Search Results (11,350)

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with hypercholesterolemia identified as a major, but modifiable risk factor. This review serves as the second part of a comprehensive analysis of dyslipidemia management. The first installment laid the groundwork by detailing the key pathophysiological mechanisms of lipid metabolism, the development of atherosclerosis, major complications of hyperlipidemia, and the importance of cardiovascular risk assessment in therapeutic decision-making. It also examined non-pharmacological interventions and conventional therapies, with a detailed focus on statins and ezetimibe. Building upon that foundation, the present article focuses exclusively on emerging pharmacological therapies designed to overcome limitations of standard treatment. It explores the mechanisms, clinical applications, safety profiles, and pharmacogenetic aspects of novel agents such as proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors (alirocumab, evolocumab), small interfering RNA (siRNA) therapy (inclisiran), adenosine triphosphate–citrate lyase (ACL) inhibitor (bempedoic acid), microsomal triglyceride transfer protein (MTP) inhibitor (lomitapide), and angiopoietin-like protein 3 (ANGPTL3) inhibitor (evinacumab). These agents offer targeted strategies for patients with high residual cardiovascular risk, familial hypercholesterolemia (FH), or statin intolerance. By integrating the latest advances in precision medicine, this review underscores the expanding therapeutic landscape in dyslipidemia management and the evolving potential for individualized care. Full article

Since the initial use of biological ion channels to detect single-stranded genomic base pair differences, label-free and highly sensitive resistive pulse sensing (RPS) with nanopores has made remarkable progress in single-molecule analysis. By monitoring transient ionic current disruptions caused by molecules translocating through a nanopore, this technology offers detailed insights into the structure, charge, and dynamics of the analytes. In this work, the RPS platforms based on biological, solid-state, and other sensing pores, detailing their latest research progress and applications, are reviewed. Their core capability is the high-precision characterization of tiny particles, ions, and nucleotides, which are widely used in biomedicine, clinical diagnosis, and environmental monitoring. However, current RPS methods involve bottlenecks, including limited sensitivity (weak signals from sub-nanometer targets with low SNR), complex sample interference (high false positives from ionic strength, etc.), and field consistency (solid-state channel drift, short-lived bio-pores failing POCT needs). To overcome this, bio-solid-state fusion channels, in-well reactors, deep learning models, and transfer learning provide various options. Evolving into an intelligent sensing ecosystem, RPS is expected to become a universal platform linking basic research, precision medicine, and on-site rapid detection. Full article

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator. The generator creates synthetic noise patterns to augment training data, while the dual-path-UNet denoiser uses multiple receptive field modules to preserve fine details and dense feature fusion to maintain global structural integrity. The discriminator provides adversarial feedback to enhance denoising performance. This dual-path adversarial training mechanism addresses the limitations of traditional methods by simultaneously capturing both local details and global structures. Experiments on the SIDD, DND, and PolyU datasets demonstrate superior performance. We compare our architecture with the latest state-of-the-art GAN variants through comprehensive qualitative and quantitative evaluations. These results confirm the effectiveness of noise removal with minimal loss of critical image details. The proposed architecture enhances image denoising capabilities in complex noise scenarios, providing a robust solution for applications that require high image fidelity. By enhancing adaptability to various types of noise while maintaining structural integrity, this method provides a versatile tool for image processing tasks that require preserving detail. Full article

In the preceding and early stages of cancer progression, local drug delivery to pre-cancerous and cancerous skin lesions may be applied as an alternative or supplementary therapy. At present, 5-Fluorouracil, imiquimod, and tirbanibulin creams and ointments have established their place in practice, while several other active pharmaceutical ingredients (APIs) (e.g., calcipotriol, tretinoin, diclofenac) have been repurposed, used off-label, or are currently being investigated in mono- or combined chemotherapies of skin cancers. Apart from them, dozens to hundreds of therapeutics of natural and synthetic origin are proven to possess anti-tumor activity against melanoma, squamous cell carcinoma (SCC), and other skin cancer types in in vitro studies. Their clinical introduction is most often limited by low skin permeability, challenged targeted drug delivery, insufficient chemical stability, non-selective cytotoxicity, or insufficient safety data. A variety of prodrug and nanotechnological approaches, including vesicular systems, micro- and nanoemulsions, solid lipid nanoparticles, nanostructured lipid carriers, polymeric nanoparticles, and others, offer versatile solutions for overcoming the biophysical barrier function of the skin and the undesirable physicochemical nature of some drug molecules. This review aims to present the most significant aspects and latest achievements on the subject. Full article

