

Impact
The present study unveiled a significant overexpression of inosine monophosphate dehydrogenase 2 (IMPDH2) in hepatoblastoma (HB) tissues, particularly in association with metastasis and recurrence of the disease. The pronounced upregulation of IMPDH2 was found to be intimately correlated with adverse outcomes in HB patients. This overexpression appears to accelerate the progression of the cell cycle, suggesting that IMPDH2 may serve as a promising candidate for both a prognostic marker and a therapeutic target in the context of HB.
Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification
Authors: Padigepati Samskruthi Reddy , Stafford David A. , Tan Christopher A. , Silvis Melanie R. , Jamieson Kirsty , Keyser Andrew , Nunez Paola Alejandra Correa , Nicoludis John M. , Manders Toby , Fresard Laure , Kobayashi Yuya , Araya Carlos L. , Aradhya Swaroop , Johnson Britt , Nykamp Keith , Reuter Jason A. 
Published: August 2024

AbstractAs the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.
Optimal decision rules for marked point process models
Authors: van Lieshout M. N. M. 
Published: August 2024

AbstractWe study a Markov decision problem in which the state space is the set of finite marked point patterns in the plane, the actions represent thinnings, the reward is proportional to the mark sum which is discounted over time, and the transitions are governed by a birth-death-growth process. We show that thinning points with large marks maximises the discounted total expected reward when births follow a Poisson process and marks grow logistically. Explicit values for the thinning threshold and the discounted total expected reward over finite and infinite horizons are also provided. When the points are required to respect a hard core distance, upper and lower bounds on the discounted total expected reward are derived.
Suspicious transaction alert and blocking system for cryptocurrency exchanges in metaverse’s social media universes: RG-guard
Authors: Gürfidan Remzi 
Published: August 2024

AbstractIn this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk >  = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies.
Ground-truth generation through crowdsourcing with probabilistic indexes
Authors: Sánchez Joan Andreu , Vidal Enrique , Bosch Vicente , Quirós Lorenzo 
Published: August 2024

AbstractAutomatic transcription of large series of historical handwritten documents generally aims at allowing to search for textual information in these documents. However, automatic transcripts often lack the level of accuracy needed for reliable text indexing and search purposes. Probabilistic Indexing (PrIx) offers a unique alternative to raw transcripts. Since it needs training data to achieve good search performance, PrIx-based crowdsourcing techniques are introduced in this paper to gather the required data. In the proposed approach, PrIx confidence measures are used to drive a correction process in which users can amend errors and possibly add missing text. In a further step, corrected data are used to retrain the PrIx models. Results on five large series are reported which show consistent improvements after retraining. However, it can be argued whether the overall costs of the crowdsourcing operation pay off for the improvements, or perhaps it would have been more cost-effective to just start with a larger and cleaner amount of professionally produced training transcripts.
Treatment outcomes in non-occlusive mesenteric ischemia and post-treatment return to social activities
Authors: Ohira Gaku , Hayano Koichi , Tochigi Toru , Maruyama Tetsuro , Toyozumi Takeshi , Kurata Yoshihiro , Maruyama Michihiro , Arai Satoko , Nakada Taka-Aki , Matsubara Hisahiro 
Published: August 2024

Abstract

Purpose
To investigate the treatment outcomes of patients with non-occlusive mesenteric ischemia (NOMI) at our institution, we focused on their post-treatment return to social activities.

Methods
This study included patients with suspected NOMI who were referred to our department between 2011 and 2023. In-hospital mortality was also investigated as a prognostic factor. The Glasgow–Pittsburgh Outcome Categories (GPOC) score was used to evaluate the return to social activities. The relationship between in-hospital mortality and GPOC scores and patient background and treatment factors was examined.

Results
Eighty-two patients were included in the study. Among them, 54 (65.9%) died during hospitalization. Only 9 patients (11%) returned to their social activities. In the multivariate analysis, non-surgical management was found to be the only independent factor for in-hospital mortality. Positive portal venous gas on computed tomography, no open abdomen, no pre-onset catecholamine administration, platelet count < 100,000/µL, lactate level < 5 mmol/L, APTT < 46 s, and Sequential Organ Failure Assessment score < 11 were factors significantly associated with an increased likelihood of return to social activities.

