BMJ Public Health. 2024 Nov 27;2(2):e001459. doi: 10.1136/bmjph-2024-001459. eCollection 2024 Dec.

ABSTRACT

INTRODUCTION: Newborn screening (NBS) is an essential public health initiative for early diagnosis of inborn errors of metabolism (IEM), where timely intervention can reduce morbidity and mortality. While routine in developed countries, NBS is not widely practised in India. This study aimed to implement NBS programme in a tertiary care hospital in South India and validate predetermined cut-off values tailored to the regional population.

METHODS: Between 2020 and 2022, 5157 neonates were screened for congenital hypothyroidism (CH), congenital adrenal hyperplasia (CAH), cystic fibrosis (CF), glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD), phenylketonuria (PKU), galactosemia and biotinidase deficiency. Screening was performed using dissociation-enhanced lanthanide fluorescent immunoassay technology on Victor2D platform (Revvity). Markers assessed included 17-α-OH progesterone, neonatal thyroid stimulating hormone, total galactose, immunoreactive trypsinogen, G6PD enzyme, biotinidase enzyme and phenylalanine levels. Data analysis was conducted using R V.4.1.1 software.

RESULTS: Of the 5157 neonates, the recall rates were consistent with those reported in similar studies. However, only 26.7% of screen-positive newborns returned for retesting, indicating a significant gap in awareness about IEMs and the importance of follow-up. Of these, none were diagnosed with CAH; however, four were found to have CH, two had galactosemia, three had G6PDD, one had CF, one had PKU and none had biotinidase deficiency. The confirmed cases were promptly treated and monitored regularly. The distribution of each marker's values fell within 2.5th-97.5th percentiles suggesting consistency.

CONCLUSION: The reference ranges provided by the manufacturer appear valid in the Indian context. A key challenge identified was low follow-up compliance for screen-positive infants, highlighting the need for enhanced public education on IEM and NBS. Future research will focus on determining the incidence of IEMs and improving parental awareness and follow-up rates.

PMID:40018536 | PMC:PMC11816103 | DOI:10.1136/bmjph-2024-001459

J Family Community Med. 2025 Jan-Mar;32(1):74-77. doi: 10.4103/jfcm.jfcm_217_24. Epub 2025 Jan 17.

ABSTRACT

Aquagenic wrinkling of the palms (AWP) is a rare transient dermatological disorder, characterized by an extreme and early wrinkling and pruning of the palms that occurs within a few minutes of exposure to water. Greater awareness of AWP is needed since the vast majority of AWP cases are linked with cystic fibrosis (CF) and CF carrier state. Initial assessment of AWP cases can be done in a primary care setting. A comprehensive medical history and physical examination should be done upon the diagnosis of AWP with additional investigations based on patients' presentation. We report two cases of AWP diagnosed at a family medicine center in Dammam, Saudi Arabia. The first case was related to the use of ibuprofen and discounting this medication led to a marked improvement in the patient's symptoms. The second case was related to primary palmar hyperhidrosis that was managed successfully with topical 20% aluminum chloride.

PMID:40018333 | PMC:PMC11864362 | DOI:10.4103/jfcm.jfcm_217_24

Mol Ther Methods Clin Dev. 2024 Jun 24;32(3):101292. doi: 10.1016/j.omtm.2024.101292. eCollection 2024 Sep 12.

ABSTRACT

Lung cystic fibrosis (CF) is a lethal inherited disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, leading to a dysfunctional CFTR protein. Gene therapy offers promise for the treatment of lung CF. However, the development and clinical application of CF gene therapy have long been hampered by the absence of safe and highly efficient delivery vectors. In this work, a novel polymer-based gene replacement treatment approach was developed. A series of poly (β-amino esters) (PAEs) with various topological structures and chemical compositions were screened to create non-viral therapeutic systems for CFTR restoration in lung CF disease. A nanoparticle, formed by the selected highly branched PAE (HPAE) with a CpG-depleted CFTR plasmid, demonstrated CFTR gene expression and biocompatibility in lung epithelial cells, outperforming leading commercial gene transfection reagents such as Lipofectamine 3000 and Xfect. The newly developed gene therapy system successfully restored functional CFTR protein production in lung CF epithelial monolayers. This therapeutic approach holds great potential for use as an efficient and safe non-viral treatment for CF patients.

