J Pak Med Assoc. 2025 Feb;75(2):313-316. doi: 10.47391/JPMA.20212.

ABSTRACT

Gorham-Stout disease is an exceptionally rare disease which is characterised by massive osteolysis of the bone, oedema, and in severe cases pleural effusion and chylothorax. Its aetiopathology is unknown, and no specific treatment has been modulated thus far. We report the case of a 17-year-old male with osteolysis in the bones of his entire left arm and persistent chylothorax. Due to the late presentation and patient's desire for a better quality of life, amputation was the only choice left for treatment. This case was evaluated and treated at the Orthopaedic Surgery and Trauma department of Rehman Medical Institute in Peshawar, Pakistan.

PMID:39948797 | DOI:10.47391/JPMA.20212

J Intellect Disabil Res. 2025 Feb 13. doi: 10.1111/jir.13218. Online ahead of print.

ABSTRACT

BACKGROUND: Dravet Syndrome (DS), Helsmoortel-Van Der Aa Syndrome (HVDAS) and Tuberous Sclerosis Complex (TSC) are rare genetic syndromes, sharing intellectual disability (ID) and motor delay. In DS, two distinct gait patterns, crouch and non-crouch, have been described using instrumented 3D gait analysis (i3DGA). This cross-sectional study measures gait in participants with TSC and HVDAS. The findings are compared to the known crouch and non-crouch gait patterns observed in DS and to typical gait.

METHODS: Participants (6-22 years) with DS (n = 37; 19 crouch and 18 non-crouch), HVDAS (n = 12) or TSC (n = 8) were compared with typically developing (TD) peers (n = 33). All participants underwent i3DGA (Plugin Gait model processed with Vicon Nexus and MATLAB®) to investigate spatiotemporal and lower-limb kinematics.

RESULTS: All three genetic syndromes showed increased step width. Participants with HVDAS and DS, but not participants with TSC walked with decreased step length and velocity compared to TD. HVDAS demonstrated increased knee flexion during the stance phase, lack of hip extension during pre-swing, and increased ankle dorsiflexion during some phases of the gait cycle (p < 0.001). Additionally, HVDAS showed similar kinematic deviations to DS-NonCrouch. No significant differences were found in terms of kinematics between TSC and TD peers (p > 0.05).

CONCLUSION: The current study reveals differences in gait characteristics from typical functional gait in rare genetic disorders. DS-Crouch, DS-NonCrouch and HVDAS display a more impaired gait from a biomechanical perspective than TSC. The variability of clinical and genetic features might explain heterogeneity in gait deviations and should be further explored.

PMID:39948735 | DOI:10.1111/jir.13218

Expert Rev Clin Pharmacol. 2025 Feb 14. doi: 10.1080/17512433.2025.2465426. Online ahead of print.

ABSTRACT

INTRODUCTION: Therapeutic drug monitoring (TDM) is important to optimize drug exposure and minimize toxicity for the individual patient.

AREAS COVERED: This narrative review covers the pharmacokinetics (PK), -dynamics (PD) and-genetics of classic chemotherapeutic drugs used in frontline therapy for acute lymphoblastic leukemia (ALL), including anthracyclines, asparaginase, busulfan, cyclophosphamide, cytarabine, glucocorticoids, methotrexate, nelarabine, thiopurines, tyrosine kinase inhibitors, and vincristine. Furthermore, novel immunotherapies including blinatumomab, inotuzumab ozogamicin, and chimeric antigen receptor T-cells that are rapidly moving into frontline therapy are addressed. This review focuses on TDM already used in clinical practice as well as the unused potential and feasibility of TDM. Finally, important factors affecting PK/PD such as obesity and transition to adolescence and young adulthood are discussed.

EXPERT OPINION: Investigation of TDM as standard of care for antileukemic agents is highly warranted to personalize curative yet toxic anticancer regimens within frontline ALL treatment. Some of the drugs have been used in ALL treatment regimens for decades, but a wide range of new compounds are being introduced, some like blinatumomab reaching standard-of-care designation. Not least, optimized drug efficacy and reduction of the risk of serious toxicities may render TDM implementation cost-effective.

PMID:39949259 | DOI:10.1080/17512433.2025.2465426

Front Tuberc. 2024;2:1473341. doi: 10.3389/ftubr.2024.1473341. Epub 2024 Oct 30.

