J Neuromuscul Dis. 2024 Nov;11(6):1211-1220. doi: 10.1177/22143602241283391.

ABSTRACT

BACKGROUND: The accurate diagnosis of titin-related myopathies (TTN-RM) is challenging due to the "gigantism" of the coding gene TTN with an incompletely understood landscape of normal genetic variation, an increasing number of pathogenic variants, and wide phenotypic variability of both cardiac and muscle involvement. Particularly in situations of potentially incomplete genotypes, clinicians need more phenotyping tools to help confidently determine the pathogenicity of variants in TTN and accurately diagnose titinopathies.

OBJECTIVE: To illustrate the pattern of muscle involvement found by muscle imaging in patients with TTN-RM.

METHODS: We reviewed the clinical and imaging data of patients with TTN-RM. Cross secitonal MR images of the lower extremity muscles were scored for degree of abnormality using the Mercuri scoring system and patterns were identified with comparison across muscle groups. Ultrasound images were also reviewed and described.

RESULTS: Eleven patients with TTN-RM had clinical and imaging data available for review. The relatively more severe involvement of the semitendinosus muscle in the hamstring group ("semitendinosus sign") emerged as a consistent feature in patients with recessive TTN-RM despite clinical heterogeneity.

CONCLUSIONS: Here we find that despite considerable complexity, the pattern of muscle involvement on MRI and ultrasound may aid in the confirmation of TTN-RM by establishing compatibility with the diagnosis.

PMID:39967429 | DOI:10.1177/22143602241283391

Pharmgenomics Pers Med. 2025 Feb 14;18:55-69. doi: 10.2147/PGPM.S502355. eCollection 2025.

ABSTRACT

BACKGROUND: Pharmacogenomics holds significant promise in improving the efficacy and safety of chemotherapy for childhood cancers. However, the field remains underexplored in Africa, where high genetic diversity and substantial disease burdens, including cancers, create unique challenges. This review investigates the current state of pharmacogenomics research in childhood cancer chemotherapies across Africa, focusing on genetic variations influencing chemotherapy efficacy and adverse drug reactions. It also highlights critical gaps, such as limited infrastructure and insufficient healthcare worker knowledge, and emphasizes the importance of capacity-building initiatives in the region.

METHODS: A scoping review was conducted encompassing studies published up to September 2024 that examined pharmacogenomic variations associated with chemotherapies in childhood cancer patients across Africa. The review included laboratory genetic analyses and surveys assessing healthcare workers' knowledge, attitudes, and perceptions regarding pharmacogenomics, particularly in the context of pediatric oncology.

RESULTS: A total of 12 genes were identified across eight studies, including TPMT, CYP3A5, MDR1, MAPT, NUDT15, ITPA, IMPDH1, SLC29A1, SLC28A2, SLC28A3, ABCC4, and MTHFR. The most studied genes were TPMT and CYP3A5, which are involved in the metabolism of 6-mercaptopurine (6-MP) and vincristine, respectively. These studies spanned five African countries, including Kenya, Egypt, Zimbabwe, Nigeria, Tunisia, and Libya, and focused primarily on childhood cancers, particularly acute lymphoblastic leukemia. Chemotherapies frequently studied were 6-MP (reported in five studies), vincristine, cyclophosphamide, and methotrexate. Knowledge of pharmacogenomics among healthcare workers in Africa remains low, though a positive attitude towards its clinical applications was observed.

CONCLUSION: Pharmacogenomic variants, such as TPMT*3A, 3C, and CYP3A53, *6, significantly impact drug metabolism in African children with cancer. However, research remains regionally limited, and gaps in infrastructure and healthcare worker training persist. Expanding research efforts and improving pharmacogenomics capacity through pharmacist training and capacity-building initiatives are essential to advancing personalized medicine in Africa, ultimately improving treatment outcomes for pediatric cancer patients.

PMID:39968370 | PMC:PMC11834739 | DOI:10.2147/PGPM.S502355

J Public Health Afr. 2025 Jan 24;16(1):786. doi: 10.4102/jphia.v16i1.786. eCollection 2025.

