Literature Watch
Artificial Intelligence Iterative Reconstruction for Dose Reduction in Pediatric Chest CT: A Clinical Assessment via Below 3 Years Patients With Congenital Heart Disease
J Thorac Imaging. 2025 Feb 27. doi: 10.1097/RTI.0000000000000827. Online ahead of print.
ABSTRACT
PURPOSE: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-year-old patients with congenital heart disease (CHD).
MATERIALS AND METHODS: The lung image available from routine-dose cardiac CT angiography (CTA) on below 3 years patients with CHD was employed as a reference for evaluating the paired low-dose chest CT. A total of 191 subjects were prospectively enrolled, where the dose for chest CT was reduced to ~0.1 mSv while the cardiac CTA protocol was kept unchanged. The low-dose chest CT images, obtained with the AIIR and the hybrid iterative reconstruction (HIR), were compared in image quality, ie, overall image quality and lung structure depiction, and in diagnostic performance, ie, severity assessment of pneumonia and airway stenosis.
RESULTS: Compared with the reference, lung image quality was not found significantly different on low-dose AIIR images (all P>0.05) but obviously inferior with the HIR (all P<0.05). Compared with the HIR, low-dose AIIR images also achieved a closer pneumonia severity index (AIIR 4.32±3.82 vs. Ref 4.37±3.84, P>0.05; HIR 5.12±4.06 vs. Ref 4.37±3.84, P<0.05) and airway stenosis grading (consistently graded: AIIR 88.5% vs. HIR 56.5% ) to the reference.
CONCLUSIONS: AIIR has the potential for large dose reduction in chest CT of patients below 3 years of age while preserving image quality and achieving diagnostic results nearly equivalent to routine dose scans.
PMID:40013381 | DOI:10.1097/RTI.0000000000000827
Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis
J Evid Based Med. 2025 Mar;18(1):e70005. doi: 10.1111/jebm.70005.
ABSTRACT
OBJECTIVE: Leukemia is a type of blood cancer that begins in the bone marrow and results in high numbers of abnormal white blood cells. Automated detection and classification of leukemia and its subtypes using artificial intelligence (AI) and machine learning (ML) algorithms plays a significant role in the early diagnosis and treatment of this fatal disease. This study aimed to review and synthesize research findings on AI-based approaches in leukemia detection and classification from peripheral blood smear images.
METHODS: A systematic literature search was conducted across four e-databases (Web of Science, PubMed, Scopus, and IEEE Xplore) from January 2015 to March 2023 by searching the keywords "Leukemia," "Machine Learning," and "Blood Smear Image," as well as their synonyms. All original journal articles and conference papers that used ML algorithms in detecting and classifying leukemia were included. The study quality was assessed using the Qiao Quality Assessment tool.
RESULTS: From 1325 articles identified through a systematic search, 190 studies were eligible for this review. The mean validation accuracy (ACC) of the ML methods applied in the reviewed studies was 95.38%. Among different ML methods, modern techniques were mostly considered to detect and classify leukemia (60.53% of studies). Supervised learning was the dominant ML paradigm (79% of studies). Studies utilized common ML methodologies for leukemia detection and classification, including preprocessing, feature extraction, feature selection, and classification. Deep learning (DL) techniques, especially convolutional neural networks, were the most widely used modern algorithms in the mentioned methodologies. Most studies relied on internal validation (87%). Moreover, K-fold cross-validation and train/test split were the commonly employed validation strategies.
CONCLUSION: AI-based algorithms are widely used in detecting and classifying leukemia with remarkable performance. Future studies should prioritize rigorous external validation to evaluate generalizability.
PMID:40013326 | DOI:10.1111/jebm.70005
Detecting Eating and Social Presence with All Day Wearable RGB-T
IEEE Int Conf Connect Health Appl Syst Eng Technol. 2023 Jun;2023:68-79. doi: 10.1145/3580252.3586974. Epub 2024 Jan 22.
