Hum Mutat. 2024 Oct 16;2024:6165547. doi: 10.1155/2024/6165547. eCollection 2024.

ABSTRACT

Cystic fibrosis (CF) is a complex monogenic disorder with a large variability in disease severity. Growing evidence suggests that the variation observed depends not only on variations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene but also on modifier genes. Utilizing five databases (including CINAHL, PubMed, Science Direct, Scopus, and Web of Science), a systematic review was conducted to examine the current literature on the known impacts of genomic variations in modifier genes on the CF disease progression, severity, and therapeutic response. A total of 70 full-text articles describing over 80 gene modifiers associated with CF were selected. The modifier genes included genes associated with the CFTR interactome, the inflammatory response, microbial profiles, and other genes affecting the critical physiological pathways of multiple organ systems, such as the respiratory and gastrointestinal systems. Limitations of the existing literature embrace the lack of clinical studies investigating pharmacogenetic impacts and the significance of gene modifiers on the CF clinical picture, including a limited number of replication and validation studies. Further investigations into other potential gene modifiers using genome-wide association studies are needed to critically explore new therapeutic targets and provide a better understanding of the CF disease phenotype under specific drug treatments.

PMID:40225935 | PMC:PMC11919198 | DOI:10.1155/2024/6165547

J Thorac Dis. 2025 Mar 31;17(3):1424-1443. doi: 10.21037/jtd-24-1525. Epub 2025 Mar 27.

ABSTRACT

BACKGROUND: An expanding array of inhaled antibiotic therapies can be effective for the treatment of chronic Pseudomonas aeruginosa (P. aeruginosa) infection in patients with cystic fibrosis (CF) and non-CF bronchiectasis (NCFB). Nonetheless, there is a paucity of direct studies comparing the curative effects of these regimens. This network meta-analysis (NMA) aimed to assess the efficacy and safety of different inhaled antibiotic therapies for the relative short-term (4 weeks) and long-term (≥4 months) management of chronic P. aeruginosa infection in patients with CF and NCFB, respectively.

METHODS: We searched PubMed, Web of Science, Embase, and Cochrane Library database as at 25th February, 2024. Randomized controlled trials (RCTs) involving inhaled antibiotic therapies for treatment of CF or NCFB were thoroughly screened. We conducted this NMA within a Bayesian framework. The surface under the cumulative ranking curve (SUCRA) was calculated to estimate relative effects of interventions per outcome.

RESULTS: A total of 39 RCTs were included, involving 18 inhaled antibiotic treatment regimens and 7,486 participants. The primary outcomes assessed were microbiological efficacy and tolerability. According to SUCRA results, for patients with CF, tobramycin inhalation powder (TIP) had the best profile regarding microbiological efficacy at both short-term and long-term follow-up (SUCRA, 94.5%; 90.5%). Colistin for inhalation (SUCRA, 84.0%) and tobramycin inhalation solution (TIS; SUCRA, 75.7%) had the best tolerability profile at short-term and long-term follow-up, respectively. For patients with NCFB, TIP (SUCRA, 84.2%) and gentamicin injectable solution (GM) for inhalation (SUCRA, 92.2%) had the best profile regarding microbiological efficacy at short-term and long-term follow-up, respectively. Ciprofloxacin inhalation powder had the best tolerability profile at both short-term and long-term follow-up (SUCRA, 66.4%; 85.6%).

CONCLUSIONS: The present study suggests that inhalation of TIS and GM are deemed exhibiting favorable profile across various outcomes for treating chronic P. aeruginosa infection in patients with CF and NCFB, respectively. Further large-scale and higher-quality studies are needed to support the conclusion.

PMID:40223951 | PMC:PMC11986750 | DOI:10.21037/jtd-24-1525

Cancers (Basel). 2025 Apr 7;17(7):1245. doi: 10.3390/cancers17071245.

ABSTRACT

BACKGROUND/OBJECTIVES: Identifying tumor budding (TB) in colorectal cancer (CRC) is vital for better prognostic assessment as it may signify the initial stage of metastasis. Despite its importance, TB detection remains challenging due to subjectivity in manual evaluations. Identifying TBs remains difficult, especially at high magnification levels, leading to inconsistencies in prognosis. To address these issues, we propose an automated system for TB classification using deep learning.

