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PubMed1979

Quantitative analysis of intermediary metabolism in Tetrahymena. Cells grown in proteose-peptone and resuspended in a defined nutrient-rich medium.

Tetrahymena pyriformis were grown to early-stationary phase and resuspended in a defined mixture containing glucose, fructose, ribose, glycerol, acetate, pyruvate, bicarbonate, glutamate, and hexanoate, with only one substrate labeled with 14C in any flask. Incorporation of label into CO2, glycogen, RNA, alanine, glutamate, glycine, lipid glycerol, and lipid fatty acids was measured 20, 40, and 60 min after the start of the incubation. To develop a model suitable for quantitative analysis of the data, it was necessary to join two preceding models, one for carbohydrate-metabolizing cells and one for acetate-metabolizing cells, eliminating the over-simplified sections of each. Equations were written and programmed for a digital computer to allow computation of the amount of label expected to be incorporated into any of the products measured for any given set of steady state flux values in the metabolic network. The model formed by simply joining the two preceding models did not yield satisfactory agreement with the complete data obtained in the present study, although each partial set of data could be fit well by the appropriate partial model. Analysis of the ways in which the model failed to yield good fits to the data indicated that another pool of P-enolpyruvate, of pyruvate, and of acetyl-CoA had to be added at the junction of the two models. The presence of such poolte into fatty acids as compared to the incorporation of label from glucose into fatty acids. A new model was therefore constructed which differed from the preceding model only in its structural organization at the level of P-enolpyruvate, pyruvate, and acetyl-CoA. The model is consistent with all known information on the compartmental structure of metabolism in Tetrahymena, on enzyme localization, and on the enzyme complement of this cell. Over 70 measurements of label incorporation into products were made at each time. These, plus a large number of "limit" measurements which constrain any possible solutions, were in sufficient excess of the 39 independent flux values to permit a stringent assessment of the model. A set of flux values was found which yielded a good fit to the data. These flux values therefore provide a quantitative description of metabolite flux in the intact cell during the slow adaptation to the nine-substrate mixture. The rates of utilization of glucose, fructose, glycerol, and ribose were in the ratio of about 10:1:0.33:0.16, i.e. fairly similar to the ratio observed with carbohydrate-metabolizing cells. Initial flux through phosphofructokinase is about 160 nmol/10(6) cells.h, increasing over 3-fold during tje jpir incubation. Initial flux through fructose-1,6-diphosphatase is about 110 nmol/10(6) cells.h and also increases almost 3-fold during the incubation. Thus net flux is glycolytic and increases 4-fold during the hour with a large amount of futile cycling at this step...

Acetates↗

PubMed2026

Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.

Humans↗

PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗

PubMed1978

[Synaptoarchitectonics of the ganglia of the celiac plexus in white rats].

In 50 intact white rats at the age of 6, 15, 23 and 30 months synapsoarchitectonics of the celiac plexus nodes was studied by an electron microscopy method. Peculiarities in synapsoarchitectonics are stipulated by pericaryon processes in neurons, some of them have no contacts with the axonal terminals, while others have contacts with the axonal terminals. The former include small (about 0.5 mkm) drop-like and large (up to 1.5 mkm) polymorphous processes within the limits of perisomatic membrane, as well as processes penetrating the neuronal capsule. All of them contain, in different combinations, vesicles, ribosomas, fibrillae, and the largest processes--small cisterns of granular cytoplasmatic network and single mitochondria. The processes of the first group are considered as original stages for the development of the second group processes. The latter are represented by different in size (about 1.0--2.0 mkm) in form (digital, cone-, pin-, goblet-shaped, cylindrical, branching) and in content formations. There is, as a rule, one contact on the processes of an uncomplicated form, while on the branching processes there can be up to three and more contacting axonal terminals. Peculiar features in the composition of the processes taken as a whole (specific forms, absence of dendritic tubes, sometimes numerous contacts with axonal terminals in spite of small size) distinguish them from newly forming dendritic processes and these formations are considered as independent specialized receptor apparatus in the pericaryon of neurons of the celiac plexus.

Animals↗

PubMed2023

H4C5 missense variant leads to a neurodevelopmental phenotype overlapping with Angelman syndrome.