ABSTRACT
Social presence has been known to impact eating behavior among people with obesity; however, the dual study of eating behavior and social presence in real-world settings is challenging due to the inability to reliably confirm the co-occurrence of these important factors. High-resolution video cameras can detect timing while providing visual confirmation of behavior; however, their potential to capture all-day behavior is limited by short battery lifetime and lack of autonomy in detection. Low-resolution infrared (IR) sensors have shown promise in automating human behavior detection; however, it is unknown if IR sensors contribute to behavior detection when combined with RGB cameras. To address these challenges, we designed and deployed a low-power, and low-resolution RGB video camera, in conjunction with a low-resolution IR sensor, to test a learned model's ability to detect eating and social presence. We evaluated our system in the wild with 10 participants with obesity; our models displayed slight improvement when detecting eating (5%) and significant improvement when detecting social presence (44%) compared with using a video-only approach. We analyzed device failure scenarios and their implications for future wearable camera design and machine learning pipelines. Lastly, we provide guidance for future studies using low-cost RGB and IR sensors to validate human behavior with context.
PMID:40013103 | PMC:PMC11864367 | DOI:10.1145/3580252.3586974
Approximating Human-Level 3D Visual Inferences With Deep Neural Networks
Open Mind (Camb). 2025 Feb 16;9:305-324. doi: 10.1162/opmi_a_00189. eCollection 2025.
ABSTRACT
Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g., rapid classification of object or scene categories), they often fail in tasks requiring inferences about the underlying shape of objects or scenes. Here, we ask whether and how this gap in 3D shape representation between DNNs and humans can be closed. First, we define the problem space: after generating a stimulus set to evaluate 3D shape inferences using a match-to-sample task, we confirm that standard DNNs are unable to reach human performance. Next, we construct a set of candidate 3D-aware DNNs including 3D neural field (Light Field Network), autoencoder, and convolutional architectures. We investigate the role of the learning objective and dataset by training single-view (the model only sees one viewpoint of an object per training trial) and multi-view (the model is trained to associate multiple viewpoints of each object per training trial) versions of each architecture. When the same object categories appear in the model training and match-to-sample test sets, multi-view DNNs approach human-level performance for 3D shape matching, highlighting the importance of a learning objective that enforces a common representation across viewpoints of the same object. Furthermore, the 3D Light Field Network was the model most similar to humans across all tests, suggesting that building in 3D inductive biases increases human-model alignment. Finally, we explore the generalization performance of multi-view DNNs to out-of-distribution object categories not seen during training. Overall, our work shows that multi-view learning objectives for DNNs are necessary but not sufficient to make similar 3D shape inferences as humans and reveals limitations in capturing human-like shape inferences that may be inherent to DNN modeling approaches. We provide a methodology for understanding human 3D shape perception within a deep learning framework and highlight out-of-domain generalization as the next challenge for learning human-like 3D representations with DNNs.
PMID:40013087 | PMC:PMC11864798 | DOI:10.1162/opmi_a_00189
MRpoxNet: An enhanced deep learning approach for early detection of monkeypox using modified ResNet50
Digit Health. 2025 Feb 16;11:20552076251320726. doi: 10.1177/20552076251320726. eCollection 2025 Jan-Dec.
ABSTRACT
OBJECTIVE: To develop an enhanced deep learning model, MRpoxNet, based on a modified ResNet50 architecture for the early detection of monkeypox from digital skin lesion images, ensuring high diagnostic accuracy and clinical reliability.
METHODS: The study utilized the Kaggle MSID dataset, initially comprising 1156 images, augmented to 6116 images across three classes: monkeypox, non-monkeypox, and normal skin. MRpoxNet was developed by extending ResNet50 from 177 to 182 layers, incorporating additional convolutional, ReLU, dropout, and batch normalization layers. Performance was evaluated using metrics such as accuracy, precision, recall, F1 score, sensitivity, and specificity. Comparative analyses were conducted against established models like ResNet50, AlexNet, VGG16, and GoogleNet.
