Nat Microbiol. 2025 Jan 13. doi: 10.1038/s41564-024-01891-8. Online ahead of print.

ABSTRACT

Despite ongoing antibiotic development, evolution of resistance may render candidate antibiotics ineffective. Here we studied in vitro emergence of resistance to 13 antibiotics introduced after 2017 or currently in development, compared with in-use antibiotics. Laboratory evolution showed that clinically relevant resistance arises within 60 days of antibiotic exposure in Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa, priority Gram-negative ESKAPE pathogens. Resistance mutations are already present in natural populations of pathogens, indicating that resistance in nature can emerge through selection of pre-existing bacterial variants. Functional metagenomics showed that mobile resistance genes to antibiotic candidates are prevalent in clinical bacterial isolates, soil and human gut microbiomes. Overall, antibiotic candidates show similar susceptibility to resistance development as antibiotics currently in use, and the corresponding resistance mechanisms overlap. However, certain combinations of antibiotics and bacterial strains were less prone to developing resistance, revealing potential narrow-spectrum antibacterial therapies that could remain effective. Finally, we develop criteria to guide efforts in developing effective antibiotic candidates.

PMID:39805953 | DOI:10.1038/s41564-024-01891-8

Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70088. doi: 10.1002/pds.70088.

ABSTRACT

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

PMID:39805810 | DOI:10.1002/pds.70088

Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70097. doi: 10.1002/pds.70097.

ABSTRACT

PURPOSE: This study aimed to obtain a better understanding of the characteristics of the risk management plans (RMP) and the background regulatory policies governing them, in the European Union (EU) and Japan. This was done by descriptively comparing the safety concerns (SCs) listed in the RMP and examining their relationships with product labeling.

METHODS: Information regarding SCs was collected from the published RMP of both the EU and Japan for the targeted products-all of which were commonly approved in both regions. The concordance rate of the SCs for each product between the EU- and Japan-RMP was calculated. The warning information for each product was collected from the product labeling, summary of product characteristics for the EU, and package insert for Japan, and compared with the SCs listed in the corresponding RMP.

RESULTS: A total of 259 products that were approved for sale in both the EU and Japan (1998-2023), for which RMP were available in both regions, were analyzed. While 51.0% of the SCs labeled as important identified risks (IIRs) in the EU-RMP were concordant with those in the Japan-RMP, 20.4% of the SCs listed as IIRs in the Japan-RMP were concordant with those in the EU-RMP. The concordance rate between the SCs identified as IIRs and the warning information was 18.6% for the EU-RMP and 88.4% for the Japan-RMP.

CONCLUSIONS: The low SC concordance rate between the EU- and Japan-RMP indicates a different approach to selecting RMP SCs by the two regulatory authorities.

PMID:39805803 | DOI:10.1002/pds.70097

Pigment Cell Melanoma Res. 2025 Jan;38(1):e13225. doi: 10.1111/pcmr.13225.

ABSTRACT

Beta-blockers have generated an exciting discourse for their potential as a cheap, safe, and effective adjunctive therapy for cutaneous melanoma patients, but the field remains murky. This systematic review investigates the association between beta-blocker use and survival outcomes in cutaneous melanoma patients. We reviewed 12 studies with 21,582 patients in a network meta-analysis and found a benefit between beta-blocker use and disease-free survival but no other significant association for melanoma-specific or overall survival. However, some evidence suggests that pan-selective beta-blockers, rather than cardio-selective ones, may have a protective effect. We conclude that the current evidence is insufficient to recommend beta-blockers for melanoma treatment but suggest further research focusing on pan-selective beta-blockers to clarify their potential benefits.

PMID:39804765 | DOI:10.1111/pcmr.13225

Adv Sci (Weinh). 2025 Jan 13:e2410981. doi: 10.1002/advs.202410981. Online ahead of print.

ABSTRACT

Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites. MMFuncPhos outperforms existing functional phosphorylation site prediction approaches. EFuncType is further developed based on transfer learning to predict whether phosphorylation of a residue upregulates or downregulates enzyme activity for the first time. The functional phosphorylation sites predicted by MMFuncPhos and the regulatory types predicted by EFuncType align with experimental findings from several newly reported protein phosphorylation studies. The study contributes to the understanding of the functional regulatory mechanism of phosphorylation and provides valuable tools for precision medicine, enzyme engineering, and drug discovery. For user convenience, these two prediction models are integrated into a web server which can be accessed at http://pkumdl.cn:8000/mmfuncphos.

