Pharmacogenomics J. 2025 Apr 9;25(3):8. doi: 10.1038/s41397-025-00368-z.

ABSTRACT

This review offers an overview of advanced in silico methods crucial for drug discovery, emphasizing their integration with data science, and investigates the effectiveness of data science, machine learning, and artificial intelligence via a thorough meta-analysis of existing technologies. This meta-analysis aims to rank these technologies based on their applications and accessibility of knowledge. Initially, a search strategy yielded 900 papers, which were then refined into two subsets: the top 300 most-cited papers since 2000 and papers selected for systematic review based on high impact. From these, 97 articles were identified for discussion, categorized by their influence on society. The focus remains on the qualitative impact of these disciplines rather than solely on metrics like new drug approvals. Ultimately, the review underscores the role of big data in enhancing our comprehension of drug candidate trajectories from development to commercialization, utilizing information stored in publicly available databases to chemical space. Graphical extrapolation of some keywords (Drug Discovery; Big Data; Database; Metadata) discussed in this article and their evolution (in terms of absolute items that are available) by time.

PMID:40204715 | DOI:10.1038/s41397-025-00368-z

Mol Metab. 2025 Apr 7:102136. doi: 10.1016/j.molmet.2025.102136. Online ahead of print.

ABSTRACT

Recent modifications to glucagon-like peptide 1 (GLP-1), known for its insulinotropic and satiety-inducing effects, have focused on conjugating small molecules to enable selective delivery into GLP-1R+ tissues to achieve targeted synergy and improved metabolic outcomes. Despite continued advancements in GLP-1/small molecule conjugate strategies, the intracellular mechanisms facilitating concurrent GLP-1R signaling and small molecule cargo release remain poorly understood. We evaluate an estradiol (E2)-conjugated GLP-1 (GLP-1-CEX/E2) for relative differences in GLP-1R signaling and trafficking, and elucidate endolysosomal dynamics that lead to estrogenic activity using various live-cell, reporter, imaging, and mass-spectrometry techniques. We find GLP-1-CEX/E2 does not differentially activate or traffic the GLP-1R relative to its unconjugated GLP-1 backbone (GLP-1-CEX), but uniquely internalizes the E2 moiety and stimulates estrogenic signaling. Endolysosomal pH-dependent proteolytic activity likely mediates E2 moiety liberation, as evidenced by clear amplification in estrogenic activity following co-administration with lysosomal VATPase activator EN6. The hypothesized liberated metabolite from GLP-1-CEX/E2, E2-3-ether, exhibits partial estrogenic efficacy through ERα, and is predisposed toward estrone-3-sulfate conversion. Finally, we identify relative increases in intracellular E2, estrone, and estrone-3-sulfate following GLP-1-CEX/E2 incubation in GLP-1R+ cells, demonstrating proof-of-principle for desired cargo release. Together, our data suggest that GLP-1-CEX/E2 depends on GLP-1R trafficking and lysosome acidification for estrogenic efficacy, with a likely conversion of the liberated E2-3-ether metabolite into estrone-3-sulfate, resulting in residual downstream flux into active estradiol. Our current findings aim to improve the understanding of small molecule targeting and the efficacy behind GLP-1/small molecule conjugates.

PMID:40204014 | DOI:10.1016/j.molmet.2025.102136

BMC Microbiol. 2025 Apr 9;25(1):203. doi: 10.1186/s12866-025-03920-w.

ABSTRACT

BACKGROUND: Pseudomonas aeruginosa is a major pathogen in cystic fibrosis (CF), where chronic and intermittent infections significantly affect patient outcomes. This study aimed to investigate the molecular epidemiology of P. aeruginosa in CF patients from the Brazilian Amazon, focusing on genotypic diversity, resistance profiles, and virulence factors.

METHODS: A cross-sectional study included 72 P. aeruginosa isolates from 44 CF patients treated at a regional reference center between 2018 and 2019. Antimicrobial susceptibility patterns were determined using VITEK-2 system and Kirby-Bauer disk diffusion. Virulotypes were defined by molecular detection of exoS, exoU, exoT, exoY, algU, and algD genes. Genetic diversity was assessed using multilocus sequence typing (MLST). Demographic data, clinical severity, and spirometry results were also collected.

