J Neuroeng Rehabil. 2025 Feb 18;22(1):29. doi: 10.1186/s12984-025-01570-7.

ABSTRACT

BACKGROUND: Postural instability greatly reduces quality of life in people with Parkinson's disease (PD). Early and objective detection of postural impairments is crucial to facilitate interventions. Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body sway. We hypothesized the time-frequency content of body sway to be predictive of PD, even when impairments are not yet clinically apparent.

METHODS: 18 people with idiopathic PD and 15 healthy controls (HC) participated in the study. We tracked participants' center of pressure (COP) using a Wii Balance Board and their full-body motion using a Microsoft Kinect, out of which we calculated the trajectory of their center of mass (COM). We used 30 s-snippets of motion data from which we acquired wavelet-based time-frequency spectrograms that were fed into a custom-built CNN as labeled images. We used binary classification to have the network differentiate between individuals with PD and controls (n = 15, respectively).

RESULTS: Classification performance was best when the medio-lateral motion of the COM was considered. Here, our network reached a predictive accuracy, sensitivity, specificity, precision and F1-score of 100%, respectively, with a receiver operating characteristic area under the curve of 1.0. Moreover, an explainable AI approach revealed high frequencies in the postural sway data to be most distinct between both groups.

CONCLUSION: Heeding our small and heterogeneous sample, our findings suggest a CNN classifier based on cost-effective and conveniently obtainable posturographic data to be a promising approach to detect postural impairments in early to mid-stage PD and to gain novel insight into the subtle characteristics of impairments at this stage of the disease.

PMID:39966853 | DOI:10.1186/s12984-025-01570-7

Cardiovasc Diabetol. 2025 Feb 18;24(1):80. doi: 10.1186/s12933-025-02640-9.

ABSTRACT

BACKGROUND: Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning methods.

METHODS: We used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and calibration.

RESULTS: Among n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well calibrated.

CONCLUSIONS: Discrimination performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive Predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature richness and complexity were exploited before the choice of algorithm could become critical to maximize performance. Future research might focus on the impact of different feature derivation approaches on performance ceilings. In the absence of other more powerful screening alternatives, applying transparent regression-based models in routine claims, though certainly imperfect, remains a promising scalable low-cost approach for population-based cardiovascular risk prediction and stratification.

PMID:39966813 | DOI:10.1186/s12933-025-02640-9

BMC Cancer. 2025 Feb 18;25(1):291. doi: 10.1186/s12885-025-13635-w.

ABSTRACT

OBJECTIVE: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.

METHODS AND MATERIALS: All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort (n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. Radiomics analysis pipeline modules were developed for analyzing features. Radiomic scores, clinical scores, and combined nomograms were analyzed to predict 2-year, 3-year, and 5-year overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance.

FINDINGS: Both clinical and radiomic scores showed good performance for overall survival and disease-free survival prediction in internal (median AUC of 0.82 and 0.72 respectively) and external validation (median AUC of 0.70 and 0.74 respectively). The combined nomograms had AUCs of 0.80-0.93 and 0.73-0.89 in the internal and external validation, which had best predictive performance in all tasks (p < 0.05), especially for 5-year OS (p < 0.01). For the overall evaluation of six tasks, combined models obtained better performance than clinical and radiomic scores [AUCs of 0.83 (0.73,0.93), 0.81 (0.72,0.93), and 0.70 (0.61,0.85) respectively].

INTERPRETATION: The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.

PMID:39966783 | DOI:10.1186/s12885-025-13635-w

BMC Pregnancy Childbirth. 2025 Feb 18;25(1):182. doi: 10.1186/s12884-025-07313-9.

ABSTRACT

BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.

METHODS: This study implemented predictive SSC approaches based on the data obtained from the "Iranian Maternal and Neonatal Network (IMaN Net)" from January 2020 to January 2022. A predictive model was built using nine statistical learning models (linear regression, logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Demographic, obstetric, and maternal and neonatal clinical factors were considered as potential predicting factors and were extracted from the patient's medical records. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F_1 Score were measured to evaluate the diagnostic performance.