ABSTRACT

The intersection of mpox and mental health is a critical concern, particularly for individuals with pre-existing mental disorders, who face heightened psychological stress and exacerbation of symptoms. This study explores the potential of genetic testing, such as Polygenic Risk Scores and pharmacogenetics, in enhancing mental disorders and mpox management. By tailoring treatment and prevention strategies to an individual's genetic profile, clinicians can provide more personalised care, reducing adverse effects and improving outcomes. Furthermore, genetic insights can inform the development of safer vaccines and early interventions, particularly for vulnerable populations. The study underscores the importance of integrating mental and public health strategies, advocating for targeted research and fostering interdisciplinary collaboration to effectively address these complex health challenges.

PMID:39968354 | PMC:PMC11830839 | DOI:10.4102/jphia.v16i1.786

Arch Med Sci. 2022 Jun 30;20(6):1993-2001. doi: 10.5114/aoms/150867. eCollection 2024.

ABSTRACT

INTRODUCTION: Cholecystokinin (CCK) is involved in several metabolic pathways, and CCK agonists are considered as a potential novel treatment option in populations with increased metabolic risk, including polycystic ovary syndrome (PCOS). As genetic variability of cholecystokinin A and B receptor genes (CCKAR and CCKBR, respectively) may modify their biological actions, we investigated the impact of CCKAR and CCKBR genetic variability on anthropometric and metabolic parameters in patients with PCOS.

MATERIAL AND METHODS: Our cross-sectional study included 168 patients with PCOS and 82 healthy female controls genotyped for polymorphisms in CCKAR (rs6448456 and rs1800857) and CCKBR (rs2929180, rs1800843, rs1042047 and rs1042048) genes.

RESULTS: The investigated polymorphisms were not associated with anthropometric characteristics of patients with PCOS. However, among healthy controls, carriers of at least one polymorphic CCKBR rs1800843 allele had a larger waist circumference (p = 0.027) and more visceral fat (p = 0.046). Among PCOS patients, carriers of at least one polymorphic CCKAR rs6448456 C allele had significantly higher total blood cholesterol and LDL, and significantly lower blood glucose levels after 30, 60 and 90 min of the oral glucose tolerance test (all p < 0.05). Healthy controls with at least one polymorphic CCKAR rs1800857 C allele were less likely to have a high metabolic syndrome burden (p = 0.029).

CONCLUSIONS: Genetic variability in CCKAR affects lipid profile and post-load glucose levels in patients with PCOS and is associated with metabolic syndrome burden in healthy young women. Further investigation of the role of genetic variability in CCKAR and CCKBR could contribute to development of individually tailored treatment strategies with CCK receptor agonists.

PMID:39967955 | PMC:PMC11831332 | DOI:10.5114/aoms/150867

Clin Transl Sci. 2025 Feb;18(2):e70126. doi: 10.1111/cts.70126.

ABSTRACT

Pharmacogenetic testing provides patient genotype information which could influence medication selection and dosing for optimal patient care. Insurance coverage for pharmacogenetic testing varies widely. A better understanding of the commonly used medications with clinically important pharmacogenetic recommendations can inform which medications and/or genes should be prioritized for coverage and reimbursement in the context of finite healthcare resources. The aim of this scoping review was to collate previous studies that investigated the utilization rate of medications that could be guided by pharmacogenetic testing. Included studies utilized electronic medical records or claims data to assess pharmacogenetic medication prescription rates for older adults (≥ 65 years old). Identified pharmacogenetic medications were classified according to therapeutic class and assessed for actionability based on the Clinical Pharmacogenetics Implementation Consortium guidelines. Across the 31 included studies, analgesic (n = 29), psychotropic (n = 29), and cardiovascular (n = 27) therapeutic classes were most commonly investigated. Study populations were primarily generalized (48%); however, some studies focused on specific populations, such as, cancer (n = 6), mental health (n = 1), and nursing home (n = 2) cohorts. A total of 215 unique pharmacogenetic medications were reported, of which, 82 were associated with actionable pharmacogenetic recommendations. The most frequent genes implicated in potential drug-gene interactions with these actionable pharmacogenetic drugs were CYP2D6 (25.6%), CYP2C19 (18.3%), and CYP2C9 (11%). Medications most frequently prescribed included pantoprazole (range 0%-49.6%), simvastatin (range 0%-54.9%), and ondansetron (range 0.1%-62.6%). Overall, the frequently prescribed medications and associated genes identified in this review could guide pharmacogenetic testing implementation into clinical practice, including insurer subsidization.

