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At least 127 records · Page 7Linked to original sources

PubMed2026

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗

PubMed1991

Work experiences of minority managers and professionals: individual and organizational costs of perceived bias.

The present study examined the relations of the way minority managers and professionals described their treatment within their organizations, and their organizations' acceptance and openness to minorities within measures of satisfaction, commitment, skill utilization, and integration. Data were collected from 81 minority managers and professionals in early career stages using questionnaires completed anonymously. Minority managers experiencing more positive treatment in their organization, and employed in organizations more accepting of minorities, were more satisfied, committed, and integrated.

Achievement↗

PubMed1991

An optically scanned EMS reporting form and analysis system for statewide use: development and five years' experience.

Analysis of emergency medical services (EMS) systems data is crucial to planning, education, research, and quality assurance programs. Currently, comparative analysis of EMS data between regions or states is virtually impossible due to wide variations in data collection and analysis methods. To devise a practical and uniform EMS reporting system, we referenced the minimum data set (MDS) established by the federal government in 1974 and surveyed 22 states known to be using uniform reporting systems. In developing our final data set, elements were added based on inclusion in the MDS, national survey results, a review of current EMS literature, and consensus of local EMS providers. This set of 48 elements then was incorporated into a reporting form using narrative and optically scanned formats, allowing automated data collection for computer analysis. After a pilot study, the system was improved to allow high-speed ink reading and large volume data storage and analysis using a microcomputer. This system has subsequently been adopted by seven states. The combined data base exceeds 250,000 cases. Error screening algorithms ensure data integrity and are also used for quality assurance. Customized output reports can be generated within minutes and have assisted in EMS quality assurance, planning, and research. We believe that the successful performance of this system supports the use of the suggested data elements as well as optical scanning and microcomputer analysis of EMS data.

Data Collection↗

PubMed1990

A multiuser system for whole body plethysmographic measurements and interpretation.

A multiuser system for whole body plethysmographic measurements and interpretation which has been developed under clinical conditions is described. The following measurements can be carried out in a rapid way and in one session with the patient: specific airway resistance during spontaneous breathing, determination of functional residual capacity, static lung volumes, and maximal forced expiratory data. Each section is normally measured twice and can be repeated up to ten times. The final results are displayed and printed together with a consistent system of normal reference values. All values and selected original curves are stored automatically in an integrated data base system. Obstructive patients are measured again after the inhalation of a bronchodilator. All results are evaluated by an automatic interpretation program. This analyzes and graduates airway obstruction, lung volumes, and pharmacological airway reversibility using standardized texts which are written below all numerical printouts and graphical plots. The interpretation algorithm is tree structured and uses the normal reference values as a knowledge base. The system supports up to four online laboratories with their own A/D converter and up to 20 video terminals, printers, plotters, and modems. Our laboratory performs 8,297 such complete measurements on 4,671 different patients per year with one body box.

Diagnosis, Computer-Assisted↗

PubMed1992

Modification of the OMED nomenclature: a system approach based on the SISCOPE data model.

The OMED nomenclature represented a turning point in endoscopic computer systems by supplying software developers with an internationally recognized scientific document on which prototypes could be based. The main pitfalls of the OMED system are related to its hierarchical structure, probably not the most effective design to represent endoscopic findings. Based on our experience during the development of SISCOPE, an integrated data management system for endoscopy, an alternative scheme is proposed: Endoscopic descriptions are modeled as a set of objects represented by a data structure whose elements are location, morphology, associated lesions and hemorrhage. 72 objects appear to be sufficient for an accurate representation of all endoscopic scenes and a consistent data model could be created with this approach. Efforts should be made to decrease redundancy in the OMED nomenclature, but extension to other endoscopic data types, such as clinical and pathological diagnosis, is more urgently required. Furthermore, if data exchange between systems is desired, the definition of an Endoscopy Metafile is an absolute requirement.

Database Management Systems↗

PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics↗

PubMed2026

Trans-omics integration underscores distinct roles of polyunsaturated phospholipids in bidirectional offspring birth weight deviations.

