Curr Protein Pept Sci. 2025 May 8. doi: 10.2174/0113892037364237250402151440. Online ahead of print.

ABSTRACT

The COVID-19 outbreak, caused by the SARS-CoV-2 coronavirus, has threatened and taken many lives since the end of 2019. Given the importance of COVID-19 worldwide, since its spread, many research groups have been seeking blood markers that could help to understand the disease establishment and prognosis. Usually, those markers are proteins with a differential accumulation only during infection. Based on that, proteomic studies have played a crucial role in elucidating diseases. Mass spectrometry (MS) is a promising technique in COVID-19 studies, allowing the identification and quantification of proteins present in the plasma or serum of affected patients. It helps us to understand pathological mechanisms, predict clinical outcomes, and develop specific therapies. MS proteomics revealed biomarkers associated with infection, disease severity, and immune response. Plasma or blood serum is easy to collect and store; however, its composition and the higher concentration of proteins (e.g., albumins) shadow the identification of less abundant proteins, which usually are essential markers. So, clean-up approaches such as depletion strategies and fractionating are often required to analyze blood samples, allowing the identification of low-abundant proteins. This review will discuss many proteomic approaches to discovering new plasma biomarkers of COVID-19 employed in recently published studies. The challenges inherent to blood samples will also be discussed, such as sample preparation, data processing, and identifying reliable biomarkers.

PMID:40353410 | DOI:10.2174/0113892037364237250402151440

Alzheimers Dement (Amst). 2025 May 9;17(2):e70111. doi: 10.1002/dad2.70111. eCollection 2025 Apr-Jun.

ABSTRACT

INTRODUCTION: The apolipoprotein E (APOE) ɛ4 allele is a well-established risk factor for neurocognitive impairment (NCI), with varying impacts between men and women. This study investigates the distinct roles of sex and gender in modifying APOE ɛ4-related NCI.

METHODS: Biological sex was inferred from sex chromosomes, and a femininity score (FS) was used as a proxy for gender. We analyzed 276,596 UK Biobank participants without prior NCI to assess whether sex and FS modified the effect of APOE ɛ4 on NCI.

RESULTS: NCI risk was higher in APOE ɛ4 carriers compared to non-carriers (hazard ratio [HR] = 2.48 in females; HR = 1.96 in males) with significant interaction by sex (P < 0.0001). FS was associated with an increased NCI risk after accounting for sex (HR = 1.07, 95% confidence interval: 1.04-1.10, P < 0.0001) with no significant differences by sex or APOE ɛ4 carrier status.

DISCUSSION: Our findings show that APOE ɛ4 increases NCI risk more in females, while FS independently elevates risk across sexes.

HIGHLIGHTS: Apolipoprotein E (APOE) ɛ4 increases neurocognitive impairment (NCI) risk, with a greater impact in females (hazard ratio [HR] = 2.48) than males (HR = 1.96).Sex significantly modifies the effect of APOE ɛ4 on NCI (P < 0.0001f).Femininity score increases NCI risk (HR = 1.07) independently of sex and APOE ɛ4.Understanding the distinct sex and gender contributions to APOE ɛ4-related NCI can improve interventions.

PMID:40352685 | PMC:PMC12064333 | DOI:10.1002/dad2.70111

J Med Internet Res. 2025 May 12;27:e67156. doi: 10.2196/67156.

ABSTRACT

BACKGROUND: Accurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidence is often influenced by socioeconomic factors, such as abrupt events or changes, including pandemics, economic crises, and social unrest, creating complex structural break scenarios in the time-series data. These structural breaks can affect the performance of forecasting methods in various ways. Therefore, understanding and comparing different models across these scenarios is essential.

OBJECTIVE: This study aimed to develop depression incidence forecasting models and compare the performance of autoregressive integrated moving average (ARIMA) and vector-ARIMA (VARIMA) and temporal fusion transformers (TFT) under different structural break scenarios.

METHODS: We developed population-wide depression incidence forecasting models and compared the performance of ARIMA and VARIMA-based methods to TFT-based methods. Using monthly depression incidence from 2002 to 2022 in Hong Kong, we applied sliding windows to segment the whole time series into 72 ten-year subsamples. The forecasting models were trained, validated, and tested on each subsample. Within each 10-year subset, the first 7 years were used for training, with the eighth year for setting hold-out validation, and the ninth and tenth years for testing. The accuracy of the testing set within each 10-year subsample was measured by symmetric mean absolute percentage error (SMAPE).

