bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficacy and safety of non-pharmacological, pharmacological and surgical treatments for hand osteoarthritis in 2024: a systematic review
Authors: Kjeken Ingvild , Bordvik Daniel Huseby , Osteras Nina , Haugen Ida K , Aasness Fjeldstad Kristine , Skaalvik Ingrid , Kloppenburg Margreet , Kroon Féline P B , Tveter Anne Therese , Smedslund Geir
Published: January 2025

Background

We aimed to update the 2018 systematic literature review on the efficacy and safety of treatments for hand osteoarthritis (OA), which was based on 126 studies.

Methods

We performed a systematic literature search on randomised controlled trials from June 2017 up to 31 December 2023. Risk of bias was assessed using the RoB2 tool. Meta-analyses of previous and new studies regarding the efficacy for pain, function, grip strength and OMERACT/OARSI responders were performed. Certainty of evidence was judged using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) tool.

Results

Sixty-five new studies were included. For non-pharmacological interventions, there was low-certainty evidence for a small long-term effect of hand exercises and a moderate long-term effect of thumb orthoses for pain, and moderate-certainty evidence that assistive devices had a moderate long-term effect on function. Concerning pharmacological interventions, there was low-certainty evidence for a moderate short-term effect of oral non-steroidal anti-inflammatory drugs (NSAIDs) on pain, high- and moderate-certainty evidence for a small short-term effect of topical NSAIDs and oral glucocorticoids on function, respectively, and low-certainty evidence that oral glucocorticoids had a small short-term effect on function. Further, there was low-certainty evidence that methotrexate had a small long-term effect on pain. The heterogeneity of studies did not allow for any meta-analyses on surgery.

Conclusion

The results largely support current treatment recommendations. However, there is a lack of interventions that efficiently improve grip strength, and the evidence for most current treatments is still limited. To better understand action mechanism of different treatments, future trials should include hand OA subtyping and be powered for subgroup analyses.
A review of paleomagnetic studies from Northern Alaska and Yukon: implications for terrane reconstructions
Authors: Biasi Joseph , Strauss Justin V.
Published: January 2025

Here, we present a comprehensive overview of available paleomagnetic studies and datasets from northern Alaska, USA, and Yukon, Canada. Most studies in this region were conducted when our understanding of best-practices paleomagnetism was still developing; as a result, many do not meet modern standards of data quality and many interpretations of the paleomagnetic data—though valid at the time—are now also outdated. In this review, we assess what data are reliable, what interpretations have stood the test of time, and what the existing data can constrain about the tectonic history of this region. We find that although a middle to Late Cretaceous (Aptian–Turonian) overprint pervades much of this area, many sites still retain primary remanence directions, some dating as far back as the Neoproterozoic. Studies that found complete overprinting in the Cretaceous also typically analyzed poor lithologic recorders of paleomagnetic directions, such as carbonates. Based on our assessment of the most reliable data from the study region, relative motion between the examined outboard terranes and Laurentia was not yet complete by the early Late Cretaceous. We also find that “high latitude” dinosaur fossil sites were more northerly than today, confirming previous assumptions about the paleolatitudes at these sites. Finally, we discuss how the widely cited Jurassic–Cretaceous counterclockwise rotation hypothesis for the Arctic Alaska terrane is no longer supported by the existing paleomagnetic data, and the validity of this hypothesis should be critically re-examined.
Synchronization of three Rössler systems coupled in linear configuration
Authors: Uriostegui-Legorreta U. , Tututi E. S. , Estevez-Delgado J. , Pérez-Aguilar H.
Published: January 2025

In this paper, we study the synchronization of three Piecewise Linear (PWL) Rössler systems that exhibit coexistence of two attractors, coupled in linear configuration. In particular, we analyze the four linear configurations of the network motifs. We analyze the interaction of the systems by varying the unidirectional or bidirectional coupling strengths depending on the case under study, in order to characterize the conditions under which complete synchronization between the three PWL Rössler systems are obtained. We have found, by means of the error functions, the range of values of the coupling strengths where the different types of synchronization occurs. The numerical results show that the interaction of various multistable chaotic systems causes different types of synchronization and very complex dynamics between the systems.
STABLE REDUCED HIP AFTER OPEN REDUCTION IN DEVELOPMENTAL DYSPLASIA OF THE HIP
Authors: Maleki Arash , Qoreishy Mohamad , Tajik Katayoon , Valinejad Ghazal , Tahririan Mohammad Ali
Published: January 2025

Introduction: Developmental dysplasia of the hip (DDH) has a wide spectrum of conditions and treatment plans. Our aim in this study is to investigate related and controllable factors that can lead to secondary redislocation, and use them to foresee a concomitant pelvic osteotomy requirement.

Methods: This is a retrospective case-control study on DDH cases reduced via an anterior approach without pelvic osteotomy in our orthopedics hospital from 2019 to 2021. After 24 months, we compared clinical and radiological findings between the failure and successful groups.

Results: We reduced 49 hips (38 cases) using an anterior approach without osteotomy. The success rate was 89.8% (five cases re-dislocated), which is accountable for favorable odds. We found no statistically significant correlation between re-dislocation and bilaterality ([Formula: see text] = 0.64), gender ([Formula: see text] = 0.27) or age ([Formula: see text] = 0.82), or the AI before reduction ([Formula: see text] = 0.19).