RESULTS: Among the patients, 54.55% experienced intermittent infections, while 45.45% had chronic infections. Chronic infections were associated with older age, lower FEV1, and reduced Shwachman-Kulczycki scores. Multidrug resistance was observed in 15.3% of isolates, particularly against ciprofloxacin and piperacillin/tazobactam. The exoU gene was present in 55.56% of isolates, an uncommon finding in CF populations. High genetic diversity was evident, with 37 sequence types (STs), including 14 novel STs. High-risk clones (HRCs) constituted 25% of isolates, with ST274 being the most prevalent (12.5%). Longitudinal analysis revealed transient colonization in intermittent infections, while chronic infections were dominated by stable clones.

CONCLUSION: This study highlights the molecular and clinical dynamics of P. aeruginosa in CF patients from the Brazilian Amazon. Chronic infections were linked to severe lung impairment , while intermittent infections were dominated by HRCs. These findings underscore the need for robust genotypic surveillance to mitigate the burden of P. aeruginosa in CF populations.

PMID:40205346 | DOI:10.1186/s12866-025-03920-w

Respir Med. 2025 Apr 7:108086. doi: 10.1016/j.rmed.2025.108086. Online ahead of print.

ABSTRACT

This study evaluates the clinical impact of transitioning from the GLI 2012 to the race-neutral GLI 2022 spirometry equations in people with cystic fibrosis (pwCF). Spirometry data from a large adult CF centre showed an increase in average ppFEV1 (71.1% to 75%, p<0.01), with White patients showing the greatest change (4.56%). Fewer patients met lung transplantation thresholds, and 1.7% became newly eligible for clinical trials, while 7% became ineligible. These findings suggest the need for further research on the long-term implications of GLI 2022 across respiratory conditions.

PMID:40204244 | DOI:10.1016/j.rmed.2025.108086

J Transl Med. 2025 Apr 9;23(1):412. doi: 10.1186/s12967-025-06428-z.

ABSTRACT

BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical early symptoms, causing patients to miss optimal treatment opportunities. Traditional endoscopic and pathological diagnostic processes are highly dependent on expert experience, facing problems such as high misdiagnosis rates and significant inter-observer variations. With the development of artificial intelligence (AI) technologies such as deep learning, real-time lesion detection with endoscopic assistance and automated pathological image analysis have shown potential in improving diagnostic accuracy and efficiency. However, relevant applications still face challenges including insufficient data standardization, inadequate interpretability, and weak clinical validation.

OBJECTIVE: This study aims to systematically review the current applications of artificial intelligence in diagnosing malignant digestive tract tumors, focusing on the progress and bottlenecks in two key areas: endoscopic examination and pathological diagnosis, and to provide feasible ideas and suggestions for subsequent research and clinical translation.

METHODS: A systematic literature search strategy was adopted to screen relevant studies published between 2017 and 2024 from databases including PubMed, Web of Science, Scopus, and IEEE Xplore, supplemented with searches of early classical literature. Inclusion criteria included studies on malignant digestive tract tumors such as esophageal cancer, gastric cancer, or colorectal cancer, involving the application of artificial intelligence technology in endoscopic diagnosis or pathological analysis. The effects and main limitations of AI diagnosis were summarized through comprehensive analysis of research design, algorithmic methods, and experimental results from relevant literature.

RESULTS: In the field of endoscopy, multiple deep learning models have significantly improved detection rates in real-time polyp detection, early gastric cancer, and esophageal cancer screening, with some commercialized systems successfully entering clinical trials. However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. In pathological analysis, using convolutional neural networks, multimodal pre-training models, etc., automatic tissue segmentation, tumor grading, and assisted diagnosis can be achieved, showing good scalability in interactive question-answering. Nevertheless, clinical implementation still faces obstacles such as non-uniform data standards, lack of large-scale prospective validation, and insufficient model interpretability and continuous learning mechanisms.