Recurrent de novo missense variants in H4 histone genes have recently been associated with a novel neurodevelopmental syndrome that is characterized by intellectual disability and developmental delay as well as more variable findings that include short stature, microcephaly, and facial dysmorphisms. A 4-year-old male with autism, developmental delay, microcephaly, and a happy demeanor underwent evaluation through the Undiagnosed Disease Network. He was clinically suspected to have Angelman syndrome; however, molecular testing was negative. Genome sequencing identified the H4 histone gene variant H4C5 NM_003545.4: c.295T>C, p.Tyr99His, which parental testing confirmed to be de novo. The variant met criteria for a likely pathogenic classification and is one of the seven known disease-causing missense variants in H4C5. A comparison of our proband's findings to the initial description of the H4-associated neurodevelopmental syndrome demonstrates that his phenotype closely matches the spectrum of those reported among the 29 affected individuals. As such, this report corroborates the delineation of neurodevelopmental syndrome caused by de novo missense H4 gene variants. Moreover, it suggests that cases of clinically suspected Angelman syndrome without molecular confirmation should undergo exome or genome sequencing, as novel neurodevelopmental syndromes with phenotypes overlapping with Angelman continue to be discovered.

Male↗

PubMed1976

Centralized drug list production in a multihospital system.

A project established to maintain individualized station drug lists for a nationwide network of hospitals and clinics is described. The structure of the drug lists and procedures used in their production are described in detail. A central location controls keypunching, data processing and printing. The pharmacist at each station is responsible for drug list maintenance. Each staton's formulary is stored on digital magnetic tape. Sharing development costs by all stations and using a single printing facility resulted in a cost-effective system.

Computers↗

PubMed2026

Airway microbiome diversity, intramucosal bacteria, and spatial immunity in asthmatic adults and controls.

RATIONALE: Asthma is characterized by disruption of the thoracic airway mucosae and loss of microbial diversity. Spatial profiling of the mucosal transcriptome may systematically discover mechanisms for microbial influences on immunity. OBJECTIVES: We investigated relationships between clinical measures, microbial communities, and the host mucosal transcriptome within different strata of bronchial biopsies in subjects with and without asthma. METHODS: We performed bronchoscopy in 65 asthmatic adults and 44 healthy controls, quantifying bacterial operational taxonomic units (OTUs) in bronchial brushings by 16S ribosomal RNA (rRNA) gene amplicon sequences. Biopsy histologic features were scored blind to diagnosis. Following 16S rRNA in situ hybridization of 44 biopsies, bacterial foci were scored in epithelium, basement membrane, and stroma. Global human gene expression was quantified in epithelial and stromal compartments using digital spatial profiling. MEASUREMENTS AND MAIN RESULTS: Clinical asthma was independently predicted by basement membrane abnormalities (BaseMA), endobronchial bacterial diversity, and circulating eosinophil counts, but not by specific OTU abundances. 16S rRNA staining revealed bacteria within epithelium and mucosa of all biopsies. Intramucosal bacteria counts correlated negatively with spatially organized coexpression networks encoding antigen-specific immunity, neutrophil functions, and matrix activation, whereas BaseMA correlated positively with the adaptive immunity module. Eosinophil counts correlated with epithelial bacterial counts and senescence pathways. Clinical asthma was accompanied by upregulation of a regulatory T-cell network. CONCLUSIONS: Asthma and its related phenotypes are accompanied by complex mucosal events that extend beyond eosinophilic pathways. Components of diverse airway microbiota may modify immunity by beneficial interactions within the mucosa.

Humans↗

PubMed1975

Associative recall and formation of stable modes of activity in neural network models.