Conclusion: When the reduction has a wide safe zone in the operating room, the reduction of the hip without osteotomy can have satisfactory results, so based on the findings of our study, invasive procedures such as osteotomy will not be necessary if the hip is stable after the open reduction in the operating room.
Identifying States of Global Oil and Gas Markets Based on Visibility Graph Embedding Algorithms
Authors: Wei Na , Xie Wen-Jie
Published: January 2025

Oil and natural gas are indispensable energy commodities. Investigating the evolution and transformation of energy market dynamics represented by oil and natural gas holds significant implications for global energy governance, as well as political and economic development. We have employed the visibility graph algorithm to transform the most intuitive and readily available time series data of oil and natural gas prices into complex networks. Then, three network embedding algorithms based on machine learning are utilized to embed the network into multidimensional vector spaces, enabling us to identify market state changes in both energy products through the sequential clustering method. We find that the visibility graph algorithm and network embedding methods effectively preserve internal structural characteristics of data, capture similarities between price change rate nodes across different trading days in the oil and gas markets, while also identifying the memorability of price volatility trends. Beyond simple price trends, critical market fluctuations such as those induced by events like the COVID-19 or Russia–Ukraine conflict can be discerned from similarity matrix partitions of network node representation vectors. Furthermore, the sequential clustering algorithm accurately identifies transition points in the oil and gas markets’ states. Unlike conventional time series analysis methods, this innovative combination of visibility graph and network embedding algorithms allows for a multi-faceted exploration of market state changes at various levels; thereby facilitating deeper insights into underlying logic behind price time series.
Large deviations of one-hidden-layer neural networks
Authors: Hirsch Christian , Willhalm Daniel
Published: January 2025

We study large deviations in the context of stochastic gradient descent for one-hidden-layer neural networks with quadratic loss. We derive a quenched large deviation principle, where we condition on an initial weight measure, and an annealed large deviation principle for the empirical weight evolution during training when letting the number of neurons and the number of training iterations simultaneously tend to infinity. The weight evolution is treated as an interacting dynamic particle system. The distinctive aspect compared to prior work on interacting particle systems lies in the discrete particle updates, simultaneously with a growing number of particles.
Performance Dependence Multi-Criteria Decision-Making: A Preference Model Based on an Improved Choquet Integral
Authors: Gao Yu , Cai Mei , Xiao Jingmei , Yang Guang
Published: January 2025

In multi-criteria decision-making, the Choquet integral can handle the interaction between criteria; however, the interaction between criteria is not only related to the criteria themselves but may also depend on the performance values of criteria. We present a further extension of the Choquet integral considering this fact. A classic example is used to illustrate the limitations of the Choquet integral, and a performance-dependent assumption is proposed that describes the interactions influenced by the performance of alternatives on criteria. Based on this assumption, we propose an alternative capacity and construct an improved Choquet integral to obtain alternative’s global utility. A preference model based on the improved Choquet integral with a wider range of applicability and more realistic decision results is proposed. Finally, a numerical example is provided to illustrate the feasibility and validity of our reference model.
Angle-Based Discontinuous Deformation Analysis
Authors: Zhang Hong , Zhu Hua-Rui , Li Xi-Long , Wu Wei , Zheng Lu
Published: January 2025

Due to the nonlinear nature of contacts, the real contact information is indeterminate and there is no analytical solution for discontinuous computation. In the context of two-dimensional discontinuous deformation analysis (2D DDA) for arbitrary polygon block systems, the contact indeterminacy arises from at least two aspects, namely kinematics and geometry. This study provides a specific numerical solution for 2D DDA to address the indeterminacy problem of contacts between arbitrary polygon blocks. First, local contact boundary is established by defining the solid angle of 2D blocks and the entrance solid angle between two 2D solid angles of two blocks. Second, the linearized first entrance formulas are derived to estimate the first entrance time and position. Third, an enhanced shortest exit strategy based on entrance angles is introduced to provide a unified solution of the contact points and direction of closed contacts. Some extreme and challenging examples, including oblique collision contacts, undue penetration contacts, genuine vertex–vertex contacts (including absolutely symmetric contacts), extremely abnormal block contacts (i.e., contacts between extremely short edges and/or sharp angles), multi-connected block contacts and numerous singular contacts, demonstrate the accuracy, robustness and application prospects of the proposed method in addressing difficult contact problems.
Effect of Synthesis Parameters on the Optical, Structural and Morphological Properties of Wet-Synthesized Zinc Oxide (ZnO) Nanostructures
Authors: Vishnoi Shweta , Tyagi Anurag , Singh Beer Pal , Goel Raj Kumar
Published: January 2025