CONCLUSION: Artificial intelligence provides new technological opportunities for endoscopic and pathological diagnosis of malignant digestive tract tumors, achieving positive results in early lesion identification and assisted decision-making. However, to achieve the transition from research to widespread clinical application, data standardization, model reliability, and interpretability still need to be improved through multi-center joint research, and a complete regulatory and ethical system needs to be established. In the future, artificial intelligence will play a more important role in the standardization and precision management of diagnosis and treatment of digestive tract tumors.

PMID:40205603 | DOI:10.1186/s12967-025-06428-z

Br J Radiol. 2025 Apr 9:tqaf077. doi: 10.1093/bjr/tqaf077. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction(DLIR) in coronary computed tomography angiography(CCTA) for slender patients with body-mass-index (BMI)≤25kg/m2.

METHODS: Sixty patients for CCTA were randomly divided into two groups: group A with 120 kV and contrast agent dose of 0.8 ml/kg, and group B with 70 kV and contrast agent dose of 0.5 ml/kg.Group A used adaptive statistical iterative reconstruction-V(ASIR-V) with 50% strength level(50%ASIR-V) while group B used 50%ASIR-V, DLIR of low level(DLIR-L),DLIR of medium level(DLIR-M) and DLIR of high level(DLIR-H) for image reconstruction. The CT values and SD values of coronary arteries and pericardial fat were measured, and signal-to-noise ratio(SNR) and contrast-to-noise ratio(CNR) were calculated. The image quality was subjectively evaluated by two radiologists using a five-point scoring system. The effective radiation dose(ED) and contrast dose were calculated and compared.

RESULTS: Group B significantly reduced radiation dose by 75.6% and contrast dose by 32.9% compared to group A. Group B exhibited higher CT values of coronary arteries than group A, and DLIR-L, DLIR-M and DLIR-H in group B provided higher SNR values and CNR values and subjective scores, among which DLIR-H had the lowest noise and highest subjective scores.

CONCLUSION: Using 70 kV combined with DLIR significantly reduces radiation and contrast dose while improving image quality in CCTA for slender patients with DLIR-H having the best effect on improving image quality.

ADVANCES IN KNOWLEDGE: The 70 kV and DLIR-H may be used in CCTA for slender patients to significantly reduce radiation dose and contrast dose while improving image quality.

PMID:40205479 | DOI:10.1093/bjr/tqaf077

J Neuroeng Rehabil. 2025 Apr 9;22(1):79. doi: 10.1186/s12984-025-01605-z.

ABSTRACT

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients' clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide an overview of the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The 201 selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

PMID:40205472 | DOI:10.1186/s12984-025-01605-z

World J Surg Oncol. 2025 Apr 9;23(1):135. doi: 10.1186/s12957-025-03774-6.

ABSTRACT

BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.

PURPOSE: To develop and test a transformer model utilizing dual-energy computed tomography (DECT) for predicting LNM in patients with PDAC.

MATERIALS AND METHODS: This retrospective study examined patients who had undergone surgical resection and had pathologically confirmed PDAC, with DECT performed between August 2016 and October 2022. Six predictive models were constructed: a DECT report model, a clinical model, 100 keV DL model, 150 keV DL model, a combined 100 + 150 keV DL model, and a model that integrated clinical information with DL-derived signatures. Multivariable logistic regression analysis was employed to develop the integrated model. The efficacy of these models was assessed by comparing their areas under the receiver operating characteristic curve (AUC) using the Delong test. Survival analysis was conducted using Kaplan-Meier curves.

RESULTS: In brief, 223 patients (mean age, 57 years ± 11 standard deviation; 93 men) were evaluated. All patients were divided into training (n = 160) and test (n = 63) sets. Patients with LNM accounted for 96 of the 223 patients (43%). In the test set, the integrated model, which integrated DECT parameters such as IC and Z, CA- 199 levels, DECT reports, and DL signatures, demonstrated the highest performance in predicting LNM, with an AUC of 0.93. In contrast, the radiologists'assessment and the clinical model yielded AUCs of 0.60 and 0.62, respectively. The integrated model-predicted positive LNM was associated with worse overall survival (hazard ratio, 1.75; 95% confidence interval: 1.22 - 2.83; P =.023).