RESULTS: We found that in subsamples without significant slope or trend change (structural break), multivariate TFT significantly outperformed univariate TFT, vector-ARIMA (VARIMA), and ARIMA, with an average SMAPE of 11.6% compared to 13.2% (P=.01) for univariate TFT, 16.4% (P=.002) for VARIMA, and 14.8% (P=.003) for ARIMA. Adjusting for the unemployment rate improved TFT performance more effectively than VARIMA. When fluctuating outbreaks happened, TFT was more robust to sharp interruptions, whereas VARIMA and ARIMA performed better when incidence surged and remained high.

CONCLUSIONS: This study provides a comparative evaluation of TFT and ARIMA and VARIMA models for forecasting depression incidence under various structural break scenarios, offering insights into predicting disease burden during both stable and unstable periods. The findings support a decision-making framework for model selection based on the nature of disruptions and data characteristics. For public health policymaking, the results suggest that TFT may be a more suitable tool for disease burden forecasting during periods of stable burden level or when sudden temporary interruption, such as pandemics or socioeconomic variation, impacts disease occurrence.

PMID:40354111 | DOI:10.2196/67156

Eur Arch Paediatr Dent. 2025 May 12. doi: 10.1007/s40368-025-01046-1. Online ahead of print.

ABSTRACT

PURPOSE: An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction.

METHODS: The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent-child pairs, providing a robust dataset for examining the research questions.

RESULTS: In the current study, the predictors named, "mother's education" (β1: 0.423; p < 0.25), "parent's knowledge of bottle-feeding habit during sleep can cause tooth decay" (β2: -1.264; p < 0.25), "attitude towards the importance of oral health as general health" (β4: -1.052; p < 0.25) and "parent's self-reported oral pain among their children" (β5: -2.107; p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05).

CONCLUSION: It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention.

PMID:40354021 | DOI:10.1007/s40368-025-01046-1

Dentomaxillofac Radiol. 2025 May 12:twaf044. doi: 10.1093/dmfr/twaf044. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate deep learning (DL)-based models for detecting periapical bone rarefaction (PBRs) in panoramic radiographs (PRs), analyzing their feasibility and performance in dental practice.

METHODS: A search was conducted across seven databases and partial grey literature up to November 15, 2024, using Medical Subject Headings and entry terms related to DL, PBRs, and PRs. Studies assessing DL-based models for detecting and classifying PBRs in conventional PRs were included, while those using non-PR imaging or focusing solely on non-PBR lesions were excluded. Two independent reviewers performed screening, data extraction, and quality assessment using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, with conflicts resolved by a third reviewer.

RESULTS: Twelve studies met the inclusion criteria, mostly from Asia (58.3%). The risk of bias was moderate in 10 studies (83.3%) and high in 2 (16.7%). DL models showed moderate to high performance in PBR detection (sensitivity: 26-100%; specificity: 51-100%), with U-NET and YOLO being the most used algorithms. Only one study (8.3%) distinguished Periapical Granuloma from Periapical Cysts, revealing a classification gap. Key challenges included limited generalization due to small datasets, anatomical superimpositions in PRs, and variability in reported metrics, compromising models comparison.

CONCLUSION: This review underscores that DL-based has the potential to become a valuable tool in dental image diagnostics, but it cannot yet be considered a definitive practice. Multicenter collaboration is needed to diversify data and democratize those tools. Standardized performance reporting is critical for fair comparability between different models.

PMID:40353850 | DOI:10.1093/dmfr/twaf044

Magn Reson Med. 2025 May 12. doi: 10.1002/mrm.30486. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study is to perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR).

METHODS: To address low SNR encountered in single-shot imaging, especially at low field strengths, we propose a fast deep learning (DL)-based image registration method, called Averaging Morph with Edge Detection (AiM-ED). AiM-ED jointly registers multiple noisy source images to a noisy target image and utilizes a noise-robust pre-trained edge detector to define the training loss. We validate AiM-ED using synthetic late gadolinium enhanced (LGE) images from the MR extended cardiac-torso (MRXCAT) phantom and free-breathing single-shot LGE images from healthy subjects (24 slices) and patients (5 slices) under various levels of added noise. Additionally, we demonstrate the clinical feasibility of AiM-ED by applying it to data from patients (6 slices) scanned on a 0.55T scanner.