ABSTRACT

INTRODUCTION: Chronic pulmonary infection with Mycobacterium abscessus (M. abscessus) is a significant cause of morbidity and mortality in people with cystic fibrosis (CF). Developing an animal model of M. abscessus pulmonary infection, especially under CF conditions, is essential to understanding clinical pulmonary M. abscessus infection. βENaC transgenic mice are known to develop spontaneous CF-like disease characterized by airway mucus obstruction and inflammation. The aim of this study was to evaluate the suitability of βENaC mice as a preclinical model and characterize their respiratory function during M. abscessus lung infection.

METHODS: Mice received an intrapulmonary aerosol of M. abscessus using a high-pressure syringe device (Penn-Century) for subsequent characterization of disease progression and respiratory function. Whole body unrestrained plethysmography (WBP) data was collected to monitor lung function and endpoints determined organ bacterial burden and associated pathology.

RESULTS: Endpoint CFU data in the lung and spleen showed that there was no significant difference in bacterial clearance between βENaC and WT mice. WBP data showed an impairment in overall respiratory function during and after M. abscessus infection in both strains of mice. Interestingly, even in wildtype control mice, lung dysfunction persisted after bacterial clearance.

DISCUSSION: Even with CF-like features, the βENaC transgenic mice cleared M. abscessus at a similar rate than WT mice, however, the associated respiratory monitoring revealed that there are long-term implications of M. abscessus lung exposure. The clear decline in respiratory function, even after M. abscessus clearance, suggests that WBP coupled animal modeling provides important insight that is relevant to disease burden and treatment efficacy. The M. abscessus clearance in the βENaC mice may help improve the fields understanding of CF-modulated immune deficiencies in M. abscessus pulmonary infection.

PMID:39950136 | PMC:PMC11822858 | DOI:10.3389/ftubr.2024.1473341

Med Pharm Rep. 2025 Jan;98(1):29-35. doi: 10.15386/mpr-2806. Epub 2025 Jan 31.

ABSTRACT

BACKGROUND: Patients with cystic fibrosis (CF) frequently require modulatory therapies such as Lumacaftor/Ivacaftor (LI) and Elexacaftor/Tezacaftor/Ivacaftor (ETI) to manage their condition. Given the potential hepatic complications associated with CF, it is critical to understand the impact of these therapies on liver function and fibrosis indices. This study aimed to evaluate the changes in liver function markers and fibrosis indices in CF patients undergoing LI and ETI therapies, with a specific focus on the influence of underlying hepatic disease.

METHODS: In this retrospective analysis, liver function markers and fibrosis indices were assessed in CF patients receiving ETI (n=24), LI (n=4), or LI transitioned to ETI (LI/ETI, n=8). Key liver function markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, platelet count, and fibrosis indices (APRI and FIB-4), were measured at baseline and at various time points up to 12 months.

RESULTS: In patients receiving LI therapy, ALT and AST levels demonstrated a slight but non-significant decrease over six months, accompanied by significant fluctuations in total bilirubin levels. Among those receiving ETI therapy, ALT and AST levels initially increased but stabilized over time, while total bilirubin levels significantly increased from baseline to 12 months. No significant differences were observed in liver function markers between patients with and without hepatic disease under ETI therapy. Trends in fibrosis indices (APRI and FIB-4) were modest and largely non-significant across both therapies.

CONCLUSIONS: ETI therapy appears to be safe for CF patients, including those with pre-existing hepatic disease, with no significant deterioration in liver function over a 12-month period. However, the observed fluctuations in bilirubin levels underscore the necessity for ongoing monitoring. Further research is warranted to investigate the long-term hepatic effects of LI and ETI therapies.

PMID:39949910 | PMC:PMC11817584 | DOI:10.15386/mpr-2806

Cureus. 2025 Jan 13;17(1):e77407. doi: 10.7759/cureus.77407. eCollection 2025 Jan.

ABSTRACT

The syndrome of inappropriate antidiuretic hormone secretion (SIADH) is a complex and often underdiagnosed disorder characterized by impaired water homeostasis, leading to hyponatremia and associated complications. This comprehensive review explores the intersection of SIADH with chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis, cystic fibrosis, and interstitial lung disease. The review looks at current evidence on pathophysiology, diagnostic challenges, and treatment approaches, highlighting the need for specialized management strategies to improve patient outcomes. Through an analysis of clinical and observational studies, this review highlights the significant impact of SIADH on morbidity and mortality among patients with respiratory diseases. It illustrates the necessity for further research to refine diagnostic and therapeutic modalities.