CONCLUSION: A transformer-based model outperformed radiologists and clinical model for prediction of LNM at DECT in patients with PDAC.

PMID:40205450 | DOI:10.1186/s12957-025-03774-6

BMC Cancer. 2025 Apr 9;25(1):648. doi: 10.1186/s12885-025-13611-4.

ABSTRACT

OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies.

METHODS: GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. Utilizing the genes from these key pathways, multi-omics bioinformatics methods were employed to refine the construction of a gene signature, termed responseScore, aimed at enhancing the precision of immunotherapy response predictions. Subsequently, responseScore was explored from the perspectives of prognosis, genetic variation, pathway enrichment, and the tumor microenvironment. Concurrently, the association among 13 genes contributing to responseScore and factors such as immunotherapy response, prognosis, and the tumor microenvironment was investigated. Among these genes, PSMB6 was subjected to an in-depth analysis of its biological effect through experimental approaches like transfection and co-culture.

RESULTS: In the finalized model utilizing GNNs, it has revealed an AUC of 0.854 within the training dataset and 0.824 within the testing set, pinpointing key pathways such as R-HSA-70,268. The indicator named as responseScore excelled in its predictive accuracy regarding immunotherapy response and patient prognosis. Investigations into genetic variation, pathway enrichment, tumor microenvironment disclosed a profound association between responseScore and the enhancement of immune cell infiltration and anti-tumor immunity. A negative correlation was observed between the expression of PSMB6 and immune genes, with elevated PSMB6 expression correlating with poor prognosis. ELISA detection after co-cultivation experiments revealed significant reductions in the levels of cytokines IL-6 and IL-1β in specimens from the PCDH-PSMB6 group.

CONCLUSION: The GNNs prediction model and the responseScore developed in this research effectively indicate the immunotherapy response and prognosis for patients with skin melanoma. Additionally, responseScore provides insights into the tumor microenvironment and the characteristics of tumor immunity of melanoma. Thirteen genes identified in this study show promise as potential tumor markers or therapeutic targets. Notably, PSMB6 emerges as a potential therapeutic target for skin melanoma, where its elevated expression exhibits an inhibitory effect on the tumor immunity.

PMID:40205338 | DOI:10.1186/s12885-025-13611-4

J Imaging Inform Med. 2025 Apr 9. doi: 10.1007/s10278-025-01491-w. Online ahead of print.

ABSTRACT

This study aimed to develop and validate a predictive model for early recurrence of high-grade glioma (HGG) within 180 days, assess the prognostic value of preoperative and postoperative temporalis muscle metrics (area and thickness), and explore their significance in postoperative follow-up. Seventy-one molecularly confirmed HGG patients were included, with data sourced from local data and TCIA (The Cancer Imaging Archive) RHUH-GBM (Río Hortega University Hospital Glioblastoma) dataset. Tumor segmentation was performed using deep learning, and radiomic features were extracted following comparison with manual segmentation. Feature selection was conducted using mutual information and recursive feature elimination. A comprehensive model integrating 3D tumor radiomics and temporalis muscle metrics was developed and compared with a tumor-only model to identify the optimal predictive framework. SHAP analysis was used to evaluate model interpretability and feature importance. The TM_Tumor_HistGradientBoosting model, incorporating 16 features including temporalis muscle metrics, outperformed the tumor-only model in accuracy (0.89), recall (0.87), and F1 score (0.88). SHAP analysis highlighted that preoperative temporalis muscle cross-sectional area was strongly associated with early recurrence risk, while postoperative temporalis muscle thickness significantly contributed to recurrence prediction. Combining temporalis muscle metrics with preoperative tumor MRI substantially improved the accuracy of early recurrence prediction in HGG. Temporalis muscle metrics serve as objective and sustainable prognostic indicators with significant clinical value in postoperative follow-up.

PMID:40205255 | DOI:10.1007/s10278-025-01491-w

Nature. 2025 Apr;640(8058):470-477. doi: 10.1038/s41586-025-08829-y. Epub 2025 Apr 9.