In this research Zinc Nitrate Hexahydrate [Formula: see text] was used to synthesize Zinc oxide (ZnO) nanostructures and polyethylene glycol ([Formula: see text] was used as the capping agent. During the synthesis process temperature and concentration of ammonium hydroxide [Formula: see text] and [Formula: see text] were changed to control the size and morphology of the synthesized ZnO nanostructures. The structural and physical investigations of synthesized ZnO nanostructures were done using UV–Vis spectrophotometry, scanning electron microscopy (SEM) equipped with energy dispersive X-ray analysis (EDXA) and X-ray powder diffraction (XRD). The XRD analysis indicated an enhancement in crystallite size from 16.14[Formula: see text]nm to 17.98[Formula: see text]nm, which further increased to 43.39[Formula: see text]nm as the concentration of [Formula: see text] was varied from 5[Formula: see text]mL to 15[Formula: see text]mL (at the rate of increment of 5[Formula: see text]mL each time). While the crystallite decreases from 17.98[Formula: see text]nm to 4.75[Formula: see text]nm as the concentration of [Formula: see text] increases from 0.5[Formula: see text]g to 1.5[Formula: see text]g (at the rate of increment of [Formula: see text] 0.5[Formula: see text]g in each step). The maximum crystallite size of 47.15[Formula: see text]nm was observed in ZnO.7 at room temperature and decreased with increment of temperature. Optical absorption spectra showed that ZnO.5 has a maximum direct bandgap of 3.68[Formula: see text]eV at [Formula: see text]C with the concentration of [Formula: see text] and [Formula: see text] of 10[Formula: see text]mL and 1.5[Formula: see text]g respectively. Overall, the study demonstrated that the size, morphology and optical properties of ZnO nanostructures can be effectively controlled by varying temperature, the concentration [Formula: see text] and [Formula: see text] during the synthesis process. The results could have significant implications for the development of ZnO-based nanoelectronics devices and other applications.
Hybrid optimal load balancing with modified fuzzy clustering-based virtual machine categorization
Authors: More Nitin Sudam , Borhade Ratnaprabha Ravindra , Apare Ravindra Sadashivrao , Bangare Manoj Limchand , Bangare Pushpa Manoj
Published: January 2025

In cloud computing, load balancing is crucial for effective resource management. Keeping servers from getting overworked entails dividing up incoming network traffic or computational tasks among several servers. Better resource management, increased throughput, and quicker reaction times result from this. Several heuristic and metaheuristic techniques have been used to disperse the load across the available virtual machines. Researchers have worked very hard to find a solution for the load balancing issue. This paper uses the following stages to develop a novel load-balancing model with optimization assistance: Virtual Machine (VM) classification, load balancing, and replica management are the three main processes. For the VM classification process, a modified version of the fuzzy clustering approach is suggested. For the load balancing procedure, the COOT Insisted Bald Eagle Search (COOTIBES) model is suggested. This optimization-assisted load balancing takes into account a number of limitations, including frequency, makespan, memory usage, resource utilization, and execution time. Additionally, the suggested COOTIBES algorithm manages replicas while taking load, put cost, and storage cost into account. Lastly, using various performance indicators, the suggested work’s performance is contrasted with traditional models. While the Inquisitive Genetic Algorithm with Grey Wolf Optimization Algorithm (IG-GWO), Ant Colony Optimization for Continuous Domains (ACOR), Life Choice-Based Optimizer (LCBO), Cat Swarm Optimization (CSO), Genetic Algorithm Combined with First Come First Serve + Genetic Algorithm Combined with Round Robin (GA-FCFS[Formula: see text]+[Formula: see text]GA-RR), Jellyfish Optimization (JFO), and Namib Beetle Optimization (NBO) offer bigger makespan values, the COOTIBES scheme gives a smaller makespan of 180 for a task count of 2000.
Synergetic enhancement of electric polarization and magnetization in Fe-doped Pb(Zr0.52Ti0.48)O3 modulated with “magnetoelectric composite” defect-dipoles
Authors: Chen Long , Wu Yao , Ul Hasnain Bakhtiar Syed , Yang Ying , Huang Hao , Xia Zhengcai , Zhang Guangzu , Jiang Shenglin , Dong Wen , Fu Qiuyun
Published: January 2025

Multiferroics have many novel applications such as multifunctional photo-magneto-electric sensors and ultra-low power consumption storage. However, it is still a challenge to couple magnetic order and ferroelectric order for all-in-one materials that are highly piezoelectric multiferroics. In this work, room temperature multiferroicity as well as piezoelectricity were realized in Fe-doped Pb(Ti[Formula: see text]Fe[Formula: see text]Zr[Formula: see text]O3 ([Formula: see text]–[Formula: see text]). An increase in the remnant magnetization (from 0 to 44.5[Formula: see text]memu/g) is accompanied by an increase in [Formula: see text] (from 17 to 65[Formula: see text]pC/N) which is achieved with an optimal doping level. In addition, broad UV–Vis–NIR light absorption was achieved (200–2500[Formula: see text]nm). The coupling of intrinsic ferroelectric polarization with defect dipoles formed by Fe and oxygen vacancy should be mainly responsible for the magnetization enhancement at appropriate Fe concentration. The defect energy levels induced by defect dipole contribute to a broad UV–Vis–NIR light absorption. This material has the potential for multifunctional photo-magneto-electric sensors and storage technology.
Machine Learning-Based Approach for the Gambling Problem Identification
Authors: Kozak Jan , Probierz Barbara , Juszczuk Przemysław , Dziczkowski Grzegorz , Jach Tomasz , Stefański Piotr , Głowania Szymon , Hrabia Anita , Wołek Gabriel , Sznapka Wojciech , Świerk Łukasz , Joniec Natalia
Published: January 2025