RESULTS: MRpoxNet achieved a diagnostic accuracy of 98.1%, outperforming baseline models in all key metrics. The enhanced architecture demonstrated superior robustness in distinguishing monkeypox lesions from other skin conditions, highlighting its potential for reliable clinical application.
CONCLUSION: MRpoxNet provides a robust and efficient solution for early monkeypox detection. Its superior performance suggests readiness for integration into diagnostic workflows, with future enhancements aimed at dataset expansion and multimodal adaptability to diverse clinical scenarios.
PMID:40013075 | PMC:PMC11863262 | DOI:10.1177/20552076251320726
Decoding the effects of mutation on protein interactions using machine learning
Biophys Rev (Melville). 2025 Feb 21;6(1):011307. doi: 10.1063/5.0249920. eCollection 2025 Mar.
ABSTRACT
Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.
PMID:40013003 | PMC:PMC11857871 | DOI:10.1063/5.0249920
MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images
Front Med (Lausanne). 2025 Feb 12;12:1507258. doi: 10.3389/fmed.2025.1507258. eCollection 2025.
ABSTRACT
INTRODUCTION: Pulmonary granulomatous nodules (PGN) often exhibit similar CT morphological features to solid lung adenocarcinomas (SLA), making preoperative differentiation challenging. This study aims to address this diagnostic challenge by developing a novel deep learning model.
METHODS: This study proposes MAEMC-NET, a model integrating generative (Masked AutoEncoder) and contrastive (Momentum Contrast) self-supervised learning to learn CT image representations of intra- and inter-solitary nodules. A generative self-supervised task of reconstructing masked axial CT patches containing lesions was designed to learn intra- and inter-slice image representations. Contrastive momentum is used to link the encoder in axial-CT-patch path with the momentum encoder in coronal-CT-patch path. A total of 494 patients from two centers were included.
RESULTS: MAEMC-NET achieved an area under curve (95% Confidence Interval) of 0.962 (0.934-0.973). These results not only significantly surpass the joint diagnosis by two experienced chest radiologists (77.3% accuracy) but also outperform the current state-of-the-art methods. The model performs best on medical images with a 50% mask ratio, showing a 1.4% increase in accuracy compared to the optimal 75% mask ratio on natural images.
DISCUSSION: The proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.
PMID:40012977 | PMC:PMC11861088 | DOI:10.3389/fmed.2025.1507258
A Deep Learning Framework for End-to-End Control of Powered Prostheses
IEEE Robot Autom Lett. 2024 May;9(5):3988-3994. doi: 10.1109/lra.2024.3374189. Epub 2024 Mar 6.
ABSTRACT
Deep learning offers a potentially powerful alternative to hand-tuned control of active lower-limb prostheses, being capable of generating continuous joint-level assistance end-to-end. This eliminates the need for conventional task classification, state machines and mid-level control equations by collapsing the entire control problem into a deep neural network. In this letter, sensor data and conventional commanded torque from an open-source powered knee-ankle prosthesis (OSL) were collected across five locomotion modes: level ground, ramp incline/decline and stair ascent/descent. Reference commanded torques were generated using an expert-tuned finite state machine-based impedance controller for each mode and transfemoral amputee participant (N = 12). Stance phases of the output were then estimated using a temporal convolutional network (TCN), which produced mode- and user-independent knee and ankle torques with RMSE of 0.154 ± 0.06 and 0.106 ± 0.06 Nm/kg, respectively. Training the model on mode-specific data only produced significant reductions in stair descent, lowering knee and ankle RMSE by 0.06 ± 0.028 and 0.033 ± 0.008 Nm/kg respectively (p < 0.05). In addition, the TCN adapted to walking speed and slope shifts in reference commanded torque. These results demonstrate that this deep learning model not only removes the need for heuristic state machines and mode classification but can also reduce or remove the need for prosthesis assistance tuning entirely.