METHODS: We trained a deep learning model to identify TBs through weakly supervised learning by aggregating positive and negative bags from the tumor invasive front. We assessed various foundation models for feature extraction and compared their performance. Attention heatmaps generated by attention-based multi-instance learning (ABMIL) were analyzed to verify alignment with TBs, providing insights into the interpretability of the features. The dataset includes 29 WSIs for training and 70 whole slide images (WSIs) for the hold-out test set.

RESULTS: In six-fold cross-validation, Phikon-v2 achieved the highest average AUC (0.984 ± 0.003), precision (0.876 ± 0.004), and recall (0.947 ± 0.009). Phikon-v2 again achieved the highest AUC (0.979) and precision (0.980) on the external hold-out test set. Moreover, its recall rate (0.910) was still higher than that of UNI's (0.879). UNI exhibited a balanced performance on the hold-out test set, with an AUC of 0.960 and a precision of 0.968. CtransPath showed strong precision on the external hold-out test set (0.947) but had a slightly lower recall (0.911).

CONCLUSIONS: The proposed technique enhances the accuracy of TB assessment, offering potential applications for CRC and other cancer types.

PMID:40227783 | DOI:10.3390/cancers17071245

Cancers (Basel). 2025 Mar 28;17(7):1132. doi: 10.3390/cancers17071132.

ABSTRACT

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

PMID:40227709 | DOI:10.3390/cancers17071132

Cancers (Basel). 2025 Mar 25;17(7):1092. doi: 10.3390/cancers17071092.

ABSTRACT

Background: A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is time management. Without a good time management scheme, a grid search can easily be set off as a "mission" that will not finish in our lifetime. In this study, we introduce a heuristic three-stage mechanism for managing the running time of low-budget grid searches with deep learning, sweet-spot grid search (SSGS) and randomized grid search (RGS) strategies for improving model prediction performance, in an application of predicting the 5-year, 10-year, and 15-year risk of breast cancer metastasis. Methods: We develop deep feedforward neural network (DFNN) models and optimize the prediction performance of these models through grid searches. We conduct eight cycles of grid searches in three stages, focusing on learning a reasonable range of values for each of the adjustable hyperparameters in Stage 1, learning the sweet-spot values of the set of hyperparameters and estimating the unit grid search time in Stage 2, and conducting multiple cycles of timed grid searches to refine model prediction performance with SSGS and RGS in Stage 3. We conduct various SHAP analyses to explain the prediction, including a unique type of SHAP analyses to interpret the contributions of the DFNN-model hyperparameters. Results: The grid searches we conducted improved the risk prediction of 5-year, 10-year, and 15-year breast cancer metastasis by 18.6%, 16.3%, and 17.3%, respectively, over the average performance of all corresponding models we trained using the RGS strategy. Conclusions: Grid search can greatly improve model prediction. Our result analyses not only demonstrate best model performance but also characterize grid searches from various aspects such as their capabilities of discovering decent models and the unit grid search time. The three-stage mechanism worked effectively. It not only made our low-budget grid searches feasible and manageable but also helped improve the model prediction performance of the DFNN models. Our SHAP analyses not only identified clinical risk factors important for the prediction of future risk of breast cancer metastasis, but also DFNN-model hyperparameters important to the prediction of performance scores.

PMID:40227603 | DOI:10.3390/cancers17071092

Clin Oral Investig. 2025 Apr 14;29(5):246. doi: 10.1007/s00784-025-06325-1.

ABSTRACT

OBJECTIVES: Dental caries remains a significant global health concern. Recognising the diagnostic potential of cone-beam computed tomography (CBCT) in caries assessment, this study aimed to develop an artificial intelligence (AI)-driven tool for accurate caries localisation and classification on CBCT images, thereby enhancing early diagnosis and precise treatment planning.