PMID:39967300 | DOI:10.1111/cts.70126

Biofilm. 2025 Jan 25;9:100257. doi: 10.1016/j.bioflm.2025.100257. eCollection 2025 Jun.

ABSTRACT

BACKGROUND: Pseudomonas aeruginosa chronic lung infection is the leading cause of death in the cystic fibrosis (CF) population. The high genome versatility of this microorganism allows it to adapt to the hostile CF lung where the same clone can persist for decades. Paranasal sinuses serve as a reservoir for bacterial adaptation before lung infection. Our study investigates biofilm compatibility among identical and different P. aeruginosa genotypes from sinus and lungs of CF patients. Strains were further characterized by whole genome sequencing and motility assays were performed.

METHODOLOGY: Motility, gentamicin susceptibility and growth rates were assessed in four strains coming from three CF patients. The strains were subjected to whole genome sequencing with the Illumina MiSeq platform.Conjugation assays using the mini Tn7 transposon were performed in order to tag bacteria with the fluorescent proteins YFP (yellow) and CFP (cyan). Biofilm experiments were carried out in a flow cell system and images were acquired using a confocal laser microscope (CLSM) on days 3 and 5. Four experiments were performed: Experiment 1 with two clonal isolates from sinus and lungs from patient P01 (CF430-142, CF430-11621); experiments 2 (CF430-11621 + 75885-B) and 3 (CF430-11621 + 80271-B) with two lung isolates belonging to two different clones from different patients (P02, P03) and experiment 4 with one lung strain (CF430-11621) and P. aeruginosa PAO1 reference strain.

RESULTS: P. aeruginosa clonal isolates coming from paranasal sinuses and lungs from the same patient were able to form mixed biofilm. When different clones were employed no mixed biofilms were observed. Similar results were observed when combining the lung strain and the reference strain PAO1. Biofilms of both strains were observed in the flow-cell channels but no mixed biofilms of them were observed, with the exception of strain 75887-B which did not appear to form any biofilm when mixed with strain CF430-11621. All strains performed swarming while strains CF430-142 and 75887B lacked twitching motility. An aminoacidic change in SadB was observed in the strain 75887B.

CONCLUSION: Mixed biofilms were only observed when identical clones from the same patient were cultured together. Our experiments indicate that twitching motility does not significantly affect biofilm formation or architecture in our isolates.

PMID:39968375 | PMC:PMC11834076 | DOI:10.1016/j.bioflm.2025.100257

Digit Health. 2025 Feb 17;11:20552076251320288. doi: 10.1177/20552076251320288. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Accurate segmentation of brain tumors in medical imaging is essential for diagnosis and treatment planning. Current techniques often struggle with capturing complex tumor features and are computationally demanding, limiting their clinical application. This study introduces the attention-based convolutional U-Net (ACU-Net) model, designed to improve segmentation accuracy and efficiency in fMRI images by incorporating attention mechanisms that selectively highlight critical features while preserving spatial context.

METHODS: The ACU-Net model combines convolutional neural networks (CNNs) with attention mechanisms to enhance feature extraction and spatial coherence. We evaluated ACU-Net on the BraTS 2018 and BraTS 2020 fMRI datasets using rigorous data splitting for training, validation, and testing. Performance metrics, particularly Dice scores, were used to assess segmentation accuracy across different tumor regions, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes.

RESULTS: ACU-Net demonstrated high segmentation accuracy, achieving Dice scores of 99.23%, 99.27%, and 96.99% for WT, TC, and ET, respectively, on the BraTS 2018 dataset, and 98.72%, 98.40%, and 97.66% for WT, TC, and ET on the BraTS 2020 dataset. These results indicate that ACU-Net effectively captures tumor boundaries and subregions with precision, surpassing traditional segmentation approaches.