PMID:39949461 | PMC:PMC11822327 | DOI:10.7759/cureus.77407

Plant Biotechnol J. 2025 Feb 14. doi: 10.1111/pbi.14577. Online ahead of print.

ABSTRACT

Genomic selection (GS) is a new breeding strategy. Generally, traditional methods are used for predicting traits based on the whole genome. However, the prediction accuracy of these models remains limited because they cannot fully reflect the intricate nonlinear interactions between genotypes and traits. Here, a novel single nucleotide polymorphism (SNP) feature extraction technique based on the Pearson-Collinearity Selection (PCS) is firstly presented and improves prediction accuracy across several known models. Furthermore, gene network prediction model (NetGP) is a novel deep learning approach designed for phenotypic prediction. It utilizes transcriptomic dataset (Trans), genomic dataset (SNP) and multi-omics dataset (Trans + SNP). The NetGP model demonstrated better performance compared to other models in genomic predictions, transcriptomic predictions and multi-omics predictions. NetGP multi-omics model performed better than independent genomic or transcriptomic prediction models. Prediction performance evaluations using several other plants' data showed good generalizability for NetGP. Taken together, our study not only offers a novel and effective tool for plant genomic selection but also points to new avenues for future plant breeding research.

PMID:39950326 | DOI:10.1111/pbi.14577

J Pathol. 2025 Feb 14. doi: 10.1002/path.6399. Online ahead of print.

ABSTRACT

Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene sequencing for mutation testing is expensive and time-consuming and is susceptible to a variety of preanalytical factors. To overcome the challenges associated with genetic screening by sequencing, in the current study we developed an artificial intelligence-based deep-learning （DL） model that uses convolutional neural networks (CNN) to analyze digitized hematoxylin and eosin staining in tumor histological sections to predict potential response to imatinib or avapritinib treatment in GIST patients. Assessment with an independent testing set showed that our DL model could predict imatinib sensitivity with an area under the curve (AUC) of 0.902 in case-wise analysis and 0.807 in slide-wise analysis. Case-level AUCs for predicting imatinib-dose-adjustment cases, avapritinib-sensitive cases, and wildtype GISTs were 0.920, 0.958, and 0.776, respectively, while slide-level AUCs for these respective groups were 0.714, 0.922, and 0.886, respectively. Our model showed comparable or better prediction of actual response to TKI than sequencing-based screening (accuracy 0.9286 versus 0.8929; DL model versus sequencing), while predictions of nonresponse to imatinib/avapritinib showed markedly higher accuracy than sequencing (0.7143 versus 0.4286). These results demonstrate the potential of a DL model to improve predictions of treatment response to TKI therapy from histology in GIST patients. © 2025 The Pathological Society of Great Britain and Ireland.

PMID:39950223 | DOI:10.1002/path.6399

Front Glob Womens Health. 2025 Jan 30;6:1447579. doi: 10.3389/fgwh.2025.1447579. eCollection 2025.

ABSTRACT

INTRODUCTION: Estimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard.

METHODS: A literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed.

RESULTS: Out of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images (n = 6) and blind sweep videos (n = 4) was 4.32 days (95% CI: 2.82, 5.83; l 2: 97.95%) and 2.55 days (95% CI: -0.13, 5.23; l 2: 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain.

CONCLUSION: Preliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited.

SYSTEMATIC REVIEW REGISTRATION: PROSPERO, identifier (CRD42022319966).

PMID:39950139 | PMC:PMC11821921 | DOI:10.3389/fgwh.2025.1447579

Digit Health. 2025 Feb 13;11:20552076251319667. doi: 10.1177/20552076251319667. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Based on the current research status, this paper proposes a deep learning model named Covid-DenseNet for COVID-19 detection from CXR (computed tomography) images, aiming to build a model with smaller computational complexity, stronger generalization ability, and excellent performance on benchmark datasets and other datasets with different sample distribution features and sample sizes.

METHODS: The proposed model first extracts and obtains features of multiple scales from the input image through transfer learning, followed by assigning internal weights to the extracted features through the attention mechanism to enhance important features and suppress irrelevant features; finally, the model fuses these features of different scales through the multi-scale fusion architecture we designed to obtain richer semantic information and improve modeling efficiency.