RESULTS: Of 8031 eligible mothers, 3759 (46.8%) experienced SSC. The algorithms created by deep learning (AUROC: 0.81, accuracy: 0.75, precision: 0.67, recall: 0.77, and F_1 Score: 0.73) and linear regression (AUROC: 0.80, accuracy: 0.75, precision: 0.66, recall: 0.75, and F_1 Score: 0.71) had the highest performance in predicting SSC. Doula support, neonatal weight, gestational age, attending childbirth classes, and maternal age were the critical predictors for SSC based on the top two algorithms with superior performance.

CONCLUSIONS: Although this study found that the ML model performed well in predicting SSC, more research is needed to make a better conclusion about its performance.

PMID:39966775 | DOI:10.1186/s12884-025-07313-9

BMC Cancer. 2025 Feb 18;25(1):289. doi: 10.1186/s12885-025-13669-0.

ABSTRACT

BACKGROUND: Treatment planning systems (TPS) often exclude immobilization devices from optimization and calculation, potentially leading to inaccurate dose estimates. This study employed deep learning methods to automatically segment 3D-printed head and neck immobilization devices and evaluate their dosimetric impact in head and neck VMAT.

METHODS: Computed tomography (CT) positioning images from 49 patients were used to train the Mask2Former model to segment 3D-printed headrests and MFIFs. Based on the results, four body structure sets were generated for each patient to evaluate the impact on dose distribution in volumetric modulated arc therapy (VMAT) plans: S (without immobilization devices), S_MF (with MFIFs), S_3D (with 3D-printed headrests), and S_3D+MF (with both). VMAT plans (P, P_MF, P_3D, and P_3D+MF) were created for each structure set. Dose-volume histogram (DVH) data and dose distribution of the four plans were compared to assess the impact of the 3D-printed headrests and MFIFs on target and normal tissue doses. Gafchromic EBT3 film measurements were used for patient-specific verification to validate dose calculation accuracy.

RESULTS: The Mask2Former model achieved a mean average precision (mAP) of 0.898 and 0.895, with a Dice index of 0.956 and 0.939 for the 3D-printed headrest on the validation and test sets, respectively. For the MFIF, the Dice index was 0.980 and 0.981 on the validation and test sets, respectively. Compared to P, P_MF reduced the V100% for PGTVnx, PGTVnd, PGTVrpn, PTV1, and PTV2 by 5.99%, 6.51%, 5.93%, 2.24%, and 1.86%, respectively(P ≤ 0.004). P_3D reduced the same targets by 1.78%, 2.56%, 1.75%, 1.16%, and 1.48%(P < 0.001), with a 31.3% increase in skin dose (P < 0.001). P_3D+MF reduced the V100% by 9.15%, 10.18%, 9.16%, 3.36%, and 3.28% (P < 0.001), respectively, while increasing the skin dose by 31.6% (P < 0.001). EBT3 film measurements showed that the P_3D+MF dose distribution was more aligned with actual measurements, achieving a mean gamma pass rate of 92.14% under the 3%/3 mm criteria.

CONCLUSIONS: This study highlights the potential of Mask2Former in 3D-printed headrest and MFIF segmentation automation, providing a novel approach to enhance personalized radiation therapy plan accuracy. The attenuation effects of 3D-printed headrests and MFIFs reduce V100% and Dmean for PTVs in head and neck cancer patients, while the buildup effects of 3D-printed headrests increases the skin dose (31.3%). Challenges such as segmentation inaccuracies for small targets and artifacts from metal fasteners in MFIFs highlight the need for model optimization and validation on larger, more diverse datasets.

PMID:39966735 | DOI:10.1186/s12885-025-13669-0

J Dent Res. 2025 Feb 18:220345251317097. doi: 10.1177/00220345251317097. Online ahead of print.

NO ABSTRACT

PMID:39966688 | DOI:10.1177/00220345251317097

Sci Rep. 2025 Feb 18;15(1):5924. doi: 10.1038/s41598-025-90214-w.

