BMC Med Inform Decis Mak. 2025 Jan 9;25(1):13. doi: 10.1186/s12911-024-02819-2.

ABSTRACT

BACKGROUND: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method. In this regard, complementary methods are demanded. In the recent decade, machine learning strategies that employ mathematical models on a dataset to extract the most informative hidden information are the center of interest for prediction and diagnosis purposes.

METHOD: In this study, machine learning approaches were used for finding the important variables for a reliable prediction of UTI. Several types of machines including classical and deep learning models were used for this purpose.

RESULTS: Eighteen selected features from urine test, blood test, and demographic data were found as the most informative features. Factors extracted from urine such as WBC, nitrite, leukocyte, clarity, color, blood, bilirubin, urobilinogen, and factors extracted from blood test like mean platelet volume, lymphocyte, glucose, red blood cell distribution width, and potassium, and demographic data such as age, gender and previous use of antibiotics were the determinative factors for UTI prediction. An ensemble combination of XGBoost, decision tree, and light gradient boosting machines with a voting scheme obtained the highest accuracy for UTI prediction (AUC: 88.53 (0.25), accuracy: 85.64 (0.20)%), according to the selected features. Furthermore, the results showed the importance of gender and age for UTI prediction.

CONCLUSION: This study highlighted the potential of machine learning strategies for UTI prediction.

PMID:39789596 | DOI:10.1186/s12911-024-02819-2

BMC Cancer. 2025 Jan 9;25(1):45. doi: 10.1186/s12885-025-13424-5.

ABSTRACT

OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

METHODS: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed. Feature selection was performed using Mann-Whitney U test, LASSO regression, and SelectKBest. Prediction models were established by gaussian process (GP) and decision tree (DT) algorithms and evaluated by the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA).

RESULTS: Compared to the DL (AUCtraining = 0.830, AUCtest = 0.779, and AUCvalidation = 0.711), radiomics (AUCtraining = 0.810, AUCtest = 0.710, and AUCvalidation = 0.839), and clinical (AUCtraining = 0.780, AUCtest = 0.685, and AUCvalidation = 0.695) models, the combined model based on the GP algorithm, which consisted of four DL features, five radiomics features, and two clinical variables, not only demonstrated the highest diagnostic efficacy (AUCtraining = 0.949, AUCtest = 0.877, and AUCvalidation = 0.914) but also led to an improvement in risk reclassification of the TP53 mutation (NIRtraining = 66.38%, 56.98%, and 83.48%, NIRtest = 50.72%, 80.43%, and 89.49%, and NIRvalidation = 64.58%, 87.50%, and 120.83%, respectively). In addition, the combined model exhibited good agreement and clinical utility in calibration curves and DCA analyses, respectively.

CONCLUSIONS: A prediction model based on the GP algorithm and consisting of DL and radiomics features of DWI as well as clinical variables can effectively assess TP53 mutation in EC.

PMID:39789538 | DOI:10.1186/s12885-025-13424-5

Int J Cardiovasc Imaging. 2025 Jan 9. doi: 10.1007/s10554-025-03324-x. Online ahead of print.

ABSTRACT

The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment. We developed a deep learning model to classify cine loops into left or right coronary artery (LCA/RCA) or "other". Data were obtained by manual annotation. Using these classifications, cine loops before revascularization were identified and curated automatically. Separate deep learning models for LCA and RCA were developed to estimate stenosis using these identified cine loops. From a cohort of 19,414 patients and 332,582 cine loops, we identified cine loops for 13,480 patients for model development and 5056 for internal testing. External testing was conducted using automated identified cine loops from 608 patients. For identification of significant stenosis (visual assessment of diameter stenosis > 70%), our model obtained a receiver operator characteristic (ROC) area under the curve (ROC-AUC) of 0.903 (95% CI: 0.900-0.906) on the internal test. The performance was evaluated on the external test set against visual assessment, 3D quantitative coronary angiography, and fractional flow reserve (≤ 0.80), obtaining ROC AUC values of 0.833 (95% CI: 0.814-0.852), 0.798 (95% CI: 0.741-0.842), and 0.780 (95% CI: 0.743-0.817), respectively. The deep-learning-based stenosis estimation models showed promising results for predicting stenosis. Compared to previous work, our approach demonstrates performance increase, includes all 16 segments, does not exclude revascularized patients, is externally tested, and is simpler using fewer steps.