PMID:40012860 | PMC:PMC11864809 | DOI:10.1109/lra.2024.3374189
Paving the way for new antimicrobial peptides through molecular de-extinction
Microb Cell. 2025 Feb 20;12:1-8. doi: 10.15698/mic2025.02.841. eCollection 2025.
ABSTRACT
Molecular de-extinction has emerged as a novel strategy for studying biological molecules throughout evolutionary history. Among the myriad possibilities offered by ancient genomes and proteomes, antimicrobial peptides (AMPs) stand out as particularly promising alternatives to traditional antibiotics. Various strategies, including software tools and advanced deep learning models, have been used to mine these host defense peptides. For example, computational analysis of disulfide bond patterns has led to the identification of six previously uncharacterized β-defensins in extinct and critically endangered species. Additionally, artificial intelligence and machine learning have been utilized to uncover ancient antibiotics, revealing numerous candidates, including mammuthusin, and elephasin, which display inhibitory effects toward pathogens in vitro and in vivo. These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.
PMID:40012704 | PMC:PMC11853161 | DOI:10.15698/mic2025.02.841
Unifying fragmented perspectives with additive deep learning for high-dimensional models from partial faceted datasets
NPJ Biol Phys Mech. 2025;2(1):5. doi: 10.1038/s44341-025-00009-3. Epub 2025 Feb 24.
ABSTRACT
Biological systems are complex networks where measurable functions emerge from interactions among thousands of components. Many studies aim to link biological function with molecular elements, yet quantifying their contributions simultaneously remains challenging, especially at the single-cell level. We propose a machine-learning approach that integrates faceted data subsets to reconstruct a complete view of the system using conditional distributions. We develop both polynomial regression and neural network models, validated with two examples: a mechanical spring network under external forces and an 8-dimensional biological network involving the senescence marker P53, using single-cell data. Our results demonstrate successful system reconstruction from partial datasets, with predictive accuracy improving as more variables are measured. This approach offers a systematic method to integrate fragmented experimental data, enabling unbiased and holistic modeling of complex biological functions.
PMID:40012561 | PMC:PMC11850287 | DOI:10.1038/s44341-025-00009-3
Plant species richness promotes the decoupling of leaf and root defence traits while species-specific responses in physical and chemical defences are rare
New Phytol. 2025 Feb 27. doi: 10.1111/nph.20434. Online ahead of print.
ABSTRACT
The increased positive impact of plant diversity on ecosystem functioning is often attributed to the accumulation of mutualists and dilution of antagonists in diverse plant communities. While increased plant diversity alters traits related to resource acquisition, it remains unclear whether it reduces defence allocation, whether this reduction differs between roots and leaves, or varies among species. To answer these questions, we assessed the effect of plant species richness, plant species identity and their interaction on the expression of 23 physical and chemical leaf and fine root defence traits of 16 plant species in a 19-yr-old biodiversity experiment. Only leaf mass per area, leaf and root dry matter content and root nitrogen, traits associated with both, resource acquisition and defence, responded consistently to species richness. However, species richness promoted a decoupling of these defences in leaves and fine roots, possibly in response to resource limitations in diverse communities. Species-specific responses were rare and related to chemical defence and mutualist collaboration, likely responding to species-specific antagonists' dilution and mutualists' accumulation. Overall, our study suggests that resource limitation in diverse communities might mediate the relationship between plant defence traits and antagonist dilution.
PMID:40013369 | DOI:10.1111/nph.20434
A review of mathematical modeling of bone remodeling from a systems biology perspective
Front Syst Biol. 2024;4:1368555. doi: 10.3389/fsysb.2024.1368555. Epub 2024 Apr 8.