ABSTRACT

The presence of a diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidates for recanalization treatment. However, the visual assessment of DWI-FLAIR mismatch is subject to limitations due to variability among raters, which affects accuracy and consistency. To overcome these challenges, we aimed to develop and validate a deep learning-based classifier to categorize the mismatch. We screened consecutive acute ischemic stroke patients who underwent DWI and FLAIR imaging from a four stroke centers. Two centers were used for model development and internal testing (derivation cohort), while two independent centers served as external validation cohorts. We developed Convolutional Neural Network-based classifiers for two binary classifications: DWI-FLAIR match versus non-match (Label Set I) and match versus mismatch (Label Set II). A total of 2369 patients from the derivation set and 679 patients from two external validation sets (350 and 329 patients) were included in the analysis. For Label Set I, the internal test set AUC was 0.862 (95% CI 0.841-0.884, with external validation AUCs of 0.829 (0.785-0.873) and 0.835 (0.790-0.879). Label Set II showed higher performance with internal test AUC of 0.934 (0.911-0.957) and external validation AUCs of 0.883 (0.829-0.938) and 0.913 (0.876-0.951). A deep learning-based classifier for the DWI-FLAIR mismatch can be used to diminish subjectivity and support targeted decision-making in the treatment of acute stroke patients.

PMID:39966647 | DOI:10.1038/s41598-025-90214-w

Sci Rep. 2025 Feb 18;15(1):5888. doi: 10.1038/s41598-025-89961-7.

ABSTRACT

Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a novel Hybrid Deep Learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. The proposed methodology involves pre-processing the OCT dataset using discrete wavelet transform and ACO-optimized augmentation, followed by multiscale patch embedding to generate image patches of varying sizes. The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. Furthermore, a Transformer-based feature extraction module integrates content-aware embeddings, multi-head self-attention, and feedforward neural networks to improve classification performance. Experimental results demonstrate that HDL-ACO outperforms state-of-the-art models, including ResNet-50, VGG-16, and XGBoost, achieving 95% training accuracy and 93% validation accuracy. The proposed framework offers a scalable, resource-efficient solution for real-time clinical OCT image classification.

PMID:39966596 | DOI:10.1038/s41598-025-89961-7

Sci Rep. 2025 Feb 18;15(1):5959. doi: 10.1038/s41598-025-88728-4.

ABSTRACT

A complex biological process involves physical changes and hormonal fluctuation in the menstrual cycle. The traditional nutrition recommendation models often offer general guidelines but fail to address the specific requirements of women during various menstrual cycle stages. This paper proposes a novel Optimization Hybrid Deep Learning (OdriHDL) model to provide a personalized health nutrition recommendation for women during their menstrual cycle. It involves pre-processing the data through Missing Value Imputation, Z-score Normalization, and One-hot encoding. Next, feature extraction is accomplished using the Layered Sparse Autoencoder Network. Then, the extracted features are utilized by the Hybrid Attention-based Bidirectional Convolutional Greylag Goose Gated Recurrent Network (HABi-ConGRNet) for nutrient recommendation. The hyper-parameter tuning of HABi-ConGRNet is carried out using Greylag Goose Optimization Algorithm to enhance the model performance. The Python platform is used for the simulation of collected data, and several performance metrics are employed to analyze the performance. The OdriHDL model demonstrates superior performance, achieving a maximum accuracy of 97.52% and enhanced precision rate in contrast to the existing methods, like RNN, CNN-LSTM, and attention GRU. The findings suggest that OdriHDL captures complex patterns between nutritional needs and menstrual symptoms and provides robust solutions to unique physiological changes experienced by women.

PMID:39966547 | DOI:10.1038/s41598-025-88728-4

Sci Rep. 2025 Feb 18;15(1):5904. doi: 10.1038/s41598-025-89639-0.

ABSTRACT

Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed to detect and classify label-free cytopathic effects (CPEs) induced by SARS-CoV-2, BAdV-1, BPIV3, BoAHV-1, and two strains of BoGHV-4 in Vero and MDBK cell lines. AIRVIC utilizes convolutional neural networks, with ResNet50 as the primary architecture, trained on 40,369 microscopy images at various magnifications. AIRVIC demonstrated strong CPE detection, achieving 100% accuracy for the BoGHV-4 DN-599 strain in MDBK cells, the highest among tested strains. In contrast, the BoGHV-4 MOVAR 33/63 strain in Vero cells showed a lower accuracy of 87.99%, the lowest among all models tested. For virus classification, a multi-class accuracy of 87.61% was achieved for bovine viruses in MDBK cells; however, it dropped to 63.44% when the virus was identified without specifying the cell line. To the best of our knowledge, this is the first research article published in English to utilize AI for distinguishing animal virus infections in cell culture. AIRVIC's hierarchical structure highlights its adaptability to virological diagnostics, providing unbiased infectivity scoring and facilitating viral isolation and antiviral efficacy testing. Additionally, AIRVIC is accessible as a web-based platform, allowing global researchers to leverage its capabilities in viral diagnostics and beyond.