RESULTS: We evaluated our model and compared it with advanced models on three publicly available chest radiology datasets of different types, one of which is the baseline dataset, on which we constructed the model Covid-DenseNet, and the recognition accuracy on this test set was 96.89%, respectively. With recognition accuracy of 98.02% and 96.21% on the other two publicly available datasets, our model performs better than other advanced models. In addition, the performance of the model was further evaluated on external test sets, trained on data sets with balanced sample distribution (experiment 1) and unbalanced sample distribution (experiment 2), identified on the same external test set, and compared with DenseNet121. The recognition accuracy of the model in experiment 1 and experiment 2 is 80% and 77.5% respectively, which is 3.33% and 4.17% higher than that of DenseNet121 on external test set. On this basis, we also changed the number of samples in experiment 1 and experiment 2, and compared the impact of the change in the number of training set samples on the recognition accuracy of the model on the external test set. The results showed that when the number of samples increased and the sample features became more abundant, the trained Covid-DenseNet performed better on the external test set and the model became more robust.

CONCLUSION: Compared with other advanced models, our model has achieved better results on multiple datasets, and the recognition effect on external test sets is also quite good, with good generalization performance and robustness, and with the enrichment of sample features, the robustness of the model is further improved, and it has better clinical practice ability.

PMID:39949849 | PMC:PMC11822832 | DOI:10.1177/20552076251319667

Data Min Knowl Discov. 2024 May;38(3):1493-1519. doi: 10.1007/s10618-024-01006-1. Epub 2024 Feb 9.

ABSTRACT

The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success of these approaches relies on access to the massive amount of labeled data (i.e., annotating or assigning tags to each sample that shows its corresponding category). However, obtaining a massive amount of labeled data is usually very time-consuming and expensive in many real-world applications such as medicine, because it requires domain experts' knowledge to annotate data. Insufficient labeled data prevents these models from learning discriminative features, resulting in poor margins that reduce generalization performance. To address this challenge, we propose a novel approach: supervised contrastive learning for time series classification (SupCon-TSC). This approach improves the classification performance by learning the discriminative low-dimensional representations of multivariate time series, and its end-to-end structure allows for interpretable outcomes. It is based on supervised contrastive (SupCon) loss to learn the inherent structure of multivariate time series. First, two separate augmentation families, including strong and weak augmentation methods, are utilized to generate augmented data for the source and target networks, respectively. Second, we propose the instance-level, and cluster-level SupCon learning approaches to capture contextual information to learn the discriminative and universal representation for multivariate time series datasets. In the instance-level SupCon learning approach, for each given anchor instance that comes from the source network, the low-variance output encodings from the target network are sampled as positive and negative instances based on their labels. However, the cluster-level approach is performed between each instance and cluster centers among batches, as opposed to the instance-level approach. The cluster-level SupCon loss attempts to maximize the similarities between each instance and cluster centers among batches. We tested this novel approach on two small cardiopulmonary exercise testing (CPET) datasets and the real-world UEA Multivariate time series archive. The results of the SupCon-TSC model on CPET datasets indicate its capability to learn more discriminative features than existing approaches in situations where the size of the dataset is small. Moreover, the results on the UEA archive show that training a classifier on top of the universal representation features learned by our proposed method outperforms the state-of-the-art approaches.

PMID:39949582 | PMC:PMC11825059 | DOI:10.1007/s10618-024-01006-1

Front Artif Intell. 2025 Jan 30;8:1518049. doi: 10.3389/frai.2025.1518049. eCollection 2025.