PMID:39804866 | DOI:10.1002/advs.202410981

FEBS J. 2025 Jan 13. doi: 10.1111/febs.17398. Online ahead of print.

ABSTRACT

Vesicle trafficking is pivotal in heparan sulfate (HS) biosynthesis, influencing its spatial and temporal regulation within distinct Golgi compartments. This regulation modulates the sulfation pattern of HS, which is crucial for governing various biological processes. Here, we investigate the effects of silencing Rab1A and Rab2A expression on the localisation of 3-O-sulfotransferase-5 (3OST5) within Golgi compartments and subsequent alterations in HS structure and levels. Interestingly, silencing Rab1A led to a shift in 3OST5 localization towards the trans-Golgi, resulting in increased HS levels within 24 and 48 h, while silencing Rab2A caused 3OST5 accumulation in the cis-Golgi, with a delayed rise in HS content observed after 48 h. Furthermore, a compensatory mechanism was evident in Rab2A-silenced cells, where increased Rab1A protein expression was detected. This suggests a dynamic interplay between Rab1A and Rab2A in maintaining the fine balance of vesicle trafficking processes involved in HS biosynthesis. Additionally, we demonstrate that the trafficking of 3OST5 in COPI vesicles is facilitated by GOLPH3 protein. These findings identify novel vesicular transport mechanisms regulating HS biosynthesis and reveal a compensatory relationship between Rab1A and Rab2A in maintaining baseline HS production.

PMID:39804811 | DOI:10.1111/febs.17398

Turk Arch Pediatr. 2025 Jan 2;60(1):22-28. doi: 10.5152/TurkArchPediatr.2025.24003.

ABSTRACT

Objective: Nebulizer contamination has potential harmful effects on the respiratory system. The aim was to investigate the contamination profile of the nebulizers in cystic fibrosis patients and evaluate the relationship between hygiene practices and microbial contamination. Materials and Methods: Microbiological swab samples were taken from 3 different locations of the nebulizers of 102 patients. A questionnaire regarding nebulizer hygiene practices was applied to participants. Results: Contamination rate was 40.2%, while chambers were the most contaminated area. The bacterial contamination rate was 37.3%, with gram-negative bacterial growth being predominant. The organisms identified were mostly environmental or floral. Only 3 of the patients were performing the whole steps correctly. This number was not sufficient to assess the relationship between nebulizer cleaning and disinfection practices and microbial growth from nebulizers. When the relationship between nebulizer cleaning/disinfection frequencies, methods, and storage locations was evaluated separately with microbial growth from nebulizers, no statistically significant relationship was found for all (P > .05 for all). Conclusion: The nebulizer contamination rate with pathogenic microorganisms is low in the present study. Regular educational interventions regarding nebulizer hygiene practices should be implemented in all Cystic Fibrosis Centers.

PMID:39803923 | DOI:10.5152/TurkArchPediatr.2025.24003

J Med Internet Res. 2025 Jan 13;27:e63004. doi: 10.2196/63004.

ABSTRACT

BACKGROUND: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech. Deficits in any of these systems can cause changes in speech signal patterns. Increasing efforts are being made to develop speech-based clinical decision support systems.

OBJECTIVE: This systematic scoping review investigated the technological revolution and recent digital clinical speech signal analysis trends to understand the key concepts and research processes from clinical and technical perspectives.

METHODS: A systematic scoping review was undertaken in 6 databases guided by a set of research questions. Articles that focused on speech signal analysis for clinical decision-making were identified, and the included studies were analyzed quantitatively. A narrower scope of studies investigating neurological diseases were analyzed using qualitative content analysis.

RESULTS: A total of 389 articles met the initial eligibility criteria, of which 72 (18.5%) that focused on neurological diseases were included in the qualitative analysis. In the included studies, Parkinson disease, Alzheimer disease, and cognitive disorders were the most frequently investigated conditions. The literature explored the potential of speech feature analysis in diagnosis, differentiating between, assessing the severity and monitoring the treatment of neurological conditions. The common speech tasks used were sustained phonations, diadochokinetic tasks, reading tasks, activity-based tasks, picture descriptions, and prompted speech tasks. From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing-based speech features (such as wavelet transformation-based features), and spectrograms in the form of audio images were analyzed. Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. Model evaluations primarily focused on analytical validations. A significant research gap was identified: the need for a structured research process to guide studies toward potential technological intervention in clinical settings. To address this, a research framework was proposed that adapts a design science research methodology to guide research studies systematically.