PMID:39966536 | DOI:10.1038/s41598-025-89639-0

BMC Cancer. 2025 Feb 18;25(1):292. doi: 10.1186/s12885-025-13713-z.

NO ABSTRACT

PMID:39966798 | DOI:10.1186/s12885-025-13713-z

BMC Genomics. 2025 Feb 18;26(1):158. doi: 10.1186/s12864-025-11351-0.

ABSTRACT

BACKGROUND: The nematode phylum includes many species key to soil food webs with trophic behaviours extending from feeding on microbes to macrofauna and plant roots. Among these, the plant parasitic cyst nematodes retain their eggs in protective cysts prolonging their survival under harsh conditions. These nematodes, including those from the genus Heterodera, cause significant economic losses in agricultural systems. Understanding of nematode diversity and ecology has expanded through application of genomic research, however, for Heterodera species there are very few available whole genome sequences. Sequencing and assembling Heterodera genomes is challenging due to various technical limitations imposed by the biology of Heterodera. Overcoming these limitations is essential for comprehensive insights into Heterodera parasitic interactions with plants, population studies, and for Australian biosecurity implications.

RESULTS: We hereby present draft genomes of six species of which Heterodera australis, H. humuli, H. mani and H. trifolii are presently recorded in Australia and two species, H. avenae and H. filipjevi, currently absent from Australia. The draft genomes were sequenced from genomic DNA isolated from 50 cysts each using an Illumina NovaSeq short read sequencing platform. The data revealed disparity in sequencing yield between species. What was previously identified as H. avenae in Australia using morphological traits is now confirmed as H. australis and may have consequences for wheat breeding programs in Australia that are breeding for resistance to H. avenae. A multigene phylogeny placed the sequenced species into taxonomic phylogenetic perspective. Genomic comparisons within the Avenae species group revealed orthologous gene clusters within the species, emphasising the shared and unique features of the group. The data also revealed the presence of a Wolbachia species, a putative bacterial endosymbiont from Heterodera humuli short read sequencing data.

CONCLUSION: Genomic research holds immense significance for agriculture, for understanding pest species diversity and the development of effective management strategies. This study provides insight into Heterodera, cyst nematode genomics and the associated symbionts and this work will serve as a baseline for further genomic analyses in this economically important nematode group.

PMID:39966714 | DOI:10.1186/s12864-025-11351-0

Nat Methods. 2025 Feb 18. doi: 10.1038/s41592-025-02594-6. Online ahead of print.

ABSTRACT

An ideal tool for the study of cellular biology would enable the measure of molecular activity nondestructively within living cells. Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in cells but have historically been limited by a necessary tradeoff between spatiotemporal resolution and throughput. Here we address these limitations using oblique line scan (OLS), a robust single-objective light-sheet-based illumination and detection modality that achieves nanoscale spatial resolution and sub-millisecond temporal resolution across a large field of view. We show that OLS can be used to capture protein motion up to 14 μm2 s-1 in living cells. We further extend the utility of OLS with in-solution SMT for single-molecule measurement of ligand-protein interactions and disruption of protein-protein interactions using purified proteins. We illustrate the versatility of OLS by showcasing two-color SMT, STORM and single-molecule fluorescence recovery after photobleaching. OLS paves the way for robust, high-throughput, single-molecule investigations of protein function required for basic research, drug screening and systems biology studies.

PMID:39966678 | DOI:10.1038/s41592-025-02594-6

Nat Metab. 2025 Feb 18. doi: 10.1038/s42255-025-01220-1. Online ahead of print.