ABSTRACT

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset2. Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.

PMID:40205215 | DOI:10.1038/s41586-025-08829-y

Nature. 2025 Apr;640(8058):368-374. doi: 10.1038/s41586-025-08854-x. Epub 2025 Apr 9.

ABSTRACT

Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning1-4, as a path towards enhanced energy efficiency and performance5-14. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore's law and Dennard scaling era15-19. Despite these advances, no photonic chip has achieved the precision necessary for practical AI applications, and demonstrations have been limited to simplified benchmark tasks. Here we introduce a photonic AI processor that executes advanced AI models, including ResNet3 and BERT20,21, along with the Atari deep reinforcement learning algorithm originally demonstrated by DeepMind22. This processor achieves near-electronic precision for many workloads, marking a notable entry for photonic computing into competition with established electronic AI accelerators23 and an essential step towards developing post-transistor computing technologies.

PMID:40205212 | DOI:10.1038/s41586-025-08854-x

J Med Ultrason (2001). 2025 Apr 9. doi: 10.1007/s10396-025-01528-1. Online ahead of print.

ABSTRACT

PURPOSE: In recent years, computer-aided diagnosis (CAD) using deep learning methods for medical images has been studied. Although studies have been conducted to classify ultrasound images of tumors of the liver into four categories (liver cysts (Cyst), liver hemangiomas (Hemangioma), hepatocellular carcinoma (HCC), and metastatic liver cancer (Meta)), no studies with additional information for deep learning have been reported. Therefore, we attempted to improve the classification accuracy of ultrasound images of hepatic tumors by adding hepatitis virus infection information to deep learning.

METHODS: Four combinations of hepatitis virus infection information were assigned to each image, plus or minus HBs antigen and plus or minus HCV antibody, and the classification accuracy was compared before and after the information was input and weighted to fully connected layers.

RESULTS: With the addition of hepatitis virus infection information, accuracy changed from 0.574 to 0.643. The F1-Score for Cyst, Hemangioma, HCC, and Meta changed from 0.87 to 0.88, 0.55 to 0.57, 0.46 to 0.59, and 0.54 to 0.62, respectively, remaining the same for Hemangioma but increasing for the rest.

CONCLUSION: Learning hepatitis virus infection information showed the highest increase in the F1-Score for HCC, resulting in improved classification accuracy of ultrasound images of hepatic tumors.

PMID:40205118 | DOI:10.1007/s10396-025-01528-1

NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3.

ABSTRACT

Transthoracic echocardiography (TTE), commonly used for initial screening of pulmonary hypertension (PH), often lacks sufficient accuracy. To address this gap, we developed and validated a multimodal fusion model for improved PH screening (MMF-PH). The study was registered in the ClinicalTrials.gov (NCT05566002, 09/30/2022). The MMF-PH underwent extensive training, validation, and testing, including comparisons with TTE and evaluations across various patient subgroups to assess robustness and reliability. We analyzed 2451 patients who underwent right heart catheterization, supplemented by a prospective dataset of 477 patients and an external dataset. The MMF-PH demonstrated robust performance across different datasets. The model outperformed TTE in terms of specificity and negative predictive value across all test datasets. An ablation study using the external test dataset confirmed the essential role of each module in the MMF-PH. The MMF-PH significantly advances PH detection, offering robust and reliable diagnostic accuracy across diverse patient populations and clinical settings.

PMID:40205021 | DOI:10.1038/s41746-025-01593-3

Respir Res. 2025 Apr 9;26(1):128. doi: 10.1186/s12931-025-03201-0.

ABSTRACT

BACKGROUND: Cellular senescence is one of the key steps in the progression of pulmonary fibrosis, and the senescence of type II alveolar epithelial cells (AEC IIs) may potentially accelerate the progression of pulmonary fibrosis. However, the molecular mechanisms underlying cellular senescence in pulmonary fibrosis remain unclear.