BACKGROUND: Abnormal birth weights are associated with adverse pregnancy outcomes and future metabolic consequences. We aimed to examine cord blood lipidomes from low, normal and high birth weight (LBW, NBW, HBW) infants to identify core lipid signatures associated with non-optimum birth weight, and to derive biological insights through trans-omics data integration with placental proteome, maternal plasma lipidome and clinical phenome. METHODS: We conducted quantitative lipidomics of cord blood samples from two independent cohorts: a retrospective discovery cohort (n = 147) and a prospective validation cohort (n = 73). Integration with placental proteomics, maternal plasma lipidomics and clinical phenomics was conducted to elucidate potential biological implications. FINDINGS: We identified substantial reductions in cord blood polyunsaturated phospholipids (PUFA-PLs) (FDR <0.05) associated with placental vesicle trafficking and formation in LBW, and altered neutrophil degranulation in HBW. Combinatorial analyses of paired maternal plasma and cord blood samples indicated that cord blood PUFA-PL reductions were not attributable to deficient maternal supply, but rather to impeded assimilation (LBW) and increased utilisation (HBW). INTERPRETATION: Our findings provide biological insights that may inform targetable, lipid-oriented nutritional and/or pharmacological strategies to modulate foetal growth and development, with the goal of optimising clinical outcomes for both mother and child. FUNDING: This work was supported by the National Natural Science Foundation of China (82170854, 81870579, 81870545, 82571043, 2357308); National High Level Hospital Clinical Research Funding (2022-PUMCH-C-019); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0530200 and 2024ZD0530204); Beijing Municipal Science & Technology Commission (Z201100005520011); Peking University Clinical Scientist Training Program (No. BMU2023PYJH022); Beijing Municipal Natural Science Foundation (7202163, 7184252).

Humans↗

PubMedDate not supplied

Automated data collection and presentation in the operating room.

An 'Operating Room Data Integration System', is described which is used to collect, present and archive all important physiological parameters during open heart surgery. The system requires very little attention, and provides an easy to understand and coherent interface to the user. The system is adaptable to a large extend and thus data can be presented to the user in a manner, with which he or she is already familiar. Simple drivers can be written to enable connection of the system to almost any other piece of medical equipment, if the latter provides an analog or digital, output signal. Automatic logging of the acquired signals is then possible.

Computer Systems↗

PubMed2026

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans↗

PubMed1990

Construction of validated, non-redundant composite protein sequence databases.

A strategy has been developed for the construction of a validated, comprehensive composite protein sequence database. Entries are amalgamated from primary source data bases by a largely automated set of processes in which redundant and trivially different entries are eliminated. A modular approach has been adopted to allow scientific judgement to be used at each stage of database processing and amalgamation. Source databases are assigned a priority depending on the quality of sequence validation and commenting. Rejection of entries from the lower priority database, in each pairwise comparison of databases, is carried out according to optionally defined redundancy criteria based on sequence segment mismatches. Efficient algorithms for this methodology are embodied in the COMPO software system. COMPO has been applied for over 2 years in construction and regular updating of the OWL composite protein sequence database from the source databases NBRF-PIR, SWISS-PROT, a GenBank translation retrieved from the feature tables, NBRF-NEW, NEWAT86, PSD-KYOTO and the sequences contained in the Brookhaven protein structure databank. OWL is part of the ISIS integrated data resource of protein sequence and structure [Akrigg et al. (1988) Nature, 335, 745-746]. The modular nature of the integration process greatly facilitates the frequent updating of OWL following releases of the source databases. The extent of redundancy in these sources is revealed by the comparison process. The advantages of a robust composite database for sequence similarity searching and information retrieval are discussed.

Amino Acid Sequence↗

PubMed2026

Scalable medium-density genotyping platforms for cultivar identification, pedigree authentication, marker-assisted and genomic selection, and other applications in strawberry.