Neoxanthin is a xanthophyll carotenoid with high-value nutritional functions for human health due to its anti-cancer, anti-oxidative, and anti-obesity activities. In this present work, we systematically reviewed the structure, source, and biosynthetic pathways of neoxanthin, and discussed the advantages and disadvantages of the prevailing extraction methods of neoxanthin. Meanwhile, this review described the latest research progress on the pharmacological activities of neoxanthin. Finally, we concluded with a discussion on the main challenges of neoxanthin production from microalgae, and proposed some future development prospects and potential solutions. Full article

Air pollution is linked to childhood mortality and morbidity in low- and middle-income countries globally. There is growing evidence linking air pollution to asthma and other respiratory diseases in children. Studies have shown that children are likely to experience asthma due to their narrow airways and their heightened sensitivity to environmental irritants. This study aims to investigate the relationship between ambient air pollution and respiratory diseases in children under the age of 5. The study will be conducted in the informal township of Alexandra, north of Johannesburg, South Africa. A quantitative approach will be used in this cross-sectional analytical study. Data will be collected using different tools that include a questionnaire to determine the prevalence of asthma and respiratory disease and potential risk factors. While environmental air pollution will be measured using Radiello passive samplers and Gillian pumps. Data will be analyzed using the latest version of the STATANow/MP 19.5 software. Furthermore, health risk assessment will be conducted for lifetime non-carcinogenic and carcinogenic risk estimation following the USEPA framework. The study will identify environmental triggers that exacerbate asthma and other respiratory conditions in other similar community settings and will contribute to the body of knowledge in public health. Ethical approval was obtained from the Research Ethics Committee, Faculty of Health Sciences at the University of Johannesburg. Full article

This study was conducted in the context of China’s latest “double carbon” policy. The objective of this study was twofold: firstly, to examine the characteristics of traditional Chinese residential skywell; and secondly, to investigate and develop climate-adaptive technologies for these structures. To this end, a study was conducted on the Huizhou skywell architecture in China. Firstly, we obtained a large amount of basic data, such as traditional buildings and skywell parameters in Huizhou through field research and on-site mapping. Second, we combined a large number of parameters and typical cases to analyze the data and determine a more scientifically typical model of Huizhou architecture and experimental coverage of skywell data. Different shapes and sizes of skywell were formed by changing the combination of skywell length and width, and the indoor wind environments of these skywell dwelling models were simulated one by one using PHOENIX (v2016) software. Finally, the data obtained from these simulations are analyzed using the variable control method, and the simulated indoor wind environments of skywells under different scale combinations in terms of skywell length, width, and length–width coupling effects are summarized and compared. The following conclusions were drawn: (1) The length of the skywell has a certain effect on all indoor wind environments, and the variation in indoor wind speed is smallest when the length of the skywell is 7.5 m. When selecting the design size of the skywell, the length of the skywell can be increased accordingly. (2) The width of the skywell for the indoor wind environment is divided into two cases, when the length of the skywell is less than 3.75 m, increasing the width of the skywell will make the indoor wind environment more unstable. However, when the length of the skywell is greater than 3.75 m, the width of the skywell is positively related to the comfort of the indoor wind environment. (3) The area of the skywell should not be too large, usually between 11.25 m² and 18.75 m². A slender skywell is more likely to provide a comfortable indoor wind environment. (4) After extensive evaluation, we found that the comfort of the indoor wind environment is high when the width of the skywell is 2 m. The optimum dimensions for the ventilation performance of the skywell are 7.5 m in length and 2 m in width. Full article