CONCLUSIONS: The findings highlight how data science techniques can enhance speech signal analysis to support clinical decision-making. By combining knowledge from clinical practice, speech science, and data science within a structured research framework, future research may achieve greater clinical relevance.

PMID:39804693 | DOI:10.2196/63004

Bioinformatics. 2025 Jan 13:btaf016. doi: 10.1093/bioinformatics/btaf016. Online ahead of print.

ABSTRACT

MOTIVATION: Recent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.

RESULTS: We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera. Compared against recent tools, PHIStruct exhibits the best balance of precision and recall, with the highest and most stable F1 score across a wide range of confidence thresholds and sequence similarity settings. The margin in performance is most pronounced when the sequence similarity between the training and test sets drops below 40%, wherein, at a relatively high-confidence threshold of above 50%, PHIStruct presents a 7% to 9% increase in class-averaged F1 over machine learning tools that do not directly incorporate structure information, as well as a 5% to 6% increase over BLASTp.

AVAILABILITY AND IMPLEMENTATION: The data and source code for our experiments and analyses are available at https://github.com/bioinfodlsu/PHIStruct.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39804673 | DOI:10.1093/bioinformatics/btaf016

Bioinformatics. 2025 Jan 13:btaf018. doi: 10.1093/bioinformatics/btaf018. Online ahead of print.

ABSTRACT

MOTIVATION: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

RESULTS: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RNA-binding protein sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights.

AVAILABILITY AND IMPLEMENTATION: EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39804669 | DOI:10.1093/bioinformatics/btaf018

Insights Imaging. 2025 Jan 13;16(1):14. doi: 10.1186/s13244-024-01874-7.

ABSTRACT

OBJECTIVE: To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS).

METHODS: A total of 663 ultrasound images of adnexal mass were collected and divided into two sets according to experienced radiologists: a low malignant risk set (n = 446) and an intermediate-high malignant risk set (n = 217). Deep learning segmentation models were trained and selected to automatically segment adnexal masses. Radiomics features were extracted utilizing a feature analysis system in Pyradiomics. Feature selection was conducted using the Spearman correlation analysis, Mann-Whitney U-test, and least absolute shrinkage and selection operator (LASSO) regression. A nomogram integrating radiomic and clinical features using a machine learning model was established and evaluated. The SHapley Additive exPlanations were used for model interpretability and visualization.

RESULTS: The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925-0.996). The nomogram using the LightGBM algorithm reached the best AUC (0.966, 95% CI: 0.927-1.000). The diagnostic performance of the nomogram was comparable to that of experienced radiologists (p > 0.05) and outperformed that of less-experienced radiologists (p < 0.05). The model significantly improved the diagnostic accuracy of less-experienced radiologists.

CONCLUSIONS: The segmentation model serves as a valuable tool for the automated delineation of adnexal lesions. The machine learning model exhibited commendable classification capability and outperformed the diagnostic performance of less-experienced radiologists.

CRITICAL RELEVANCE STATEMENT: The ultrasound radiomics-based machine learning model holds the potential to elevate the professional ability of less-experienced radiologists and can be used to assist in the clinical screening of ovarian cancer.

KEY POINTS: We developed an image segmentation model to automatically delineate adnexal masses. We developed a model to classify adnexal masses based on O-RADS. The machine learning model has achieved commendable classification performance. The machine learning model possesses the capability to enhance the proficiency of less-experienced radiologists. We used SHapley Additive exPlanations to interpret and visualize the model.

PMID:39804536 | DOI:10.1186/s13244-024-01874-7

Int J Cardiovasc Imaging. 2025 Jan 13. doi: 10.1007/s10554-025-03327-8. Online ahead of print.

