J Orthop Surg Res. 2025 Jan 11;20(1):41. doi: 10.1186/s13018-024-05334-2.

ABSTRACT

PURPOSE: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.

METHODS: We conducted a retrospective multicenter diagnostic study using 560 full-spine sagittal plane X-ray images from five hospitals in Inner Mongolia. The model was trained and validated using 500 images, with an additional 60 images for independent external validation. We evaluated the consistency of keypoint annotations among different physicians, the accuracy of model-predicted keypoints, and the accuracy of model measurement results compared to manual measurements.

RESULTS: The consistency percentages of keypoint annotations among different physicians and the model were 90-97% within the 4-mm range. The model's prediction accuracies for key points were 91-100% within the 4-mm range compared to the reference standards. The model's predictions for 15 anatomical parameters showed high consistency with experienced physicians, with intraclass correlation coefficients ranging from 0.892 to 0.991. The mean absolute error for SVA was 1.16 mm, and for other parameters, it ranged from 0.22° to 3.32°. A significant challenge we faced was the variability in data formats and specifications across different hospitals, which we addressed through data augmentation techniques. The model took an average of 9.27 s to automatically measure the 15 anatomical parameters per X-ray image.

CONCLUSION: The deep learning model based on RTMpose can effectively enhance clinical efficiency by automatically measuring the sagittal plane parameters of the spine in X-rays of patients with AIS. The model's performance was found to be highly consistent with manual measurements by experienced physicians, offering a valuable tool for clinical diagnostics.

PMID:39799363 | DOI:10.1186/s13018-024-05334-2

BMC Bioinformatics. 2025 Jan 11;26(1):10. doi: 10.1186/s12859-025-06033-3.

ABSTRACT

Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models. Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. Subsequently, we analyze the relations among peptide sequences, manually extracted features, and automatically inferred information by deep learning models, leading to observations that the inferred information is more comprehensive and non-redundant for the task of predicting AMPs. Moreover, this approach alleviates the impact of the scarcity of positive data and demonstrates great potential in future research and applications.

PMID:39799358 | DOI:10.1186/s12859-025-06033-3

Commun Biol. 2025 Jan 11;8(1):43. doi: 10.1038/s42003-025-07469-2.

ABSTRACT

Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a generative adversarial network (GAN) training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality.

PMID:39799275 | DOI:10.1038/s42003-025-07469-2

NPJ Precis Oncol. 2025 Jan 11;9(1):11. doi: 10.1038/s41698-024-00778-5.

ABSTRACT

Cervical cancer remains the fourth most common cancer among women worldwide. This study proposes an end-to-end deep learning framework to predict consensus molecular subtypes (CMS) in HPV-positive cervical squamous cell carcinoma (CSCC) from H&E-stained histology slides. Analysing three CSCC cohorts (n = 545), we show our Digital-CMS scores significantly stratify patients by both disease-specific (TCGA p = 0.0022, Oslo p = 0.0495) and disease-free (TCGA p = 0.0495, Oslo p = 0.0282) survival. In addition, our extensive tumour microenvironment analysis reveals differences between the two CMS subtypes, with CMS-C1 tumours exhibit increased lymphocyte presence, while CMS-C2 tumours show high nuclear pleomorphism, elevated neutrophil-to-lymphocyte ratio, and higher malignancy, correlating with poor prognosis. This study introduces a potentially clinically advantageous Digital-CMS score derived from digitised WSIs of routine H&E-stained tissue sections, offers new insights into TME differences impacting patient prognosis and potential therapeutic targets, and identifies histological patterns serving as potential surrogate markers of the CMS subtypes for clinical application.

PMID:39799271 | DOI:10.1038/s41698-024-00778-5

NPJ Digit Med. 2025 Jan 12;8(1):23. doi: 10.1038/s41746-024-01418-9.

ABSTRACT

The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results. The latent space ECG model identified associations with 645 prevalent and 606 incident Phecodes. Associations were most enriched in the circulatory (n = 140, 82% of category-specific Phecodes), respiratory (n = 53, 62%) and endocrine/metabolic (n = 73, 45%) categories, with additional associations across the phenome. The strongest ECG association was with hypertension (p < 2.2×10-308). The ECG latent space model demonstrated more associations than models using standard ECG intervals, and offered favorable discrimination of prevalent disease compared to models comprising age, sex, and race. We further demonstrate how latent space models can be used to generate disease-specific ECG waveforms and facilitate individual disease profiling.

