bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent advancements in the diverse roles of polymerase-associated proteins in the replication and pathogenesis of Newcastle disease virus
Authors: Zhou Jinghang , Duan Yuqi , Liu Menglan , Liu Jinyang , Hu Zenglei , Duan Zhiqiang
Published: January 2025

Newcastle disease virus (NDV) is a significant member of the Paramyxoviridae family, known for causing epidemics and substantial economic losses in the poultry industry worldwide. The NDV RNA genome primarily encodes six structural proteins (N, P, M, F, HN, and L) and two non-structural proteins (V and W). Among these, the polymerase-associated proteins (N, P, and L) and the viral RNA (vRNA) genome form the ribonucleoprotein complex, which plays a crucial role in the synthesis and transcription of NDV vRNA. In the last two decades, numerous studies have demonstrated that the polymerase-associated proteins are linked to the virulence, pathotype, and thermostability of NDV. Additionally, the interactions between these polymerase-associated proteins and host proteins are closely related to the NDV’s replication and pathogenicity. Despite significant progress in understanding the unique and shared functions of NDV polymerase-associated proteins, research on these viral proteins’ structure and function is less comprehensive than other NDV proteins, and the available information is often scattered. Therefore, this article systematically summarises and reviews the research progress made in understanding the structural features, virulence, pathotype, and thermostability correlation of NDV polymerase-associated proteins, as well as the critical roles of interactions between polymerase-associated proteins and host proteins in NDV replication and pathogenicity. This review aims to enhance our understanding of the complex functions of polymerase-associated proteins in NDV replication and pathogenesis and to contribute to the development of more effective vaccines and antiviral drugs against NDV challenges.
DNA methylation of ACADS promotes immunogenic cell death in hepatocellular carcinoma
Authors: Qian Ze , Jiang Yifan , Wang Yacong , Li Yu , Zhang Lin , Xu Xiaofeng , Chen Diyu
Published: January 2025

Background

Altered metabolism has become an important characteristic of cancer, and acyl-CoA dehydrogenase short-chain (ACADS), a regulator of lipid synthesis, is involved in carcinogenesis-associated metabolic pathways. DNA methylation is an important mechanism for silencing ACADS in various malignancies. However, the specific role of ACADS in hepatocellular carcinoma (HCC) pathogenesis remains poorly understood.

Methods and results

Using RNA sequencing data from different tumours in The Cancer Genome Atlas database, we observed that ACADS was downregulated and hypermethylated in HCC. Three potential CpG island sites (cg01535453, cg08618068, and cg10174836) were identified in the ACADS promoter. Through in vivo and in vitro experiments, we confirmed that cg08618068 was methylated in HCC. We defined this site as ACADS methylation site-2 (ACADS MS-2). Methylation of ACADS MS-2 was associated with worse survival, and mutation of MS-2 increased ACADS mRNA levels in five HCC cell lines. Sustained overexpression of ACADS not only suppressed the proliferation, migration, and invasion of HCC cells but also promoted immunogenic cell death (ICD) via the upregulation of calreticulin. Subsequently, we established a specific nomogram based on ACADS methylation levels to evaluate the 3- and 5-year overall survival rates of patients with HCC who underwent surgical resection.

Conclusions

Our work clarified that ACADS acts as a putative tumour suppressor in HCC and confirmed that a nomogram including ACADS methylation had good predictive performance in HCC. We also discovered a correlation between ACADS and ICD, suggesting that ACADS is an essential target for immunotherapy in HCC.

Graphical Abstract
Sound recurrence analysis for acoustic scene classification
Authors: Abeßer Jakob , Liang Zhiwei , Seeber Bernhard
Published: January 2025

In everyday life, people experience different soundscapes in which natural sounds, animal noises, and man-made sounds blend together. Although there have been several studies on the importance of recurring sound patterns in music and language, the relevance of this phenomenon in natural soundscapes is still largely unexplored. In this article, we study the repetition patterns of harmonic and transient sound events as potential cues for acoustic scene classification (ASC). In the first part of our study, our aim is to identify acoustic scene classes that exhibit characteristic sound repetition patterns concerning harmonic and transient sounds. We propose three metrics to measure the overall prevalence of sound repetitions as well as their repetition periods and temporal stability. In the second part, we evaluate three strategies to incorporate self-similarity matrices as an additional input feature to a convolutional neural network architecture for ASC. We observe the characteristic repetition of transient sounds in recordings of “park” and “street traffic” as well as harmonic sound repetitions in acoustic scene classes related to public transportation. In the ASC experiments, hybrid network architectures, which combine spectrogram features and features from sound recurrence analysis, show increased accuracy for those classes with prominent sound repetition patterns. Our findings provide additional perspective on the distinctions among acoustic scenes previously primarily ascribed in the literature to their spectral features.
Experimental Testing and Numerical Simulation of Recycled Concrete Aggregate in a Concrete Mix
Authors: Neupane Bini , Sahani Kameshwar , Khadka Shyam Sundar
Published: January 2025