PMID:39789341 | DOI:10.1007/s10554-025-03324-x

J Imaging Inform Med. 2025 Jan 9. doi: 10.1007/s10278-024-01351-z. Online ahead of print.

ABSTRACT

PURPOSE: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.

METHODS: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled. Mode models aggregating single image predictions, trained with the full or "key" datasets, and a multiple instance learning (MIL) model were developed for location classification of the DSA sequences. Model performance was evaluated with a primary endpoint of multiclass classification accuracy and compared by McNemar's test.

RESULTS: A total of 819 unique angiographic sequences from 205 patients and 276 procedures were included in the training, validation, and testing data and split into partitions at the patient level to preclude data leakage. The data demonstrate substantial information sparsity as a minority of the images were designated as "key" with sufficient information for localization by a domain expert. A Mode model, trained and tested with "key" images, demonstrated an overall multiclass classification accuracy of 0.975 (95% CI 0.941-1). A MIL model, trained and tested with all data, demonstrated an overall multiclass classification accuracy of 0.966 (95% CI 0.932-0.992). Both the Mode model with "key" images (p < 0.001) and MIL model (p < 0.001) significantly outperformed a Mode model trained and tested with the full dataset. The MIL model additionally automatically identified a set of top-5 images with an average overlap of 92.5% to manually labelled "key" images.

CONCLUSION: Deep learning algorithms can identify anatomic locations in abdominopelvic DSA with high fidelity using manual or automatic methods to manage information sparsity.

PMID:39789320 | DOI:10.1007/s10278-024-01351-z

Clin Rheumatol. 2025 Jan 10. doi: 10.1007/s10067-025-07304-3. Online ahead of print.

ABSTRACT

OBJECTIVES: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex interaction of clinical variables, and routine extensive US is impractical. Machine learning (ML) models, particularly those integrating the 18-joint ultrasound scoring system (US18), have shown potential to address these issues but remain underexplored. This study aims to evaluate ML models integrating US18 with clinical data to improve early identification of high-risk patients and support personalized treatment strategies.

METHODS: In this prospective cohort, 432 UA patients were followed for 1 year to track progression to RA. Four ML algorithms and one deep learning model were developed using baseline clinical and US18 data. Comparative experiments on a testing cohort identified the optimal model. SHAP (SHapley Additive exPlanations) analysis highlighted key variables, validated through an ablation experiment.

RESULTS: Of the 432 patients, 152 (35.2%) progressed to the RA group, while 280 (64.8%) remained in the non-RA group. The Random Forest (RnFr) model demonstrated the highest accuracy and sensitivity. SHAP analysis identified joint counts at US18 Grade 2, total US18 score, and swollen joint count as the most influential variables. The ablation experiment confirmed the importance of US18 in enhancing early RA detection.

CONCLUSIONS: Integrating the US18 assessment with clinical data in an RnFr model significantly improves early detection of RA progression in UA patients, offering potential for earlier and more personalized treatments. Key Points • A machine learning model integrating clinical and ultrasound features effectively predicts rheumatoid arthritis progression in undifferentiated arthritis patients. • The 18-joint ultrasound scoring system (US18) enhances predictive accuracy, particularly when incorporated with clinical variables in a Random Forest model. • SHAP analysis underscores that joint severity levels in US18 contribute significantly to early identification of high-risk patients. • This study offers a feasible and efficient approach for clinical implementation, supporting more personalized and timely RA treatment strategies.

PMID:39789318 | DOI:10.1007/s10067-025-07304-3

Int J Comput Assist Radiol Surg. 2025 Jan 9. doi: 10.1007/s11548-024-03319-4. Online ahead of print.

ABSTRACT

PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.

METHODS: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e., support). Segmentation starts by detecting the rectum using a Markov Random Field-based algorithm. Then, supervised sequential episodic training is applied to the remaining slices, while contrastive learning is employed to enhance feature discriminability, thereby improving segmentation accuracy.

RESULTS: The proposed method, evaluated on 98 abdominal scans of prepped patients, achieved a Dice coefficient of 97.3% and a polyp information preservation accuracy of 98.28%. Statistical analysis, including 95% confidence intervals, underscores the method's robustness and reliability. Clinically, this high level of accuracy is vital for ensuring the preservation of critical polyp details, which are essential for accurate automatic diagnostic evaluation. The proposed method performs reliably in scenarios with limited annotated data. This is demonstrated by achieving a Dice coefficient of 97.15% when the model was trained on a smaller number of annotated CT scans (e.g., 10 scans) than the testing dataset (e.g., 88 scans).