PMID:39799251 | DOI:10.1038/s41746-024-01418-9

Sci Rep. 2025 Jan 11;15(1):1670. doi: 10.1038/s41598-025-85632-9.

ABSTRACT

This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFormerB0 network is trained to provide an initial segmentation. In the second phase, the network is further refined using the Pix2Pix structure, which enhances attention to details and corrects any erroneous or additional segments in the output. This hybrid method effectively combines the strengths of both SegFormer and Pix2Pix to produce highly accurate segmentation results. We have assembled the Spleenex dataset, consisting of 450 ultrasound images of the spleen, which is the first dataset of its kind in this field. Our method has been validated on this dataset, and the experimental results show that it outperforms existing state-of-the-art models. Specifically, our approach achieved a mean Intersection over Union (mIoU) of 94.17% and a mean Dice (mDice) score of 96.82%, surpassing models such as Splenomegaly Segmentation Network (SSNet), U-Net, and Variational autoencoder based methods. The proposed method also achieved a Mean Percentage Length Error (MPLE) of 3.64%, further demonstrating its accuracy. Furthermore, the proposed method has demonstrated strong performance even in the presence of noise in ultrasound images, highlighting its practical applicability in clinical environments.

PMID:39799236 | DOI:10.1038/s41598-025-85632-9

Sci Rep. 2025 Jan 11;15(1):1736. doi: 10.1038/s41598-024-84193-7.

ABSTRACT

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) models in practical settings limits their clinical application. This study aims to review and analyze current SOTA deep learning models for lung cancer risk prediction (malignant-benign classification). To evaluate our model's general performance, we selected 253 out of 467 patients from a subset of the National Lung Screening Trial (NLST) who had CT scans without contrast, which are the most commonly used, and divided them into training and test cohorts. The CT scans were preprocessed into 2D-image and 3D-volume formats according to their nodule annotations. We evaluated ten 3D and eleven 2D SOTA deep learning models, which were pretrained on large-scale general-purpose datasets (Kinetics and ImageNet) and radiological datasets (3DSeg-8, nnUnet and RadImageNet), for their lung cancer risk prediction performance. Our results showed that 3D-based deep learning models generally perform better than 2D models. On the test cohort, the best-performing 3D model achieved an AUROC of 0.86, while the best 2D model reached 0.79. The lowest AUROCs for the 3D and 2D models were 0.70 and 0.62, respectively. Furthermore, pretraining on large-scale radiological image datasets did not show the expected performance advantage over pretraining on general-purpose datasets. Both 2D and 3D deep learning models can handle lung cancer risk prediction tasks effectively, although 3D models generally have superior performance than their 2D competitors. Our findings highlight the importance of carefully selecting pretrained datasets and model architectures for lung cancer risk prediction. Overall, these results have important implications for the development and clinical integration of DL-based tools in lung cancer screening.

PMID:39799226 | DOI:10.1038/s41598-024-84193-7

Sci Rep. 2025 Jan 11;15(1):1726. doi: 10.1038/s41598-025-85866-7.

ABSTRACT

Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that would help to prevent the attack.Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) are the vector figures incorporated into the study through the results. These models are subjected to various test to established the best results on the identification and prevention of network violation. Based on the obtained results, it can be stated that all the tested models are capable of organizing data originating from network traffic. thus, recognizing the difference between normal and intrusive behaviors, models such as SVM, KNN, RF, and DT showed effective results. Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. For the companies seeking IDS solutions which are reliable and at the same time more interpretable, these models can be promising. Additionally, LSTM and ANN, with their ability to catch successive conditions, are suitable for situations involving nuanced, advancing dangers.

PMID:39799225 | DOI:10.1038/s41598-025-85866-7

Sci Rep. 2025 Jan 11;15(1):1718. doi: 10.1038/s41598-025-85240-7.

