Current Limitations Research Articles

How to return? experiences of patients in working age after first Ischaemic stroke: an interpretative phenomenological analysis of patient´s perspective at 12 - 24 months post-stroke.

Limited evidence of young adult patient-reported outcomes and experiences after ischaemic stroke has been conducted. To investigate the meaning of the lived experiences of stroke patients in working age 12-24 months after their first IS. The exploratory qualitative study used an interpretative phenomenological analysis (IPA) design. Nine ischaemic stroke patients (with age ranges from 41 to 50 years) took part in semi-structured qualitative interviews. Even with mild residual neurological deficit, IS negatively impacted the quality of life daily and social life. Six subthemes and three interconnected group experiential themes were generated: (i) From confusion to understanding (ii) Triggers for rebuilding; and (iii) Challenges and benefits. The study highlights the current gaps and limitations in supporting the needs of stroke patients in working age in long-term post-stroke care. The findings are crucial for healthcare professionals to develop improved age- and mild- impairment-appropriate strategies or tailor self-management interventions for stroke patients of working age.ClinicalTrials.gov: NCT04839887.

Recent progress, challenges and future propects of applied deep reinforcement learning : A practicial perspecive in path planning

Path planning is one of the most crucial elements in the field of robotics, such as autonomous driving, minimally invasive surgery and logistics distribution. This review begins by summarizing the limitations of conventional path planning methods and recent work on DRL-based path planning methods. Subsequently, the paper systematically reviews the construction of key elements of DRL methods in recent work, with the aim of assisting readers in comprehending the foundation of DRL research, along with the underlying logic and considerations from a practical perspective. Facing issues of sparse rewards and the exploration-exploitation balance during the practical training process, the paper reviews enhancement methods for training efficiency and optimization results in DRL path planning. In the end, the paper summarizes the current research limitations and challenges in practical path planning applications, followed by future research directions.

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Temperature extraction from Brillouin sensing based on temporal convolutional networks

The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps.

Preventative and curative treatment of venous thromboembolic disease in cancer patients

Cancer-associated venous thromboembolism (CAT) is common in patients with cancer and associated with significant morbidity and mortality. The incidence of CAT continues to rise, complicating patient care and burdening healthcare systems. Patients with cancer experiencing VTE face poorer prognoses, making prevention and effective management imperative. This narrative review synthesizes evidence on thromboprophylaxis in ambulatory patients with cancer receiving systemic therapy and acute treatment strategies for CAT. Risk assessment models (e.g., Khorana score) aid in identifying high-risk patients who may benefit from thromboprophylaxis. Pharmacological thromboprophylaxis with low molecular weight heparins (LMWHs) direct oral anticoagulants (DOACs) has been shown to reduce the risk of CAT without significantly increasing the risk of bleeding complications. However, implementation of risk-based strategies remains limited in clinical practice. For acute CAT management, LMWHs have been the standard of care, but DOACs are increasingly favored due to their convenience and efficacy. However, challenges persist, including bleeding risks and drug interactions. Emerging therapies targeting Factor XI inhibitors present promising alternatives, potentially addressing current limitations in anticoagulation management for CAT.

Optimizing Cardiomegaly Detection: A Random Forest Approach to Processed Chest X-ray Imagery

This study explores the application of a Random Forest Classifier for the automated detection of Cardiomegaly from chest X-ray images, utilizing a dataset processed and derived from the NIH Chest X-ray Dataset. Given the crucial need for accurate and timely diagnosis of Cardiomegaly to inform appropriate treatment decisions, this research aims to determine the efficacy of machine learning models in augmenting diagnostic processes. Employing image pre-processing techniques such as Sobel filtering for edge detection and Hu Moments for feature extraction, the study enhances the input features for the model. The performance of the classifier was evaluated using a 5-fold cross-validation approach, yielding results with average accuracy, precision, recall, and F1-scores ranging approximately between 52% and 54%. These findings suggest a moderate level of reliability and consistency, indicating the potential utility of ensemble machine learning methods in medical imaging analysis. However, the variability in performance across different data subsets highlights the challenges and necessitates further optimization. This research contributes to the ongoing discourse on integrating machine learning into clinical settings, demonstrating the potential benefits and current limitations. Future research is recommended to expand the dataset variety, integrate advanced deep learning methodologies, and rigorously test these models in clinical environments. The findings hold significant implications for the development of automated diagnostic tools in healthcare, potentially leading to enhanced diagnostic accuracy and efficiency.