ABSTRACT

ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accuracy on USMLE, 78.2% on PubMedQA). ChatGPT offers personalized learning platforms, automated scoring, and instant access to vast medical knowledge in medical education, addressing resource limitations and enhancing training efficiency. It streamlines clinical workflows by supporting triage processes, generating discharge summaries, and alleviating administrative burdens, allowing healthcare professionals to focus more on patient care. Additionally, ChatGPT facilitates remote monitoring and chronic disease management, providing personalized advice, medication reminders, and emotional support, thus bridging gaps between clinical visits. Its ability to process and synthesize vast amounts of data accelerates research workflows, aiding in literature reviews, hypothesis generation, and clinical trial designs. This paper aims to gather and analyze published studies involving ChatGPT, focusing on exploring its advantages and disadvantages within the healthcare context. To aid in understanding and progress, our analysis is organized into six key areas: (1) Information and Education, (2) Triage and Symptom Assessment, (3) Remote Monitoring and Support, (4) Mental Healthcare Assistance, (5) Research and Decision Support, and (6) Language Translation. Realizing ChatGPT's full potential in healthcare requires addressing key limitations, such as its lack of clinical experience, inability to process visual data, and absence of emotional intelligence. Ethical, privacy, and regulatory challenges further complicate its integration. Future improvements should focus on enhancing accuracy, developing multimodal AI models, improving empathy through sentiment analysis, and safeguarding against artificial hallucination. While not a replacement for healthcare professionals, ChatGPT can serve as a powerful assistant, augmenting their expertise to improve efficiency, accessibility, and quality of care. This collaboration ensures responsible adoption of AI in transforming healthcare delivery. While ChatGPT demonstrates significant potential in healthcare transformation, systematic evaluation of its implementation across different healthcare settings reveals varying levels of evidence quality-from robust randomized trials in medical education to preliminary observational studies in clinical practice. This heterogeneity in evidence quality necessitates a structured approach to future research and implementation.

PMID:39949509 | PMC:PMC11821943 | DOI:10.3389/frai.2025.1518049

Multivariate Behav Res. 2025 Feb 14:1-15. doi: 10.1080/00273171.2025.2455497. Online ahead of print.

ABSTRACT

Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and Py-Feat. We present their advantages, disadvantages, and provide sample code so that researchers can immediately begin designing, collecting, and analyzing emotion data. Furthermore, we provide an introductory level explanation of the machine learning, deep learning, and computer vision algorithms that underlie most emotion detection programs in order to improve literacy of explainable artificial intelligence in the social and behavioral science literature.

PMID:39949325 | DOI:10.1080/00273171.2025.2455497

J Med Eng Technol. 2025 Feb 14:1-9. doi: 10.1080/03091902.2025.2463574. Online ahead of print.

ABSTRACT

The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals are segmented by R peak detection process. Thereafter, the greyscale and scalograms images have been formed. The features of the images are extracted using the EfficientNet-B0 deep learning model. These features are normalised using z-score normalisation method and then optimal features are selected using the hybrid feature selection method. The hybrid feature selection is constructed utilising two filter methods and Self Adaptive Bald Eagle Search (SABES) optimisation algorithm. The proposed methodology has been applied to the ECG signals for the classification of the five types of beats. The methodology acquired 99.31% of accuracy.

PMID:39949269 | DOI:10.1080/03091902.2025.2463574

Math Biosci Eng. 2025 Jan 3;22(1):138-151. doi: 10.3934/mbe.2025007.

ABSTRACT

Bone age assessment (BAA) is a widely used clinical practice for the biological development of adolescents. The Tanner Whitehouse (TW) method is a traditionally mainstream method that manually extracts multiple regions of interest (ROIs) related to skeletal maturity to infer bone age. In this paper, we propose a deep learning-based method for fully automatic ROIs localization and BAA. The method consists of two parts: a U-net-based backbone, selected for its strong performance in semantic segmentation, which enables precise and efficient localization without the need for complex pre- or post-processing. This method achieves a localization precision of 99.1% on the public RSNA dataset. Second, an InceptionResNetV2 network is utilized for feature extraction from both the ROIs and the whole image, as it effectively captures both local and global features, making it well-suited for bone age prediction. The BAA neural network combines the advantages of both ROIs-based methods (TW3 method) and global feature-based methods (GP method), providing high interpretability and accuracy. Numerical experiments demonstrate that the method achieves a mean absolute error (MAE) of 0.38 years for males and 0.45 years for females on the public RSNA dataset, and 0.41 years for males and 0.44 years for females on an in-house dataset, validating the accuracy of both localization and prediction.

PMID:39949166 | DOI:10.3934/mbe.2025007

Math Biosci Eng. 2025 Jan;22(1):73-105. doi: 10.3934/mbe.2025004. Epub 2024 Dec 25.

ABSTRACT

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00-99.00%) and specificity (89.63-99.00%) across all tasks highlighted the model's robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life.

PMID:39949163 | DOI:10.3934/mbe.2025004

Plant Cell Environ. 2025 Feb 14. doi: 10.1111/pce.15429. Online ahead of print.