ABSTRACT

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans. A retrospective analysis of 677 PET/CT scans from a medical center was conducted. The dataset was divided into training (88%) and testing (12%) sets. The DLA-3D model was employed for high-resolution representation learning of cardiac CT images. Data preprocessing techniques were applied to normalize and augment the images. Performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity and p-values. The AI model achieved an average AUC of 0.85 on the training set and 0.80 on the testing set. The model demonstrated expert-level performance with a specificity of 0.79, a sensitivity of 0.67, and an overall accuracy of 0.73 for the test group. In real-world scenarios, the model yielded a specificity of 0.8, sensitivity of 0.6, and an accuracy of 0.76. Comparison with human experts showed comparable performance. This study developed an AI method utilizing DLA-3D for automated CAC detection in non-gated PET/CT images. Findings indicate reliable CAC detection in routine PET/CT scans, potentially enhancing both cancer diagnosis and cardiovascular risk assessment. The DLA-3D model shows promise in aiding non-specialist physicians and may contribute to improved cardiovascular risk assessment in oncological imaging, encouraging additional CAC interpretation.

PMID:39804436 | DOI:10.1007/s10554-025-03327-8

Clin Oral Investig. 2025 Jan 13;29(1):59. doi: 10.1007/s00784-024-06136-w.

ABSTRACT

OBJECTIVES: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

MATERIALS AND METHODS: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.

RESULTS: The mean DSC and IoU between DL and SA segmentations were 0.96 ± 0.01 and 0.92 ± 0.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62 mm ± 0.16 mm and 0.77 mm ± 0.31 mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85 ± 0.08; 0.78 ± 0.07 and 0.91 ± 0.92 mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.

CONCLUSIONS: The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model's robustness.

CLINICAL RELEVANCE: Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.

PMID:39804427 | DOI:10.1007/s00784-024-06136-w

J Gen Virol. 2025 Jan;106(1). doi: 10.1099/jgv.0.002067.

ABSTRACT

The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more. This review explores how machine learning has been applied to and has impacted the study of viruses, before addressing the strengths and limitations of its techniques and finally highlighting the next steps that are needed for the technology to reach its full potential in this challenging and ever-relevant research area.

PMID:39804261 | DOI:10.1099/jgv.0.002067

Eur Heart J. 2025 Jan 13:ehae914. doi: 10.1093/eurheartj/ehae914. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.

METHODS: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator.

RESULTS: Among 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

CONCLUSIONS: An AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.

PMID:39804243 | DOI:10.1093/eurheartj/ehae914

J Cell Mol Med. 2025 Jan;29(1):e70351. doi: 10.1111/jcmm.70351.

ABSTRACT

Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental identification and validation of cancer driver genes are time-consuming and costly. Studies have demonstrated that interactions among genes are associated with similar phenotypes. Therefore, identifying cancer driver genes using molecular network-based approaches is necessary. Molecular network-based random walk-based approaches, which integrate mutation data with protein-protein interaction networks, have been widely employed in predicting cancer driver genes and demonstrated robust predictive potential. However, recent advancements in deep learning, particularly graph-based models, have provided novel opportunities for enhancing the prediction of cancer driver genes. This review aimed to comprehensively explore how machine learning methodologies, particularly network propagation, graph neural networks, autoencoders, graph embeddings, and attention mechanisms, improve the scalability and interpretability of molecular network-based cancer gene prediction.

PMID:39804102 | DOI:10.1111/jcmm.70351

JMIR Med Educ. 2025 Jan 10;11:e62669. doi: 10.2196/62669.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training.

OBJECTIVE: This study aims to assess and compare medical AI-related attitudes among medical students in general medicine and in one of the visually oriented fields (pathology), along with illuminating their anticipated role of AI in the rapidly evolving landscape of AI-enhanced health care.

METHODS: This was a cross-sectional study that used a web-based survey composed of a closed-ended questionnaire. The survey addressed medical students at all educational levels across the 5 public medical schools, along with pathology residents in 4 residency programs in Jordan.

RESULTS: A total of 394 respondents participated (328 medical students and 66 pathology residents). The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. There was a statistically significant difference in awareness among respondents who consider themselves tech experts compared with those who do not (P=.03). More than half of the respondents believed that AI could be used to diagnose diseases automatically (213/394, 54.1% agreement), with medical students agreeing more than pathology residents (P=.04). However, more than one-third expressed fear about recent AI developments (167/394, 42.4% agreed). Two-thirds of respondents disagreed that their medical schools had educated them about AI and its potential use (261/394, 66.2% disagreed), while 46.2% (182/394) expressed interest in learning about AI in medicine. In terms of pathology-specific questions, 75.4% (297/394) agreed that AI could be used to identify pathologies in slide examinations automatically. There was a significant difference between medical students and pathology residents in their agreement (P=.001). Overall, medical students and pathology trainees had similar responses.