Living biodrugs and how tissue source influences mesenchymal stem cell therapeutics for heart failure

In this editorial we comment on the article by Safwan M et al . We especially focused on the cardiac function restoration by the use of mesenchymal stem cells (MSCs) therapy for heart failure (HF), which has emerged as a new treatment approach as “Living Biodrugs”. HF remains a significant clinical challenge due to the heart’s inability to pump blood effectively, despite advancements in medical and device-based therapies. MSCs have emerged as a promising therapeutic approach, offering benefits beyond traditional treatments through their ability to modulate inflammation, reduce fibrosis, and promote endogenous tissue regeneration. MSCs can be derived from various tissues, including bone marrow and umbilical cord. Umbilical cord-derived MSCs exhibit superior expansion capabilities, making them an attractive option for HF therapy. Conversely, bone marrow-derived MSCs have been extensively studied for their potential to improve cardiac function but face challenges related to cell retention and delivery. Future research is focusing on optimizing MSC sources, enhancing differentiation and immune modulation, and improving delivery methods to overcome current limitations.

3D Printing Hierarchical Porous Nanofibrous Scaffold for Bone Regeneration.

Current limitations in 3D printing pose significant challenges for the fabrication of hierarchical 3D scaffolds with nanofibrous structures that simulate the natural bone extracellular matrix (ECM) for enhanced bone regeneration. This study presents an innovative approach to 3D printing customized hierarchical porous scaffolds with nanofiber structures using biodegradable nanofibrous microspheres as the bio-ink. In vitro investigations demonstrate that the hierarchical porous architecture substantially enhances cell infiltration and proliferation rates, while the nanofiber topology provides physical cues to guide osteogenic differentiation and ECM deposition. When serving as a cell carrier, the 3D-printed nanofibrous scaffold promotes bone tissue regeneration and integration in vivo. Additionally, the facile and versatile chemical modification facilitates the precise tailoring of the scaffold's functionality. Using nanofibrous microspheres with highly biomimetic and versatile modification properties as the foundational constituent in this universal 3D printing methodology enables comprehensive manipulation of scaffolding biological properties, spanning from macroscopic external morphology to molecular-scale biochemical kinetics, thereby addressing a diverse spectrum of clinical requisites.

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Biocomposite Scaffolds for Tissue Engineering: Materials, Fabrication Techniques and Future Directions

Tissue engineering is an interdisciplinary field that combines materials, methods, and biological molecules to engineer newly formed tissues to replace or restore functional organs. Biomaterials-based scaffolds play a crucial role in developing new tissue by interacting with human cells. Tissue engineering scaffolds with ideal characteristics, namely, nontoxicity, biodegradability, and appropriate mechanical and surface properties, are vital for tissue regeneration applications. However, current biocomposite scaffolds face significant limitations, particularly in achieving structural durability, controlled degradation rates, and effective cellular integration. These qualities are essential for maintaining long-term functionality in vivo. Although commonly utilized biomaterials can provide physical and chemical properties needed for tissue regeneration, inadequate biomimetic properties, as well as insufficient interactions of cells-scaffolds interaction, still need to be improved for the application of tissue engineering in vivo. It is impossible to achieve some essential features using a single material, so combining two or more materials may accomplish the requirements. In order to achieve a proper scaffold design, a suitable fabrication technique and combination of biomaterials with controlled micro or nanostructures are needed to achieve the proper biological responses. This review emphasizes advancements in scaffold durability, biocompatibility, and cellular responsiveness. It focuses on natural and synthetic polymer combinations and innovative fabrication techniques. Developing stimulus-responsive 3D scaffolds is critical, as these scaffolds enhance cell adhesion and promote functional tissue formation while maintaining structural integrity over time. This review also highlights the natural polymers, smart materials, and recent advanced techniques currently used to create emerging scaffolds for tissue regeneration applications.

Probing stochastic ultralight dark matter with space-based gravitational-wave interferometers

Ultralight particles are theoretically well-motivated dark matter candidates. In the vicinity of the Solar System, these ultralight particles can be described as a superposition of plane waves, resulting in a stochastic field with sizable amplitude fluctuations on scales determined by the velocity dispersion of dark matter. In this work, we systematically investigate the sensitivity of space-based gravitational-wave interferometers to the stochastic ultralight dark matter field within the frequentist framework. We derive the projected sensitivity of a single detector using the time-delay interferometry. Our results show that space-based gravitational-wave interferometers have the potential to probe unconstrained regions in parameter space and improve the current limit on coupling strengths. Furthermore, we explore the sensitivity of a detector network and investigate the optimal configuration for ultralight dark matter detection. We introduce the overlap reduction function for ultralight dark matter, which quantifies the degree of correlation between the signals observed by different detectors. We find that the configuration, where the signals observed by two detectors are uncorrelated, is the optimal choice for ultralight dark matter detection due to a smaller chance of missing signals. This contrasts with the detection of stochastic gravitational-wave background, where the correlated configuration is preferred. Our results may provide useful insights for potential joint observations involving space-based gravitational-wave detectors like LISA and Taiji, as well as other ultralight dark matter detection networks operating in the coherence limit. Published by the American Physical Society 2024

Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.