Watching sports matches is a beloved pastime for every fan, and predicting the results and betting on which team will win adds to the excitement. Unfortunately, for some users, a lack of self-control can turn initially innocent play into a long-term problem. For this reason, problem gambling is among the crucial issues facing modern societies. Bookmaker companies, therefore, put considerable effort into implementing responsible gambling practices; however, identifying at-risk individuals remains a major challenge. Our paper focuses on identifying users who may currently have, or soon develop, potential issues with gambling. To achieve this, we introduce a set of machine learning methods combined with preprocessing tools that allow us to initially acquire and anonymize user data. This anonymized database is then used to identify high-risk groups. We tested our approach on a large dataset that was obtained and preprocessed specifically for this study. The experiments were conducted on actual data and verified by specialists responsible for identifying gambling problems. Using our method, we successfully identified and detected the early signs of potential gambling problems in multiple users.
Advanced Skin Cancer Classification Using Yet Another Hill Climbing
Authors: Takefuji Yoshiyasu
Published: January 2025

This paper introduces a novel hill-climbing technique aimed at maximizing the prediction accuracy in skin cancer classification. It highlights the efficacy of epoch–baseline termination management coupled with data augmentation, especially when dealing with the severely imbalanced HAM10000 data set. The method alters the hill-climbing function’s termination condition, enabling the system to evade local minima and find superior solutions. Termination conditions using baseline and epochs significantly enhance prediction accuracy, while data augmentation balances the data set. The method achieves a prediction accuracy of over 0.99 in classifying seven types of skin cancers using the HAM10000 data set, as validated by repetitive random cross-validation and the confusion matrix. The paper concludes that the proposed method, when combined with data augmentation, can enhance deep learning and is applicable to oncology classification. The advanced skin classification algorithm proposed herein has achieved the highest prediction accuracy in the benchmark with the HAM10000 data set, which can be used for cancer research classification in general.
Law and Morality: The Decisive Oath as a Means of Resolving Legal Disputes
Authors: Coşgel Metin M. , Miceli Thomas J.
Published: January 2025

How does a society resolve legal disputes lacking evidence? Although such cases would typically be dismissed by modern courts, an interesting but little-known alternative element observed in some legal systems is the option to request a decisive oath in a lawsuit. Distinct from ordinary (testimonial) oaths that are routinely administered to witnesses at trial with the aim of eliciting only truthful testimony, a decisive oath can end a lawsuit in cases where the plaintiff has no evidence. We argue that the oath option may improve on burden-of-proof rules because standard theory overlooks the possibility that some defendants will truthfully admit their guilt after having professed an oath to be truthful. The reason is that people with strong religious or moral beliefs have internalised an ethic of honesty that overrides material consequences. We show that when the fraction of such people is large enough, the oath option achieves a more accurate resolution of disputes than burden-of-proof rules. Evidence from the Ottoman courts in Istanbul during the 16th and 17th centuries supports the argument regarding the positive relationship between religiosity and the use of the decisive oath for dispute resolution. The viability of the decisive oath declined in recent centuries, despite remaining ‘on the books’ in some countries. We suggest that its global demise can be attributed to rising intellectual opposition, increasing complexity of laws and/or legal disputes, falling cost of gathering evidence, and a general trend toward the separation of religion and state.
Detecting Empathy in Roman Urdu Using Transfer Learning
Authors: Sattar Hafsa , Munir Mubasher , Malik Muhammad Kamran , Nasar Zara
Published: January 2025

Empathy means to understand the other person’s feelings and emotions to become familiar with their situation. This work aims to create a Roman Urdu empathy dataset containing two types of sentences, either “Depressed” or “Not-Depressed,” and execute different machine learning, deep learning, and pre-trained models on the dataset for experimentation. The dataset has been created by extorting depressed Roman Urdu and motivational sentences from web pages and social media websites such as Facebook, Twitter, and YouTube. Multiple evaluation results led to the observation that the extraction of attributes from the union of word- and character-level features improves the accuracy of machine learning algorithms. The extraction of features by the unification of character-level unigram, bigram, trigram, and 5-gram feature extraction methods demonstrated improved results. In contrast, character-level unigrams and word-level trigrams have retrieved fewer significant parameters from sentences. Naive Bayes and Random Forest have exhibited the lowest accuracies on word-level, character-level, and the union of word- and character-level features as well. However, Logistic Regression, Linear Support Vector Classifier, and Support Vector Machine have obtained better accuracy in all three feature extraction methods. The perceptron-based algorithm has illustrated improved results than most of the machine learning methods. Almost all deep learning algorithms produce results that are a step ahead of those of machine learning and perceptron-based models. Analysis of deep learning experiments shows that FastText word embeddings support the models in achieving high accuracy. Recurrent Neural Network, and a combination of Convolutional Neural Network and Gated Recurrent Unit, performed well overall on all word and sentence embeddings. However, Recurrent Convolutional Neural Network had the lowest final results across all three types of embeddings. Observing the outputs of pre-trained models revealed that they produce more precise findings than many machine learning and deep learning models. In comparison to all pre-trained models, Universal Language Model Fine-Tuning has outperformed the rest. Hence, the results show that pre-trained language models have produced outstanding results on the Roman Urdu empathy dataset.
PersianMHQA: A Dataset for Open Domain Persian Multi-hop Question Answering Based on Wikipedia Encyclopedia
Authors: Taji Mobina , Ghafouri Arash , Naderi Hasan , Minaei-Bidgoli Behrouz
Published: January 2025