This study focuses on exploring the potential of utilizing demolished concrete and promoting sustainable practices through the use of recycled concrete aggregate (RCA) as a substitute for natural aggregates, particularly in the context of Nepal. The region’s susceptibility to frequent earthquakes results in significant volumes of concrete rubble, posing challenges in waste disposal. To address this issue and mitigate resource depletion, the research focuses on concrete recycling. By conducting a thorough analysis of mechanical properties, crack patterns, strength variations, and specific gravity evaluations across different RCA compositions, the study emphasizes the ongoing endeavors toward sustainable concrete practices. A comparative examination of test results involving varying percentages of coarse recycled aggregate content (0%, 25%, 50%, 75%, and 100%) denoted as R0, R25, R50, R75 and R100, respectively, provides insights into the performance of different mixes. The compressive strength of cube for R25 increased by 20.13%, while R50 and R75 showed gains of 8.08% and 1.28%, respectively, while cylinder showed an increase of 25.86%, 18.88%, 9.54% and 2.65% for R25, R50, R75 and R100, respectively, compared to R0 concrete mix when tested at 28 days of curing. Tensile strength of concrete cylinder also improved, with R25 showing an 18.52% increase and R50 showing a 9.26% increase. Additionally, the RCA increased the flexural strength, with R25 leading with a 5% increase and R50 following with a 1.66% increase at 28 days of testing. The inclusion of numerical analysis in ABAQUS CAE using the Kent and Park Model serves to reinforce and support the experimental findings, establishing the credibility of both approaches. In essence, the study strongly advocates for the integration of recycled aggregate in concrete as a means to foster sustainable development and environmentally friendly construction methods.
Combinatorial functionomics identifies HDAC6-dependent molecular vulnerability of radioresistant head and neck cancer
Authors: Chan Sharon Pei Yi , Yeo Celestia Pei Xuan , Hong Boon Hao , Tan Evelyn Mui Cheng , Beh Chaw Yee , Yeo Eugenia Li Ling , Poon Dennis Jun Jie , Chu Pek Lim , Soo Khee Chee , Chua Melvin Lee Kiang , Chow Edward Kai-Hua
Published: January 2025

Background

Radiotherapy is the primary treatment modality for most head and neck cancers (HNCs). Despite the addition of chemotherapy to radiotherapy to enhance its tumoricidal effects, almost a third of HNC patients suffer from locoregional relapses. Salvage therapy options for such recurrences are limited and often suboptimal, partly owing to divergent tumor and microenvironmental factors underpinning radioresistance. In this study, we utilized a combinatorial functionomics approach, the Quadratic Phenotypic Optimization Platform (QPOP), to rationally design drug pairings that exploit the molecular fingerprint and vulnerability of established in vitro isogenic radioresistant (RR)-HNC models.

Methods

A QPOP-specific protocol was applied to RR-HNC models to rank and compare all possible drug combinations from a 12-drug set comprising standard chemotherapy, small molecule inhibitors and targeted therapies specific to HNC. Drug combination efficacy was evaluated by computing combination index scores, and by measuring apoptotic response. Drug targeting was validated by western blot analyses, and the Comet assay was used to quantify DNA damage. Enhanced histone deacetylase inhibitor (HDACi) efficacy in RR models was further examined by in vivo studies, and genetic and chemical inhibition of major Class I/II HDACs. Regulatory roles of HDAC6/SP1 axis were investigated using immunoprecipitation, gel shift and ChIP-qPCR assays. Comparative transcriptomic analyses were employed to determine the prognostic significance of targeting HDAC6.

Results

We report the therapeutic potential of combining panobinostat (pan-HDAC inhibitor) with AZD7762 (CHK1/2 inhibitor; AstraZeneca) or ionizing radiation (IR) to re-sensitize RR-HNC cells and showed increased DNA damage underlying enhanced synergy. We further refined this RR-specific drug combination and prioritized HDAC6 as a targetable dependency in reversing radioresistance. We provide mechanistic insights into HDAC6-mediated regulation via a crosstalk involving SP1 and oncogenic and repair genes. From two independent patient cohorts, we identified a four-gene signature that may have discriminative ability to predict for radioresistance and amenable to HDAC6 inhibition.

Conclusion

We have uncovered HDAC6 as a promising molecular vulnerability that should be explored to treat RR-HNC.
Landslide susceptibility assessment for Uttarakhand, a Himalayan state of India, using multi-criteria decision making, bivariate, and machine learning models
Authors: Chauhan Vipin , Gupta Laxmi , Dixit Jagabandhu
Published: January 2025

Background

Landslides, among the most catastrophic natural hazards, result from natural and anthropogenic factors, causing substantial financial losses, infrastructural damage, fatalities, and environmental degradation. Uttarakhand, with its unique topographical and hydrological conditions, unplanned human settlements, and changing precipitation patterns, is highly susceptible to landslides.