PMID:40017666 | PMC:PMC11866167 | DOI:10.1016/j.omtm.2024.101292

Diagn Interv Radiol. 2025 Feb 28. doi: 10.4274/dir.2025.243100. Online ahead of print.

ABSTRACT

PURPOSE: The primary objective of this research is to enhance the accuracy and efficiency of information extraction from radiology reports. In addressing this objective, the study aims to develop and evaluate a deep learning framework for named entity recognition (NER).

METHODS: We used a synthetic dataset of 1,056 Turkish radiology reports created and labeled by the radiologists in our research team. Due to privacy concerns, actual patient data could not be used; however, the synthetic reports closely mimic genuine reports in structure and content. We employed the four-stage DYGIE++ model for the experiments. First, we performed token encoding using four bidirectional encoder representations from transformers (BERT) models: BERTurk, BioBERTurk, PubMedBERT, and XLM-RoBERTa. Second, we introduced adaptive span enumeration, considering the word count of a sentence in Turkish. Third, we adopted span graph propagation to generate a multidirectional graph crucial for coreference resolution. Finally, we used a two-layered feed-forward neural network to classify the named entity.

RESULTS: The experiments conducted on the labeled dataset showcase the approach's effectiveness. The study achieved an F1 score of 80.1 for the NER task, with the BioBERTurk model, which is pre-trained on Turkish Wikipedia, radiology reports, and biomedical texts, proving to be the most effective of the four BERT models used in the experiment.

CONCLUSION: We show how different dataset labels affect the model's performance. The results demonstrate the model's ability to handle the intricacies of Turkish radiology reports, providing a detailed analysis of precision, recall, and F1 scores for each label. Additionally, this study compares its findings with related research in other languages.

CLINICAL SIGNIFICANCE: Our approach provides clinicians with more precise and comprehensive insights to improve patient care by extracting relevant information from radiology reports. This innovation in information extraction streamlines the diagnostic process and helps expedite patient treatment decisions.

PMID:40018795 | DOI:10.4274/dir.2025.243100

Diagn Interv Radiol. 2025 Feb 28. doi: 10.4274/dir.2025.242950. Online ahead of print.

ABSTRACT

PURPOSE: Gastrointestinal (GI) dilatations are frequently observed in radiographs of pediatric patients who visit emergency departments with acute symptoms such as vomiting, pain, constipation, or diarrhea. Timely and accurate differentiation of whether there is an obstruction requiring surgery in these patients is crucial to prevent complications such as necrosis and perforation, which can lead to death. In this study, we aimed to use convolutional neural network (CNN) models to differentiate healthy children with normal intestinal gas distribution in abdominal radiographs from those with GI dilatation or obstruction. We also aimed to distinguish patients with obstruction requiring surgery and those with other GI dilatation or ileus.

METHODS: Abdominal radiographs of patients with a surgical, clinical, and/or laboratory diagnosis of GI diseases with GI dilatation were retrieved from our institution's Picture Archiving and Communication System archive. Additionally, abdominal radiographs performed to detect abnormalities other than GI disorders were collected to form a control group. The images were labeled with three tags according to their groups: surgically-corrected dilatation (SD), inflammatory/infectious dilatation (ID), and normal. To determine the impact of standardizing the imaging area on the model's performance, an additional dataset was created by applying an automated cropping process. Five CNN models with proven success in image analysis (ResNet50, InceptionResNetV2, Xception, EfficientNetV2L, and ConvNeXtXLarge) were trained, validated, and tested using transfer learning.