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

Carbon dots (CDs) have attracted much attention as new types of luminescent carbon nanomaterials in recent years because of their tunable fluorescence, good biocompatibility, high stability, and low cost. In this review, the classification of CDs is overviewed based on their differences in structure. Subsequently, the latest research progress of CDs in fluorescence sensing is systematically summarized and various sensing principles are elucidated in detail, including fluorescence resonance energy transfer, aggregation-induced emission, aggregation-caused quenching, electron transfer, and the inner filter effect. Finally, the challenges and future direction of CD fluorescent probes are discussed in detail. The purpose of this review is to stimulate the design of advanced CD fluorescent probes and achieve the accurate and reliable measurement of analytes in complex samples. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations. Full article

Background/Objectives: Borderline Personality Disorder (BPD) involves emotional dysregulation, interpersonal instability and impulsivity. Although treatments have advanced, evaluating the latest innovations remains essential. This rapid review aimed to (1) identify and classify recent therapeutic innovations for BPD, (2) assess their effects on clinical and functional outcomes, and (3) highlight research gaps to inform future priorities. Methods: Employing a rapid review design, we searched PubMed/MEDLINE, PsycINFO, and Embase for publications from 1 January 2019 to 28 March 2025. Eligible studies addressed adult or adolescent BPD populations and novel interventions—psychotherapies, pharmacological agents, digital tools, and neuromodulation. Two independent reviewers conducted screening, full-text review, and data extraction using a standardised form. Results: Sixty-nine studies—predominantly from Europe and North America—were included. Psychotherapeutic programmes dominated, ranging from entirely novel models to adaptations of established treatments (for example, extended or modified Dialectical Behavior Therapy). Pharmacological research offered fresh insights, particularly into ketamine, while holistic approaches such as adventure therapy and digital interventions also emerged. Most investigations centred on symptom reduction; far fewer examined psychosocial functioning, mortality, or social inclusion. Conclusions: Recent innovations show promise in BPD treatment but underserve the needs of mortality and societal-level outcomes. Future research should adopt inclusive, equity-focused agendas that align with patient-centred and recovery-oriented goals, supported by a coordinated, integrated research strategy. Full article

Lung cancer remains one of the most common and deadliest forms of cancer worldwide despite notable advancements in its management. Conventional treatments, such as chemotherapy, often have limitations in effectively targeting cancer cells, which frequently lead to off-target side effects. In this context, the pulmonary delivery of inhalable nanomaterials offers the advantages of being rapid, efficient, and target-specific, with minimal systemic side effects. This concise review summarizes the basic research and clinical translation of inhalable nanomaterials for the treatment of lung cancer. We also provide insights into the latest advances in pulmonary drug delivery systems, focusing on various types of pulmonary devices and nanomaterials. Furthermore, this paper discusses significant challenges in translating the discoveries of inhalable nanomaterials into clinical care for lung cancer and shares strategies to overcome these issues. Full article

T cells play a vital role in resisting pathogen invasion and maintaining immune homeostasis. However, T cells gradually become exhausted under chronic antigenic stimulation, and this exhaustion is closely related to mitochondrial dysfunction in T cells. Mitochondria play a crucial role in the metabolic reprogramming of T cells to achieve the desired immune response. Here, we compiled the latest research on how mitochondrial metabolism determines T cell function and differentiation, with the mechanisms mainly including mitochondrial biogenesis, fission, fusion, mitophagy, and mitochondrial transfer. In addition, the alterations in mitochondrial metabolism in T-cell exhaustion were also reviewed. Furthermore, we discussed intervention strategies targeting mitochondrial metabolism to reverse T cell exhaustion in detail, including inducing PGC-1α expression, alleviating reactive oxygen species (ROS) production or hypoxia, enhancing ATP production, and utilizing mitochondrial transfer. Targeting mitochondrial metabolism in exhausted T cells may achieve the goal of reversing and preventing T cell exhaustion. Full article