Conclusion
This is the first study to assess the post-treatment return to social activities among patients with NOMI. Our findings highlight the concerning reality that survivors may face prolonged dependence on medical care.
Estimating Time-to-Failure and Long-Term Strength of Rocks Based on Creep Strain Rate Model
Authors: Aghajanzadeh Mohammadreza , Masoumi Hossein , Heidarpour Amin , Alejano Leandro R. 
Published: August 2024

AbstractSustainable mining development requires structures on or within rock masses that can withstand deformation over a long period without compromising safety. Understanding of time-dependent behaviour of rocks is essential for such a purpose which is commonly investigated under sustained loading or so-called “creep” condition within the laboratory environment. A large number of experimental and analytical studies have examined creep behaviour of different rock types. However, some questions have still remained unanswered, particularly regarding the estimation of long-term strength of rocks and predicting their time-to-failure. This study proposes a novel method for prediction of time-to-failure of rock materials under creep loading governed by the secondary creep strain rate as well as estimation of their long-term strength through laboratory data. To do so, six different stress magnitudes ranging from 0.4 to 0.95 of the uniaxial compressive strength were selected for conventional creep compressive tests on Gosford sandstone. Throughout each experiment, the stress magnitude was kept constant until the sample reached failure. The results demonstrated that the secondary creep strain rate is strongly dependent on the magnitude of applied stress. A mere 10% reduction in the applied stress resulted in a decrease in the secondary creep strain rate of approximately three orders of magnitude. The proposed approach for time-to-failure prediction under creep loading included utilisation of secondary creep strain rates as a set of predictive indicators to overcome inherent variability or heterogeneity in rocks. Finally, the validation study was conducted based on the creep data obtained from various rock types to highlight consistent linear correlation between the secondary creep strain rate and the time-to-failure regardless of the magnitude of applied stress. Such an innovative approach can be a suitable tool for practitioners to better predict the stability of rock structures subjected to long-term loading leading to sustainable mining operation.
Synthesis and characterization of polysaccharide-cryogel and its application to the electrochemical detection of DNA
Authors: Tunca Nilay , Maral Meltem , Yildiz Esma , Sengel Sultan Butun , Erdem Arzum 
Published: August 2024

AbstractThe main goal of our study is to demonstrate the applicability of the PPy-cryogel-modified electrodes for electrochemical detection of DNA. First, a polysaccharide-based cryogel was synthesized. This cryogel was then used as a template for chemical polypyrrole synthesis. This prepared polysaccharide-based conductive cryogel was used for electrochemical biosensing on DNA. Carrageenan (CG) and sodium alginate (SA) polysaccharides, which stand out as biocompatible materials, were used in cryogel synthesis. Electron transfer was accelerated by polypyrrole (PPy) synthesized in cryogel networks. A 2B pencil graphite electrode with a diameter of 2.00 mm was used as a working electrode. The prepared polysaccharide solution was dropped onto a working electrode as a support material to improve the immobilization capacity of biomolecules and frozen to complete the cryogelation step. PPy synthesis was performed on the electrodes whose cryogelation process was completed. In addition, the structures of cryogels synthesized on the electrode surface were characterized by thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Surface characterization of the modified electrodes was performed by energy-dispersive X-ray spectroscopy (EDX) analysis. Electrochemical determination of fish sperm DNA (fsDNA) was performed using a PPy-cryogel-modified electrode. The use of a porous 3D cryogel intermediate material enhanced the signal by providing a large surface area for the synthesis of PPy and increasing the biomolecule immobilization capacity. The detection limit was 0.98 µg mL−1 in the fsDNA concentration range 2.5–20 µg mL−1. The sensitivity of the DNA biosensor was estimated to 14.8 µA mM−1 cm−2. The stability of the biosensor under certain storage conditions was examined and observed to remain 66.95% up to 45 days.
Graphical Abstract
A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability
Authors: Coskun Dilcan Cigdem , Aydinalp Koksal Merih 
Published: August 2024

AbstractWater and energy have become essential resources and must be wisely managed for a sustainable future. This paper explores the relationship between water consumption and electricity generation in hydropower plants with dams under different climate change scenarios to contribute to the policy perspectives with a new tool and method to sustain the future. Still, as a reliable forecasting tool, the evaluation of the adaptive neuro-fuzzy inference system model has not been tested for forecasting water consumption during electricity generation. Thus, this study uses this modeling approach to generate reliable water consumption estimates based on electricity generation. The operational data of 78 hydroelectric power plants with dams and meteorological parameters were used as input variables, while water consumption was the output parameter in the model. The dataset was randomly divided into training and testing sets, and 85–15% data splitting presented the best-fitted model. The lowest mean average percent error of the hydroelectric power plants' model resulted in 9.59%, and the coefficient of determination of the model was 0.97, which showed that the developed model presented acceptable prediction performance. Various climate change scenarios are applied to analyze the effects of climate parameters on the water consumption of hydropower plants. The annual hydroelectric power plant water consumption and water intensity were estimated between 2,609 million m3 and 4,393 million m3, and 50,768 m3/GWh and 85,487 m3/GWh, respectively, based on climate change scenarios. The study concludes with significant policy suggestions to endorse this approach.
Graphical abstract