RESULTS: A total of 540 normal, 298 SD, and 314 ID were used in this study. In the differentiation between normal and abnormal images, the highest accuracy rates were achieved with ResNet50 (93.3%) and InceptionResNetV2 (90.6%) CNN models. Then, after using automated cropping preprocessing, the highest accuracy rates were achieved with ConvNeXtXLarge (96.9%), ResNet50 (95.5%), and InceptionResNetV2 (95.5%). The highest accuracy in the differentiation between SD and ID was achieved with EfficientNetV2L (94.6%).

CONCLUSION: Deep learning models can be integrated into radiographs located in the emergency departments as a decision support system with high accuracy rates in pediatric GI obstructions by immediately alerting the physicians about abnormal radiographs and possible etiologies.

CLINICAL SIGNIFICANCE: This paper describes a novel area of utilization of well-known deep learning algorithm models. Although some studies in the literature have shown the efficiency of CNN models in identifying small bowel obstruction with high accuracy for the adult population or some specific diseases, our study is unique for the pediatric population and for evaluating the requirement of surgical versus medical treatment.

PMID:40018794 | DOI:10.4274/dir.2025.242950

Front Microbiol. 2025 Feb 13;16:1536131. doi: 10.3389/fmicb.2025.1536131. eCollection 2025.

ABSTRACT

Developing new antibiotics poses a significant challenge in the fight against antimicrobial resistance (AMR), a critical global health threat responsible for approximately 5 million deaths annually. Finding new classes of antibiotics that are safe, have acceptable pharmacokinetic properties, and are appropriately active against pathogens is a lengthy and expensive process. Therefore, high-throughput platforms are needed to screen large libraries of synthetic and natural compounds. In this review, we present bacterial cytological profiling (BCP) as a rapid, scalable, and cost-effective method for identifying antibiotic mechanisms of action. Notably, BCP has proven its potential in drug discovery, demonstrated by the identification of the cellular target of spirohexenolide A against methicillin-resistant Staphylococcus aureus. We present the application of BCP for different bacterial organisms and different classes of antibiotics and discuss BCP's advantages, limitations, and potential improvements. Furthermore, we highlight the studies that have utilized BCP to investigate pathogens listed in the Bacterial Priority Pathogens List 2024 and we identify the pathogens whose cytological profiles are missing. We also explore the most recent artificial intelligence and deep learning techniques that could enhance the analysis of data generated by BCP, potentially advancing our understanding of antibiotic resistance mechanisms and the discovery of novel druggable pathways.

PMID:40018674 | PMC:PMC11865948 | DOI:10.3389/fmicb.2025.1536131

BJA Open. 2024 Oct 12;12:100347. doi: 10.1016/j.bjao.2024.100347. eCollection 2024 Dec.

ABSTRACT

BACKGROUND: Maintaining an appropriate depth of anaesthesia is important for avoiding adverse effects from undermedication or overmedication during surgery. Electroencephalography (EEG) has become increasingly used to achieve this balance. Investigating the predictive power of intracranial EEG (iEEG) and scalp EEG for different levels of sedation could increase the utility of EEG monitoring.

METHODS: Simultaneous iEEG, scalp EEG, and Observer's Assessment of Alertness/Sedation (OAA/S) scores were recorded during emergence from anaesthesia in seven patients undergoing placement of intracranial electrodes for medically refractory epilepsy. A deep learning model was constructed to predict an OAA/S score of 0-2 vs 3-5 using iEEG, scalp EEG, and their combination. An additional five patients with only scalp EEG data were used for independent validation. Models were evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS: Combining scalp EEG and iEEG yielded significantly better prediction (AUC=0.795, P<0.001) compared with iEEG only (AUC=0.750, P=0.02) or scalp EEG only (AUC=0.764, P<0.001). The validation scalp EEG only data resulted in an AUC of 0.844. Combining the two modalities appeared to capture spatiotemporal advantages from both modalities.