METHODS: The researchers first conducted in vitro experiments to investigate whether AEC IIs cultured on high matrix stiffness would lead to cellular senescence. Next, samples from mouse pulmonary fibrosis models and clinical idiopathic pulmonary fibrosis (IPF) patients were tested to observe extracellular matrix deposition, lamin A/C levels, and cellular senescence status in lung tissue. Construct lamin A/C knockdown and overexpression systems separately in AEC IIs, and observe whether changes in lamin A/C levels lead to cellular senescence. Further explore the degradation mechanism of lamin A/C using protein degradation inhibitors.

RESULTS: In vitro experiments have found that high matrix stiffness promotes senescence of AEC IIs. In a mouse model of pulmonary fibrosis, AEC IIs were found to exhibit significant cellular senescence on day 21. In clinical IPF samples, it was found that senescent cells expressed low levels of lamin A/C. In the lamin A/C SiRNA knockdown system, it was further confirmed that AEC IIs with low levels of lamin A/C are more prone to cellular senescence. Under high matrix stiffness, lamin A/C in AEC IIs is degraded through the autophagy lysosome pathway. The use of chloroquine can effectively alleviate cellular senescence.

CONCLUSIONS: High matrix stiffness degrades lamin A/C in pulmonary fibrosis through lysosomal degradation pathways, promoting AEC II senescence. Inhibition the degradation of lamin A/C could alleviate AEC II senescence.

PMID:40205454 | DOI:10.1186/s12931-025-03201-0

Arerugi. 2025;74(2):83-88. doi: 10.15036/arerugi.74.83.

ABSTRACT

A 74-year-old man was treated with nintedanib for idiopathic pulmonary fibrosis (IPF). Thirty-six days after starting to take nintedanib, he admitted to our hospital due to respiratory failure with ground-glass opacities and mediastinal emphysema on chest CT. Acute exacerbation of IPF was suspected. Because lung injury due to nintedanib was suspected, nintedanib was discontinued. Although he was treated with pulse corticosteroid therapy, interstitial pneumonia and mediastinal emphysema deteriorated. Despite continuing corticosteroid therapy, he died 21 days after admission due to respiratory failure. Subsequently drug lymphocyte stimulation test for nintedanib was found to be positive. We should pay attention to possibility of drug-induced lung injury caused by nintedanib.

PMID:40204485 | DOI:10.15036/arerugi.74.83

Theor Appl Genet. 2025 Apr 9;138(5):95. doi: 10.1007/s00122-025-04877-0.

ABSTRACT

Understanding the genetic basis of nutrient accumulation in wheat is crucial for improving its nutritional content and addressing global food security challenges. Here, we identified stable pleiotropic loci controlling the accumulation of 13 nutritional elements in wheat across diverse environments using a large wheat population of 1470 individuals. Our analysis revealed significant variability in SNP-based heritability values across 13 essential elements. Genetic correlations among elements uncovered complex relations, with positive correlations observed within two distinct groups, where calcium (Ca), cobalt (Co), potassium (K), and sodium (Na) formed one group, and copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), phosphorus (P), and zinc (Zn) formed the other. Negative correlations were observed among elements across both groups. Through MetaGWAS analysis, we identified stable QTL associated with individual elements and elements with high positive correlations. We identified 67 stable QTL across environments that are independent from grain yield, of which 56 were detected using the MetaGWAS analysis indicating their pleiotropic effect on multiple elements. A major QTL on chromosome 7D that can shift the phenotype up to one standard deviation compared to the mean phenotype in the population exhibited differential effects on multiple elements belonging to both groups. Our findings offer novel insights into the genetic architecture of nutrient accumulation in wheat and have practical implications for breeding programmes aimed at enhancing multiple nutrients simultaneously. By targeting stable QTL, breeders can develop wheat varieties with improved nutritional profiles, contributing to global food security and human health.

PMID:40205176 | DOI:10.1007/s00122-025-04877-0

Nature. 2025 Apr 9. doi: 10.1038/s41586-025-08878-3. Online ahead of print.