RESULTS: Compared with a traditional energy-minimization-based image registration method and DL-based VoxelMorph, images registered using AiM-ED exhibit higher values of recovery SNR and three perceptual image quality metrics. An ablation study shows the benefit of both jointly processing multiple source images and using an edge map in AiM-ED.

CONCLUSION: For single-shot LGE imaging, AiM-ED outperforms existing image registration methods in terms of image quality. With fast inference, minimal training data requirements, and robust performance at various noise levels, AiM-ED has the potential to benefit single-shot CMR applications.

PMID:40353517 | DOI:10.1002/mrm.30486

Curr Pharm Des. 2025 May 9. doi: 10.2174/0113816128381171250415171256. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) can completely transform drug development methods by delivering faster, more accurate, efficient results. However, the effective use of AI requires the accessibility of data of excellent quality, the resolution of ethical dilemmas, and an awareness of the drawbacks of AI-based techniques. Moreover, the application of AI in drug discovery is gaining popularity as an alternative to both the complex and time-consuming process of discovering as well as developing novel medications. Importantly, machine learning (ML) as well as natural language processing, for example, may boost both productivity as well as accuracy by analyzing vast volumes of data. This review article discusses in detail the promise of AI in drug discovery as well as offers insights into various topics such as societal issues related to the application of AI in medicine (e.g., legislation, interpretability and explainability, privacy and anonymity, and ethics and fairness), the importance of AI in the development of drug delivery systems, causability and explainability of AI in medicine, and opportunities and challenges for AI in clinical adoption, threat or opportunity of AI in medical imaging, the missing pieces of AI in medicine, approval of AI and ML-based medical devices.

PMID:40353469 | DOI:10.2174/0113816128381171250415171256

Med Sci Educ. 2025 Jan 8;35(2):939-948. doi: 10.1007/s40670-024-02256-w. eCollection 2025 Apr.

ABSTRACT

INTRODUCTION: Reflection is a critical cognitive process that involves thinking deeply about experiences, actions, or concepts to gain insights and enhance learning. Online collaborative environments, such as forums, facilitate peer learning and reflection on educational experiences. This study aimed to analyze medical students' opinions on electronic Peer-Assisted Reflection (ePAR) in an educational forum focused on psychology-related courses.

METHODS: This qualitative study utilized content analysis methods. The sample consisted of opinions gathered across 16 forums encompassing a total of 389 students specializing in medicine, laboratory science, and public health at Jahrom University of Medical Sciences (JUMS), IRAN. The instructor delivered the courses to the class and posed questions based on the discussion forum within the Learning Management System. Following each course, students' experiences regarding discussions and question-and-answer sessions were assessed in a forum setting. The data were analyzed using inductive content analysis.

RESULTS: A content analysis of the data obtained from students' opinions yielded 51 codes (items), which were classified into two main categories: effectiveness and setting. The most significant factors were effectiveness (Including: Thinking, Peer-Assisted Learning, Active Learning, and Self-Critique) and setting (Environment, Teachers, and Students). The open codes (items) most frequently mentioned by students included the potential to correct inaccurate interpretations of others' learning experiences and misconceptions (94 mentions), improving students' ability to compare and analyze (93 mentions), promoting and strengthening criticism and analysis (93 mentions), and fostering active and deep learning (92 mentions).

CONCLUSION: The forum environment can enhance participation, active learning, and critical thinking among students while providing a rich educational context for medical education. Therefore, utilizing this method in education without time or place constraints can be a practical approach to improving medical science education.

PMID:40352990 | PMC:PMC12058557 | DOI:10.1007/s40670-024-02256-w

Front Artif Intell. 2025 Apr 25;8:1444891. doi: 10.3389/frai.2025.1444891. eCollection 2025.