CONCLUSIONS: The combination of iEEG and scalp EEG better predicted sedation level than either modality alone. The scalp EEG only model achieved a similar AUC to the combined model and maintained its performance in additional patients, suggesting that scalp EEG models are likely sufficient for real-time monitoring. Deep learning approaches using multiple leads to capture a wider area of brain activity may help augment existing EEG monitors for prediction of sedation.

PMID:40018289 | PMC:PMC11867133 | DOI:10.1016/j.bjao.2024.100347

Acute Med Surg. 2025 Feb 27;12(1):e70049. doi: 10.1002/ams2.70049. eCollection 2025 Jan-Dec.

ABSTRACT

AIM: Most previous research on AI-based image diagnosis of acute cholecystitis (AC) has utilized ultrasound images. While these studies have shown promising outcomes, the results were based on still images captured by physicians, introducing inevitable selection bias. This study aims to develop a fully automated system for precise gallbladder detection among various abdominal structures, aiding clinicians in the rapid assessment of AC requiring cholecystectomy.

METHODS: The dataset comprised images from 250 AC patients and 270 control participants. The VGG-16 architecture was employed for gallbladder recognition. Post-processing techniques such as the flood fill algorithm and centroid calculation were integrated into the model. U-Net was utilized for segmentation and features extraction. All models were combined to develop a fully automated AC detection system.

RESULTS: The gallbladder identification accuracy among various abdominal organs was 95.3%, with the model effectively filtering out CT images lacking a gallbladder. In diagnosing AC, the model was tested on 120 cases, achieving an accuracy of 92.5%, sensitivity of 90.4%, and specificity of 94.1%. After integrating all components, the ensemble model achieved an overall accuracy of 86.7%. The automated process required 0.029 seconds of computation time per CT slice and 3.59 seconds per complete CT set.

CONCLUSIONS: The proposed system achieves promising performance in the automatic detection and diagnosis of gallbladder conditions in patients requiring cholecystectomy, with robust accuracy and computational efficiency. With further clinical validation, this computer-assisted system could serve as an auxiliary tool in identifying patients requiring emergency surgery.

PMID:40018053 | PMC:PMC11865635 | DOI:10.1002/ams2.70049

PeerJ. 2025 Feb 24;13:e18878. doi: 10.7717/peerj.18878. eCollection 2025.

ABSTRACT

N6-methyladenosine (m6A) represents one of the most prevalent methylation modifications in eukaryotes and it is crucial to accurately identify its modification sites on RNA sequences. Traditional machine learning based approaches to m6A modification site identification primarily focus on RNA sequence data but often incorporate additional biological domain knowledge and rely on manually crafted features. These methods typically overlook the structural insights inherent in RNA sequences. To address this limitation, we propose M6A-SAI, an advanced predictor for RNA m6A modifications. M6A-SAI leverages a transformer-based deep learning framework to integrate structure-aware insights into sequence representation learning, thereby enhancing the precision of m6A modification site identification. The core innovation of M6A-SAI lies in its ability to incorporate structural information through a multi-step process: initially, the model utilizes a Transformer encoder to learn RNA sequence representations. It then constructs a similarity graph based on Manhattan distance to capture sequence correlations. To address the limitations of the smooth similarity graph, M6A-SAI integrates a structure-aware optimization block, which refines the graph by defining anchor sets and generating an awareness graph through PageRank. Following this, M6A-SAI employs a self-correlation fusion graph convolution framework to merge information from both the similarity and awareness graphs, thus producing enriched sequence representations. Finally, a support vector machine is utilized for classifying these representations. Experimental results validate that M6A-SAI substantially improves the recognition of m6A modification sites by incorporating structure-aware insights, demonstrating its efficacy as a robust method for identifying RNA m6A modification sites.

PMID:40017651 | PMC:PMC11867033 | DOI:10.7717/peerj.18878

Front Oncol. 2025 Feb 13;14:1454411. doi: 10.3389/fonc.2024.1454411. eCollection 2024.