ABSTRACT
Bone remodeling is an essential, delicately balanced physiological process of coordinated activity of bone cells that remove and deposit new bone tissue in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important aspects frequently found in mathematical models for bone remodeling and discuss how and why these aspects are included when considering the physiology of the bone basic multicellular unit, which is the term used for the collection of cells responsible for bone remodeling. The review also emphasizes the view of bone remodeling from a systems biology perspective. Understanding the systemic mechanisms involved in remodeling will help provide information on bone pathology associated with aging, endocrine disorders, cancers, and inflammatory conditions and enhance systems pharmacology. Furthermore, some features of the bone remodeling cycle and interactions with other organ systems that have not yet been modeled mathematically are discussed as promising future directions in the field.
PMID:40012834 | PMC:PMC11864782 | DOI:10.3389/fsysb.2024.1368555
The LAM Is Not Enough-An Idea to Watch Regarding Adipose Tissue Macrophages and Their Disease Relevance: Why Lipid-Associated Macrophage (LAM) Accumulation in Adipose Tissue Is a Systems Biology Problem
Bioessays. 2025 Feb 26:e202500020. doi: 10.1002/bies.202500020. Online ahead of print.
NO ABSTRACT
PMID:40012408 | DOI:10.1002/bies.202500020
Erratum to 'Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial' [Clin Mol Hepatol 2024;30:807-823]
Clin Mol Hepatol. 2025 Feb 27. doi: 10.3350/cmh.2024.0333e. Online ahead of print.
NO ABSTRACT
PMID:40012401 | DOI:10.3350/cmh.2024.0333e
Everolimus induced pneumonitis in a liver transplant patient: Dilemma in the discrimination of pneumonia
Turk J Surg. 2025 Feb 27;41(1):105-107. doi: 10.47717/turkjsurg.2022.5489.
ABSTRACT
Everolimus is one of the immunosuppressive drugs used in solid organ transplantation. Many side effects have been described for these immunosuppressive drugs, similar to other drugs in this category. The purpose of this case presentation is to draw attention to drug-induced pneumonitis, which is a rare and life-threatening side effect of everolimus. A nineteen-year-old female patient who received liver transplantation for toxic hepatitis was admitted to our institute with cough and dyspnea. Everolimus had been started in conjunction with tacrolimus therapy 6 months prior to admission. Her chest imaging were consistent with pneumonitis. Markers of infection and cultures were all negative. After discontinuation of everolimus, symptoms and radiological findings resolved. The adverse effects of the drug should be kept in mind while investigating possible infectious agents in liver transplant recipients who are prone to opportunistic infections.
PMID:40012361 | DOI:10.47717/turkjsurg.2022.5489
Retrospective Analysis of the Safety of High-Volume Dental Articaine Preparations for Japanese Patients
Acta Med Okayama. 2025 Feb;79(1):31-37. doi: 10.18926/AMO/68356.
ABSTRACT
We retrospectively analyzed the safety of the use of articaine, an amide-type local anesthetic, in Japanese dental patients (n=300) treated in Thailand in 2015-2017. The dosage, adverse events (AEs) caused by local anesthesia, and treatment efficacy were examined. Articaine, which is safe for patients with liver impairments due to its unique metabolism, has not been thoroughly tested in Japan for doses above 5.1 mL. Eighty of the present patients had undergone root canal treatment (RCT), 71 underwent tooth extraction, and 149 underwent implant-related surgery. More than three articaine cartridges were used in 41 patients, and no AEs occurred in these cases. The only AE occurred in a 52-year-old woman who was treated with three cartridges and presented with what appeared to be hyperventilation syndrome; she later recovered and received her dental treatment as scheduled. Most treatments were completed with three or fewer cartridges, suggesting that this number is generally sufficient. Our findings, particularly the low AE risk even with doses exceeding three cartridges, support the potential applicability of the overseas recommended maximum dose of articaine (7 mg/kg) in Japanese patients. This conclusion is significant for advancing dental anesthetic practices and ensuring patient safety and treatment efficacy in Japan.