MATERIALS AND METHODS: A three-dimensional (3D) convolutional neural network (CNN) was developed using a large annotated dataset comprising 1,778 single-tooth CBCT images. The network's performance in localising and classifying multi-stage caries was compared with that of three dentists. Performance metrics included precision, recall, F1-score, Dice similarity coefficient (DSC), and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS: The proposed CNN achieved overall precision, recall, and DSC values of 0.712, 0.899, and 0.776, respectively, for lesion localisation. In comparison, the average metrics values for the dentists were 0.622, 0.886, and 0.700. For caries classification, the CNN achieved precision, recall, and F1-score values of 0.855, 0.857, and 0.856, respectively, whereas the corresponding values for the dentists were 0.700, 0.684, and 0.678. Overall, the CNN significantly outperformed the dentists in both localisation and classification tasks.

CONCLUSIONS: This study developed a high-performance 3D CNN for the localisation and classification of multi-stage caries on CBCT images. The CNN demonstrated significantly superior diagnostic performance compared to a group of three dentists, underscoring its potential for clinical integration.

CLINICAL RELEVANCE: The integration of AI into CBCT image analysis may improve the efficiency and accuracy of caries diagnosis. The proposed CNN represents a promising tool to enhance early diagnosis and precise treatment planning, potentially supporting clinical decision-making in dental practice.

PMID:40227550 | DOI:10.1007/s00784-025-06325-1

Skeletal Radiol. 2025 Apr 14. doi: 10.1007/s00256-025-04927-0. Online ahead of print.

ABSTRACT

OBJECTIVE: Prior work has demonstrated that AI access can help residents more accurately detect pediatric fractures. We wished to evaluate the effectiveness of an unsupervised AI-based training module as a pediatric fracture detection educational tool.

MATERIALS AND METHODS: Two hundred forty radiographic examinations from throughout the pediatric upper extremity were split into two groups of 120 examinations. A previously developed open-source deep learning fracture detection algorithm ( www.childfx.com ) was used to annotate radiographs. Four medical students and four PGY-2 radiology residents first evaluated 120 examinations for fracture without AI assistance and subsequently reviewed AI annotations on these cases via a training module. They then interpreted 120 different examinations without AI assistance. Pre- and post-intervention fracture detection accuracy was evaluated using a chi-squared test.

RESULTS: Overall resident fracture detection accuracy significantly improved from 71.3% pre-intervention to 77.5% post-intervention (p = 0.032). Medical student fracture detection accuracy was not significantly changed from 56.3% pre-intervention to 57.3% post-intervention (p = 0.794). Eighty-eight percent of responding participants (7/8) would recommend this model of learning.

CONCLUSION: We found that a tailored AI-based training module increased resident accuracy for detecting pediatric fractures by 6.2%. Medical student accuracy was not improved, likely due to their limited background familiarity with the task. AI offers a scalable method for automatically generating annotated teaching cases covering varied pathology, allowing residents to efficiently learn from simulated experience.

PMID:40227327 | DOI:10.1007/s00256-025-04927-0

J Integr Plant Biol. 2025 Apr 14. doi: 10.1111/jipb.13914. Online ahead of print.

ABSTRACT

Deep learning-based genomic prediction (DL-based GP) has shown promising performance compared to traditional GP methods in plant breeding, particularly in handling large, complex multi-omics data sets. However, the effective development and widespread adoption of DL-based GP still face substantial challenges, including the need for large, high-quality data sets, inconsistencies in performance benchmarking, and the integration of environmental factors. Here, we summarize the key obstacles impeding the development of DL-based GP models and propose future developing directions, such as modular approaches, data augmentation, and advanced attention mechanisms.

PMID:40226955 | DOI:10.1111/jipb.13914

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf289. doi: 10.1093/nar/gkaf289.

ABSTRACT

The three-dimensional (3D) organization of the genome is crucial for gene regulation, with disruptions linked to various diseases. High-throughput Chromosome Conformation Capture (Hi-C) and related technologies have advanced our understanding of 3D genome organization by mapping interactions between distal genomic regions. However, capturing enhancer-promoter interactions at high resolution remains challenging due to the high sequencing depth required. We introduce pC-SAC (probabilistically Constrained Self-Avoiding Chromatin), a novel computational method for producing accurate high-resolution Hi-C matrices from low-resolution data. pC-SAC uses adaptive importance sampling with sequential Monte Carlo to generate ensembles of 3D chromatin chains that satisfy physical constraints derived from low-resolution Hi-C data. Our method achieves over 95% accuracy in reconstructing high-resolution chromatin maps and identifies novel interactions enriched with candidate cis-regulatory elements (cCREs) and expression quantitative trait loci (eQTLs). Benchmarking against state-of-the-art deep learning models demonstrates pC-SAC's performance in both short- and long-range interaction reconstruction. pC-SAC offers a cost-effective solution for enhancing the resolution of Hi-C data, thus enabling deeper insights into 3D genome organization and its role in gene regulation and disease. Our tool can be found at https://github.com/G2Lab/pCSAC.