Today, one of the most important tasks in natural language processing is answering user questions. Especially, users' questions nowadays moved from simple questions to complex questions. In recent years, several question answering datasets have been produced for Persian language, but none of them support complex open-domain and explainable questions. In this article, the PersianMHQA dataset is introduced which is the first open-domain question answering dataset for complex questions based on the unstructured Persian Wikipedia encyclopedia. This dataset contains 7,000 complex questions and sentence-level supporting facts are provided for each question that allows question answering systems to explain the predictions. The questions in this dataset are diverse and explainable and are not limited to any previous knowledge base. Various types of complexity are provided in this dataset, and the questions are designed in such a way that answering them requires reasoning over more than one paragraph.
Ge'ez Grammar Error Handling Using Neural Machine Translation Approach
Authors: Emiru Eshete Derb , Wendyifraw Desalegn Mamo
Published: January 2025

The goal of natural language processing (NLP), which has recently gained popularity, is to improve the capacity of computers to comprehend and interact with human language. Consequently, in order to converse using natural language, it's crucial that spoken language be grammatically correct, especially for Ge'ez language. Geéz language sentences must follow certain norms of agreement in terms of number, person, gender, tense, and other factors in order to be considered grammatically correct. If the input sentence in Geéz language is improper, it can have problems with subject-verb agreement, object-verb agreement, adjective-noun agreement, and adverb-verb agreement. The goal of the proposed work is to provide a neural machine translation approach for detecting and correcting grammar errors in Ge'ez sentences. We have prepared manually 11490 Geéz parallel corpuses (Geéz language grammatically incorrect and grammatically correct sentences). After we have prepared a parallel Ge'ez sentence, we have used normalization, tokenization, padding, and one hot encoding as preprocesses. We have used two deep learning algorithms, including a bidirectional long-short-term memory encoder decoder and a long-short-term memory encoder decoder, for training the proposed model. Keras and TensorFlow were used for importing the required libraries, and we used the Python 3.7 environment for implementation. Two test cases are used for the evaluation technique. The first one is for the long-short-term memory encoder decoder model, and the second one is for the bidirectional long-short-term memory encoder decoder model. Finally, the bidirectional long short-term memory encoder decoder model achieved best results with an accuracy of 82%, recall of 82%, precision of 85%, and f1 measure of 83% with balanced error type classes.
LLMs and Their Applications in Medical Artificial Intelligence
Authors: Mao Wenji , Qiu Xipeng , Abbasi Ahmed
Published: January 2025

Medical artificial intelligence (AI) is a cross-disciplinary field focused on developing advanced computing and AI technologies to benefit medicine and healthcare. Globally, medical AI has tremendous potential to support the United Nations’ sustainable development goals pertaining to health and well-being. In particular, large language models (LLMs) afford opportunities for positively disrupting medical AI-related research and practice. We present a research framework for LLMs in medical AI. Our framework considers the interplay between health and well-being goals, disease lifecycle stages, and the important emerging role of LLMs in medical AI processes related to various lifecycle stages. As part of our framework, we describe the LLM multiplex - important multimodal, multi-model, multicultural, and multi-responsibility considerations for LLMs in medical AI. We discuss how the five articles in the special issue relate to this framework and are helping us learn about the opportunities and challenges for LLMs in medical AI.
Not fleeting but lasting: limited influence of aging on implicit adaptative motor learning and its short-term retention
Authors: Hermans Pauline , Vandevoorde Koen , Orban de Xivry Jean-Jacques
Published: January 2025

In this study, we tested whether aging influences the ability to learn to counteract a perturbation during reaching movements and to recall previously learned motor memories. In contrast to a previously published paper, we found that the ability of older participants to adapt to a perturbation and to recall motor memories remains unimpaired.
Nitrogen Utilization by Grain Sorghum as Affected by Rate of Nitrogen Fertilization and Plant Density: A Preliminary Study
Authors: Tóth Ariel , Tóth Zoltán
Published: January 2025

Grain sorghum (Sorghum bicolor (L.) Moench) has become a popular alternative to maize in drought‐stricken areas of Hungary. A three‐factorial small plot field experiment was conducted in Keszthely (Hungary) in order to examine the effects of plant density (D1 = 240,000, D2 = 280,000 plants ha−1) and nitrogen (N) application (N0–N5 = 0−40−80−120−160−200 kg ha−1) on the N utilization characteristics and grain content indicators of four commercial grain sorghum cultivars (G1 = KWS Nemesis, G2 = RTG Huggo, G3 = GK Erzsébet, G4 = ES Foehn). The results revealed that the utilization of applied N was better in D2, which also resulted in a significant increase in most quantitative parameters. This can be verified based on generally higher total N uptake, nitrogen harvest index (HIN%), nitrogen utilization efficiency (NUtE), and agronomic N use efficiency (AEN) values. Plant density treatment did not have a significant effect on grain N content (p = 0.096), but compared to D1, the densification of grain sorghum significantly reduced crude protein (p  < 0.001) and starch (p  < 0.001) contents. An inverse proportional relationship between the evolution of grain protein and starch contents was also found. Optimum N rates varied between 120 and 160 kg N ha−1 for G1 and G2, both in D1 and D2, 40–80 kg N ha−1 for G3 in D1 and 80–120 kg N ha−1 in D2 and 120–160 kg N ha−1 for G4 in D1 and 160–200 kg N ha−1 in D2. In conclusion, a genotype‐specific approach in the optimization of Hungarian grain sorghum cultivation technology is required to ensure potential performance and to reduce N loss.
An Unusual and Severe Thyrotoxicosis in a Twin Pregnancy: Fortune Favors the Brave
Authors: Maltese Virginia , Gatta Elisa , Silvestrini Irene , Anelli Valentina , Bambini Francesca , Buoso Caterina , Cavadini Maria , Ugoccioni Massimiliano , Saullo Maura , Marini Fiorella , Gandossi Elena , Delbarba Andrea , Pirola Ilenia , Cappelli Carlo
Published: January 2025