CONCLUSIONS: AI education should be introduced into medical school curricula to improve medical students' understanding and attitudes. Students agreed that they need to learn about AI's applications, potential hazards, and legal and ethical implications. This is the first study to analyze medical students' views and awareness of AI in Jordan, as well as the first to include pathology residents' perspectives. The findings are consistent with earlier research internationally. In comparison with prior research, these attitudes are similar in low-income and industrialized countries, highlighting the need for a global strategy to introduce AI instruction to medical students everywhere in this era of rapidly expanding technology.

PMID:39803949 | DOI:10.2196/62669

Cardiovasc Res. 2025 Jan 10:cvae267. doi: 10.1093/cvr/cvae267. Online ahead of print.

ABSTRACT

AIMS: The gastrointestinal (GI) tract is composed of distinct sub-regions, which exhibit segment-specific differences in microbial colonization and (patho)physiological characteristics. Gut microbes can be collectively considered as an active endocrine organ. Microbes produce metabolites, which can be taken up by the host and can actively communicate with the immune cells in the gut lamina propria with consequences for cardiovascular health. Variation in bacterial load and composition along the GI tract may influence the mucosal microenvironment and thus be reflected its interstitial fluid (IF). Characterization of the segment-specific microenvironment is challenging and largely unexplored because of lack of available tools.

METHODS AND RESULTS: Here, we developed methods, namely tissue centrifugation and elution, to collect IF from the mucosa of different intestinal segments. These methods were first validated in rats and mice, and the tissue elution method was subsequently translated for use in humans. These new methods allowed us to quantify microbiota-derived metabolites, mucosa-derived cytokines, and proteins at their site-of-action. Quantification of short-chain fatty acids showed enrichment in the colonic IF. Metabolite and cytokine analyses revealed differential abundances within segments, often significantly increased compared to plasma, and proteomics revealed that proteins annotated to the extracellular phase were site-specifically identifiable in IF. Lipopolysaccharide injections in rats showed significantly higher ileal IL-1β levels in IF compared to the systemic circulation, suggesting the potential of local as well as systemic effect.

CONCLUSION: Collection of IF from defined segments and the direct measurement of mediators at the site-of-action in rodents and humans bypasses the limitations of indirect analysis of faecal samples or serum, providing direct insight into this understudied compartment.

PMID:39804196 | DOI:10.1093/cvr/cvae267

Elife. 2025 Jan 13;13:RP92683. doi: 10.7554/eLife.92683.

ABSTRACT

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

PMID:39804159 | DOI:10.7554/eLife.92683

Anal Chem. 2025 Jan 13. doi: 10.1021/acs.analchem.4c04692. Online ahead of print.

ABSTRACT

Simultaneous analysis of multiple phosphorylated metabolites (phosphorylated metabolome) in biological samples is vital to reveal their physiological and pathophysiological functions, which is extremely challenging due to their low abundance in some biological matrices, high hydrophilicity, and poor chromatographic behavior. Here, we developed a new method with ion-pair reversed-phase ultrahigh-performance liquid chromatography and mass spectrometry using BEH C18 columns modified with hybrid surface technology. This method demonstrated good performances for various phosphorylated metabolites, including phosphorylated sugars and amino acids, nucleotides, NAD-based cofactors, and acyl-CoAs in a single run using standard LC systems. Specifically, the method showed good retention (capacity factor > 2) and reproducibility (ΔtR < 0.09 min, n = 6), peak symmetry (tailing factor < 2), and sensitivity (limit-of-detection < 238 fmol-on-column with QTOFMS) for all tested analytes especially for the medium- and/or long-chain acyl-CoAs. The method demonstrated reproducible applicability across numerous biological matrices, including tissue (liver), human biofluids (urine, plasma), cells, and feces, and revealed significant molecular phenotypic differences in phosphorylated metabolite composition.

PMID:39804109 | DOI:10.1021/acs.analchem.4c04692