Models of neural networks with recurrent inhibition are studied, as well as one model which also includes recurrent excitation. The models are intended as possible descriptions of the cerebral cortex. Each network model is composed of neuron models called pyramidal cells and stellate cells in accordance with the names of two types of cells in the cortex. Inputs and outputs of the network are connected to the pyramidal cells while feedback is provided by the stellate cells. Connections within the network are random. During a learning phase the pyramidal cell excitatory synapses become facilitated according to a two-conditional facilitation rule. This is the basis of the model's ability for associative learning. The associative retrieval of information can be studied during a subsequent association phase. This has been done by simulation on a digital computer. It was shown that all of the models considered can be designed to perform a so-called decision-making function. This means that if the associating input pattern is similar to several patterns which occurred during learning the model can decide which similarity is greatest by responding with the appropriate associated pattern. The model also including recurrent excitation differs from the simpler models in that it can become stabilized in so-called stable modes of activity which are self-sustaining and remain even after the input has been turned off. Normally, only one stable mode can be active at a time. However, through careful choice of construction parameters it was possible to obtain a model in which a maximum of two stable modes could be activated independently of each other. Physiological and psychological interpretations are discussed and so are the limitations of the models, which are evident in certain situations.

Cerebral Cortex↗

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed1979

Correlation between nuclear morphology and rate of deoxyribonucleic acid synthesis in a normal cell line.

Chinese hamster fibroblasts were investigated for the existence of correlations between proliferative activity and nuclear morphology. As a proliferative parameter, the rate of DNA synthesis of individual cells was determined by quantitative 14C-autoradiography. In a second step the images of the Feulgen-stained nuclei were digitized for extraction of features of morphology and texture. These features were correlated with the corresponding DNA synthesis rate values. The following relationships were found: Round nuclei have higher rates of DNA synthesis than flat ones. The more chromatin is packed at the nuclear rim, possibly representing heterochromatin, the lower the rate of DNA synthesis. The DNA synthesis rate also correlates with the graininess of chromatin. Larger areas of condensed chromatin are associated with lower rate values. A fine and irregular network of chromatin, as is typical of immature cell types, is associated with a high rate of DNA synthesis. Although these results are presently confined to the cell line investigated, parallels seem to exist to other cell types, such as erythropoietic cells, which await further investigation.

Animals↗

PubMedDate not supplied

Gamma cameras--state of the art.

The growth of nuclear medicine has paralleled the development and acceptance of gamma cameras, which are stationary instruments that produce an image of an isotope distribution. The most successful gamma camera is based on a large, single crystal of the scintillator sodium iodide coupled to an array of photomultiplier tubes and electronic networks to determine the location of a gamma ray. Current cameras are capable of spatial resolution of 4.0 mm or better. Other types of gamma cameras have been developed, including the multi-crystal camera and a limited number of instruments using gas detectors and semiconductor detectors. The scintillation type of gamma camera will continue to be the dominant camera for the next 5 to 10 years, and will continue to gradually improve. Most significant improvements will result from application of digital electronics to position circuits.

Data Display↗

PubMedDate not supplied

Experience of transferring an integrated hospital-administration system from a CODASYL data-base to a standard MUMPS file structure.

University College Hospital, a teaching hospital situated in Central London, has been using computers since 1964. Development of an integrated on-line hospital administration computer system was started in 1974. By mid 1977, the components of the system which had been implemented included a patient Master Index, Registration of new patients, Waiting Lists, Bed State, Out-patient Appointments, X-ray, Microbiology and Disease Index. The system was implemented using the Rank Xerox Data Systems (CODASYL based data-base management software, with the majority of applications running on a terminal network supported by transaction processing programs. The experience gained during three years of using such a system is reviewed, with particular emphasis on the ways in which the system matched up in practice to the expected benefits of data-base management facilities. In early 1977, it was decided to replace the Rank Xerox mainframe with a minicomputer which was considered to be a more cost-effective solution to the hospital's computing requirements. The machine selected a PDP 11-70, is running the Digital Equipment Corporation's implementation of Standard MUMPS, an interpretive language developed in a hospital environment, with its own file management software. The decisions which were made in redesigning the existing computer systems for the new machine are discussed. The progress made to date in the transfer of applications from the mainframe to the mini is reviewed, and some of the features of the software available on each machine and its suitability for the implementation of on-line data management and retrieval systems are compared.

Computers↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed2026

Hormone priming and metabolic engineering of phytohormone crosstalk in rice under combined biotic and abiotic stresses: a multi-omics perspective for climate-resilient crop development.