CONCLUSION: The ACU-Net model shows significant potential to enhance clinical diagnosis and treatment planning by providing precise and efficient brain tumor segmentation in fMRI images. The integration of attention mechanisms within a CNN architecture proves beneficial for identifying complex tumor structures, suggesting that ACU-Net can be a valuable tool in medical imaging applications.

PMID:39968528 | PMC:PMC11833834 | DOI:10.1177/20552076251320288

Geohealth. 2025 Feb 18;9(2):e2024GH001234. doi: 10.1029/2024GH001234. eCollection 2025 Feb.

ABSTRACT

The association between ambient ozone (O3) and acute myocardial infarction (AMI) onset is unclear, particularly for younger patients and AMI subtypes. This study examined the short-term association of O3 with AMI onset in patients aged 18-55 years and explored differences by AMI subtypes and patient characteristics. We analyzed 2,322 AMI patients admitted to 103 US hospitals (2008-2012). Daily maximum 8-hr O3 concentrations estimated using a spatiotemporal deep learning approach were assigned to participants' home addresses. We used a time-stratified case-crossover design with conditional logistic regression to assess the association between O3 and AMI, adjusting for fine particulate matter, air temperature, and relative humidity. We conducted stratified analyses to examine associations for AMI subtypes and effect modification by sociodemographic status, lifestyle factors, and medical history. An interquartile range (16.6 ppb) increase in O3 concentrations was associated with an increased AMI risk at lag 4 days (odds ratio [OR] = 1.21, 95% confidence interval [CI]: 1.08-1.34) and lag 5 days (OR = 1.11, 95% CI: 1.00-1.24). The association was more pronounced for non-ST-segment elevation AMI and type 2 AMI compared with ST-segment elevation AMI and type 1 AMI, respectively. Stronger O3-AMI associations were observed in non-Hispanic Blacks than in non-Hispanic Whites. Our study provides evidence that short-term O3 exposure is associated with increased AMI risk in younger patients, with varying associations across AMI subtypes. The effect modification by race/ethnicity highlights the need for population-specific intervention strategies.

PMID:39968338 | PMC:PMC11833228 | DOI:10.1029/2024GH001234

Comput Struct Biotechnol J. 2025 Jan 17;27:531-540. doi: 10.1016/j.csbj.2025.01.011. eCollection 2025.

ABSTRACT

Somatic driver mutations play important roles in cancer and must be precisely identified to advance our understanding of tumorigenesis and its promotion and progression. However, identifying somatic driver mutations remains challenging in Homo sapiens genomics due to the random nature of mutations and the high cost of qualitative experiments. Building on the powerful sequence interpretation capabilities of language models, we propose a self-attention-based contextualized pretrained language model for somatic driver mutation identification. We pretrained the model with the Homo sapiens reference genome to equip it with the ability to understand genome sequences and then fine-tuned it for oncogene and tumor suppressor gene prediction tasks, enabling it to extract features related to driver genes from the original genome sequence. The fine-tuned model was used to obtain the mutations' carcinogenic effect characteristics to further identify whether the mutation is a driver or a passenger. Compared with other computational algorithms, our method achieved excellent somatic driver mutation identification performance on the test set, with an absolute improvement of 4.31% in AUROC over the best comparison method. The strong performance of our method indicates that it can provide new insights into the discovery of cancer drivers.

PMID:39968174 | PMC:PMC11833646 | DOI:10.1016/j.csbj.2025.01.011

Front Artif Intell. 2025 Feb 4;8:1551661. doi: 10.3389/frai.2025.1551661. eCollection 2025.

ABSTRACT

A smart grid is a structure that regulates, operates, and utilizes energy sources that are incorporated into the smart grid using smart communications techniques and computerized techniques. The running and maintenance of Smart Grids now depend on artificial intelligence methods quite extensively. Artificial intelligence is enabling more dependable, efficient, and sustainable energy systems from improving load forecasting accuracy to optimizing power distribution and guaranteeing issue identification. An intelligent smart grid will be created by substituting artificial intelligence for manual tasks and achieving high efficiency, dependability, and affordability across the energy supply chain from production to consumption. Collection of a large diversity of data is vital to make effective decisions. Artificial intelligence application operates by processing abundant data samples, advanced computing, and strong communication collaboration. The development of appropriate infrastructure resources, including big data, cloud computing, and other collaboration platforms, must be enhanced for this type of operation. In this paper, an attempt has been made to summarize the artificial intelligence techniques used in various aspects of smart grid system.