ABSTRACT

OBJECTIVE: To analyze the research hotspots and potential of Artificial Intelligence (AI) in cholangiocarcinoma (CCA) through visualization.

METHODS: A comprehensive search of publications on the application of AI in CCA from January 1, 2014, to December 31, 2023, within the Web of Science Core Collection, was conducted, and citation information was extracted. CiteSpace 6.2.R6 was used for the visualization analysis of citation information.

RESULTS: A total of 736 publications were included in this study. Early research primarily focused on traditional treatment methods and care strategies for CCA, but since 2019, there has been a significant shift towards the development and optimization of AI algorithms and their application in early cancer diagnosis and treatment decision-making. China emerged as the country with the highest volume of publications, while Khon Kaen University in Thailand was the academic institution with the highest number of publications. A core group of authors involved in a dense network of international collaboration was identified. HEPATOLOGY was found to be the most influential journal in the field. The disciplinary development pattern in this domain exhibits the characteristic of multiple disciplines intersecting and integrating.

CONCLUSION: The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA. The complementarity and interdependence among different AI applications will facilitate future applications of AI in the CCA field.

PMID:40017633 | PMC:PMC11865243 | DOI:10.3389/fonc.2024.1454411

Front Plant Sci. 2025 Feb 13;15:1491593. doi: 10.3389/fpls.2024.1491593. eCollection 2024.

ABSTRACT

A variety of diseased leaves and background noise types are present in images of diseased tomatoes captured in real-world environments. However, existing tomato leaf disease recognition models are limited to recognizing only a single leaf, rendering them unsuitable for practical applications in real-world scenarios. Additionally, these models consume significant hardware resources, making their implementation challenging for agricultural production and promotion. To address these issues, this study proposes a framework that integrates tomato leaf detection with leaf disease recognition. This framework includes a leaf detection model designed for diverse and complex environments, along with an ultra-lightweight model for recognizing tomato leaf diseases. To minimize hardware resource consumption, we developed five inverted residual modules coupled with an efficient attention mechanism, resulting in an ultra-lightweight recognition model that effectively balances model complexity and accuracy. The proposed network was trained on a dataset collected from real environments, and 14 contrasting experiments were conducted under varying noise conditions. The results indicate that the accuracy of the ultra-lightweight tomato disease recognition model, which utilizes the efficient attention mechanism, is 97.84%, with only 0.418 million parameters. Compared to traditional image recognition models, the model presented in this study not only achieves enhanced recognition accuracy across 14 noisy environments but also significantly reduces the number of required model parameters, thereby overcoming the limitation of existing models that can only recognize single disease images.

PMID:40017620 | PMC:PMC11865201 | DOI:10.3389/fpls.2024.1491593

Front Plant Sci. 2025 Feb 13;15:1448669. doi: 10.3389/fpls.2024.1448669. eCollection 2024.

ABSTRACT

UAV image acquisition and deep learning techniques have been widely used in field hydrological monitoring to meet the increasing data volume demand and refined quality. However, manual parameter training requires trial-and-error costs (T&E), and existing auto-trainings adapt to simple datasets and network structures, which is low practicality in unstructured environments, e.g., dry thermal valley environment (DTV). Therefore, this research combined a transfer learning (MTPI, maximum transfer potential index method) and an RL (the MTSA reinforcement learning, Multi-Thompson Sampling Algorithm) in dataset auto-augmentation and networks auto-training to reduce human experience and T&E. Firstly, to maximize the iteration speed and minimize the dataset consumption, the best iteration conditions (MTPI conditions) were derived with the improved MTPI method, which shows that subsequent iterations required only 2.30% dataset and 6.31% time cost. Then, the MTSA was improved under MTPI conditions (MTSA-MTPI) to auto-augmented datasets, and the results showed a 16.0% improvement in accuracy (human error) and a 20.9% reduction in standard error (T&E cost). Finally, the MTPI-MTSA was used for four networks auto-training (e.g., FCN, Seg-Net, U-Net, and Seg-Res-Net 50) and showed that the best Seg-Res-Net 50 gained 95.2% WPA (accuracy) and 90.9% WIoU. This study provided an effective auto-training method for complex vegetation information collection, which provides a reference for reducing the manual intervention of deep learning.