ABSTRACT

Dietary intake is tightly coupled to gut microbiota composition, human metabolism and the incidence of virtually all major chronic diseases. Dietary and nutrient intake are usually assessed using self-reporting methods, including dietary questionnaires and food records, which suffer from reporting biases and require strong compliance from study participants. Here, we present Metagenomic Estimation of Dietary Intake (MEDI): a method for quantifying food-derived DNA in human faecal metagenomes. We show that DNA-containing food components can be reliably detected in stool-derived metagenomic data, even when present at low abundances (more than ten reads). We show how MEDI dietary intake profiles can be converted into detailed metabolic representations of nutrient intake. MEDI identifies the onset of solid food consumption in infants, shows significant agreement with food frequency questionnaire responses in an adult population and shows agreement with food and nutrient intake in two controlled-feeding studies. Finally, we identify specific dietary features associated with metabolic syndrome in a large clinical cohort without dietary records, providing a proof-of-concept for detailed tracking of individual-specific, health-relevant dietary patterns without the need for questionnaires.

PMID:39966520 | DOI:10.1038/s42255-025-01220-1

Commun Biol. 2025 Feb 18;8(1):256. doi: 10.1038/s42003-025-07685-w.

ABSTRACT

The multi-petal "Hutou" jasmine (Jasminum sambac var. Trifoliatum) is highly valued for bonsai cultivation and landscape design, however, the aroma profile and mechanisms underlying floral scent formation remain elusive. In this study, we generate a nearly complete telomere-to-telomere (T2T) genome assembly of "Hutou" jasmine (487.45 Mb with contig N50 of 38.93 Mb). Metabolomic profiling unveils that 16 significantly differential volatiles (SDVs) may play a crucial role in the formation of flower aroma. Among them, five scented SDVs, particularly α-farnesene and pentanoic acid 1-ethenyl-1,5-dimethyl-4-hexenyl ester, contribute to the characteristic aroma profile of "Hutou" jasmine flowers. Weighted gene co-expression network analysis (WGCNA) identifies HTWRKY41, HTWRKY53, and HTHSP90 as the hub genes potentially regulating the production of these 16 metabolites. The expression of selected genes and duplication events drive the increased relative content of major sesquiterpenoids in terpenoid biosynthetic pathway. Four structural genes (BEAT3, BSMT1, BPBT2, and BPBT3) are potentially implicated in the emission of downstream key volatile esters (benzyl acetate, methyl benzoate, and benzyl benzoate) in the phenylpropanoids synthesis. Our integrated dataset of genomics, transcriptomics, and metabolomics present here provides a theoretical basis for the practical utilization of fragrance and genetic improvement in horticultural applications of "Hutou" jasmine.

PMID:39966493 | DOI:10.1038/s42003-025-07685-w

Nat Commun. 2025 Feb 18;16(1):1735. doi: 10.1038/s41467-025-56890-y.

ABSTRACT

The carbohydrates that fuel gut colonization by S. Typhimurium are not fully known. To investigate this, we designed a quality-controlled mutant pool to probe the metabolic capabilities of this enteric pathogen. Using neutral genetic barcodes, we tested 35 metabolic mutants across five different mouse models with varying microbiome complexities, allowing us to differentiate between context-dependent and context-independent nutrient sources. Results showed that S. Typhimurium uses D-mannose, D-fructose and likely D-glucose as context-independent carbohydrates across all five mouse models. The utilization of D-galactose, N-acetylglucosamine and hexuronates, on the other hand, was context-dependent. Furthermore, we showed that D-fructose is important in strain-to-strain competition between Salmonella serovars. Complementary experiments confirmed that D-glucose, D-fructose, and D-galactose are excellent niches for S. Typhimurium to exploit during colonization. Quantitative measurements revealed sufficient amounts of carbohydrates, such as D-glucose or D-galactose, in the murine cecum to drive S. Typhimurium colonization. Understanding these key substrates and their context-dependent or -independent use by enteric pathogens will inform the future design of probiotics and therapeutics to prevent diarrheal infections such as non-typhoidal salmonellosis.

PMID:39966379 | DOI:10.1038/s41467-025-56890-y

BMC Infect Dis. 2025 Feb 18;25(1):230. doi: 10.1186/s12879-025-10627-w.

ABSTRACT

BACKGROUND: Isavuconazole has been used to treat invasive fungal infections, however, it is unclear whether the efficacy of isavuconazole is superior to that of voriconazole. The purpose of this meta-analysis was to assess the efficacy and safety of isavuconazole compared to voriconazole in treating invasive fungal infections.