ABSTRACT

The effective prevention of many infectious and non-infectious diseases relies on people concurrently adopting multiple prevention behaviors. Individual characteristics, opinion leaders, and social networks have been found to explain why people take up specific prevention behaviors. However, it remains challenging to understand how these factors shape multiple interdependent behaviors. We propose a multilevel social network framework that allows us to study the effects of individual and social factors on multiple disease prevention behaviors simultaneously. We apply this approach to examine the factors explaining eight malaria prevention behaviors, using unique interview data collected from 1529 individuals in 10 hard-to-reach, malaria-endemic villages in Meghalaya, India in 2020-2022. Statistical network modelling reveals exposure to similar behaviors in one's social network as the most important factor explaining prevention behaviors. Further, we find that households indirectly shape behaviors as key contexts for social ties. Together, these two factors are crucial for explaining the observed patterns of behaviors and social networks in the data, outweighing individual characteristics, opinion leaders, and social network size. The results highlight that social network processes may facilitate or hamper disease prevention efforts that rely on a combination of behaviors. Our approach is well suited to study these processes in the context of various diseases.

PMID:39799220 | DOI:10.1038/s41598-025-85240-7

Bioorg Chem. 2025 Jan 3;155:108127. doi: 10.1016/j.bioorg.2025.108127. Online ahead of print.

ABSTRACT

Novel pH-sensitive polymeric photosensitizer carriers from the phthalocyanine (Pc) group were investigated as potential photodynamic therapy drugs for the treatment of breast cancer. Their high antiproliferative activity was confirmed by photocytotoxicity studies, which indicated their high efficacy and specificity toward the SK-BR-3 cell line. Importantly, the Pcs encapsulated in the polymeric nanoparticle (NP) carrier exhibited a much better penetration into the acidic environment of tumor cells than their free form. The investigated Pc4-NPs and TT1-NPs exhibited a high selectivity to healthy fibroblasts as well as non-toxicity without irradiation. This paper describes the detailed mechanism of action of the evaluated compounds by measuring reactive oxygen species (ROS), including singlet oxygen; imaging cellular localization; and analyzing key signaling pathway proteins. An additional advantage of the evaluated compounds is their ability to inhibit the Akt protein expression, including its phosphorylation, which the Western blot test confirmed. This is particularly important because breast cancers often overexpress the HER-2 receptor-related signaling proteins. Moreover, an analysis of proteins such as GLUT-1, HO-1, phospho-p42/44, and BID revealed the significant involvement of ROS in disrupting cellular homeostasis, thereby leading to the induction of oxidative stress and resulting in apoptotic cell death.

PMID:39798455 | DOI:10.1016/j.bioorg.2025.108127

Food Chem. 2024 Dec 29;471:142691. doi: 10.1016/j.foodchem.2024.142691. Online ahead of print.

ABSTRACT

Gluten-free products must not contain more than 20 mg/kg of gluten to be safe for consumption by celiac disease patients. Almost all analytical methods are calibrated to wheat, wheat gluten or gliadin, and there is no rye-specific reference material available. The aim of this study was to assess the effect of the harvest year on rye gluten composition and to generate distinct rye isolates to serve as calibration standards. Four different extraction procedures of a specific rye cultivar mixture were tested yielding prolamins (PROL), glutelins (GLUT), gluten (G) and acetonitrile/water-extractable proteins (AWEP). The isolates were characterized using different methods such as RP-HPLC, GP-HPLC, SDS-PAGE and LC-MS/MS. The isolates were evaluated in the R5 ELISA which resulted in the following response order: PROLiso > AWEPiso > Giso > GLUTiso. This paper represents a significant step towards improving gluten analysis, particularly in the context of rye-contaminated gluten-free products.

PMID:39798370 | DOI:10.1016/j.foodchem.2024.142691

Virology. 2025 Jan 7;603:110397. doi: 10.1016/j.virol.2025.110397. Online ahead of print.