PMID:40017619 | PMC:PMC11864880 | DOI:10.3389/fpls.2024.1448669

Front Plant Sci. 2025 Feb 13;15:1474906. doi: 10.3389/fpls.2024.1474906. eCollection 2024.

ABSTRACT

Mung bean seeds are very important in agricultural production and food processing, but due to their variety and similar appearance, traditional classification methods are challenging, to address this problem this study proposes a deep learning-based approach. In this study, based on the deep learning model MobileNetV2, a DMS block is proposed for mung bean seeds, and by introducing the ECA block and Mish activation function, a high-precision network model, i.e., HPMobileNet, is proposed, which is explored to be applied in the field of image recognition for the fast and accurate classification of different varieties of mung bean seeds. In this study, eight different varieties of mung bean seeds were collected and a total of 34,890 images were obtained by threshold segmentation and image enhancement techniques. HPMobileNet was used as the main network model, and by training and fine-tuning on a large-scale mung bean seed image dataset, efficient feature extraction classification and recognition capabilities were achieved. The experimental results show that HPMobileNet exhibits excellent performance in the mung bean seed grain classification task, with the accuracy improving from 87.40% to 94.01% on the test set, and compared with other classical network models, the results show that HPMobileNet achieves the best results. In addition, this study analyzes the impact of the learning rate dynamic adjustment strategy on the model and explores the potential for further optimization and application in the future. Therefore, this study provides a useful reference and empirical basis for the development of mung bean seed classification and smart agriculture technology.

PMID:40017618 | PMC:PMC11865048 | DOI:10.3389/fpls.2024.1474906

Chem Biomed Imaging. 2025 Jan 17;3(2):85-94. doi: 10.1021/cbmi.4c00065. eCollection 2025 Feb 24.

ABSTRACT

Disease-recapitulating animal models are valuable tools in preclinical development for the study of compounds. In the case of fibrotic pulmonary diseases such as idiopathic pulmonary fibrosis (IPF), the bleomycin model of lung injury in the mouse is widely used. To evaluate bleomycin-induced changes in the lung, we employed a quantitative, multimodal approach. Using in vivo microcomputed tomography (μCT), we demonstrated radiographic changes associated with disease progression in aeration levels of the lung parenchyma. There exists an unmet need for a quantitative, high-resolution imaging probe to detect pulmonary fibrosis, particularly that can differentiate between inflammatory and fibrotic components of the disease. Matrix remodeling and overexpression of extracellular matrix (ECM) proteins such as collagen and fibronectin are hallmarks of organ fibrosis. A splice variant of fibronectin containing extra domain A (FnEDA) is of particular interest in fibrosis due to its high level of expression in diseased tissue, which is confirmed here using immunohistochemistry (IHC) in mouse and human lungs. An antibody against FnEDA was evaluated for use as an imaging tool, particularly by using in vivo single-photon emission computed tomography (SPECT) and ex vivo near-infrared (NIR) fluorescence imaging. These data were further corroborated with histological tissue staining and fibrosis quantitation based on a Modified Ashcroft (MA) score and a digital image analysis of whole slide lung tissue sections. The fusion of these different approaches represents a robust integrated workflow combining anatomical and molecular imaging technologies to enable the visualization and quantitation of disease activity and treatment response with an inhibitor of the TGFβ signaling pathway.

PMID:40018646 | PMC:PMC11863149 | DOI:10.1021/cbmi.4c00065

J Integr Neurosci. 2025 Feb 19;24(2):36279. doi: 10.31083/JIN36279.