ABSTRACT

Human cells consist of a complex hierarchy of components, many of which remain unexplored1,2. Here we construct a global map of human subcellular architecture through joint measurement of biophysical interactions and immunofluorescence images for over 5,100 proteins in U2OS osteosarcoma cells. Self-supervised multimodal data integration resolves 275 molecular assemblies spanning the range of 10-8 to 10-5 m, which we validate systematically using whole-cell size-exclusion chromatography and annotate using large language models3. We explore key applications in structural biology, yielding structures for 111 heterodimeric complexes and an expanded Rag-Ragulator assembly. The map assigns unexpected functions to 975 proteins, including roles for C18orf21 in RNA processing and DPP9 in interferon signalling, and identifies assemblies with multiple localizations or cell type specificity. It decodes paediatric cancer genomes4, identifying 21 recurrently mutated assemblies and implicating 102 validated new cancer proteins. The associated Cell Visualization Portal and Mapping Toolkit provide a reference platform for structural and functional cell biology.

PMID:40205054 | DOI:10.1038/s41586-025-08878-3

Nature. 2025 Apr 9. doi: 10.1038/s41586-025-08864-9. Online ahead of print.

ABSTRACT

Given the large number of genes significantly associated with risk for neuropsychiatric disorders, a critical unanswered question is the extent to which diverse mutations-sometimes affecting the same gene-will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, which encodes a presynaptic cell-adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain and are differentially affected by unique (non-recurrent) deletions1. We contrast the cell-type-specific effect of patient-specific mutations in NRXN1 using human-induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Through distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/- deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Reciprocal isogenic manipulations causally demonstrate that aberrant splicing drives these changes in synaptic activity. For NRXN1 deletions, and perhaps more broadly, precision medicine will require stratifying patients based on whether their gene mutations act through LOF or GOF mechanisms, to achieve individualized restoration of NRXN1 isoform repertoires by increasing wild-type and/or ablating mutant isoforms. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disorders, our findings add nuance to future considerations of precision medicine.

PMID:40205044 | DOI:10.1038/s41586-025-08864-9

Cell Death Differ. 2025 Apr 9. doi: 10.1038/s41418-025-01502-x. Online ahead of print.

ABSTRACT

The stress-associated chaperone system is an actionable target in cancer therapies. It is ubiquitously upregulated in cancer tissues and enables tumorigenicity by stabilizing oncoproteins. Most inhibitors target the key component, heat-shock protein 90 (HSP90). Although HSP90 inhibitors are highly tumor-selective, they fail in clinical trials. These failures are partly due to interference with a negative regulatory feedback loop in the heat-shock response (HSR): in response to HSP90 inhibition, there is compensatory synthesis of stress-inducible chaperones, mediated by the transcription factor heat-shock-factor 1 (HSF1). We recently identified that wild-type p53 reduces the HSR by repressing HSF1 via a p21-CDK4/6-MAPK-HSF1 axis. Here, we test whether in HSP90-based therapies, simultaneous p53 activation or direct cell cycle inhibition interrupts the deleterious HSF1-HSR axis and improves the efficiency of HSP90 inhibitors. We found that the clinically relevant p53 activator Idasanutlin suppresses the HSF1-HSR activity in HSP90 inhibitor-based therapies. This combination synergistically reduces cell viability and accelerates cell death in p53-proficient colorectal cancer (CRC) cells, murine tumor-derived organoids, and patient-derived organoids (PDOs). Mechanistically, upon combination therapy, CRC cells upregulate p53-associated pathways, apoptosis, and inflammatory pathways. Likewise, in a CRC mouse model, dual HSF1-HSP90 inhibition represses tumor growth and remodels immune cell composition. Importantly, inhibition of the cyclin-dependent kinases 4/6 (CDK4/6) under HSP90 inhibition phenocopies synergistic repression of the HSR in p53-proficient CRC cells. Moreover, in p53-deficient CRC cells, HSP90 inhibition in combination with CDK4/6 inhibitors similarly suppresses the HSF1-HSR and reduces cancer growth. Likewise, p53-mutated PDOs respond to dual HSF1-HSP90 inhibition, providing a strategy to target CRC independent of the p53 status. In sum, we provide new options to improve HSP90-based therapies to enhance CRC therapies.

PMID:40204953 | DOI:10.1038/s41418-025-01502-x