ABSTRACT

Previous work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks. In this work we investigate how Layer wise Scaled Gaussian Priors perform with Markov Chain Monte Carlo trained Bayesian neural networks. From our experiments on 8 classifications datasets of various complexity, the results indicate that using Layer wise Scaled Gaussian Priors makes the sampling process more efficient as compared to using an Isotropic Gaussian Prior, an Isotropic Cauchy Prior, or an Isotropic Laplace Prior. We also show that the cold posterior effect does not arise when using a either an Isotropic Gaussian or a layer wise Scaled Prior for small feed forward Bayesian neural networks. Since Bayesian neural networks are becoming popular due to their advantages such as uncertainty estimation, and prevention of over-fitting, this work seeks to provide improvements in the efficiency of Bayesian neural networks learned using Markov Chain Monte Carlo methods.

PMID:40352974 | PMC:PMC12061901 | DOI:10.3389/frai.2025.1444891

Biol Methods Protoc. 2025 Apr 11;10(1):bpaf030. doi: 10.1093/biomethods/bpaf030. eCollection 2025.

ABSTRACT

Three-dimensional (3D) spheroid models have advanced cancer research by better mimicking the tumour microenvironment compared to traditional two-dimensional cell cultures. However, challenges persist in high-throughput analysis of morphological characteristics and cell viability, as traditional methods like manual fluorescence analysis are labour-intensive and inconsistent. Existing AI-based approaches often address segmentation or classification in isolation, lacking an integrated workflow. We propose a scalable, two-stage deep learning pipeline to address these gaps: (i) a U-Net model for precise detection and segmentation of 3D spheroids from microscopic images, achieving 95% prediction accuracy, and (ii) a CNN Regression Hybrid method for estimating live/dead cell percentages and classifying spheroids, with an R 2 value of 98%. This end-to-end pipeline automates cell viability quantification and generates key morphological parameters for spheroid growth kinetics. By integrating segmentation and analysis, our method addresses environmental variability and morphological characterization challenges, offering a robust tool for drug discovery, toxicity screening, and clinical research. This approach significantly improves efficiency and scalability of 3D spheroid evaluations, paving the way for advancements in cancer therapeutics.

PMID:40352793 | PMC:PMC12064216 | DOI:10.1093/biomethods/bpaf030

Narra J. 2025 Apr;5(1):e1361. doi: 10.52225/narra.v5i1.1361. Epub 2025 Mar 5.

ABSTRACT

Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can indicate various lung diseases, including early COVID-19, pneumonia, and lung cancer. Artificial intelligence (AI) is a promising tool for analyzing medical images, such as chest CT scans. The aim of this study was to evaluate AI models' performance in detecting GGO nodules using metrics like accuracy, sensitivity, specificity, F1 score, area under the curve (AUC) and precision. We designed a search strategy to include reports focusing on deep learning algorithms applied to high-resolution CT scans. The search was performed on PubMed, Google Scholar, Scopus, and ScienceDirect to identify studies published between 2016 and 2024. Quality appraisal of included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, assessing the risk of bias and applicability concerns across four domains. Two reviewers independently screened studies reporting the diagnostic ability of AI-assisted CT scans in early GGO detection, where the review results were synthesized qualitatively. Out of 5,247 initially identified records, we found 18 studies matching the inclusion criteria of this study. Among evaluated models, DenseNet achieved the highest accuracy of 99.48%, though its sensitivity and specificity were not reported. WOANet showed an accuracy of 98.78%, with a sensitivity of 98.37% and high specificity of 99.19%, excelling particularly in specificity without compromising sensitivity. In conclusion, AI models can potentially detect GGO on chest CT scans. Future research should focus on developing hybrid models that integrate various AI approaches to improve clinical applicability.

PMID:40352244 | PMC:PMC12059966 | DOI:10.52225/narra.v5i1.1361

Virus Evol. 2025 Apr 29;11(1):veaf026. doi: 10.1093/ve/veaf026. eCollection 2025.

ABSTRACT

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with TDA models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3-assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.

PMID:40352163 | PMC:PMC12063592 | DOI:10.1093/ve/veaf026

Integr Comp Biol. 2025 May 12:icaf036. doi: 10.1093/icb/icaf036. Online ahead of print.