PMID:39968172 | PMC:PMC11832663 | DOI:10.3389/frai.2025.1551661

Heliyon. 2025 Jan 25;11(3):e42273. doi: 10.1016/j.heliyon.2025.e42273. eCollection 2025 Feb 15.

ABSTRACT

The fast progress of deepfake technology has caused a huge overlap between reality and deceit, leading to substantial worries over the authenticity of digital media content. Deepfakes, which involve the manipulation of image, audio and video to produce highly convincing yet completely fabricated content, present significant risks to media, politics, and personal well-being. To address this increasing problem, our comprehensive survey investigates the advancement along with evaluation of autonomous techniques for identifying and evaluating deepfake media. This paper provides an in-depth analysis of state-of-the-art techniques and tools for identifying deepfakes, encompassing image, video, and audio-based content. We explore the fundamental technologies, such as deep learning models, and evaluate their efficacy in differentiating real and manipulated media. In addition, we explore novel detection methods that utilize sophisticated machine learning, computer vision, and audio analysis techniques. The study we conducted included exclusively the most recent research conducted between 2018 and 2024, which represents the newest developments in the area. In an era where distinguishing fact from fiction is paramount, we aim to enhance the security and awareness of the digital ecosystem by advancing our understanding of autonomous detection and evaluation methods.

PMID:39968137 | PMC:PMC11834059 | DOI:10.1016/j.heliyon.2025.e42273

Acta Med Philipp. 2025 Jan 31;59(2):33-40. doi: 10.47895/amp.vi0.8950. eCollection 2025.

ABSTRACT

BACKGROUND AND OBJECTIVES: The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AI-based TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

METHODS: A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

RESULTS: With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

CONCLUSIONS: qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

PMID:39967706 | PMC:PMC11831083 | DOI:10.47895/amp.vi0.8950

J Chem Inf Model. 2025 Feb 19. doi: 10.1021/acs.jcim.4c02144. Online ahead of print.

ABSTRACT

Interleukin-6 (IL-6) is a critical cytokine involved in immune regulation, inflammation, and the pathogenesis of various diseases, including autoimmune disorders, cancer, and the cytokine storm associated with severe COVID-19. Identifying IL-6 inducing epitopes, the short peptide fragments that trigger IL-6 production, is crucial for developing epitope-based vaccines and immunotherapies. However, traditional methods for epitope prediction often lack accuracy and efficiency. This study presents RAG_MCNNIL6, a novel deep learning framework that integrates Retrieval-augmented generation (RAG) with multiwindow convolutional neural networks (MCNNs) for accurate and rapid prediction of IL-6 inducing epitopes. RAG_MCNNIL6 leverages ProtTrans, a state-of-the-art pretrained protein language model, to generate rich embedding representations of peptide sequences. By incorporating a RAG-based similarity retrieval and embedding augmentation strategy, RAG_MCNNIL6 effectively captures both local and global sequence patterns relevant for IL-6 induction, significantly improving prediction performance compared to existing methods. We demonstrate the superior performance of RAG_MCNNIL6 on benchmark data sets, highlighting its potential for advancing research and therapeutic development for IL-6-mediated diseases.

PMID:39967508 | DOI:10.1021/acs.jcim.4c02144

Chin Med J (Engl). 2025 Feb 19. doi: 10.1097/CM9.0000000000003421. Online ahead of print.

NO ABSTRACT

PMID:39967314 | DOI:10.1097/CM9.0000000000003421

Comput Biol Med. 2025 Mar;186:109597. doi: 10.1016/j.compbiomed.2024.109597. Epub 2025 Jan 1.