ABSTRACT

The influenza A virus evades the host innate immune response to establish infection by inhibiting RIG-I activation through its nonstructural protein 1 (NS1). Here, we reported that receptor-transporting protein 4 (RTP4), an interferon-stimulated gene (ISG), targets NS1 to inhibit influenza A virus infection. Depletion of RTP4 significantly increased influenza A virus multiplication, while NS1-deficient viruses were unaffected. Mechanistically, RTP4 interacts with NS1 in an RNA-dependent manner and sequesters it from the TRIM25-RIG-I complex, thereby restoring TRIM25-mediated RIG-I K63-linked ubiquitination and subsequent activation of IRF3. Antiviral activity of RTP4 requires the evolutionarily conserved CXXC motifs and an H149 residue in the zinc finger domain, mutations of which disrupted RTP4-NS1 interaction and abrogated the ability of RTP4 to rescue RIG-I-mediated signaling. Collectively, our findings provided insights into the mechanism by which an ISG restricts influenza A virus replication by reactivating host antiviral signaling.

PMID:39798334 | DOI:10.1016/j.virol.2025.110397

J Comput Chem. 2025 Jan 15;46(2):e70029. doi: 10.1002/jcc.70029.

ABSTRACT

Phosphodiesterase 5 (PDE5) inhibitors have shown great potential in treating Alzheimer's disease by improving memory and cognitive function. In this study, we evaluated fluspirilene, a drug commonly used to treat schizophrenia, as a potential PDE5 inhibitor using computational methods. Molecular docking revealed that fluspirilene binds strongly to PDE5, supported by hydrophobic and aromatic interactions. Molecular dynamics simulations confirmed that the fluspirilene-PDE5 complex is stable and maintains its structural integrity over time. Binding energy calculations further highlighted favorable interactions, indicating that the drug forms a strong and stable bond with PDE5. Additional analyses, including studies of protein dynamics and energy landscape mapping, revealed how the drug interacts dynamically with PDE5, adapting to different conformations and maintaining stability. These findings suggest that fluspirilene may modulate PDE5 activity, potentially offering therapeutic benefits for Alzheimer's disease. This study provides strong evidence for repurposing fluspirilene as a treatment for Alzheimer's and lays the foundation for further experimental and clinical investigations.

PMID:39797567 | DOI:10.1002/jcc.70029

Cancers (Basel). 2025 Jan 3;17(1):132. doi: 10.3390/cancers17010132.

ABSTRACT

BACKGROUND/OBJECTIVES: Intrahepatic cholangiocarcinoma (iCCA) is a malignant liver tumor with a rising global incidence and poor prognosis, largely due to late-stage diagnosis and limited effective treatment options. Standard chemotherapy regimens, including cisplatin and gemcitabine, often fail because of the development of multidrug resistance (MDR), leaving patients with few alternative therapies. Doxycycline, a tetracycline antibiotic, has demonstrated antitumor effects across various cancers, influencing cancer cell viability, apoptosis, and stemness. Based on these properties, we investigated the potential of doxycycline to overcome gemcitabine resistance in iCCA.

METHODS: We evaluated the efficacy of doxycycline in two MDR iCCA cell lines, MT-CHC01R1.5 and 82.3, assessing cell cycle perturbation, apoptosis induction, and stem cell compartment impairment. We assessed the in vivo efficacy of combining doxycycline and gemcitabine in mouse xenograft models.

RESULTS: Treatment with doxycycline in both cell lines resulted in a significant reduction in cell viability (IC50 ~15 µg/mL) and induction of apoptosis. Doxycycline also diminished the cancer stem cell population, as indicated by reduced cholangiosphere formation. In vivo studies showed that while neither doxycycline nor gemcitabine alone significantly reduced tumor growth, their combination led to marked decreases in tumor volume and weight at the study endpoint. Additionally, metabolic analysis revealed that doxycycline reduced glucose uptake in tumors, both as a monotherapy and more effectively in combination with gemcitabine.

CONCLUSIONS: These findings suggest that doxycycline, especially in combination with gemcitabine, can restore chemotherapy sensitivity in MDR iCCA, providing a promising new strategy for improving outcomes in this challenging disease.

PMID:39796759 | DOI:10.3390/cancers17010132

Foods. 2025 Jan 2;14(1):102. doi: 10.3390/foods14010102.