Medical Image Segmentation

Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. This paper provides a comprehensive survey of recent advances in segmentation techniques applied to various imaging modalities, including Magnetic Resonance Imaging (MRI). Traditional methods such as thresholding, region-growing, and active contours are reviewed alongside contemporary machine learning-based approaches, particularly deep learning models. The survey emphasizes the growing dominance of convolutional neural networks (CNNs) and their variants, including U-Net and Fully Convolutional Networks (FCNs), which have shown remarkable success in handling complex medical imaging challenges. Additionally, the paper discusses hybrid methods that combine classical techniques with artificial intelligence to improve accuracy and robustness in segmentation tasks. Key challenges such as class imbalance, boundary delineation, and computational efficiency are also highlighted. Future directions, including the integration of multi-modal data and advancements in self-supervised learning, are explored as potential solutions to overcome current limitations in medical image segmentation.

Blockchain Integration in Critical Systems Enhancing Transparency, Efficiency, and Real-Time Data Security in Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management

Blockchain technology has garnered significant attention for its potential to revolutionize critical systems by enhancing transparency, efficiency, and data security. This review examines the integration of blockchain in three essential domains: Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. By leveraging decentralized ledgers and smart contracts, blockchain provides a robust framework for real-time tracking, data integrity, and automated compliance, addressing long-standing challenges across these sectors. In Agile Project Management, blockchain fosters seamless collaboration and transparent decision-making, minimizing bottlenecks and improving accountability. In DeFi, blockchain strengthens security for digital transactions and identity verification while offering financial autonomy and mitigating fraud risks. Within Cold Chain Management, blockchain ensures traceability, reduces data tampering risks, and enhances visibility throughout supply chain processes, safeguarding temperature-sensitive goods. This paper evaluates existing blockchain-based applications and frameworks, identifies current limitations, and discusses future opportunities for optimizing critical systems through blockchain. The findings highlight blockchain's transformative role in driving operational efficiency, security, and data transparency across diverse applications, providing a roadmap for industries to harness its full potential in critical environments.

Inflammasome-Activating Nanovaccine for Cancer Immunotherapy.

A range of advanced nanovaccines (NV) combined with immunotherapies has recently emerged for treating malignant tumors and has demonstrated promising tumor-suppressive effects. Nevertheless, their effectiveness is often limited by immunosuppression within the tumor microenvironment. To overcome this challenge, new approaches for NV development are required to improve antigen cross-presentation and to remodel the tumor microenvironment. In this issue of Cancer Research, Zhou and colleagues have developed a photo-enhanceable inflammasome-activating nanovaccine (PIN) designed for precise, in situ delivery of a tumor antigen and a hydrophobic small molecule that activates the NLRP3 inflammasome pathway. Near infrared light exposure enables the accumulation of PINs at tumor sites by inducing a photo-triggered charge reversal in the BODIPY-modified PAMAM nanocarrier. Systemic administration of PINs resulted in effective intratumoral activation of the NLRP3 inflammasome and antigen cross-presentation in antigen-presenting cells upon light exposure, leading to enhanced immune responses through increased proinflammatory cytokine production without significant systemic toxicity. Importantly, PINs also enhanced the efficacy of immune checkpoint blockade and promoted the development of long-term immune memory in mouse models of melanoma and hepatocellular carcinoma. Overall, inflammasome-activating NVs represent a cancer immunotherapy strategy by harnessing the innate immune system to achieve robust responses against tumors. Ongoing research and development are crucial to addressing current limitations and advancing this innovative technology toward clinical application. See related article by Zhou et al., p. 3834.

The relationship between neuroticism as a personality trait and mindfulness skills: a scoping review

IntroductionIn recent decades, researchers have assessed the relationship between mindfulness and personality traits, including neuroticism, a known target in mental health associated with the development of mental health disorders and physical illnesses. The main aim of mindfulness practice is to help individuals develop the ability to regulate and accept their experiences, emotions, and thoughts. Therefore, it could be suggested that mindfulness may be useful in reducing the expression and negative experience of neuroticism. The aim of our review was to assess the relationship between neuroticism and mindfulness.MethodsWe conducted a scoping review of the literature in December 2023, using the databases PubMed and PsycINFO.ResultsForty-nine studies were included in the review, with four common themes identified: (i) mental health, (ii) cognitive outcomes, (iii) physiological symptoms, and (iv) mindfulness-based interventions. Across most of the studies, mindfulness negatively correlated with neuroticism, supporting the idea that mindfulness may be useful in reducing neuroticism and its negative effects on mental and physical health.DiscussionWhile several limitations were identified, the overall results are promising. Future research in this area should focus on overcoming the current limitations to provide a better understanding of the relationship between mindfulness and neuroticism.