PMID:40226920 | DOI:10.1093/nar/gkaf289

Med Image Comput Comput Assist Interv. 2024 Oct;15005:754-765. doi: 10.1007/978-3-031-72086-4_71. Epub 2024 Oct 4.

ABSTRACT

Manual detection of intracranial aneurysms (IAs) in computed tomography (CT) scans is a complex, time-consuming task even for expert clinicians, and automating the process is no less challenging. Critical difficulties associated with detecting aneurysms include their small (yet varied) size compared to scans and a high potential for false positive (FP) predictions. To address these issues, we propose a 3D, multi-scale neural architecture that detects aneurysms via a deformable attention mechanism that operates on vessel distance maps derived from vessel segmentations and 3D features extracted from the layers of a convolutional network. Likewise, we reformulate aneurysm segmentation as bounding cuboid prediction using binary cross entropy and three localization losses (location, size, IoU). Given three validation sets comprised of 152/138/38 CT scans and containing 126/101/58 aneurysms, we achieved a Sensitivity of 91.3%/97.0%/74.1% @ FP rates 0.53/0.56/0.87, with Sensitivity around 80% on small aneurysms. Manual inspection of outputs by experts showed our model only tends to miss aneurysms located in unusual locations. Code and model weights are available online.

PMID:40226842 | PMC:PMC11986933 | DOI:10.1007/978-3-031-72086-4_71

Depress Anxiety. 2024 Jul 14;2024:4459867. doi: 10.1155/2024/4459867. eCollection 2024.

ABSTRACT

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy.

MATERIALS AND METHODS: The fNIRS data of participants-48 patients with MDD and 68 healthy controls (HCs)-were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth-wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave-one-subject-out cross-validation strategy and compared with those of conventional machine learning and CNN models.

RESULTS: The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms-shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet-were 73.28%, 74.14%, 62.93%, and 62.07%, respectively.

CONCLUSIONS: In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS-based CAD systems.

PMID:40226684 | PMC:PMC11918759 | DOI:10.1155/2024/4459867

Data Brief. 2025 Mar 19;60:111477. doi: 10.1016/j.dib.2025.111477. eCollection 2025 Jun.

ABSTRACT

This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.

PMID:40226198 | PMC:PMC11993157 | DOI:10.1016/j.dib.2025.111477

Chin Med J Pulm Crit Care Med. 2025 Mar 7;3(1):12-21. doi: 10.1016/j.pccm.2025.02.001. eCollection 2025 Mar.

ABSTRACT

Significant advances have been made in diagnosing and treating idiopathic pulmonary fibrosis (IPF) in the last decade. The incidence and prevalence of IPF are increasing, and morbidity and mortality remain high despite the two Food and Drug Administration (FDA)-approved medications, pirfenidone and nintedanib. Hence, there is an urgent need to develop new diagnostic tools and effective therapeutics to improve early, accurate diagnosis of IPF and halt or reverse the progression of fibrosis with a better safety profile. New diagnostic tools such as transbronchial cryobiopsy and genomic classifier require less tissue and generally have good safety profiles, and they have been increasingly utilized in clinical practice. Advances in artificial intelligence-aided diagnostic software are promising, but challenges remain. Both pirfenidone and nintedanib focus on growth factor-activated pathways to inhibit fibroblast activation. Novel therapies targeting different pathways and cell types (immune and epithelial cells) are being investigated. Biomarker-based personalized medicine approaches are also in clinical trials. This review aims to summarize recent diagnostic and therapeutic development in IPF.

PMID:40226606 | PMC:PMC11993042 | DOI:10.1016/j.pccm.2025.02.001

J Thorac Dis. 2025 Mar 31;17(3):1364-1376. doi: 10.21037/jtd-24-1882. Epub 2025 Mar 14.