ABSTRACT

Oncostatin M (OSM) plays a crucial role in diverse inflammatory reactions. Although the food bioactive compound naringenin (NAR) exerts various useful effects, including antitussive, anti-inflammatory, hepatoprotective, renoprotective, antiarthritic, antitumor, antioxidant, neuroprotective, antidepressant, antinociceptive, antiatherosclerotic, and antidiabetic effects, the modulatory mechanism of NAR on OSM expression in neutrophils has not been specifically reported. In the current work, we studied whether NAR modulates OSM release in neutrophil-like differentiated (d)HL-60 cells. To assess the modulatory effect of NAR, enzyme-linked immunosorbent assay (ELISA), quantitative real-time polymerase chain reaction (qRT-PCR), Western blotting, and immunofluorescence assay were employed. While exposure to granulocyte-macrophage colony-stimulating factor (GM-CSF) induced elevated OSM release and mRNA expression, the elevated OSM release and mRNA expression were diminished by the addition of NAR in dHL-60 cells. While the phosphorylation of phosphatidylinositol 3-kinase, protein kinase B (Akt), and nuclear factor (NF)-κB was upregulated by exposure to GM-CSF, the upregulated phosphorylation was inhibited by the addition of NAR in dHL-60 cells. Consequently, the results indicate that the food bioactive compound NAR may have a positive effect on health (in health promotion and improvement) or may play a role in the prevention of inflammatory diseases.

PMID:39796391 | DOI:10.3390/foods14010102

Int J Mol Sci. 2025 Jan 3;26(1):364. doi: 10.3390/ijms26010364.

ABSTRACT

This review describes our current understanding of the role of the mitochondria in the repurposing of the anti-diabetes drugs metformin, gliclazide, GLP-1 receptor agonists, and SGLT2 inhibitors for additional clinical benefits regarding unhealthy aging, long COVID, mental neurogenerative disorders, and obesity. Metformin, the most prominent of these diabetes drugs, has been called the "Drug of Miracles and Wonders," as clinical trials have found it to be beneficial for human patients suffering from these maladies. To promote viral replication in all infected human cells, SARS-CoV-2 stimulates the infected liver cells to produce glucose and to export it into the blood stream, which can cause diabetes in long COVID patients, and metformin, which reduces the levels of glucose in the blood, was shown to cut the incidence rate of long COVID in half for all patients recovering from SARS-CoV-2. Metformin leads to the phosphorylation of the AMP-activated protein kinase AMPK, which accelerates the import of glucose into cells via the glucose transporter GLUT4 and switches the cells to the starvation mode, counteracting the virus. Diabetes drugs also stimulate the unfolded protein response and thus mitophagy, which is beneficial for healthy aging and mental health. Diabetes drugs were also found to mimic exercise and help to reduce body weight.

PMID:39796218 | DOI:10.3390/ijms26010364

Int J Mol Sci. 2025 Jan 1;26(1):315. doi: 10.3390/ijms26010315.

ABSTRACT

Eumycetoma, a chronic fungal infection caused by Madurella mycetomatis, is a neglected tropical disease characterized by tumor-like growths that can lead to permanent disability and deformities if untreated. Predominantly affecting regions in Africa, South America, and Asia, it imposes significant physical, social, and economic burdens. Current treatments, including antifungal drugs like itraconazole, often show variable efficacy, with severe cases necessitating surgical intervention or amputation. Drug discovery for eumycetoma faces challenges due to limited understanding of the disease's molecular mechanisms and the lack of 3D structures for key targets such as Madurella mycetomatis CYP51, a well-known target for azoles' antifungal agents. To address these challenges, this study employed computational approaches, including homology modeling, virtual screening, free energy calculations, and molecular dynamics simulations, to repurpose FDA-approved drugs as potential treatments for eumycetoma targeting Madurella mycetomatis CYP51. To this end, a library of 2619 FDA-approved drugs was screened, identifying three promising candidates: montelukast, vilanterol, and lidoflazine. These compounds demonstrated favorable binding affinities, strong interactions with critical residues of the homology model of Madurella mycetomatis CYP51, and stability in molecular dynamics simulations, offering potential for further investigation as effective therapeutic options for eumycetoma.

PMID:39796172 | DOI:10.3390/ijms26010315

Int J Mol Sci. 2025 Jan 3;26(1):353. doi: 10.3390/ijms26010353.