METHODS: Electronic databases, including PubMed, EMBASE, Cochrane Library, and Web of Science, were searched to identify relevant studies. Studies evaluating the effect of isavuconazole in the treatment of patients with invasive fungal infections were included. Pooled rates of overall response, all-cause mortality, drug-related adverse events (AEs), and discontinuation due to drug-related AEs were calculated.

RESULTS: Seven studies involving 890 patients were included. Meta-analysis showed that there was no significant difference between isavuconazole and voriconazole in overall response (risk ratio [RR]: 1.02, 95% confidence interval [CI]: 0.83 to 1.25, p = 0.86) and all-cause mortality (RR: 0.95, 95% CI: 0.78 to 1.16, p = 0.61). However, isavuconazole had a significantly lower incidence of drug-related AEs (RR: 0.70, 95% CI: 0.61 to 0.81, p < 0.001) and discontinuation due to drug-related AEs (RR: 0.56, 95% CI: 0.39 to 0.82, p = 0.003) compared with voriconazole. Trial sequential analysis (TSA) confirmed that the difference between isavuconazole and voriconazole in discontinuation due to drug-related AEs need further valiadation, but the results of other outcomes were conclusive. < 0.001) and discontinuation due to drug-related AEs (RR: 0.56, 95% CI: 0.39 to 0.82, p = 0.003) compared with voriconazole. Trial sequential analysis (TSA) confirmed that the difference between isavuconazole and voriconazole in discontinuation due to drug-related AEs needs further validation, but the results of other outcomes were conclusive.

CONCLUSIONS: Our findings support the use of isavuconazole as the primary therapy for invasive fungal infections. More research is needed to compare the discontinuation rates of isavuconazole and voriconazole.

PMID:39966738 | DOI:10.1186/s12879-025-10627-w

Nat Commun. 2025 Feb 18;16(1):1750. doi: 10.1038/s41467-025-56569-4.

ABSTRACT

Understanding the interaction between genetic and epigenetic variation remains a challenge due to confounding environmental factors. We propose that human induced Pluripotent Stem Cells (iPSCs) are an excellent model to study the relationship between genetic and epigenetic variation while controlling for environmental factors. In this study, we have created a comprehensive resource of high-quality genomic, epigenomic, and transcriptomic data from iPSC lines and three iPSC-derived cell types (neural stem cell (NSC), motor neuron, monocyte) from three healthy donors. We find that epigenetic variation is most strongly associated with genetic variation at the iPSC stage, and that relationship weakens as epigenetic variation increases in differentiated cells. Additionally, cell type is a stronger source of epigenetic variation than genetic variation. Further, we elucidate a utility of studying epigenetic variation in iPSCs and their derivatives for identifying important loci for GWAS studies and the cell types in which they may be acting.

PMID:39966349 | DOI:10.1038/s41467-025-56569-4

J Am Med Inform Assoc. 2025 Feb 18:ocaf035. doi: 10.1093/jamia/ocaf035. Online ahead of print.

ABSTRACT

OBJECTIVE: This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.

MATERIALS AND METHODS: We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.

RESULTS: Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.

DISCUSSION: The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.

CONCLUSION: This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.

PMID:39965087 | DOI:10.1093/jamia/ocaf035

Otol Neurotol. 2025 Jan 22. doi: 10.1097/MAO.0000000000004417. Online ahead of print.

ABSTRACT

OBJECTIVE: To provide the first description of intratympanic bacteriophage therapy for chronic mastoiditis from multidrug-resistant Pseudomonas aeruginosa in the United States.

PATIENTS: A 47-year-old woman with chronic mastoiditis in the setting of ciliary dysfunction from cystic fibrosis and immunosuppression from lung transplantation.

INTERVENTIONS: Ten concurrent parenteral and intratympanic doses of two custom phages targeting P. aeruginosa followed by IV antibiotic therapy.

MAIN OUTCOME MEASURES: Resolution of infection confirmed by symptomatology, cultures, and imaging.

RESULTS: At 5 months after phage treatment, the patient reported resolution of otorrhea, headaches, and hearing impairment. Subsequent cultures showed no growth.

CONCLUSIONS: Bacteriophages can enhance antibiotic activity in cases of drug-resistant chronic mastoiditis.

PMID:39965256 | DOI:10.1097/MAO.0000000000004417