Graves’ disease (GD) and gestational transient thyrotoxicosis (GTT) are the most common causes of thyrotoxicosis during pregnancy, with prevalence ranging from 0.1% to 1% and from 1% to 3%, respectively. Hyperthyroidism during pregnancy can have severe consequences if not promptly recognized and treated. Even more severe, if possible, is the thyroid storm, a life‐threatening complication of hyperthyroidism, characterized by severe and dramatic clinical manifestations of thyrotoxicosis. No prior history of thyroid disease, absence of GD stigmata, negative thyrotropin (TSH) receptor antibody levels, serum human chorionic gonadotropin (hCG) higher on average, and symptoms of emesis may lead to the diagnosis of GTT. Few cases of thyroid storm during pregnancy are reported in literature, mainly due to gestational trophoblastic disease. We report a rare and severe case of thyroid storm in a 24‐year‐old woman at 15 weeks’ gestation with twins, likely due to GTT, precipitated by acute myocarditis. Initially presenting with weakness, vomiting, and sinus tachycardia, the patient rapidly deteriorated into a life‐threatening condition characterized by hypokalemia, myocardial injury, and severe thyrotoxicosis. Cardiac imaging later revealed acute myocarditis. Thyroid function stabilized at the end of the pregnancy, allowing discontinuation of methimazole. Both fetuses were delivered via emergency cesarean section at 36 weeks, with no significant congenital abnormalities. This case highlights the complexity of diagnosing and managing hyperthyroidism in twin pregnancies, particularly in the context of hyperemesis gravidarum (HG).
Comparative Analysis of Hydration Status and Microbial Quality of Tap Water Between Urban and Rural Settings in the Ashanti Region of Ghana
Authors: Tandoh Marina A. , Owusu Priscilla , Nkrumah Claire-Rush N. A. , Annaful Veronica Tawiah , Asare Chris Y. , Attu Samuel Selorm
Published: January 2025

Maintaining proper hydration is crucial for human health, physiological functions, and cognitive performance. This study sought to determine the hydration status and the microbial safety of tap water in an urban setting (Kwame Nkrumah University of Science and Technology Campus) and a rural community (Adjamesu) in the Ashanti Region of Ghana. The water safety perception and hydration status of 171 study participants aged 10–61 years were assessed. Six taps were randomly selected at each site in triplicates, resulting in 36 water samples. The microbial quality of the water was assessed by testing for heterotrophic bacteria, coliforms, Staphylococcus aureus, and Salmonella. The physicochemical quality of the water samples was assessed with a pH meter. About 68.9%, 20%, and 11.1% of participants were minimally dehydrated, significantly dehydrated, and well hydrated, respectively. Furthermore, 86.7% of the urban participants did not depend on tap water (p < 0.001) compared to 46.9% of the rural participants (p = 0.579). Moreover, 78.9% of the urban participants did not trust the quality of tap water (p < 0.001), compared to 38.3% of rural dwellers (p = 0.035). The water microbial quality assessment of both the rural and urban water samples indicated the presence of some heterotrophic bacteria at similar levels (p = 0.0649) between 7.0 × 100 ± 1.15 and 5.93 × 103 ± 5.51 cfu/mL. Some coliforms in the samples exceeded safe limits with those of the rural communities recording higher levels of contamination. Further assessment revealed the absence of pathogenic bacteria such as Escherichia coli, Salmonella, or Staphylococcus aureus. However, regular monitoring and evaluation of tap water quality are crucial, given its significance as a primary hydration source for the general population. Additionally, it would be advisable for consumers to treat their water further when using it for cooking and drinking to prevent any adverse health effects.
Caffeine Promotes Adipocyte Autophagy Through the AMPK/SIRT1 Signaling Pathway and Improves High‐Fat Diet–Induced Obesity and Leptin Resistance
Authors: Yinghao Wang , Wenyuan Peng , Chunfeng Li , Jingning Yu , Jun Sheng , Chengting Zi , Xiaoyun Wu
Published: January 2025

Purpose: Caffeine has been widely studied for its lipid‐lowering and blood‐glucose–lowering effects. This study aimed to investigate whether caffeine can regulate adipocyte autophagy through the AMPK/SIRT1 signaling pathway, both in vivo and in vitro, thereby exerting a hypoglycemic effect.

Method: Western blot analysis was conducted to assess changes in the expression levels of FASN, ACC, pACC, SIRT1, LC3II/I, and pAMPKα in 3T3‐L1 cells following caffeine treatment. The size of the lipid droplets was evaluated using Oil Red O staining. Mito Tracker Red and qRT‐PCR were employed to quantify the number of mitochondria in the cells. Six‐week‐old C57BL/6J mice were divided into three groups: control, high‐fat diet (HFD), and HFD + caffeine. After 11 weeks of feeding, insulin resistance was assessed using insulin tolerance and glucose tolerance tests. The expression levels of AMPKα, SIRT1, PPARγ, P62, and LC3II/I in the adipose tissue of each group were measured using Western Blotting. Serum levels of triglycerides (TG), total cholesterol (TC), and leptin were determined by Elisa. Finally, hematoxylin and eosin (HE) staining was performed to observe the size of adipocytes in visceral adipose tissue.