CONCLUSIONS: The proposed sequential segmentation approach achieves promising results in colon segmentation. A key strength of the method is its ability to generalize effectively, even with limited annotated datasets-a common challenge in medical imaging.

PMID:39789205 | DOI:10.1007/s11548-024-03319-4

Eye (Lond). 2025 Jan 9. doi: 10.1038/s41433-024-03585-1. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).

METHODS: A retrospective cohort of 249 patients (498 eyes) diagnosed with NPDR was analysed. Structural OCT scans were obtained using the Heidelberg Spectralis HRA + OCT device. Manual segmentation of MAs was performed by five masked readers, with an expert grader ensuring consistent labeling. Two deep learning models, YOLO (You Only Look Once) and DETR (DEtection TRansformer), were trained using the annotated OCT images. Detection and classification performance were evaluated using the area under the receiver operating characteristic (ROC) curves.

RESULTS: The YOLO model performed poorly with an AUC of 0.35 for overall MA detection, with AUCs of 0.33 and 0.24 for hyperreflective and hyporeflective MAs, respectively. The DETR model had an AUC of 0.86 for overall MA detection, but AUCs of 0.71 and 0.84 for hyperreflective and hyporeflective MAs, respectively. Post-hoc review revealed that discrepancies between automated and manual grading were often due to the automated method's selection of normal retinal vessels.

CONCLUSIONS: The choice of deep learning model is critical to achieving accuracy in detecting and classifying MAs in structural OCT images. An automated approach may assist clinicians in the early detection and monitoring of diabetic retinopathy, potentially improving patient outcomes.

PMID:39789187 | DOI:10.1038/s41433-024-03585-1

Sci Rep. 2025 Jan 10;15(1):1554. doi: 10.1038/s41598-025-85248-z.

ABSTRACT

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. Nevertheless, most previously recommended IDSs exhibit a notable false alarm rate. To mitigate this challenge, exploring deep learning methodologies emerges as a viable solution, leveraging their demonstrated efficacy across various domains. Hence, this article proposes an optimized Long Short-Term Memory (LSTM) for identifying anomalies in network traffic. The presented model uses three optimization methods, i.e., Particle Swarm Optimization (PSO), JAYA, and Salp Swarm Algorithm (SSA), to optimize the hyperparameters of LSTM. In this study, NSL KDD, CICIDS, and BoT-IoT datasets are taken into consideration. To evaluate the efficacy of the proposed model, several indicators of performance like Accuracy, Precision, Recall, F-score, True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic curve (ROC) have been chosen. A comparative analysis of PSO-LSTMIDS, JAYA-LSTMIDS, and SSA-LSTMIDS is conducted. The simulation results demonstrate that SSA-LSTMIDS surpasses all the models examined in this study across all three datasets.

PMID:39789143 | DOI:10.1038/s41598-025-85248-z

Sci Rep. 2025 Jan 9;15(1):1437. doi: 10.1038/s41598-024-84386-0.

ABSTRACT

The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas. To ensure strong and reliable tumor segmentation, we employ 2D volumetric convolution neural network architectures that utilize a majority rule. This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. The model presented in this study was successful in segmenting brain tumors and predicting the outcome of enhancing tumor and real enhancing tumor. The model was evaluated using the BRATS2020 benchmarks dataset, and the obtained results are quite satisfactory and promising.

PMID:39789043 | DOI:10.1038/s41598-024-84386-0

Lung. 2025 Jan 9;203(1):25. doi: 10.1007/s00408-024-00778-z.

ABSTRACT

PURPOSE: In the INBUILD trial in patients with progressive pulmonary fibrosis (PPF), nintedanib slowed the decline in forced vital capacity (FVC) versus placebo, with a safety profile characterised mainly by gastrointestinal events. INBUILD-ON, the open-label extension of INBUILD, assessed the safety of nintedanib during longer-term treatment. Data on FVC were collected.