Methods

This study evaluates landslide susceptibility for Uttarakhand, a Himalayan state in India, by employing bivariate analysis, multi-criteria decision-making, and advanced machine learning models, such as Random Forest and Extreme Gradient Boosting (XGBoost). A total of sixteen landslide influencing factors were used for performing landslide hazard susceptibility zonation, including the innovative use of geomorphons for detailed terrain analysis.

Results

Approximately 18.47% of the study area was classified as high to very high landslide susceptibility zones, and 21% was classified into the moderate susceptibility category. High to very high susceptibility zones were concentrated in the Uttarkashi, Chamoli, and Pithoragarh districts of the Lesser and Higher Himalayas, areas characterized by rangelands and high annual rainfall. Conversely, very low to low susceptibility zones were predominantly located in the Tarai-Bhabar and Sub-Himalayan districts, including Haridwar and Udham Singh Nagar. The Random Forest and XGBoost models demonstrated superior predictive performance.

Conclusions

The spatially explicit landslide susceptibility maps provide critical insights for urban planners, disaster management agencies, and environmentalists, aiding in developing effective strategies for landslide risk reduction and promoting sustainable development in Uttarakhand. This study exemplifies applying advanced analytical techniques to address landslide susceptibility and related soil erosion and water resource management challenges in Uttarakhand.
Copula-based trading of cointegrated cryptocurrency Pairs
Authors: Tadi Masood , Witzany Jiří
Published: January 2025

This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
Unleashing the green potential: exploring the dynamic influence of the urban digital economy on carbon emissions
Authors: Sun Xin , Rui Xueyu , Cui Zhikun , Taghizadeh-Hesary Farhad , Zhao Xin
Published: January 2025

In the backdrop of “dual-carbon” strategic objectives, understanding the influence of the digital economy (DE) on carbon emissions (CEs) is imperative. However, there is limited research on the DE’s negative impact on CEs and the nonlinear relationship between the DE and CE. To address this gap, we collected data from 270 Chinese cities from 2011 to 2021 and used benchmark regression, mediated effects, and panel threshold models to explore the DE’s impact on CEs. The results showed that DE had a nonlinear, inverted U-shaped effect on CEs, with CEs initially increasing and then being suppressed. This conclusion remained consistent even after a series of robustness tests. Overall, the rate of urbanization and breadth of digital financial coverage mediate the relationship between the DE and CEs. Additionally, the combined effects of economic development, environmental regulation, fiscal decentralization, and population size contribute to the DE’s nonlinear impact on CEs. The impact of the DE on CEs varies among nonresource-based, resource-based, and resource-depleted cities and between urban and nonurban agglomerations. This paper’s findings support the development of the DE and the formulation of CE reduction policies.
Reusable and robust fuzzy extractor for CRS-dependent sources
Authors: Ma Yucheng , Shen Peisong , Tian Xue , Lv Kewei , Chen Chi
Published: January 2025

Fuzzy extractors allow for the extraction and reproduction of a nearly uniform string from a noisy and non-uniform source. Reusable and robust fuzzy extractors further require that the output string should remain pseudorandom under multiple extractions and any modification of public value should be detectable. Existing constructions of reusable and robust fuzzy extractors are all designed in the Common Reference String (CRS) model and work only for CRS-independent sources. In this work, we introduce a construction of reusable and robust fuzzy extractor for CRS-dependent sources. Our construction is built upon some well-studied cryptography, including a collision resistant hash function, a symmetric key encryption that keeps secure with respect to auxiliary input, a public key encryption and simulation-sound non-interactive zero-knowledge argument. We also present some instantiations of these primitives from Learning with Parity Noise (LPN) assumption and Short Integer Solution (SIS) assumption. These instantiations result in the first reusable and robust fuzzy extractor for CRS-dependent sources that can tolerate linear errors.
Dadih, traditional fermented buffalo milk: a comprehensive review of the aspects of gastronomy, health benefits, and product development
Authors: Pramana Angga , Yudhistira Bara , Jayalaksamana Maghfirah , Kurnia Dihan , Husnayain Naila , Pramitasari Rianita
Published: January 2025

Indonesia is a country with a rich array of traditional meals renowned for their health benefits and potential to serve as functional foods, including dadih. Several studies have shown that dadih is a traditional fermented product from Indonesia, with various culinary applications. In addition, it is typically produced from fermented buffalo milk, which forms an unchanged or broken lump when fermented at room temperature. This product is also widely consumed by the Minangkabau ethnic group in Central Sumatra (nowadays known as the provinces of West Sumatra, Riau, Kepulauan Riau, and Jambi), holding a significant position in their culinary practices. Dadih is often traditionally processed through spontaneous fermentation in bamboo containers without the need for additional inoculation with buffalo milk. The indigenous dadih lactic acid bacteria has been evaluated to have several advantages, including immunomodulatory, antioxidant, antimutagenic, hypocholesterolemic, and antimicrobial properties, by various mechanisms. In addition, this product offers significant nutritional content, promoting digestive health and enhancing the body’s resilience.
Least squares Marchenko imaging
Authors: Guan Shanshan , Liu Jinda , Huang Xingguo , Wang Naijian , Xu Yinpo
Published: January 2025