Results: Caffeine significantly inhibits the expression of proteins associated with fat synthesis and reduces lipid droplet size in 3T3‐L1 cells. In addition, caffeine robustly activates the AMPK/SIRT1 signaling pathway in 3T3‐L1 cells, promoting autophagy and enhancing mitochondrial biogenesis. In animal models, caffeine effectively attenuates weight gain and insulin resistance induced by a HFD while reducing serum TG and TC levels. Furthermore, caffeine suppresses leptin resistance and mitigates the increased food intake associated with a HFD. Notably, caffeine enhances AMPKα phosphorylation levels and SIRT1 expression in visceral fat, facilitating adipocyte autophagy and reducing lipid accumulation. At the cellular level, caffeine significantly promotes mitochondrial biogenesis.

Conclusion: Caffeine enhances autophagy in adipocytes by activating the AMPK/SIRT1 signaling pathway of adipocytes, contributing to antiobesity effects and improving insulin resistance.
Chronic Cigarette Smoke Exposure Masks Pathological Features of Helicobacter pylori Infection While Promoting Tumor Initiation
Authors: Morris Maeve T. , Duncan Benjamin C. , Piazuelo M. Blanca , Olfert I. Mark , Xu Xiaojiang , Hussain Salik , Peek Richard M. , Busada Jonathan T.
Published: January 2025

Gastric cancer is the fifth most common cancer and the fifth leading cause of cancer deaths worldwide. Chronic infection by the bacterium Helicobacter pylori is the most prominent gastric cancer risk factor, but only 1% to 3% of infected individuals will develop gastric cancer. Cigarette smoking is another independent gastric cancer risk factor, and H. pylori–infected smokers are at a 2- to 11-fold increased risk of gastric cancer development, but the direct impacts of cigarette smoke (CS) on H. pylori pathogenesis remain unknown. In this study, male C57BL/6 mice were infected with H. pylori and began smoking within 1 week of infection. The mice were exposed to CS 5 days/week for 8 weeks. CS exposure had no notable impact on gross gastric morphology or inflammatory status compared with filtered-air (FA) exposed controls in mock-infected mice. However, CS exposure significantly blunted H. pylori–induced gastric inflammatory responses, reducing gastric atrophy and pyloric metaplasia development. Despite blunting these classic pathological features of H. pylori infection, CS exposures increased DNA damage within the gastric epithelial cells and accelerated H. pylori–induced dysplasia onset in the INS-GAS gastric cancer model. These data suggest that cigarette smoking may clinically silence classic clinical symptoms of H. pylori infection but enhance the accumulation of mutations and accelerate gastric cancer initiation.

Prevention Relevance: These findings suggest that cigarette smoking suppresses pathophysiological hallmarks of H. pylori infection while accelerating gastric carcinogenesis. Therefore, smokers should receive screening for H. pylori infection to reduce gastric cancer risk.
Supplemental Table 1 from Clonal Hematopoiesis of Indeterminate Potential and its Association with Treatment Outcomes and Adverse Events in Patients with Solid Tumors
Authors: Krishnan Tharani , Solar Vasconcelos Joao Paulo , Titmuss Emma , Vanner Robert J. , Schaeffer David F. , Karsan Aly , Lim Howard , Ho Cheryl , Gill Sharlene , Yip Stephen , Chia Stephen K. , Kennecke Hagen F. , Jonker Derek J. , Chen Eric X. , Renouf Daniel J. , O’Callaghan Chris J. , Loree Jonathan M.
Published: January 2025

TRIPOD checklist
Active Components in Internet-Based Cognitive-Behavioral Therapy for Social Anxiety Disorder: A Randomized Full Factorial Trial
Authors: Šipka Dajana , Lopes Rodrigo , Krieger Tobias , Klein Jan Philipp , Berger Thomas
Published: January 2025

Introduction: Many studies have demonstrated that social anxiety disorder (SAD) can be effectively treated with psychotherapy, particularly cognitive-behavioral therapy (CBT), including internet-based CBT (ICBT). Despite evidence-based treatments, many individuals do not sufficiently benefit from them. Identifying the active components could help improve the effectiveness of SAD treatment. This study tested the effects of four treatment components (psychoeducation, cognitive restructuring, attention training, and exposure) within ICBT for SAD to investigate its active components. Methods: This randomized full factorial trial consisted of four factors (i.e., treatment components) and 16 conditions. A total of 464 adults with a diagnosed SAD were recruited from the community. The primary outcome was SAD symptoms at 8 weeks (post-assessment). Secondary outcomes included SAD diagnosis, SAD symptoms at follow-up (4 months after post), depression and anxiety symptoms, quality of life, client satisfaction, and adverse effects. Results: Conditions including psychoeducation and exposure were significantly more effective in reducing SAD symptoms at post compared to conditions without these components. Conditions including cognitive restructuring and attention training did not show superiority over conditions without them at post. However, all treatment conditions significantly reduced symptoms compared to the condition without a treatment component. At follow-up, the superiority of psychoeducation and exposure was not significant anymore due to the version without the respective components catching up. Conclusion: The findings suggest that while all treatment components of ICBT for SAD are beneficial compared to no treatment, psychoeducation and exposure include specific active components that significantly improve treatment outcomes more quickly in ICBT for SAD.
An unusual retinal presentation of a novel COL11A1 mutation: A case report.
Authors: Peschaut Tobias , Michelitsch Monja , Brandner Martina , Kamper Sandra , Ofner-Ziegenfuss Lisa , Blatterer Jasmin , Tichy Heidelis Anna , Posch-Pertl Laura
Published: January 2025