PMID:40012157 | DOI:10.18926/AMO/68356
Characteristics and patterns of adverse event reports in the Japanese Adverse Drug Event Report database over two decades (2004-2023): Exploring findings on sexes and age groups
Drug Discov Ther. 2025 Mar 6;19(1):10-21. doi: 10.5582/ddt.2024.01090. Epub 2025 Feb 26.
ABSTRACT
Recently, increased attention has been paid to the consideration of individual characteristics, including sex and age, in the context of medication use and adverse events. However, the characteristics and patterns of adverse events reported in the Japanese Adverse Drug Event Report (JADER) database stratified by sex and age have not yet been clarified. This study aimed to clarify the characteristics and patterns of adverse event reports in the JADER database over a 20-year period (April 2004-March 2024). Data were stratified into 20 groups based on sex and age (aged 0-9 years, 10-19 years, 20-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, 80-89 years, and ≥90 years). The female/male ratio of adverse event reports in JADER was 0.95. The largest group comprised males in their 70s. Adjusting for the proportion of adverse event reports in each group according to the demographic composition in 2015 highlighted that the reporting rates of adverse events were higher in people aged ≥70 years and that females aged 20-49 years reported more adverse events than males. Medical history, causative drugs, and adverse events reported to JADER were characterized by combinations of sex and age. Our results provide additional insights into the interpretation of previous studies using JADER. In addition, the results of this study will help understand the characteristics of adverse event reports contained in JADER and conduct appropriate subgroup and sensitivity analyses.
PMID:40010736 | DOI:10.5582/ddt.2024.01090
Prioritisation of head, neck, and respiratory outcomes in mucopolysaccharidosis type II: lessons from a rare disease consensus exercise and comparison of parental and clinical priorities
Orphanet J Rare Dis. 2025 Feb 26;20(1):88. doi: 10.1186/s13023-025-03581-y.
ABSTRACT
BACKGROUND: The mucopolysaccharidoses are a group of rare, inherited metabolic disorders. MPS II is a X-linked recessive disease, also known as Hunter syndrome. Clinical manifestations include upper and lower respiratory tract, and head and neck pathologies influencing quality of life, morbidity, and mortality. Medical and surgical intervention outcomes for MPS are reported inconsistently, creating a challenge when synthesising and contrasting evidence. This study set out to address the inconsistency in outcome measurement in this field. International recommendations for developing a core outcome set were adopted. Available data from qualitative studies and outcomes from a modified e-Delphi surveys were used to develop a list of candidate outcomes for consideration. Three consensus meetings with patients diagnosed with MPS II alongside their parents/carers were ran to help finalise a list of outcome domains.
RESULTS: Survival, airway obstruction, and quality of life were outcomes identified as important to always measure in all MPS II clinical trials and/or in clinical practice. Other outcomes for younger children included swallowing difficulties, cognitive development, ability to participate in education, and communication. The adolescent group included safety of chewing and swallowing, complications of anaesthesia, sleep quality and apnoea, nasal problems, and chronic otitis media. The adult group identified sleep apnoea, and hearing, as additional outcomes to measure.
CONCLUSIONS: A novel methodology for determining a core outcome set in rare diseases has been recommended. Both functional and quality of life outcomes were identified by the three age groups of individuals and/or their parents. Adoption of these sets of outcomes in future clinical trials and/or clinical practice will enable comparison of outcomes reported.
PMID:40011961 | DOI:10.1186/s13023-025-03581-y
Rare disease challenges and potential actions in the Middle East
Int J Equity Health. 2025 Feb 26;24(1):56. doi: 10.1186/s12939-025-02388-4.
ABSTRACT
BACKGROUND: Rare diseases, defined variably by global regions, collectively impact approximately 300 million individuals despite affecting small population segments individually. Historically there were no treatments developed for these conditions, leading to significant care challenges. Public interventions have incentivized treatment development, yet up to this day, many rare disease patients are deprived of timely diagnosis and treatment in comparison to patients with more common diseases. This study evaluates the challenges that rare disease patients and healthcare systems face in the Middle East and North Africa (MENA), seeking strategies to enhance treatment accessibility.