STUDY DESIGN AND METHODS: Adverse events and changes in FVC in INBUILD-ON were assessed descriptively in all patients and in two subgroups: patients who received nintedanib in INBUILD and continued nintedanib in INBUILD-ON ("continued nintedanib" group) (n = 212) and patients who received placebo in INBUILD and initiated nintedanib in INBUILD-ON ("initiated nintedanib" group) (n = 222). Changes in FVC were based on observed values.

RESULTS: Median exposure to nintedanib in INBUILD-ON was 22.0 months. Diarrhoea was the most frequent adverse event. Amongst patients who had diarrhoea, 90.0% experienced only events of mild or moderate severity. Adverse events led to discontinuation of nintedanib at a rate of 16.7 per 100 patient-years. Serious and fatal adverse events were reported at rates of 37.2 and 9.5 per 100 patient-years. Mean (SE) changes in FVC from baseline to week 48 were - 71.6 (16.1) mL [- 128.5 (25.5) mL in continued nintedanib group (n = 106), - 14.8 (18.2) mL in initiated nintedanib group (n = 106)].

CONCLUSION: The safety profile of nintedanib in INBUILD-ON was consistent with that in INBUILD. Change in FVC in INBUILD-ON was consistent with decline in FVC in the nintedanib group of INBUILD. These results support the use of nintedanib in the long-term treatment of PPF.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; NCT03820726; registered January 29, 2019.

PMID:39789408 | DOI:10.1007/s00408-024-00778-z

Sci Rep. 2025 Jan 9;15(1):1479. doi: 10.1038/s41598-025-85135-7.

ABSTRACT

Automated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing. Opacity-based fibrotic lung (OFL) was measured by a second tool that performs a simpler binary classification of tissue into either normal or opacified lung and was originally developed for quantifying pneumonia. Differences in quantification of MTFL and OFL were assessed by Mann-Whitney U-test and Pearson correlation (r). Also, correlation with spirometry parameters (percent predicted total lung capacity (TLC), percent predicted vital capacity (VC), percent predicted forced expiratory volume in 1 s (FEV1), diffusing capacity of the lungs for carbon monoxide (DLCO), partial pressure of oxygen (PO2) and carbon dioxide (PCO2)) were assessed by r. The prognostic values for 3-year patient survival of OFL, LSS and MTFL were investigated by multivariable Cox-proportional-hazards (CPH) models including sex, age and TLC and including sex, age and VC. Also, Kaplan-Meier analysis with log rank test between subgroups separated by median OFL and MTFL were conducted. No significant difference between OFL and MTFL was observed (median and interquartile range: OFL = 29% [20-38%], MTFL = 31% [19-45%]; P = 0.44). For OFL significant correlation was observed to MTFL (r = 0.93, P < 0.01) and VC (r=-0.50, P = 0.03). For MTFL no significant correlation to spirometry parameters was found. The total time for one analysis was lower for the automated MTFL (MTFL: 313 ± 25s vs. OFL: 612 ± 61s, P < 0.001). Both analyses were significant predictors in the multivariable CPH analysis including TLC (hazard-ratios: MTFL 1.03 [1.01-1.06], P = 0.02; OFL 1.03 [1.00-1.06], P = 0.03). No parameter was a significant predictor in the CPH models including VC (hazard-ratios: MTFL 1.01 [0.98-1.04], P = 1; OFL 1.01 [0.97-1.05], P = 1). OFL showed significance in Kaplan-Meier analysis (MTFL: P = 0.17; OFL: P = 0.03). Using a simple opacity-based quantification of pulmonary fibrosis in IPF patients displayed similar results and prognostic value compared to a more complex multi-texture based analysis.

PMID:39789082 | DOI:10.1038/s41598-025-85135-7

Eur Respir J. 2025 Jan 9:2400615. doi: 10.1183/13993003.00615-2024. Online ahead of print.

ABSTRACT

RATIONALE: Although a relationship between the Gas6/AXL pathway and pulmonary fibrosis (PF) has been suggested, the precise mechanisms and clinical implications of the AXL pathway in idiopathic pulmonary fibrosis (IPF) are still unclear.

METHODS: Constitutive and conditional AXL-knockout mice were generated and injected with bleomycin (BLM) to induce pulmonary fibrosis. The expression of AXL and macrophage subtypes in BLM-injected mice and patients with IPF was analysed using flow cytometry. The therapeutic effects of the AXL inhibitors were examined.