Multiple reflections significantly influence seismic migration because they can lead to spurious events in traditional methods, including reverse time migration and Kirchhoff migration. Marchenko imaging represents a transformative advancement in the field of seismic migration that relocates seismic data containing internal multiples to accurate subsurface locations. Through the continuous pursuit of methodological refinement, we developed an innovative approach: incorporation of the Marchenko method within a least-squares migration framework, named least-squares Marchenko imaging (LSMI), based on the steepest descent method to minimize the L1 norm of gradient images. In the LSMI, to solve the problem that the conventional Born approximation can only simulate primary waves, we used the Marchenko Greens function and the Born approximation to construct a Marchenko demigration operator, which can successfully simulate synthetic seismic data containing internal multiples. However, Marchenko demigration based on the Born approximation requires a large amount of calculation, and multiple iterations increase the computation time. Therefore, we introduce a parallel-computing framework implemented in the frequency domain to improve the computational efficiency of the Marchenko demigration. We partition the frequency-domain-transformed Greens function into discrete chunks and implement parallel computation. This parallel processing significantly accelerates the Marchenko demigration process. The effectiveness of the proposed method is validated by applying it to a layered model. The experimental results show that LSMI converges with the L1 norm. Moreover, the superiority of our method over conventional Marchenko imaging is evident from its notable enhancement related to artifact suppression, resolution improvement, and illumination compensation. To further validate the effectiveness of our proposed method, we conducted additional experiments on the Sigsbee2A model, demonstrating that our method is also effective for this complex model. This is a novel approach to seismic migration, particularly in areas with internal multiples.
Regularized Derivatives - Revisited
Authors: Karcol Roland , Pašteka Roman
Published: January 2025

The methods used for differentiation of potential field data play an important role in interpretation, as various derivatives are included in many of the transformations often used when interpreting such data (e.g.: Analytical Signal, Tilt Angles, Total Gradient and others). However, the evaluation of horizontal and vertical derivatives is an unstable process. The possible (partial) answer to this instability is the Tikhonov regularization based on incorporating an additional property representing maximum smoothness, which is typically obtained by cascading low-pass filtering - managed by means of a regularization parameter. Here we present and compare several ways to evaluate regularized differentiation operators and compare their properties. Among them, a newly introduced form (named as General Form) plays a very important role. As is typical for regularization algorithms, the crucial step is the setting of a quasi-optimal value of the regularization parameter, which gives the best possible solution. Several methods of its estimation (focused on the utilization of Lp norms) are presented and the resulting utility became the keystone in valuation of the presented regularized operators.
Gabor-wavelet-activation implicit neural learning for full waveform inversion
Authors: Yang Fangshu , Ma Jianwei
Published: January 2025

Seismic full waveform inversion (FWI) is an efficient imaging method for estimating subsurface physical parameters. However, when the initial models are inaccurate or seismic data lack low frequencies, most traditional discrete grid-based FWI algorithms often encounter local minimum problems. Additionally, the inversion performance and computational cost are closely related to spatial resolution. Reparameterizing velocity models using neural networks (NNs) effectively mitigates local minimum issues. However, most NN-FWI approaches perform well when overparameterized and are limited to fixed grid-like representations. In contrast, implicit neural learning (INL) enables the representation of models at any resolution using a multilayer perception (MLP) network that learns a continuous function from discrete coordinates. To enhance the capability of INL, we developed a general Gabor-wavelet-activation INL approach and applied it to FWI, referred to as wavelet implicit neural learning FWI (WinFWI), using only the vertical particle-velocity. The constructed MLP was lightweight, which reduced the computational overhead. Numerical experiments on a synthetic block model and Marmousi2 model demonstrate the robustness of the proposed method to challenges such as parameter crosstalk. This was further validated using the Chevron 2014 blind test data. All comparisons show that our method is more general and robust than traditional and INL-based FWI algorithms. Moreover, additional feature visualization and numerical analysis illustrate the potential advantages of WinFWI in balancing computational cost and inversion accuracy.
Sensor importance-based optimal seismic acquisition design for surface waves
Authors: Mukherjee Sayan , Li Yunyue Elita
Published: January 2025

Surface wave methods aim to monitor the shallow subsurface non-invasively. Traditional multichannel surface wave acquisitions adopt evenly spaced receivers for convenience and simplicity. However, seismic acquisitions are often performed with coarse sampling due to financial constraints and practical issues with physical access. Compressive sensing (CS) has been extensively studied for industrial-scale seismic explorations, predominantly utilizing random undersampling of tens of thousands of receivers. These undersampling methods do not answer the critical question of optimality when small-scale, shallow seismic surface wave acquisition only allows tens of receivers and a few source locations. We propose an optimal seismic acquisition design (OSAD) framework, where the importance of each receiver is evaluated in a constrained sparse inversion framework for more accurate data reconstruction. Instead of randomized sampling, OSAD exploits the physical information available in previously acquired or simulated synthetic data. The sparsity of the acquisition geometry and of the seismic data in a transform domain is explored by nested convex optimizations for alternating directions by solving each sub-problem to find a solution to an overarching minimization objective function. We apply the OSAD framework to synthetic and field datasets. We focus on deriving optimal receiver geometry for seismic acquisition with a single source. The derived geometry automatically honors wave physics where high- and low-frequency data are prevalent for near- and far-offsets, respectively. We explicitly demonstrate the optimality of the acquisition by comparing the data reconstruction error resulting from OSAD with two existing random undersampling methods adapted to surface wave acquisition.
Advanced Machine Learning for Low-Data Porosity and Permeability Prediction in Tight Sandstones
Authors: Wu Juan , Luo Renze , Luo Lei , Lei Canru , Chen Xingting
Published: January 2025