Rice (Oryza sativa L.) is the caloric backbone for more than half of humanity, yet it remains one of the most vulnerable crops to the simultaneous biotic and abiotic stresses exacerbated by climate change. Phytohormone priming and the complex crosstalk networks governed by transcription factor hubs like WRKY, MYB, and NAC serve as the central adaptive mechanism for stress resilience. This review synthesizes how multi-omics integration, including spatial and single-cell transcriptomics, is resolving the molecular architecture of hormonal priming and epigenetic stress memory. We critically evaluate advanced metabolic engineering and genome-editing strategies such as CRISPR-Cas9, base/prime editing, and synthetic gene circuits that enable precision modifications to decouple stress tolerance from historical yield penalties. Furthermore, we discuss the emerging roles of microbiome-assisted priming via synthetic consortia and the application of artificial intelligence and digital twins (continuously updated computational models of crop physiology) for predictive stress management. By integrating these diverse technological pillars, we propose a systems-level roadmap for developing climate-resilient rice cultivars capable of maintaining yield stability across a volatile combinatorial stress landscape. This synthesis provides a framework for translating mechanistic hormonal insights into field-applicable cultivars to ensure global food security.

CRISPR↗

PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗

PubMed1978

Microfluorimetric scanning of sympathetic nerve fibers. An improved method to quantitate formaldehyde induced fluorescence of biogenic amines.

A two dimensional scanning procedure was developed for studying quantitative aspects of formaldehyde induced fluorescence (FIF) from noradrenaline containing nerve fibers. A computer (PDP 12) controlled scanning device was equipped with a cooled photomultiplier, a 0.5 micron scanning stage and a 0.5 micron measuring spot. Photodecomposition was reduced by a high scanspeed (100/sec) and a small excitation field. Digitized images were obtained with a high resolution. Data (10,000/scan) of large scan areas (2500 micron2) in the iris of the rat were transformed into fluorescence histograms, which allowed statistical evaluation. Visible changes induced in the histochemical preparation by administration of drugs (reserpine, L-Dopa) were reflected in characteristic changes in the histograms. A minor reduction in noradrenaline content, which was too small to be detected by visual observation of the microscopical image and by classical microfluorimetric methods, did evoke a significant change in the histograms. It is concluded that microfluorimetric scanning is a very sensitive technique for detecting changes of formaldehyde induced fluorescence from neuronal networks.

Adrenergic Fibers↗

PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗

PubMed2026

Genomic and Molecular Interaction Analysis of NodD1 in a Novel Bradyrhizobium yuanmingense sp. B64 Isolate for Nodulation and Symbiosis of Legume Plants.

Rhizobial bacteria are known for their ability to fix nitrogen for leguminous plants and their essential function for sustainable agriculture. This study characterizes the taxonomic status and functional potential of the Bradyrhizobium B64 isolate using integrated genomic and molecular approaches. The whole genome of the B64 isolate was sequenced via Illumina paired-end technology. Species delimitation was performed using average nucleotide identity (ANI) and digital DNA-DNA Hybridization (dDDH). The NodD1 protein structure was modeled using AlphaFold3 and validated by Ramachandran plot analysis. Molecular docking was then conducted to evaluate interactions between NodD1 and four signaling flavonoids: Apigenin, Daidzein, Genistein, and Naringenin. Genomic analysis revealed a maximum ANI of 94.4% and dDDH values between 51.4 and 62.4%. Since these values fall below the standard prokaryotic thresholds (ANI < 95%; dDDH < 70%), the B64 isolate is identified as a novel species. Physiological assays confirmed nitrogen fixation (1.97 ppm), IAA production (3.67 ppm), and phosphate solubilization (26.10 ppm). Structural validation showed 100% of NodD1 residues in allowed regions, ensuring high model reliability. Docking simulations demonstrated strong binding affinities across all flavonoids, with binding free energies ranging from - 8.8 to - 9.0 kcal/mol. Daidzein exhibited the highest thermodynamic stability (- 9.0 kcal/mol), whereas apigenin showed the most extensive residue interaction network. The B64 isolate is a novel Bradyrhizobium species with a high symbiotic capacity. The stable NodD1-flavonoid interactions provide a molecular basis for efficient nodulation, positioning B64 as a promising candidate for developing lipo-chitooligosaccharide (LCO)-based biofertilizers.

Bradyrhizobium↗