RESULTS: AXL-deficient mice were resistant to BLM-induced pulmonary fibrosis and had a lower degree of M2-like macrophage differentiation than wild-type mice. Interestingly, AXL expression in monocytes was enhanced according to the progression of BLM-induced pulmonary fibrosis (PF), and these results were especially prominent in Ly6Chigh monocytes. Gene silencing or inhibitor treatment with AXL inhibited the differentiation of M2-like macrophages during bone marrow-derived macrophage (BMDMs) differentiation. These results were confirmed through experiments using AXLfl/flLysMCre+ mice and systems with depletion and reconstitution of macrophages. In line with these results, patients with severe IPF had higher AXL expression in monocytes, high GAS6 levels, and an enhanced population of M2-like macrophages than those with mild IPF. Lastly, treatment with AXL inhibitors ameliorated BLM-induced PF and improved survival rate.

CONCLUSIONS: The AXL pathway in classical monocytes contributed to PF progression through the induction of M2-like macrophage differentiation. Therefore, targeting AXL may be a promising therapeutic option for PF.

PMID:39788632 | DOI:10.1183/13993003.00615-2024

Nature. 2025 Jan 9. doi: 10.1038/s41586-024-08560-0. Online ahead of print.

NO ABSTRACT

PMID:39789339 | DOI:10.1038/s41586-024-08560-0

Nat Cell Biol. 2025 Jan 9. doi: 10.1038/s41556-024-01595-5. Online ahead of print.

ABSTRACT

The nuclear matrix, a proteinaceous gel composed of proteins and RNA, is an important nuclear structure that supports chromatin architecture, but its role in human pluripotent stem cells (hPSCs) has not been described. Here we show that by disrupting heterogeneous nuclear ribonucleoprotein U (HNRNPU) or the nuclear matrix protein, Matrin-3, primed hPSCs adopted features of the naive pluripotent state, including morphology and upregulation of naive-specific marker genes. We demonstrate that HNRNPU depletion leads to increased chromatin accessibility, reduced DNA contacts and increased nuclear size. Mechanistically, HNRNPU acts as a transcriptional co-factor that anchors promoters of primed-specific genes to the nuclear matrix with POLII to promote their expression and their RNA stability. Overall, HNRNPU promotes cell-type stability and when reduced promotes conversion to earlier embryonic states.

PMID:39789220 | DOI:10.1038/s41556-024-01595-5

Nat Cancer. 2025 Jan 9. doi: 10.1038/s43018-024-00846-6. Online ahead of print.

NO ABSTRACT

PMID:39789180 | DOI:10.1038/s43018-024-00846-6

Sci Rep. 2025 Jan 9;15(1):1412. doi: 10.1038/s41598-025-85580-4.

ABSTRACT

Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives.We analyzed a Huntington's disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington's disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation.In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

PMID:39789061 | DOI:10.1038/s41598-025-85580-4

Nat Commun. 2025 Jan 10;16(1):559. doi: 10.1038/s41467-025-55818-w.

ABSTRACT

Optogenetics is a valuable tool for studying the mechanisms of neurological diseases and is now being developed for therapeutic applications. In rodents and macaques, improved channelrhodopsins have been applied to achieve transcranial optogenetic stimulation. While transcranial photoexcitation of neurons has been achieved, noninvasive optogenetic inhibition for treating hyperexcitability-induced neurological disorders has remained elusive. There is a critical need for effective inhibitory optogenetic tools that are highly light-sensitive and capable of suppressing neuronal activity in deep brain tissue. In this study, we developed a highly sensitive moderately K+-selective channelrhodopsin (HcKCR1-hs) by molecular engineering of the recently discovered Hyphochytrium catenoides kalium (potassium) channelrhodopsin 1. Transcranial activation of HcKCR1-hs significantly prolongs the time to the first seizure, increases survival, and decreases seizure activity in several status epilepticus mouse models. Our approach for transcranial optogenetic inhibition of neural hyperactivity may be adapted for cell type-specific neuromodulation in both basic and preclinical settings.

PMID:39789018 | DOI:10.1038/s41467-025-55818-w

Cell Host Microbe. 2025 Jan 8;33(1):119-136.e11. doi: 10.1016/j.chom.2024.12.006.