ABSTRACT

Fucosidosis is a rare lysosomal storage disease caused by α-L-fucosidase deficiency following a mutation in the FUCA1 gene. This enzyme is responsible for breaking down fucose-containing glycoproteins, glycolipids, and oligosaccharides within the lysosome. Mutations in FUCA1 result in either reduced enzyme activity or complete loss of function, leading to the accumulation of fucose-rich substrates in lysosomes. Lysosomes become engorged with undigested substrates, which leads to secondary storage defects affecting other metabolic pathways. The central nervous system is particularly vulnerable, with lysosomal dysfunction causing microglial activation, inflammation, and neuronal loss, leading to the neurodegenerative symptoms of fucosidosis. Neuroinflammation contributes to secondary damage, including neuronal apoptosis, axonal degeneration, and synaptic dysfunction, exacerbating the disease process. Chronic neuroinflammation impairs synaptic plasticity and neuronal survival, leading to progressive intellectual disability, learning difficulties, and loss of previously acquired skills. Inflammatory cytokines and lysosomal burden in motor neurons and associated pathways contribute to ataxia, spasticity, and hypotonia, which are common motor symptoms in fucosidosis. Elevated neuroinflammatory markers can increase neuronal excitability, leading to the frequent occurrence of epilepsy in affected individuals. So, fucosidosis is characterized by rapid mental and motor loss, along with growth retardation, coarse facial features, hepatosplenomegaly, telangiectasis or angiokeratomas, epilepsy, inguinal hernia, and dysostosis multiplex. Patients usually die at an early age. Treatment of fucosidosis is a great challenge, and there is currently no definitive effective treatment. Hematopoietic cell transplantation studies are ongoing in the treatment of fucosidosis. However, early diagnosis of this disease and treatment can be effective. In addition, the body's immune system decreases due to chemotherapy applied after transplantation, leaving the body vulnerable to microbes and infections, and the risk of death is high with this treatment. In another treatment method, gene therapy, the use of retroviral vectors, is promising due to their easy integration, high cell efficiency, and safety. In another treatment approach, enzyme replacement therapy, preclinical studies are ongoing for fucosidosis, but the blood-brain barrier is a major obstacle in lysosomal storage diseases affecting the central nervous system. Early diagnosis is important in fucosidosis, a rare disease, due to the delay in the diagnosis of patients identified so far and the rapid progression of the disease. In addition, enzyme replacement therapy, which carries fewer risks, is promising.

PMID:39796208 | DOI:10.3390/ijms26010353

Int J Mol Sci. 2024 Dec 27;26(1):135. doi: 10.3390/ijms26010135.

ABSTRACT

DNA methylation is an essential epigenetic modification that plays a crucial role in regulating gene expression and maintaining genomic stability. With the advancement in sequencing technology, methylation studies have provided valuable insights into the diagnosis of rare diseases through the various identification of episignatures, epivariation, epioutliers, and allele-specific methylation. However, current methylation studies are not without limitations. This mini-review explores the current understanding of DNA methylation in rare diseases, highlighting the key mechanisms and diagnostic potential, and emphasizing the need for advanced methodologies and integrative approaches to enhance the understanding of disease progression and design more personable treatment for patients, given the nature of rare diseases.

PMID:39795993 | DOI:10.3390/ijms26010135

J Clin Med. 2024 Dec 25;14(1):24. doi: 10.3390/jcm14010024.

ABSTRACT

Background/Objectives: Periodontitis is an inflammatory disease induced by bacteria in dental plaque that can activate the host's immune-inflammatory response and invade the bloodstream. We hypothesized that a higher periodontal inflamed surface area (PISA) is associated with higher levels of inflammatory biomarkers, lower levels of antioxidants, and mitochondrial DNA copy number (mtDNAcn). Methods: Using periodontal parameters, we calculated the PISA score, measured the levels of inflammatory biomarkers and antioxidants in the serum, and took buccal swabs for mtDNA and nuclear DNA (nDNA) extraction. Results: Higher PISA was associated with higher CRP levels, higher leukocyte, neutrophil, and erythrocyte counts, and lower magnesium-to-calcium ratio, but not with mtDNAcn. A higher number of deep pockets was associated with higher leukocytes and neutrophil counts and higher uric acid levels. Conclusions: The PISA score might be an appropriate parameter to assess the inflammatory burden of periodontitis, but not to assess mitochondrial dysfunction after mtDNA isolation from buccal swabs.

PMID:39797107 | DOI:10.3390/jcm14010024