ABSTRACT

Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning methodologies, focusing on the roles of Autoencoders, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in enhancing cardiac image analysis. The study implements a comprehensive approach, combining traditional algorithms such as Sobel, Watershed, and Otsu's Thresholding with advanced deep learning models to achieve precise and accurate imaging outcomes. The Autoencoder model, developed for image enhancement and feature extraction, achieved a notable accuracy of 99.66% on the test data. Optimized for image recognition tasks, the CNN model demonstrated a high precision rate of 98.9%. The RNN model, utilized for sequential data analysis, showed a prediction accuracy of 98%, further underscoring the robustness of the hybrid framework. The research drew upon a diverse range of academic databases and pertinent publications within cardiac imaging and deep learning, focusing on peer-reviewed articles and studies published in the past five years. Models were implemented using the TensorFlow and Keras frameworks. The proposed methodology was evaluated in the clinical validation phase using advanced imaging protocols, including the QuickScan technique and balanced steady-state free precession (bSSFP) imaging. The validation metrics were promising: the Signal-to-Noise Ratio (SNR) was improved by 15%, the Contrast-to-Noise Ratio (CNR) saw an enhancement of 12%, and the ejection fraction (EF) analysis provided a 95% correlation with manually segmented data. These metrics confirm the efficacy of the models, showing significant improvements in image quality and diagnostic accuracy. The integration of adversarial defense strategies, such as adversarial training and model ensembling, have been analyzed to enhance model robustness against malicious inputs. The reliability and comparison of the model's ability have been investigated to maintain clinical integrity, even in adversarial attacks that could otherwise compromise segmentation outcomes. These findings indicate that integrating Autoencoders, CNNs, and RNNs within a hybrid deep-learning framework is promising for enhancing cardiac MRI segmentation and early diagnosis. The study contributes to the field by demonstrating the applicability of these advanced techniques in clinical settings, paving the way for improved patient outcomes through more accurate and timely diagnoses.

PMID:39967188 | DOI:10.1016/j.compbiomed.2024.109597

Food Res Int. 2025 Feb;202:115598. doi: 10.1016/j.foodres.2024.115598. Epub 2025 Jan 7.

ABSTRACT

Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspectral imaging (HSI) offering advantages such as multiple bands, rapid, minimal damage, non-contact, and non-destructive detection. However, current HSI methods require agar plate cultures, which are time-consuming and labor-intensive. This study is the first to use bacterial broth in a 24-well plate to collect HSI spectra, combined with machine learning for enhanced feature extraction and classification. After data augmentation and dimensionality reduction via principal component analysis (PCA) and linear discriminant analysis (LDA), deep neural networks and support vector machines (DNN-SVM) resulted in prediction accuracies of 100 % on the training set, 98.31 % on the testing set, and 93.33 % on the validation set for classifying B. cereus, E. coli, and S. aureus. As a result, a high-throughput, rapid, and non-destructive detection method was developed, which is expected to detect 24 bacterial broth samples in less than ten minutes. It indicates the potential of HSI to be used as a feasible, robust, and non-destructive solution for real-time monitoring of microbial pathogens in food.

PMID:39967133 | DOI:10.1016/j.foodres.2024.115598

Front Oncol. 2025 Feb 4;15:1487334. doi: 10.3389/fonc.2025.1487334. eCollection 2025.

ABSTRACT

BACKGROUND: Alpha-fetoprotein (AFP)-producing hepatoid adenocarcinoma of lung (HAL) is a rare type of lung cancer, with its characteristics being not yet fully clarified. We recently encountered a case of HAL combined with idiopathic pulmonary fibrosis (IPF), which has never been reported.

CASE PRESENTATION: A 66-year-old man consulted our hospital with a chief complaint of cough. Chest computed tomography (CT) revealed multiple nodules measuring from 8mm to 20mm in diameter located in bilateral lung, along with an enlarged left hilar lymph node. CT-guided percutaneous lung biopsy confirmed the diagnosis of AFP-producing primary HAL combined with IPF. Systemic treatment according to guidelines for advanced non-small cell lung cancer resulted in a long-term survival.

CONCLUSIONS: This case report documents the first occurrence and prognosis of AFP-producing HAL in a patient with IPF. The long-term survival brought by the diagnosis and treatment model in our case may provide significant prognostic value for this rare condition.