ABSTRACT

Polycystic ovary syndrome (PCOS) affects 6%-10% of women of reproductive age and is known to be associated with disruptions in the gut bacteria. However, the role of the gut mycobiota in PCOS pathology remains unclear. Using culture-dependent and internal transcribed spacer 2 (ITS2)-sequencing methods, we discovered an enrichment of the gut-colonizable fungus Aspergillus tubingensis in 226 individuals, with or without PCOS, from 3 different geographical areas within China. Colonization of mice with A. tubingensis led to a PCOS-like phenotype due to inhibition of Aryl hydrocarbon receptor (AhR) signaling and reduced interleukin (IL)-22 secretion in intestinal group 3 innate lymphoid cells (ILC3s). By developing a strain-diversity-based-activity metabolite screening workflow, we identified secondary metabolite AT-C1 as an endogenous AhR antagonist and a key mediator of PCOS. Our findings demonstrate that an intestinal fungus and its secondary metabolite play a critical role in PCOS pathogenesis, offering a therapeutic strategy for improving the management of the disease.

PMID:39788092 | DOI:10.1016/j.chom.2024.12.006

Curr Opin Microbiol. 2025 Jan 8;83:102578. doi: 10.1016/j.mib.2024.102578. Online ahead of print.

ABSTRACT

The gut microbiome impacts human health in direct and indirect ways. While many associations have been discovered between specific microbiome compositions and diseases, establishing causality, understanding the underlying mechanisms, and developing successful microbiome-based therapies require novel experimental approaches. In this opinion, we discuss how in vitro cultivation of diverse communities enables systematic investigation of the individual and collective functions of gut microbes. Up to now, the field has relied mostly on simple, bottom-up assembled synthetic communities or more complex, undefined stool-derived communities. Although powerful for dissecting interactions and mapping causal effects, these communities suffer either from ignoring the complexity, diversity, coevolution, and dynamics of natural communities or from lack of control of community composition. These limitations can be overcome in the future by establishing personalized culture collections from stool samples of different donors and assembling personalized communities to investigate native interactions and ecological relationships in a controlled manner.

PMID:39787728 | DOI:10.1016/j.mib.2024.102578

J Transl Med. 2025 Jan 9;23(1):37. doi: 10.1186/s12967-024-05985-z.

ABSTRACT

BACKGROUND: A novel anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate (ADC) GQ1001 was assessed in patients with previously treated HER2 positive advanced solid tumors in a global multi-center phase Ia dose escalation trial.

METHODS: In this phase Ia trial, a modified 3 + 3 study design was adopted during dose escalation phase. Eligible patients were enrolled, and GQ1001 monotherapy was administered intravenously every 3 weeks. The starting dose was 1.2 mg/kg, followed by 2.4, 3.6, 4.8, 6.0, 7.2 and 8.4 mg/kg. Extra patients were enrolled into 6.0, 7.2, and 8.4 mg/kg cohorts as dose expansion phase. The primary endpoints were safety and to determine the maximum tolerated dose (MTD) based on dose limiting toxicities (DLTs). Pharmacokinetics and anti-tumor efficacy of GQ1001 were assessed. The plasma concentration of free DM1, the payload of GQ1001, was quantitated.

RESULTS: A total of 32 patients were enrolled, predominantly in breast (9), gastric or gastro-esophageal junction (9) and salivary gland cancer (4). Median number of prior-line of therapies was 3 (0-11) and 37.5% patients received ≥ 2 lines of anti-HER2 therapies. No DLT was observed during dose escalation. MTD was not reached and dose recommended for dose expansion (DRDE) was determined as 8.4 mg/kg. Grade ≥ 3 treatment-related adverse events rate was 28.1% (9/32) and platelet count decreased (4/32, 12.5%) was the most common one. No drug-related death was observed. Objective response rate and disease control rate of 15 evaluable patients in 7.2 mg/kg and 8.4 mg/kg cohorts were 40.0% (6/15) and 60.0% (9/15). Pharmacokinetics analysis showed low exposure and accumulation of free DM1 in circulation.

CONCLUSION: GQ1001 is well tolerated and shows promising efficacy in previously treated HER2-positive advanced solid tumors. DRDE was determined as 8.4 mg/kg for following trials. Trial registration NCT, NCT04450732, Registered 23 June 2020, https://clinicaltrials.gov/study/NCT04450732.

PMID:39789619 | DOI:10.1186/s12967-024-05985-z