Accurate prediction of porosity and permeability is crucial for understanding subsurface fluids. Traditional physical methodologies, however, are both costly and time-consuming. Moreover, existing machine learning predictive methods require a substantial number of samples, leading to performance bottlenecks. In response to the acute scarcity of core data and weak logging responses in actual working areas, we propose a novel machine learning algorithm based on the REaLTabFormer-voting in Extra Trees, XGBoost, and Random Forest (RTF-vEXR) model, for porosity and permeability prediction using well logging data. RTF-vEXR consists of two primary components: First, the REaLTabFormer data generation model is introduced, which captures the intrinsic correlations between logging parameters and target parameters (porosity and permeability), thereby enhancing the quality of core data. Second, we employ a vEXR-based ensemble regression model, which exhibits robustness and fitting ability, to achieve accurate prediction of porosity and permeability in tight sandstone reservoirs. In practice, we implement the proposed RTF-vEXR model in the Sulige Gas Field for porosity and permeability prediction. Extensive experimental results indicate that, compared to other baseline methods, our proposed RTF-vEXR model provides the best fit for core porosity and permeability, achieving the highest R-squared value, as well as the lowest mean absolute error and root mean square error, demonstrating the feasibility and effectiveness of the method for predicting porosity and permeability. Furthermore, ablation experiments indicate that integrating the RTF module can significantly enhance the models performance in dealing with low-data scenarios.
Self-Potential Inversion for Regularly Polarized Ore Deposits Anomalies in Different Models Based on Quantum Genetic Algorithm
Authors: Zhang Pengfei , Cui Yi-an , Li Hao , Liu Jianxin
Published: January 2025

The self-potential (SP) method is widely used in mineral exploration for its simplicity and cost-effectiveness. Traditional inversion approaches often face challenges with local optima and computational inefficiency in complex models. A quantum genetic algorithm (QGA) for SP data inversion is introduced, combining quantum computation principles with genetic algorithms. Modal analysis and parameters analysis of inversion in different physical models are conducted to determine parameter sensitivity and the optimal parameter range. The proposed optimizer is applied on synthetic data generated from diverse geological models. Gaussian noise is added to test robustness. Indoor experiments, using a controlled environment with a 2D electrode grid, validate QGA's effectiveness in layered models. The impact of data density on the algorithm's performance is evaluated by reducing the number of measurement points. Different field cases from Turkey, Germany and USA further confirmed QGA's practical applicability, with inversion results closely matching drilling data or research published before. Uncertainty appraisal analyses show the validity of the model parameter estimations. In summary, QGA significantly improves SP data inversion, offering better accuracy, efficiency, and robustness, making it a promising tool for complex geological conditions.
Time-lapse shear-wave tomography at a test dyke with changing water levels
Authors: Minato Shohei , Ghose Ranajit
Published: January 2025

Although seismic methods using S waves can offer high-resolution images of the shallow soil layers, the use of body S-wave tomography for near-surface water monitoring remains underexplored, and the quantitative interpretation of any observed changes in S-wave velocity ( V S) in the field conditions is challenging. We conduct a time-lapse S-wave tomography experiment on a field-scale test dike with controlled water levels, allowing for detailed examination of how VS responds to water infiltration. Our results demonstrate that VS decreases progressively, starting from the high-water-side slope and extending across the dike, as the water level rises, with the most significant changes occurring in the sand body and not in the clay cover. The maximum reduction in VS is approximately 40–60 m/s, corresponding to approximately 25%–30% reduction from the initial condition. We use the squared velocity ratio to evaluate the relative contributions of bulk density and shear modulus to VS changes. In the initially unsaturated zone, both these factors contribute significantly to the observed VS changes as the zone becomes fully saturated. In fully saturated zones, we assess the changes in the effective stress using the squared VS ratio. Although the low-water side of the dike shows stress changes that are consistent with numerical modeling, the high-water side shows large stress changes than expected, possibly due to excess pore pressure during the dynamic flow conditions. These findings highlight the potential of body S-wave tomography for high-resolution, near-surface hydrologic monitoring, and provide insights into the complex interactions between physical properties that influence VS changes under varying water levels in field environments.
Chimeric Antigen Receptor-T Cells in Colorectal Cancer: Pioneering New Avenues in Solid Tumor Immunotherapy
Authors: Ouladan Shaida , Orouji Elias
Published: January 2025