PMID:39968074 | PMC:PMC11832387 | DOI:10.3389/fonc.2025.1487334

ACS Med Chem Lett. 2025 Jan 23;16(2):317-326. doi: 10.1021/acsmedchemlett.4c00559. eCollection 2025 Feb 13.

ABSTRACT

Lysophosphatidic acid (LPA) is a phospholipid activating different biological functions by binding to G protein-coupled receptors (LPA1-6). Among these, the role of the LPA1 receptor in modulating fibrotic processes is well-known, making it a therapeutic target for pulmonary fibrosis and other fibrotic disorders. Herein we report the search for a new class of LPA1 antagonists for the oral treatment of idiopathic pulmonary fibrosis with a focus on hepatobiliary safety. Compound 7 excelled in in vitro and in vivo efficacy, showing significant efficacy both in PD studies and in a rodent lung fibrosis model, with a promising in vitro hepatic safety profile. However, in a dose range finding (DRF) toxicity study, compound 7 did not ensure safety regarding potential hepatobiliary toxicity, leading to its development being halted.

PMID:39967626 | PMC:PMC11831564 | DOI:10.1021/acsmedchemlett.4c00559

Small. 2025 Feb 19:e2409126. doi: 10.1002/smll.202409126. Online ahead of print.

ABSTRACT

This study advances sustainable pharmaceutical research for endometriosis by developing in vitro 3D cell culture models of endometriotic pathophysiology that allow antifibrotic drug candidates to be tested. Fibrosis is a key aspect of endometriosis, yet current cell models to study it remain limited. This work aims to bridge the translational gap between in vitro fibrosis research and preclinical testing of non-hormonal drug candidates. When grown in a 3D matrix of sustainably produced silk protein functionalized with a fibronectin-derived cell adhesion motif (FN-silk), endometrial stromal and epithelial cells respond to transforming growth factor beta-1 (TGF-β1) in a physiological manner as probed at the messenger RNA (mRNA) level. For stromal cells, this response to TGF-β1 is not observed in spheroids, while epithelial cell spheroids behave similarly to epithelial cell FN-silk networks. Pirfenidone, an antifibrotic drug approved for the treatment of idiopathic pulmonary fibrosis, reverses TGF-β1-induced upregulation of mRNA transcripts involved in fibroblast-to-myofibroblast transdifferentiation of endometrial stromal cells in FN-silk networks, supporting pirfenidone's potential as a repurposed non-hormonal endometriosis therapy. Overall, endometrial stromal cells cultured in FN-silk networks-which are composed of a sustainably produced, fully defined FN-silk protein-recapitulate fibrotic cellular behavior with high fidelity and enable antifibrotic drug testing.

PMID:39967482 | DOI:10.1002/smll.202409126

J Sep Sci. 2025 Feb;48(2):e70099. doi: 10.1002/jssc.70099.

ABSTRACT

Metabolomics, a powerful discipline within systems biology, aims at comprehensive profiling of small molecules in biological samples. The challenges of biological sample complexity are addressed through innovative sample preparation methods, including solid-phase extraction and microextraction techniques, enhancing the detection and quantification of low-abundance metabolites. Advances in chromatographic separation, particularly liquid chromatography (LC) and gas chromatography (GC), coupled with high-resolution (HR) mass spectrometry (MS), have significantly improved the sensitivity, selectivity, and throughput of metabolomic studies. Cutting-edge techniques, such as ion-mobility mass spectrometry (IM-MS) and tandem MS (MS/MS), further expand the capacity for comprehensive metabolite profiling. These advanced analytical platforms each offer unique advantages for metabolomics, with continued technological improvements driving deeper insights into metabolic pathways and biomarker discovery. By providing a detailed overview of current trends and techniques, this review aims to offer valuable insights into the future of metabolomics in human health research and its translational potential in clinical settings. Toward the end, this review also highlights the biomedical applications of metabolomics, emphasizing its role in biomarker discovery, disease diagnostics, personalized medicine, and drug development.

PMID:39968702 | DOI:10.1002/jssc.70099