ABSTRACT

BACKGROUND: This study aims to elucidate the capability of periostin (POSTN) as a serum biomarker in diagnosing idiopathic pulmonary fibrosis (IPF), assessing disease severity, and predicting acute exacerbations of IPF (AE-IPF), and to compare it with traditional IPF serum biomarkers Krebs von den Lungen-6 (KL-6), surfactant protein A (SP-A), and surfactant protein D (SP-D).

METHODS: From October 2022 to October 2023, 55 patients who were diagnosed with IPF and treated at the Third Affiliated Hospital of Anhui Medical University were enrolled in the IPF group. Additionally, 30 patients with bacterial pneumonia (BP) and 30 healthy individuals were selected as the BP and healthy control (HC) groups, respectively. All participants underwent pulmonary function tests, and enzyme-linked immunosorbent assay (ELISA) was used to measure serum POSTN, KL-6, SP-A, and SP-D levels. IPF patients also underwent high-resolution computed tomography (HRCT) to quantify HRCT scores. Receiver operating characteristic (ROC) curves were plotted to obtain sensitivity and specificity. Binary logistic regression analysis was conducted to identify AE-IPF risk factors.

RESULTS: Serum POSTN, KL-6, SP-A, and SP-D concentrations were significantly greater in the IPF group than in the BP and HC groups (P<0.001), whereas serum SP-A and SP-D levels were greater in the BP group than in the HC group (P=0.001, P=0.04). The sensitivity of POSTN for diagnosing IPF is 94.5%, and the specificity is 93.3%, both of which are higher than those of KL-6, SP-A, and SP-D. Within the IPF group, serum POSTN levels were negatively correlated with the percentage of predicted forced expiratory volume in one second (FEV1%pred) (P=0.01) and the percentage of the predicted diffusing capacity for carbon monoxide (DLCO%pred) (P=0.003). Additionally, in IPF patients, serum POSTN, KL-6, and SP-A levels were significantly positively associated with HRCT scores. Logistic regression analysis indicated that decreased DLCO%pred and increased baseline serum KL-6 levels were both independent risk factors for AE-IPF.

CONCLUSIONS: POSTN is a valuable serum biomarker for IPF and has the highest sensitivity and specificity among the four serum markers, with a diagnostic performance superior to that of KL-6, SP-A, and SP-D. DLCO%pred and KL-6 have high predictive value for AE-IPF.

PMID:40223944 | PMC:PMC11986764 | DOI:10.21037/jtd-24-1882

J Mol Neurosci. 2025 Apr 14;75(2):49. doi: 10.1007/s12031-025-02331-w.

ABSTRACT

Multiple sclerosis (MS), an autoimmune condition of the central nervous system (CNS), can lead to demyelination and axonal degeneration in the brain and spinal cord, which can cause progressive neurologic disability. MS symptoms include dysautonomia and progressive decline in motor abilities. In this investigation, we performed an integrated bioinformatics and experimental approach to find the expression level and interaction of a novel long non-coding RNA (lncRNA), PAN3-AS1, in MS samples. Microarray analysis was performed by R Studio using GEOquery and limma packages. lncRNA-mRNA RNA interaction analysis was performed using the lncRRIsearch database. Pathway enrichment analysis was performed by KEGG and Reactome online software through the Enrichr database. Protein-protein interaction analysis was performed by STRING online software. Gene ontology (GO) analysis was performed by Enrichr database. Based on microarray analysis, lncRNA PAN3-AS1 has a significantly low expression in MS samples compared to the control (logFC - 1.2, adj. P. Val 0.03). qRT-PCR results approved bioinformatics analyses. ROC analysis revealed that PAN3-AS1 could be considered a potential diagnostic biomarker of MS. Based on lncRNA-mRNA interaction analysis, lncRNA PAN3-AS1 regulates the expression level of RPGR. RPGR and its protein interactome regulate the cilium assembly, chaperon-mediated autophagy, and microarray biogenesis. lncRNA PAN3-AS1, as a significant low-expressed lncRNA in MS samples, could be a potential diagnostic MS biomarker. PAN3-AS1 might regulate the expression level of cilium assembly and chaperon-mediated autophagy. Dysregulation of PAN3-AS1 might affect the expression of RPGR and its protein interactome.