Colorectal cancer (CRC) remains a major global health burden, being one of the most prevalent cancers with high mortality rates. Despite advances in conventional treatment modalities, patients with metastatic CRC often face limited options and poor outcomes. Chimeric antigen receptor-T (CAR-T) cell therapy, initially successful in hematologic malignancies, presents a promising avenue for treating solid tumors, including CRC. This review explores the potential of CAR-T cell therapy in CRC by analyzing clinical trials and highlighting prominent CRC-specific targets. We discuss the challenges such as immunosuppressive microenvironment, tumor heterogeneity, and physical barriers that limit CAR-T efficacy. Emerging strategies, such as logic-gated and dual-targeting CAR-T cells, offer practical solutions to overcome these hurdles. Furthermore, we explore the combination of CAR-T cell therapy with immune checkpoint inhibitors to enhance T-cell persistence and tumor infiltration. As the field continues to evolve, CAR-T cell therapies hold significant potential for revolutionizing the treatment landscape of CRC.
Lumpectomy Patients are at Highest Risk for Opioid Overprescription: A Comparison Between Practice Patterns and OPEN National Guidelines
Authors: Swafford Emily P. , Anantha Sadhana , Davis Jenna , Heath Rainya , Draper Allison , Tevis Sarah , Goel Neha , Kesmodel Susan B. , Rojas Kristin E.
Published: January 2025

Background

Nearly 25% of opioid-related deaths are from prescribed opioids, and the exacerbation of the opioid epidemic by the coronavirus disease 2019 (COVID-19) pandemic underscores the urgent need to address superfluous prescribing. Therefore, we sought to align local opioid prescribing practices with national guidelines in postoperative non-metastatic breast cancer patients.

Methods

A single-institution analysis included non-metastatic breast surgery patients treated between April 2020 and July 2021. ‘Overprescription’ was defined as a discharge prescription quantity of oral morphine equivalents (OMEs) greater than the upper limit of the procedure-specific Michigan Opioid Prescribing Engagement Network (OPEN) recommendations. Univariable and multivariate analyses identified risk factors associated with opioid prescribing.

Results

Overall, 464 patients met the inclusion criteria: 280 patients underwent lumpectomy, and 184 patients underwent mastectomy. 52% of patients were overprescribed opioids, including 74% of lumpectomy patients (p < 0.001) and 90% of patients undergoing lumpectomy with axillary surgery (p < 0.001). Mastectomy patients were overprescribed less frequently (< 25%). The quantity of opioids prescribed at discharge did not correlate to inpatient opioid requirements (r = 0.024, p = 0.604). Increased age, tobacco use, and long surgery duration were associated with higher quantities of opioids prescribed at discharge.

Conclusion

Patients undergoing less aggressive breast surgery are at very high risk of overprescription, and real-life prescribing patterns do not correlate to national guidelines or inpatient need. Future work will optimize adherence to procedure-specific guidelines and implement tailored discharge protocols.
The use of triptans as an effective form of migraine treatment – review
Authors: Borkowska Agata , Kiełb Anna , Mich Anna , Kaźmierczak Anna , Sornek Patrycja , Izdebska Wiktoria , Pawlak Igor , Ciesielski Radosław , Stanek Jakub , Perkowska Klaudia
Published: January 2025

Introduction Migraine, a prevalent neurological disorder, is characterized by recurrent episodes of moderate to severe headache, often accompanied by symptoms such as nausea, photophobia, and phonophobia. Triptans, which belong to the class of serotonin 5-HT1B/1D receptor agonists, are integral to the management of migraine, providing prompt relief from symptoms.
Aim of the study To clarify how effective triptans are in treating migraines, highlighting their role in symptom relief and improving patients' quality of life
Materials and methods The article is the result of a comprehensive analysis of present-day scientific literature, which has been collected from PubMed, Scopus, and Google Scholar. The literature was systematically reviewed utilizing specific keywords.
Results Studies consistently show high efficacy of triptans in treating acute migraines. These pharmaceuticals markedly alleviate pain and symptoms like nausea, photophobia, and phonophobia, providing substantial pain relief. Differences in triptan medications' onset and half-life affect their therapeutic duration and headache recurrence rate. The pharmacokinetic properties of each triptan significantly affect its efficacy and headache recurrence likelihood. Frovatriptan, with its long half-life, showed superior efficacy in reducing migraine recurrence. Conversely, rizatriptan, with its rapid action, offered quick pain relief. In clinical trials, all triptans showed favorable tolerability profiles. Reported adverse effects were generally mild.  
Conclusions Triptans are effective and safe for managing acute migraines. The diverse formulations and swift onset make these treatments essential for migraine management. Choose a triptan based on efficacy, safety, and patient preferences. More research is needed to improve triptan treatments for migraine, focusing on reducing headache recurrence and enhancing patient tolerability.
Ewing sarcoma – pathomechanisms, standard treatment and new therapeutic perspectives
Authors: Zezuliński Wojciech , Woliński Filip , Zyśk Julia , Korga Mateusz , Klas Jakub , Baj Jacek , Dzierżyński Eliasz
Published: January 2025