Introduction:
Stickler syndrome is a rare collagenopathy, caused by mutations in various genes coding for fibrillar collagens II, IX and XI. The disorder can be subdivided into different groups, depending on the genes affected and clinical features found in patients. Ocular symptoms, such as high myopia, retinal detachments, or anomalies in the vitreous, are present in most forms of Stickler syndrome. In this case report, we present a patient with an unusual retinal phenotype. Case presentation:
Subject of this case report is a 33-year-old woman, who was examined at the Department of Ophthalmology at Medical University of Graz. A thorough ophthalmological examination was conducted, detailed medical and family history acquired, and genetic testing performed. Best corrected visual acuity was 20/20 on both eyes, however, impaired binocular vision associated with intermittent exotropia were found. Furthermore, dilated fundoscopy showed an unusual, hypopigmented spotted retinal phenotype. Fundus autofluorescence showed multiple hyperfluorescent spots corresponding with the spotted retinal appearance. Genetic testing revealed a novel variant in the gene COL11A1. No other ocular abnormalities which are associated with COL11A1 were found.
Conclusions:
Several subtypes of Stickler syndrome have been reported in medical literature, greatly varying in clinical manifestations. Many different mutations in the gene COL11A1 have been discovered and are typically associated with Stickler syndrome type 2. To our best knowledge, this is the first report of a patient with a mutation in the COL11A1 gene presenting with an hypopigmented spotted retina.
Organization of the perioral representation of the primary somatosensory cortex in prairie voles (Microtus ochrogaster)
Authors: Pineda Carlos R. , Bresee Chris , Baldwin Mary K.L. , Seelke Adele M.H. , Krubitzer Leah
Published: January 2025

Prairie voles (Microtus ochrogaster) are one of the few mammalian species that are monogamous and engage in the biparental rearing of their offspring. Biparental care impacts the quantity and quality of care the offspring receives. The increased attention by the father may translate to heightened tactile contact the offspring receives through licking and grooming. In the current study, we used electrophysiological multiunit recording techniques to define the organization of the perioral representation in the primary somatosensory area (S1) of prairie voles. Functional representations were related to myeloarchitectonic boundaries. Our results show that most of S1 is occupied by the representation of the contralateral mystacial whiskers and the lower and upper lips. The mystacial vibrissae representation encompassed a large portion of the caudolateral S1, while the representation of the lower and upper lips occupied a large portion of the rostrolateral aspect of S1. We found that neuronal populations representing the perioral structures tended to have small receptive fields relative to other body part representations on the head. The representation of the mystacial whiskers and perioral structures was coextensive with cytoarchitectonically defined barrel fields that extend from the caudolateral to a rostrolateral aspect of S1. We discuss our findings in the context of the magnification of behaviorally relevant sensory surfaces in other rodents, the ubiquity of the barrel systems in rodents, and behaviors associated with specialized sensory surfaces.
Lungenfibrose bei systemischer Sklerose: to treat or not to treat
Authors: Xanthouli Panagiota
Published: January 2025

Background: Interstitial lung disease (ILD) is the leading cause of death in systemic sclerosis (SSc). According to expert statements, not all SSc-ILD patients require pharmacological therapy. Objectives: To describe disease characteristics and disease course in untreated SSc-ILD patients in two well characterised SSc-ILD cohorts. Methods: Patients were classified as treated if they had received a potential ILD-modifying drug. ILD progression in untreated patients was defined as (1) decline in forced vital capacity (FVC) from baseline of ≥ 10% or (2) decline in FVC of 5%–9% associated with a decline in diffusing capacity for carbon monoxide (DLCO) ≥ 15% over 12 ± 3 months or (3) start of any ILD-modifying treatment or (4) increase in the ILD extent during follow-up. Multivariable logistic regression was performed to identify factors associated with non-prescription of ILD-modifying treatment at baseline. Prognostic factors for progression in untreated patients were tested by multivariate Cox regression. Results: Of 386 SSc-ILD included patients, 287 (74%) were untreated at baseline. Anticentromere antibodies (OR: 6.75 (2.16–21.14), p = 0.001), limited extent of ILD (OR: 2.39 (1.19–4.82), p = 0.015), longer disease duration (OR: 1.04 (1.00–1.08), p = 0.038) and a higher DLCO (OR: 1.02 (1.01–1.04), p = 0.005) were independently associated with no ILD-modifying treatment at baseline. Among 234 untreated patients, the 3 year cumulative incidence of progression was 39.9% (32.9–46.2). Diffuse cutaneous SSc and extensive lung fibrosis independently predicted ILD progression in untreated patients. Conclusion: As about 40% of untreated patients show ILD progression after 3 years and effective and safe therapies for SSc-ILD are available, our results support a change in clinical practice in selecting patients for treatment.