PMID:40227518 | DOI:10.1007/s12031-025-02331-w

BioTech (Basel). 2025 Feb 20;14(1):11. doi: 10.3390/biotech14010011.

ABSTRACT

STXBP1 syndrome is a severe early-onset epileptic encephalopathy characterized by developmental delay and intellectual disability. This review addresses key challenges in STXBP1 syndrome research, focusing on advanced therapeutic approaches and experimental models. We explore gene therapy strategies, including CRISPR-Cas9, adeno-associated viral (AAV) vectors, and RNA therapies such as antisense oligonucleotides (ASOs), aimed at correcting STXBP1 genetic dysfunctions. This review presents in vivo and in vitro models, highlighting their contributions to understanding disease mechanisms. Additionally, we provide a proposal for a detailed bioinformatic analysis of a Spanish cohort of 41 individuals with STXBP1-related disorders, offering insights into specific mutations and their biological implications. Clinical and translational perspectives are discussed, emphasizing the potential of personalized medicine approaches. Future research directions and key challenges are outlined, including the identification of STXBP1 interactors, unexplored molecular pathways, and the need for clinically useful biomarkers. This comprehensive review underscores the complexity of STXBP1-related infantile epileptic encephalopathy and opens new avenues for advancing the understanding and treatment of this heterogeneous disease.

PMID:40227275 | DOI:10.3390/biotech14010011

Antioxidants (Basel). 2025 Feb 26;14(3):268. doi: 10.3390/antiox14030268.

ABSTRACT

Metabolic syndrome (MetS) constitutes a spectrum of interconnected conditions comprising obesity, dyslipidemia, hypertension, and insulin resistance (IR). While a singular, all-encompassing treatment for MetS remains elusive, an integrative approach involving tailored lifestyle modifications and emerging functional food therapies holds promise in preventing its multifaceted manifestations. Our main objective was to scrutinize the efficacy of cranberry proanthocyanidins (PAC, 200 mg/kg/day for 12 weeks) in mitigating MetS pathophysiology in male mice subjected to standard Chow or high-fat/high-fructose (HFHF) diets while unravelling intricate mechanisms. The administration of PAC, in conjunction with an HFHF diet, significantly averted obesity, evidenced by reductions in body weight, adiposity across various fat depots, and adipocyte hypertrophy. Similarly, PAC prevented HFHF-induced hyperglycemia and hyperinsulinemia while also lessening IR. Furthermore, PAC proved effective in alleviating key risk factors associated with cardiovascular diseases by diminishing plasma saturated fatty acids, as well as levels of triglycerides, cholesterol, and non-HDL-C levels. The rise in adiponectin and drop in circulating levels of inflammatory markers showcased PAC's protective role against inflammation. To better clarify the mechanisms behind PAC actions, gut-liver axis parameters were examined, showing significant enhancements in gut microbiota composition, microbiota-derived metabolites, and marked reductions in intestinal and hepatic inflammation, liver steatosis, and key biomarkers associated with endoplasmic reticulum (ER) stress and lipid metabolism. This study enhances our understanding of the complex mechanisms underlying the development of MetS and provides valuable insights into how PAC may alleviate cardiometabolic dysfunction in HFHF mice.

PMID:40227220 | DOI:10.3390/antiox14030268

ChemistryOpen. 2025 Apr 14:e202500119. doi: 10.1002/open.202500119. Online ahead of print.

ABSTRACT

Epigenetic targeting of the Hedgehog (HH) signaling pathway has emerged as a possible strategy to combat HH pathway-driven cancers. In this study, we report on benzo[cd]indol-2(1H)-ones as downstream Hedgehog pathway inhibitors. We find that benzo[cd]indol-2(1H)-one 1 has sub-micromolar potency in a variety of Hedgehog pathway cell models, including those with constitutive activity through loss of Suppressor of Fused. Compound 1 furthermore reduces cellular and ciliary GLI levels, and, like the BET bromodomain inhibitor HPI-1, increases the cellular levels of BRD2. To directly assess the ability of compound 1 to bind to BET bromodomains in cells without the need of synthetic modifications, we develop a competition assay against degrader HPP-9, the action of which was dose-dependently outcompeted by compound 1. Indeed, compound 1 reduces the viability of GLI-driven lung cancer cells and medulloblastoma spheroids, with a potency similar to its inhibitory effect on the HH pathway. Taken together, our studies highlight the potential of the benzo[cd]indol-2(1H)-one scaffold for epigenetic targeting of the HH pathway.