ABSTRACT

Diverse vertebrate species utilize hibernation as an energy-management strategy to survive long-term resource scarcity. Many hibernating and non-hibernating species employ food hoarding as a behavioral mechanism for storing surplus energy. Although the thirteen-lined ground squirrel (Ictidomys tridecemlineatus, 13LGS) relies mainly on body fat as fuel during hibernation, they also hoard food under natural and captive conditions. We tested the hypothesis that 13LGS individual variation in facultative hoarding behavior is driven by internal physiology, including body mass, sex, anxiety, and baseline stress. We recorded food hoard composition and body weight biweekly in summer active squirrels, subjected them to the open-field test (OFT) to measure state anxiety, and quantified fecal corticosterone (CORT) levels to measure baseline stress. We found that hoard sizes increased significantly across the summer, peaking at the end of June when body weight was still linearly increasing. Individual variation accounted for 10-20% of total variation in hoarding patterns. We observed a significant effect of sex on hoard size and composition, with males hoarding more than females. Contrary to our predictions, there was no relationship between hoarding and anxiety-like behavior in the OFT, and non-hoarders had significantly higher fecal CORT than hoarders. Together, our results suggest that time, sex, and baseline stress are significant factors that affect hoarding behavior, but body weight and anxiety-like behaviors are not. In the context of organismal systems biology, food hoarding is a redundant mechanism to fat storage that increases an organism's resilience against future resource scarcity and is likely regulated by dynamic interactions between multiple brain-body networks.

PMID:40353766 | DOI:10.1093/icb/icaf036

mBio. 2025 May 12:e0074925. doi: 10.1128/mbio.00749-25. Online ahead of print.

ABSTRACT

In the Arctic, phytoplankton blooms are recurring phenomena occurring during the spring-summer seasons and influenced by the strong polar seasonality. Bloom dynamics are affected by nutrient availability, especially nitrogen, which is the main limiting nutrient in the Arctic. This study aimed to investigate the changes in an Arctic microbial community using omics approaches during a phytoplankton bloom focusing on the nitrogen cycle. Using metagenomic and metatranscriptomic samples from the Dease Strait (Canada) from March to July (2014), we reconstructed 176 metagenome-assembled genomes. Bacteria dominated the microbial community, although archaea reached up to 25% of metagenomic abundance in early spring, when Nitrososphaeria archaea actively expressed genes associated with ammonia oxidation to nitrite (amt, amo, nirK). The resulting nitrite was presumably further oxidized to nitrate by a Nitrospinota bacterium that highly expressed a nitrite oxidoreductase gene (nxr). Since May, the constant increase in chlorophyll a indicated the occurrence of a phytoplankton bloom, promoting the successive proliferation of different groups of chemoorganotrophic bacteria (Bacteroidota, Alphaproteobacteria, Gammaproteobacteria). These bacteria showed different strategies to obtain nitrogen, whether it be from organic or inorganic sources, according to the expression patterns of genes encoding transporters for nitrogen compounds. In contrast, during summer, the chemolithotrophic organisms thriving during winter reduced their relative abundance and the expression of their catabolic genes. Based on our functional analysis, we see a transition from a community where nitrogen-based chemolitotrophy plays a relevant role to a chemoorganotrophic community based on the carbohydrates released during the phytoplankton bloom, where different groups seem to specialize in different nitrogen sources.IMPORTANCEThe Arctic is one of the environments most affected by anthropogenic climate change. It is expected that the rise in temperature and change in ice cover will impact the marine microbial communities and the associated biogeochemical cycles. In this regard, nitrogen is the main nutrient limiting Arctic phytoplankton blooms. In this study, we combine genetic and expression data to study the nitrogen cycle at the community level over a time series covering from March to July. Our results indicate the importance of different taxa (from archaea to bacteria) and processes (from chemolithoautotrophy to incorporation of different nitrogen sources) in the cycling of nitrogen during this period. This study provides a baseline for future research that should include additional methodologies like biogeochemical analysis to fully understand the changes occurring on these communities due to global change.

PMID:40353658 | DOI:10.1128/mbio.00749-25

Faraday Discuss. 2025 May 12. doi: 10.1039/d4fd00202d. Online ahead of print.

ABSTRACT

We consider membranes as fluid deformable surfaces and allow for higher order geometric terms in the bending energy related to the Gaussian curvature squared and the mean curvature minus the spontaneous curvature to the fourth power. The evolution equations are derived and numerically solved using surface finite elements. The two higher order geometric terms have different effects. While the Gaussian curvature squared term has a tendency to stabilize tubes and enhance the evolution towards equilibrium shapes, thereby facilitating rapid shape changes, the mean curvature minus the spontaneous curvature to the fourth power destabilizes tubes and leads to qualitatively different equilibrium shapes but also enhances the evolution. This is demonstrated in axisymmetric settings and fully three-dimensional simulations. We therefore postulate that not only surface viscosity but also higher order geometric terms in the bending energy contribute to rapid shape changes which are relevant for morphological changes of cells.