ABSTRACT

Plant-microbe interactions have been studied extensively in legumes, but the influence of host developmental stages on its microbiome remains poorly understood. The rhizospheric region enriched with microbial diversity presents an optimal environment to investigate this relationship. We employed a multi-omics meta-analysis approach to identify the rhizospheric bacteria co-existing with legumes at different developmental stages. The data from eight different legume species across various geographical locations, soil conditions and developmental stages (vegetative, reproductive and maturation) were included in the study. A total of 10 developmental stage-specific marker bacteria were identified and found to be positively associated with plant growth phenotypes. The functional profiling elucidated the expression of these marker bacterial genes, indicating the active presence of marker bacteria. Co-expression network analysis revealed the involvement of gene clusters in biological processes such as cobalt and nitrogen metabolism. Further, pathway enrichment analysis illustrated the role of these bacteria in plant metabolic pathways, such as biosynthesis of various plant secondary metabolites, biotin metabolism and carbon fixation in photosynthetic organisms. Our study identified a positive relationship between marker bacteria and the host plant, suggesting their crucial role in legume growth and development that could further aid in crop improvement strategies.

PMID:39950378 | DOI:10.1111/pce.15429

F1000Res. 2024 Dec 20;13:1545. doi: 10.12688/f1000research.160121.1. eCollection 2024.

ABSTRACT

ADNP is a multifunctional protein involved in chromatin remodeling, transcription, and microtubule interaction, playing a critical role in brain development, with mutations linked to ADNP-Related Disorder. Here we have characterized seven ADNP commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:39949964 | PMC:PMC11822250 | DOI:10.12688/f1000research.160121.1

Chin Herb Med. 2024 Nov 9;17(1):178-188. doi: 10.1016/j.chmed.2024.11.003. eCollection 2025 Jan.

ABSTRACT

OBJECTIVE: Benzylisoquinoline alkaloids (BIAs) have pharmacological functions and clinical use. BIAs are mainly distributed in plant species across the order Ranunculales and the genus Phellodendron from Sapindales. The BIA biosynthesis has been intensively investigated in Ranunculales species. However, the accumulation mechanism of BIAs in Phellodendron is largely unknown. The aim of this study is to unravel the biosynthetic pathways of BIAs in Phellodendron amurens.

METHODS: The transcriptome and metabolome data from 18 different tissues of P. amurense were meticulously sequenced and subsequently subjected to a thorough analysis. Weighted gene co-expression network analysis (WGCNA), a powerful systems biology approach that facilitates the construction and subsequent analysis of co-expression networks, was utilized to identify candidate genes involved in BIAs biosynthesis. Following this, recombinant plasmids containing candidate genes were expressed in Escherichia coli, a widely used prokaryotic expression system. The purpose of this genetic engineering endeavor was to express the candidate genes within the bacteria, thereby enabling the assessment of the resultant enzyme activity.

RESULTS: The synonymous substitutions per synonymous site for paralogs indicated that at least one whole genome duplication event has occurred. The potential BIA biosynthetic pathway of P. amurense was proposed, and two PR10/Bet v1 members, 14 CYP450s, and 33 methyltransferases were selected as related to BIA biosynthesis. One PR10/Bet v1 was identified as norcoclaurine synthase, which could catalyze dopamine and 4-hydroxyphenylacetaldehyde into (S)-norcoclaurine.

CONCLUSION: Our studies provide important insights into the biosynthesis and evolution of BIAs in non-Ranunculales species.

PMID:39949809 | PMC:PMC11814251 | DOI:10.1016/j.chmed.2024.11.003

Front Immunol. 2025 Jan 30;16:1494446. doi: 10.3389/fimmu.2025.1494446. eCollection 2025.

ABSTRACT

INTRODUCTION: New biomarkers are urgently needed to detect pancreatic ductal adenocarcinoma (PDAC) at an earlier stage for individualized treatment strategies and to improve outcomes. Autoantibodies (AAbs) in principle make attractive biomarkers as they arise early in disease, report on disease-associated perturbations in cellular proteomes, and are static in response to other common stimuli, yet are measurable in the periphery, potentially well in advance of the onset of clinical symptoms.

METHODS: Here, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).

RESULTS: Combinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.

CONCLUSION: This AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.

PMID:39949781 | PMC:PMC11821970 | DOI:10.3389/fimmu.2025.1494446