NO ABSTRACT

PMID:40018783 | DOI:10.31083/JIN36279

Chem Biomed Imaging. 2024 Dec 19;3(2):111-122. doi: 10.1021/cbmi.4c00077. eCollection 2025 Feb 24.

ABSTRACT

The COVID-19 pandemic has underscored the importance of in-depth research into the proteins encoded by coronaviruses (CoV), particularly the highly conserved nonstructural CoV proteins (nsp). Among these, the nsp13 helicase of severe pathogenic MERS-CoV, SARS-CoV-2, and SARS-CoV is one of the most preserved CoV nsp. Utilizing single-molecule FRET, we discovered that MERS-CoV nsp13 unwinds DNA in distinct steps of about 9 bp when ATP is employed. If a different nucleotide is introduced, these steps diminish to 3-4 bp. Dwell-time analysis revealed 3-4 concealed steps within each unwinding process, which suggests the hydrolysis of 3-4 dTTP. Combining our observations with previous studies, we propose an unwinding model of CoV nsp13 helicase. This model suggests that the elongated and adaptable 1B-stalk of nsp13 may enable the 1B remnants to engage with the unwound single-stranded DNA, even as the helicase core domain has advanced over 3-4 bp, thereby inducing accumulated strain on the nsp13-DNA complex. Our findings provide a foundational framework for determining the unwinding mechanism of this unique helicase family.

PMID:40018651 | PMC:PMC11863148 | DOI:10.1021/cbmi.4c00077

Front Mol Neurosci. 2025 Feb 13;18:1544971. doi: 10.3389/fnmol.2025.1544971. eCollection 2025.

ABSTRACT

INTRODUCTION: Growing recognition of microglia's role in neurodegenerative disorders has accentuated the need to characterize microglia profiles and their influence on pathogenesis. To understand changes observed in the microglial profile during the progression of synucleinopathies, microglial gene expression and DNA methylation were examined in the mThy1-α-synuclein mouse model.

METHODS: Disease progression was determined using behavioral tests evaluating locomotor deficits before DNA and RNA extraction at 7 and 10 months from isolated microglia for enzymatic methyl-sequencing and RNA-sequencing.

RESULTS: Pathway analysis of these changes at 7 months indicates a pro-inflammatory profile and changes in terms related to synaptic maintenance. Expression and methylation at both 7 and 10 months included terms regarding mitochondrial and metabolic stress. While behavior symptoms progressed at 10 months, we see many previously activated pathways being inhibited in microglia at a later stage, with only 8 of 53 shared pathways predicted to be directionally concordant. Despite the difference in pathway directionality, 21 of the 22 genes that were differentially expressed and annotated to differentially methylated regions at both 7 and 10 months had conserved directionality changes.

DISCUSSION: These results highlight a critical period in disease progression, during which the microglia respond to α-synuclein, suggesting a transition in the role of microglia from the early to late stages of the disease.

PMID:40018011 | PMC:PMC11865073 | DOI:10.3389/fnmol.2025.1544971

Front Oral Health. 2025 Feb 13;6:1543667. doi: 10.3389/froh.2025.1543667. eCollection 2025.

ABSTRACT

INTRODUCTION: Derivatives from Saccharomyces cerevisiae yeast including yeast extracts and yeast cell walls are sustainable sources of valuable nutrients, including dietary fibers and proteins. Previous studies have shown that certain components from these yeast derivatives can inhibit the growth of harmful intestinal bacteria and promote the growth of beneficial bacteria. However, the effects of yeast derivatives on oral health have not yet been investigated.

METHODS: An in vitro oral biofilm model was employed to examine the impacts of yeast derivatives on the oral microbiota and their potential benefits for maintaining oral homeostasis. The model incorporated dental plaque donor material from both healthy and periodontitis diagnosed individuals. Biofilm formation, density, and microbial composition were quantified. Additionally, the production of short-chain fatty acids in the biofilm supernatants was measured.