PMID:40353330 | DOI:10.1039/d4fd00202d

Front Pediatr. 2025 Apr 25;13:1567091. doi: 10.3389/fped.2025.1567091. eCollection 2025.

ABSTRACT

Bronchopulmonary dysplasia (BPD) is a multifactorial chronic lung disease of premature neonates. BPD development depends on prenatal and postnatal factors that induce inflammation, altering alveolar growth and pulmonary vascular development. Animal models are essential to investigate the precise molecular pathways leading to BPD. The preterm rabbit combines many advantages of small (e.g., rodents) and large BPD models (e.g., preterm lambs and baboons). Preterm rabbits display mild-to-moderate respiratory distress at delivery, which, along with continuous exposure to hyperoxia (95% O2), leads to functional and morphological lung changes resembling a BPD-like phenotype. Nevertheless, the molecular pathways leading to the BPD-like phenotype remain poorly understood. Here, we aimed to characterize the longitudinal gene expression in the lungs of preterm rabbits exposed to 95% O2, on postnatal days 3, 5, and 7. Histological analyses confirmed extensive lung injury and reduced lung development after 7 days of hyperoxia. Longitudinal transcriptomic analysis revealed different expression patterns for several genes and pathways. Over time, extracellular matrix organization and angiogenesis were increasingly downregulated. Apoptosis, RNA processing, and inflammation showed the opposite trend. We also investigated the expression of representative genes of these pathways, whose signatures could aid in developing pharmacological treatments in the context of BPD.

PMID:40352610 | PMC:PMC12063497 | DOI:10.3389/fped.2025.1567091

J Ophthalmic Inflamm Infect. 2025 May 12;15(1):44. doi: 10.1186/s12348-025-00484-8.

ABSTRACT

TOPIC: Vogt-Koyanagi-Harada (VKH)-like uveitis is uniquely reported with immune checkpoint inhibitors (ICI) and BRAF/MEK inhibitors. This article aims to provide a comprehensive portrait of the comorbidities, ocular presentations, treatments, and visual outcomes of patients with VKH-like uveitis following ICI therapy.

CLINICAL RELEVANCE: ICIs are increasingly used in cancer therapy, but poorly understood ocular immune-related adverse events (irAEs) can lead to suspension of treatment and be vision-threatening.

METHODS: We conducted a systematic review (PROSPERO #CRD42024558269) according to PRISMA guidelines. MEDLINE, Embase, CENTRAL, and Web of Science were searched for English articles published up to June 28, 2024. All study designs reporting on incident VKH-like uveitis following ICI were included. Risk of Bias was assessed using a tool modified from Murad et al. (2018).

RESULTS: Of 865 articles, we included 42 articles (4 observational studies, 28 case reports, 6 case series, 3 letters, and 1 editorial) from 12 countries, comprising 52 patients. The mean age was 60.0 ± 11.9 years, and 32 (61.5%) were females. Thirty-six (69.2%) had melanoma, and most were undergoing treatment with a PD-1 inhibitor alone (n = 33, 63.5%) or in combination with a CTLA-4 inhibitor (n = 10, 19.2%). The mean duration of ICI treatment before VKH-like uveitis symptoms was 22.2 ± 29.6 weeks, and the mean duration of ocular symptoms was 16.7 ± 18.6 weeks, with wide variation. Overall, 43 patients (73.1%) had imaging or exams suggesting bilateral involvement and 21 cases (40.4%) suggesting panuveitis. Only 31 cases (59.6%) met the acute initial-onset uveitis criteria, and 15 (28.8%) met the chronic phase criteria. Most (n = 47, 90.4%) required systemic or intravitreal steroids, termination of ICI (n = 31, 59.6%), and experienced full resolution or remission of visual symptoms (n = 43, 82.7%). Most articles (n = 40, 95.2%) were judged to be at medium risk of bias.

CONCLUSION: This descriptive systematic review consisted mostly of case reports, but it confirmed that a high proportion of VKH-like uveitis occur with PD-1 inhibitors and melanoma patients. VKH-like uveitis can lead to suspension of treatment. Further collaboration between oncologists and ophthalmologists is needed in the continuum of cancer care.