Prospects of anti-GD2 immunotherapy for retinoblastoma

Retinoblastoma is the most common type of eye tumor in infants and children. Current treatments for retinoblastoma include intravenous chemotherapy, intra-arterial chemotherapy, intravitreal chemotherapy, cryotherapy, radiotherapy, and surgery. However, these treatments come accompanied by adverse effects such as the toxic side effects of chemotherapeutic drugs, post-operative complications including blindness after surgery, or other complications caused by radiotherapy. Immunotherapy is more promising for its low toxicity on normal cells and effectively improves the quality of life of patients. Disialoganglioside (GD2), a sphingolipid expressed on the surface of retinoblastoma, is a potential therapeutic target for retinoblastoma. We summarized immunotherapeutic approaches for both preclinical studies and clinical trials of GD2. An anti-GD2 monoclonal antibody (Dinutuximab), which has been approved for the treatment of high-risk neuroblastomas, has shown promising efficacy in improving patients’ prognosis. Additionally, chimeric antigen receptors (CAR)-T therapy, GD2 vaccines and nanoparticles are also potential therapeutics. Finally, we discuss the prospects and current limitations of these immunotherapeutic approaches for treating retinoblastoma, as well as how to address these problems.

Functionalization of Emulsion Interfaces: Surface Chemistry made Liquid.

Disperse systems, and emulsions in particular, are currently massively used in fields as varied as food industry, cosmetics, health care and environmentally-friendly materials. To meet increasingly precise needs or targeted applications, these systems need to be endowed with new functionalities at their interfaces, in addition to their composition and structural properties. However, due to the fragility of drops and the low reactivity of their surface, conventional solid surface chemistry cannot be used for such a purpose. Several specific emulsion interface functionalization techniques have thus been developed for targeted systems and applications, but a general framework has yet to be drawn. In this review, we attempt to present these methods in a unified way through the prism of what we may call "liquid surface chemistry". We propose to categorize existing methods into drop-coating strategies, including layer-by-layer techniques and polymer coating, and emulsifier-carrier approaches involving particles and/or amphiphilic molecules. They are discussed in a transversal way, highlighting the underlying physico-chemical principles and providing a comparative analysis of their advantages, current limitations and potential for improvement. We also propose future directions and opportunities, involving for instance DNA-based programmability or artificial intelligence, which could make liquid surface chemistry more versatile and controlled.

Predatory pricing in platform markets: a modified test for firms within the scope of Article 3 of the DMA and super-dominant platform firms under Article 102 TFEU

ABSTRACT The paper examines predatory pricing in the context of two-sided digital platforms, arguing that traditional tests based on Average Variable Cost (AVC) may be inadequate for these markets. While predatory pricing by dominant firms is prohibited in both EU and US competition law, the current standards may not effectively capture predatory behavior in platform markets characterized by strong network effects and low marginal costs. The paper analyses cases where cross-subsidization between platform sides had predatory elements and resulted in findings of abuse of dominant position. Given platforms' unique characteristics, it proposes a modified test under Article 102 TFEU for super-dominant platforms and those within the scope of Article 3 of Digital Markets Act's scope. The proposal extends the Akzo test by presuming prices below Average Total Cost (ATC) to be abusive, rather than using AVC, with LRAIC as a proxy for ATC. This addresses the current test's limitations for low marginal cost businesses while allowing for objective justification.

Organoid-based precision medicine in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) ranks among the leading causes of cancer-related deaths worldwide. Despite advances in precision oncology in other malignancies, treatment of PDAC still largely relies on conventional chemotherapy. Given the dismal prognosis and heterogeneity in PDAC, there is an urgent need for personalized therapeutic strategies to improve treatment response. Organoids, generated from patients' tumor tissue, have emerged as a powerful tool in cancer research. These three-dimensional models faithfully recapitulate the morphological and genetic features of the parental tumor and retain patient-specific heterogeneity. This review summarizes existing precision oncology approaches in PDAC, explores current applications and limitations of organoid cultures in personalized medicine, details preclinical studies correlating in vitro organoid prediction and patient treatment response, and provides an overview of ongoing organoid-based clinical trials.