PMID:40227130 | DOI:10.1002/open.202500119

mSphere. 2025 Apr 14:e0009825. doi: 10.1128/msphere.00098-25. Online ahead of print.

ABSTRACT

Antimicrobials are a pillar of modern medicine, yet our limited arsenal of antibiotics and antifungals is currently threatened by widespread drug resistance. Ongoing efforts are focused on developing strategies to identify compounds that enhance the efficacy of current antimicrobials and develop novel, resistance-evasive therapeutic strategies. In this study, we characterized microbial isolates from two distinct environments to identify those that exhibit antimicrobial activity alone and in combination with current antimicrobials: (i) oral isolates from domesticated animals and (ii) environmental Streptomyces spp. First, conditioned media prepared from bacterial and fungal oral isolates that were collected from Canis familiaris and Felis catus were screened for antibacterial and antifungal activity. Three supernatants from bacterial isolates exhibited antifungal activity against the human fungal pathogen Candida albicans in the presence of subinhibitory concentrations of fluconazole, the most widely deployed antifungal. Additionally, two bacterial isolates displayed antibacterial activity against Escherichia coli alone and in combination with the antibacterial ampicillin. Furthermore, 32 environmental isolates of confirmed and predicted Streptomyces spp. were screened for activity against C. albicans and E. coli. Cell-free media harvested from isolates WAC5038 and WAC5287 exhibited antifungal activity against Candida spp., while only the WAC5038-conditioned medium displayed antibacterial activity. Bioactivity-guided fractionation, coupled with UV/Vis absorbance spectra, suggested that the bioactive compound in WAC5287 has a similar absorbance spectrum to the antifungal class of polyenes, while the bioactive component of WAC5038 remains unknown. Overall, this work highlights a strategy to collect and screen environmental isolates for the identification of novel antimicrobials.

IMPORTANCE: The emergence and spread of antimicrobial resistance presents a global health challenge. As such, researchers are focused on developing pipelines to discover novel antimicrobials. In this study, we screened two distinct collections of microbes for antimicrobial activity. First, we collected bacterial and fungal isolates from the oral cavities of domesticated dogs and cats and identified these isolates using 16S (bacteria) and ITS (fungi) sequencing. Follow-up analyses confirmed that some conditioned media from bacterial isolates had antibacterial activity against Escherichia coli and antifungal activity against Candida albicans both alone and in combination with the current antimicrobial drugs. Additionally, screening 32 predicted or confirmed Streptomyces environmental isolates for antifungal and antibacterial activity identified two isolates with antifungal activity (WAC5038 and WAC5287), with only one isolate demonstrating antibacterial activity (WAC5038). Overall, this study provides a framework to identify and characterize environmental microbes with antimicrobial activity.

PMID:40227049 | DOI:10.1128/msphere.00098-25

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf305. doi: 10.1093/nar/gkaf305.

ABSTRACT

Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC prediction and classification. Leveraging the transformer encoder, BGC-Prophet captures location-dependent relationships between genes. As one of the pioneering ultrahigh-throughput tools, BGC-Prophet significantly surpasses existing methods in efficiency and fidelity, enabling comprehensive pan-phylogenetic and whole-metagenome BGC screening. Through the analysis of 85 203 genomes and 9428 metagenomes, BGC-Prophet has profiled an extensive array of sub-million BGCs. It highlights notable enrichment in phyla like Actinomycetota and the widespread distribution of polyketide, NRP, and RiPP BGCs across diverse lineages. It reveals enrichment patterns of BGCs following important geological events, suggesting environmental influences on BGC evolution. BGC-Prophet's capabilities in detection of BGCs and evolutionary patterns offer contributions to deeper understanding of microbial secondary metabolites and application in synthetic biology.

PMID:40226917 | DOI:10.1093/nar/gkaf305