PMID:40354015 | DOI:10.1186/s12348-025-00484-8

Zhongguo Zhong Yao Za Zhi. 2025 Feb;50(4):853-859. doi: 10.19540/j.cnki.cjcmm.20241025.601.

ABSTRACT

The safety of traditional Chinese medicine(TCM) has always been taken very seriously, and rich and valuable theories and experiences have been developed to ensure the safe and precise use of TCM in clinical practices. In recent years, the cognitive theory of toxicity of TCM, has undergone a profound change. TCM is characterized by the existence of intrinsic toxicity, idiosyncratic toxicity, and indirect toxicity related to organic factors. Therefore, the traditional theories and experiences of TCM, which focus on the prevention and control of intrinsic toxicity, fail to be used for the development of risk prevention and control countermeasures for newly discovered TCM with idiosyncratic toxicity and indirect toxicity. Accordingly, based on the toxicity classification and mechanism characteristics of TCM, this paper proposed a new strategy and method in TCM compatibility for detoxification based on componenttarget-effect interaction. The strategy based on component-target-effect interaction is to carry out TCM compatibility for detoxification by blocking the occurrence of drug-mediated damage and promoting damage repair through component interactions, target interactions,and/or effect interactions. Based on this theory, the paper established a strategy for TCM compatibility that aligned with the cognitive theory of toxicity of TCM, so as to achieve safe and precise use of TCM in clinical practices. The strategy based on component-targeteffect interaction has been exemplarily applied to the development of countermeasures to reduce the toxicity of TCM, including Polygonum Multiflorum, Epimedii Folium, and Psoraleae Fructus, and a new mechanism of Glycyrrhizae Radix et Rhizoma to &quot; harmonize various medicines and detoxify myriad poisons&quot; was illustrated, providing a scientific basis for the safe and precise use of TCM in clinical practice. This paper explained the scientific connotation, application forms, and application examples of componenttarget-effect interaction, aiming to provide a theoretical and methodological basis for guaranteeing the precise use of TCM in clinical practice and innovate the theories and methods of TCM compatibility for detoxification.

PMID:40350805 | DOI:10.19540/j.cnki.cjcmm.20241025.601

Clin Pharmacol Ther. 2025 May 11. doi: 10.1002/cpt.3691. Online ahead of print.

ABSTRACT

The cytochrome P450 2D6 (CYP2D6) metabolizes around 20% of currently prescribed medications, including the antipsychotic risperidone. Previous studies reported greater odds of switching antipsychotic medication from risperidone among CYP2D6 poor metabolizers (PM) without considering treatment duration up to the switch. Risperidone treatment failure, defined as risperidone treatment duration up to switching medication, was analyzed among 515 patients of the PsyMetab cohort using Kaplan-Meier estimates with log-rank tests and Cox multivariate regression. Risperidone-to-paliperidone ratios were higher among CYP2D6 PMs (median: 2.78) vs. the other phenotypes (median: 0.14, P < 0.001). After 1 year of treatment, the proportion of patients who switched from risperidone was 44%. This proportion was increased to 70% among PMs, vs. 42% among the other CYP2D6 phenotypes (P = 0.026). PMs' risk of switching increased over time (interaction PM*treatment duration: 1.01; P = 0.011), becoming statistically significant after 3 months of treatment, with 1.79 (P = 0.028), 3.7 times (P < 0.001) and 16.3 times higher (P = 0.001) risk of switch at 3, 6, and 12 months, respectively (95% confidence intervals: 1.07-3.01, 1.91-7.17, and 3.13-85.37, respectively). Considering a pharmacogenetic-guided treatment, the number of patients needed to genotype to find one PM and lower the switching proportion from 70% to 42% would be 65. In conclusion, CYP2D6 PM status presented an increased risk of switching from risperidone over 1 year of treatment, the risk increasing over time and becoming statistically significant after the first 3 months of treatment.

PMID:40350722 | DOI:10.1002/cpt.3691

J Neurol Surg B Skull Base. 2024 May 23;86(3):373-376. doi: 10.1055/s-0044-1787155. eCollection 2025 Jun.

NO ABSTRACT

PMID:40351885 | PMC:PMC12064296 | DOI:10.1055/s-0044-1787155