Introduction: Ewing sarcoma is the second most common bone tumor among children, and due to its high malignancy 5-year survival rate for patients with primary lesions is around 70%. This number drops to merely 30% if metastases are present. Despite combined modality treatment, including radiotherapy, surgery, pre- and post-surgery chemotherapy, the mortality of patients is still too high. This shows a great need to look for new therapeutic options. 
Methodology: Comprehensive literature review was conducted across databases, including PubMed and Google Scholar for studies published between 2000 and 2023. This review presents factors that play a key role in pathogenesis and are potential points of targeted therapy. The paper discusses, among other things, treatment attempts based on the role of the EWS-FLI1 protein, epigenetics, tyrosine kinase inhibitors, immunotherapies and the use of nanomedicine and viruses, as well as the difficulties associated with their application.
Findings: The studies cited vary depending on the phase of the clinical trial they are on, of which teprotumumab, robatumumab, and a combination of cixutumumab/temsyrolimus, ivodesinib, Nivolumab (a PD-1 inhibitor), and Ipilimumab (a CTLA-4 inhibitor) are quite advanced as well as those conducted only on animals and in vitro like YK-4-279 molecule, mithramycin 2'-oxime, NK cells, siRNAs with cationic detonation nanodiamonds (DNDs) and Il-12 by means of lentiviruses.
Conclusions: They are new and promising approaches in cases where standard treatments fail, yet they still require further study. Knowledge of the mechanisms of Ewing's sarcoma formation and its metastases, currently accepted treatment standards, critical points in the pathomechanism and current attempts at treating Ewing's sarcoma is essential for choosing the best treatment and effectively reducing its mortality rate.
A Review of Treatment Methods and Available Therapies for Individuals with ADHD
Authors: Gałuszka Zuzanna , Papachristoforou Natalie , Michałka Daria , Makar Monika , Bartuś Tomasz , Góra Żaneta , Bąk Emilia , Głowacka Justyna , Kocjan Aleksandra , Chmiel Radosław
Published: January 2025

This review explores treatment approaches and therapies for Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental condition affecting adults, with symptoms often persisting from childhood into adulthood. The research examines the efficacy, tolerability, and accessibility of pharmacological and psychosocial interventions. ADHD management necessitates a multimodal approach combining medications, behavioral therapies, and educational interventions tailored to individual needs.
The analysis focuses on stimulant and non-stimulant medications, including methylphenidate, amphetamines, atomoxetine, and alpha-2 agonists, with regional variations in treatment guidelines. Stimulants, primarily methylphenidate and amphetamine formulations, demonstrate robust efficacy in symptom reduction for patients. Non-stimulant options like atomoxetine are recommended for patients with contraindications to stimulants. Emerging formulations, such as extended-release medications, address tolerance and adherence challenges.
Psychosocial interventions, particularly cognitive behavioral therapy (CBT), complement pharmacological treatments by targeting organizational skills, emotional regulation, and comorbidities like anxiety and depression. Evidence suggests that combined approaches improve long-term functional outcomes, such as academic performance and interpersonal relationships.
Despite advances, gaps in treatment access and the need for individualized care remain significant challenges. This review underscores the importance of ongoing research to optimize ADHD management strategies and ensure equitable access to effective therapies worldwide.
Faces of Anorexia in Adolescents: Diagnosis, Risk, and Support
Authors: Skibicka Julia Natalia , Rosińska Kamila , Janicka Ewelina Justyna , Rosiński Mateusz , Niedźwiedzka Monika
Published: January 2025

Introduction: Anorexia nervosa (AN) is an eating disorder most commonly occurring during adolescence, characterized by the highest mortality rate among mental health conditions. It is a serious disorder affecting the physical and psychological development of children and adolescents. The increase in AN cases following the COVID-19 pandemic and difficulties in its early diagnosis present significant challenges in treatment.
Objective: The aim of this paper is to review current research on anorexia nervosa, its symptoms, risk factors, the impact of the COVID-19 pandemic, and the role of social media in the development of this disorder. Therapeutic approaches, with a particular focus on family therapy and psychotherapy, are also discussed.
Methods: This study provides a literature review on anorexia nervosa, focusing on its etiology, the impact of the pandemic, and contemporary treatment methods. Available research on co-occurring mental disorders, the effects of social media, and the role of family in the therapeutic process was analyzed.
Conclusion: Anorexia nervosa is a serious disorder that requires intensified preventive and diagnostic efforts, especially in young individuals. The COVID-19 pandemic and increased social media activity have significantly exacerbated AN symptoms. Effective treatment necessitates a multifaceted approach, including early diagnosis, family therapy, and psychotherapy. A supportive family evironment can significantly enhance treatment outcomes.
The Role of Inflammatory Cytokines and lncRNAs in the Pathogenesis of Atherosclerosis and Other Inflammatory Diseases: Focus on Sarcoidosis and Alzheimer’s Disease
Authors: Sokołowska Aldona , Buszek Julia , Czerniak Piotr , Antoszewska Adrianna , Bargiel Weronika , Bąk Dominika , Wajdowicz Halszka , Warzocha Mateusz
Published: January 2025