METHODS: We followed a three-step approach for the study. First, we searched scientific publications and grey literature for the global challenges faced by rare disease patients. Our search also collected information on orphan drug regulations implemented in different countries. Subsequently, we used the findings to conduct a survey to pharmaceutical company representatives across three countries in the region (The Kingdom of Saudi Arabia, Egypt, and the United Arab Emirates). The survey assessed the challenges facing rare disease patients in the MENA region and the policies that have been implemented to overcome these challenges. The survey was then followed by governmental expert interviews to validate the survey responses and provide recommendations to mitigate the challenges.
RESULTS: The literature and survey results revealed several challenges facing rare diseases, including lack of awareness, difficulty in acquiring marketing authorization and reimbursing orphan drugs. Validation meetings provided recommendations to mitigate such challenges in the selected countries. For instance, the collaboration between the Ministry of Health and pharmaceutical companies was recommended to improve rare diseases care. A separate registration process for orphan drugs with clear criteria and timelines was suggested. A differential cost-effectiveness threshold for orphan drugs was recommended. It was also recommended to establish a definition for rare diseases and to increase the utilization of managed entry agreements for orphan drugs.
CONCLUSIONS: Rare diseases present challenges in the MENA region and globally, requiring focused attention and innovative solutions. By implementing comprehensive strategies that consider both economic efficiency and fairness, healthcare systems can better serve rare disease patients and improve their quality of life.
PMID:40011905 | DOI:10.1186/s12939-025-02388-4
Sodium valproate, a potential repurposed treatment for the neurodegeneration in Wolfram syndrome (TREATWOLFRAM): trial protocol for a pivotal multicentre, randomised double-blind controlled trial
BMJ Open. 2025 Feb 26;15(2):e091495. doi: 10.1136/bmjopen-2024-091495.
ABSTRACT
INTRODUCTION: Wolfram syndrome (WFS1-Spectrum Disorder) is an ultra-rare monogenic form of progressive neurodegeneration and diabetes mellitus. In common with most rare diseases, there are no therapies to slow or stop disease progression. Sodium valproate, an anticonvulsant with neuroprotective properties, is anticipated to mediate its effect via alteration of cell cycle kinetics, increases in p21cip1 expression levels and reduction in apoptosis and increase in Wolframin protein expression. To date, there have been no multicentre randomised controlled trials investigating the efficacy of treatments for neurodegeneration in patients with Wolfram syndrome.
METHODS AND ANALYSIS: TREATWOLFRAM is an international, multicentre, double-blind, placebo-controlled, randomised clinical trial designed to investigate whether 36-month treatment with up to 40 mg/kg/day of sodium valproate will slow the rate of loss of visual acuity as a biomarker for neurodegeneration in patients with Wolfram syndrome. Patients who satisfied the eligibility criteria were randomly assigned (2:1) to receive two times per day oral gastro-resistant sodium valproate tablets up to a maximum dose of 800 mg 12 hourly or sodium valproate-matched placebo. Using hierarchical repeated measures analyses with a 5% significance level, 80% power and accounting for an estimated 15% missing data rate, a sample size of 70 was set. The primary outcome measure, visual acuity, will be centrally reviewed and analysed on an intention-to-treat population.
ETHICS AND DISSEMINATION: The protocol was approved by the National Research Ethics Service (West of Scotland; 18/WS/0020) and by the Medicines and Healthcare products Regulatory Agency. Recruitment into TREATWOLFRAM started in January 2019 and ended in November 2021. The treatment follow-up of TREATWOLFRAM participants is ongoing and due to finish in November 2024. Updates on trial progress are disseminated via Wolfram Syndrome UK quarterly newsletters and at family conferences for patient support groups. The findings of this trial will be disseminated through peer-reviewed publications and international presentations.
TRIAL REGISTRATION NUMBER: NCT03717909.
PMID:40010822 | DOI:10.1136/bmjopen-2024-091495
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