RESULTS: Yeast extracts had only minor effects on oral biofilm formation. In contrast, yeast cell wall derivatives, which are rich in polysaccharides such as beta-glucans and mannans, significantly reduced the density of the oral biofilms in vitro. This reduction in biofilm density was associated with an overall shift in the bacterial community composition, including an increase in beneficial bacteria and a decrease in the abundance of Tannerella forsythia, an important species involved in bacterial coaggregation and the development and maturation of the oral biofilm. Furthermore, the yeast cell wall derivatives decreased the production of short-chain fatty acids, including acetic and butyric acid. These findings were consistent across both healthy and periodontitis microbiomes.

CONCLUSION: This study has demonstrated the potential of yeast cell wall derivatives to positively impact oral health by significantly reducing biofilm density, modulating the oral microbial composition, and decreasing the production of short-chain fatty acids. The observed effects highlight the promising applications of these yeast-based compounds as an approach to managing oral diseases. Further research is needed to fully elucidate the mechanisms of action and explore the clinical potential of yeast cell wall derivatives in promoting and maintaining oral health.

PMID:40017617 | PMC:PMC11865069 | DOI:10.3389/froh.2025.1543667

J Indian Soc Periodontol. 2024 Jul-Aug;28(4):494-498. doi: 10.4103/jisp.jisp_147_23. Epub 2025 Jan 6.

ABSTRACT

Drug-induced gingival enlargement often occurs due to patient's lack of awareness about the side effects of prescribed medications. This case report details an unusual instance of massive drug-induced gingival overgrowth in a 50-year-old female, successfully managed through a multidisciplinary approach, including surgical intervention and prosthetic rehabilitation. The surgical treatment involved multiple extractions and the excision of excessive tissue. Both arches healed completely after surgery, and the patient underwent prosthetic rehabilitation, with no signs of recurrence. Effective management of such cases relies on patient counseling and appropriate drug substitution. Increasing awareness about the side effects of certain medications and the connection between systemic and oral health is crucial to prevent such cases of gingival enlargement.

PMID:40018719 | PMC:PMC11864326 | DOI:10.4103/jisp.jisp_147_23

Malawi Med J. 2024 Oct 16;36(3):163-169. doi: 10.4314/mmj.v36i3.2. eCollection 2024 Oct.

ABSTRACT

BACKGROUND: Spontaneous reporting of adverse drug reaction (ADRs) is low in Malawi. We assessed the impact of training intervention on knowledge, attitudes, and practices of health care professionals (HCPs) in pharmacovigilance (PV).

METHODS: We employed a mixed-methods study design. A questionnaire was administered among HCPs who were trained in PV, followed by face-face interviews. We further extracted individual case safety reports which were submitted to the local databasewithin a period of six months prior and after the PV training. Quantitative data was analyzed using STATA 14.1. Paired t-test was used to assess the differences in PV knowledge among HCPs before and after the training. For qualitative data, we manually derived key themes from the participant's responses.

RESULTS: Overall, the mean knowledge score was significantly improved across all the participants from a mean of 56% (95% CI 53% to 58%) to 66% (95% CI 64% to 69%) after the training, p< 0.001. There was a 2.8-fold increase in the number of participants who were able to detect an ADR after the training and a 1.8-fold increase in the percentage of reporting the detected ADRs after the training. Participants expressed preference of a paper-based reporting system to other reporting tools. However, they outlined several challenges to the system which discourages HCPs from reporting ADRs, such as lack of feedback, unavailability of reporting forms and delay to transmit data to the national centre.

CONCLUSION: The survey found that in-service training for HCPs improves KAP of PV and reporting rates of ADRs. We recommend widening of the training and introducing PV courses in undergraduate programs for health care workers in Malawi.

PMID:40018398 | PMC:PMC11862858 | DOI:10.4314/mmj.v36i3.2