Introduction
Atherosclerosis, a chronic inflammatory disease of the arterial wall, remains a leading cause of morbidity and mortality worldwide. Recent advances highlight the role of inflammatory cytokines and long non-coding RNAs (lncRNAs) in its pathogenesis.
Aim of the Study
This study synthesizes current knowledge on the interplay between cytokines and lncRNAs across these conditions, emphasizing their potential as biomarkers and therapeutic targets.
Materials and Methods
Through a systematic review of recent studies, we identify key pathways and discuss translational implications for novel interventions.
Description of the State of Knowledge
Inflammatory cytokines, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6), orchestrate immune responses that exacerbate endothelial dysfunction, foam cell formation, and plaque instability. Concurrently, lncRNAs regulate gene expression through transcriptional and post-transcriptional mechanisms, influencing cellular processes such as macrophage polarization and smooth muscle cell proliferation. Beyond atherosclerosis, these mediators are implicated in the pathogenesis of other inflammatory diseases, including sarcoidosis and Alzheimer's disease.
Conclusion
Our findings underscore the critical need for integrated approaches targeting both inflammatory mediators and epigenetic regulators to mitigate the burden of these diseases.
Exploring the Potential of SGLT-2 Inhibitors in Cancer Treatment: Mechanisms, Preclinical Findings, and Clinical Implications
Authors: Sokołowska Aldona , Fikas Klaudia , Chwaliszewski Kamil , Samuła Sebastian , Mazur Michał , Waz Dorota , Szarłowicz Jakub , Goliszek Zofia , Sobek Karolina Łucja , Tabin-Barczak Wiktoria
Published: January 2025

IntroductionSodium-glucose co-transporter 2 (SGLT-2) inhibitors, originally developed for the treatment of type 2 diabetes, have recently attracted attention for their potential applications in oncology. Their potential to modulate tumor metabolism and enhance responses to cancer therapies is an area of increasing interest.
Aim of the studyThis review aims to explore the emerging role of SGLT-2 inhibitors in cancer treatment, focusing on their mechanisms of action, preclinical findings, and early clinical evidence. By understanding the potential of these agents to alter tumor metabolism and the immune microenvironment, we can evaluate their therapeutic value in cancer management.
Material and MethodsData were gathered from peer-reviewed articles, clinical trials, and ongoing research on the use of SGLT-2 inhibitors in combination with chemotherapy, immunotherapy, and other cancer treatments.
Description of the state of knowledgePreclinical studies suggest that SGLT-2 inhibitors can alter cancer cell metabolism by limiting glucose availability, potentially impeding tumor growth. These agents have demonstrated promise in models of breast, prostate, and pancreatic cancers, reducing cell proliferation and enhancing the efficacy of standard cancer treatments.
ConclusionsSGLT-2 inhibitors represent a novel therapeutic strategy in oncology, with potential to improve outcomes in combination with traditional therapies. However, more extensive clinical trials are required to validate their effectiveness, optimize treatment regimens, and better understand their safety profiles in cancer patients.
Biosimilars in Focus: Evaluating Their Role Compared to Adalimumab in Clinical Practice
Authors: Waz Dorota , Mazur Michał , Szarłowicz Jakub , Goliszek Zofia , Sobek Karolina Łucja , Tabin-Barczak Wiktoria , Sokołowska Aldona , Fikas Klaudia , Chwaliszewski Kamil , Samuła Sebastian
Published: January 2025

Introduction and Purpose:
Adalimumab, a TNF-α inhibitor, is a cornerstone treatment for autoimmune and inflammatory diseases such as rheumatoid arthritis and inflammatory bowel disease. Despite its efficacy, the high cost of adalimumab (Humira®) limits accessibility. The emergence of biosimilars offers cost-effective alternatives with comparable efficacy, safety, and immunogenicity. This article reviews the role of adalimumab biosimilars, their clinical equivalence, and potential to address healthcare challenges.
Materials and Methods:
A comprehensive literature review was conducted to evaluate the structural, functional, and clinical parity of adalimumab biosimilars with the reference product. Key studies on pharmacokinetics, efficacy, safety, therapeutic drug monitoring (TDM), and real-world outcomes were analyzed.
Results:Adalimumab biosimilars demonstrate equivalent pharmacokinetic profiles and clinical efficacy across conditions, with comparable remission rates and safety profiles. Innovations, such as citrate-free formulations and advanced delivery devices, enhance patient adherence. Biosimilars significantly reduce treatment costs, increasing accessibility, especially in resource-constrained settings. Challenges remain in patient acceptance and managing therapy transitions.
Conclusion:Biosimilars are transformative in addressing the cost barrier of biologic therapies, maintaining therapeutic equivalence to adalimumab while expanding access globally. Enhanced TDM and patient-centered strategies are essential for optimizing outcomes and maximizing their adoption in clinical practice.