A broad spectrum of high-density genotyping approaches, including single-nucleotide polymorphism (SNP) arrays, genotyping-by-sequencing, and whole-genome reduced-representation sequencing, have been shown to perform well in strawberry (Fragaria × ananassa), despite the inherent complexity of the octoploid genome. While these approaches are effective, their routine deployment in breeding programs can be constrained by cost, computational requirements, and workflow complexity. In parallel, many breeding programs continue to rely on locus-specific assays for marker-assisted selection, resulting in fragmented and inefficient genotyping strategies. Here, we describe medium-density amplicon-based genotyping platforms for strawberry designed to provide cost-effective, turnkey solutions that integrate markers used for marker-assisted selection with genome-wide markers suitable for genomic prediction in a single laboratory assay. These platforms were developed by targeting 1,650 or 4,811 target SNPs via amplicon sequencing, and are interoperable with existing high-density genotyping resources, including a widely used 50K SNP array, thereby facilitating data integration across platforms. We benchmarked their performance relative to the 50K SNP array across breeding-relevant applications, including identity and purity testing, pedigree authentication, marker-assisted selection, and genomic selection, and further evaluated the feasibility of genotype imputation to enhance genome-wide information content. Across analyses, the 1,650- and 4,811-amplicon platforms produced results comparable to higher-density platforms while substantially reducing genotyping cost and analytical overhead. This work demonstrates that targeted amplicon-based genotyping can support efficient, scalable, and integrated genome-informed breeding, enabling the routine application of both marker-assisted and genomic selection within strawberry breeding workflows. Open-source R workflows are provided to support streamlined analyses in breeding contexts.

Fragaria↗

PubMed2026

PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.

MOTIVATION: Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. RESULTS: We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. AVAILABILITY: PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article.

Software↗

PubMed2026

Comprehensive in silico genomics analysis of global trends and host-specific emergence of aminoglycoside resistance in Staphylococcus aureus: a One-Health perspective.

BACKGROUND: Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000-2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. METHODS: We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. RESULTS: Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurring in 14-22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (-2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype-phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. CONCLUSION: Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype-phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.

Aminoglycosides↗

PubMed1988

A successful experiment to reduce unnecessary laboratory use in a community hospital.

A series of interventions at a 228-bed general hospital provided physicians with feedback at regular intervals concerning the amount of laboratory services employed in treating their patients. Case-mix-adjusted estimates of laboratory tests allowed each physician to compare use of laboratory tests with that of peers in the same department at the same hospital. Physicians with "excess" practice patterns ordered hundreds more laboratory tests than average each year. A multifaceted educational program included the following: 1) meetings were held concerning costs and unnecessary laboratory tests; 2) physicians were given descriptions of their practice patterns relative to their peers as part of both large and small departmental discussions; 3) the feedback was repeated a year later; 4) a consensus conference established guidelines for test ordering; and 5) a sample of patient records was examined for appropriateness of laboratory test ordering. A total of 37% of a sample of tests ordered during the baseline period by physicians with "excess" practice patterns was classified as inappropriate. The intervention resulted in a reduction of 1.8 tests per patient (P = 0.0005). Eight of the nine tests individually showed reductions in use. Charge data from the target hospital showed a statistically significant reduction in laboratory charges per patient in the quarter following program initiation (P = 0.02) and no evidence for change in a group of five comparison hospitals. There was no evidence for reductions in the ordering of essential tests. These results demonstrate a cost-effective approach to reducing unnecessary costs that can be implemented in hospitals with integrated data systems.

Clinical Laboratory Techniques↗

PubMed1989

Differences between patient and family assessments of depression in Alzheimer's disease.

A structured interview covering the DSM-III criteria for major depression was adapted for separate use with Alzheimer's disease patients and with their families. Data from 36 patients yielded a depression rate of 13.9%, whereas information from their families indicated that the rate was 50.0%. This disagreement reflected greater family endorsement of patients' loss of interest or pleasure, irritability, fatigue, and feelings of worthlessness. Use of DSM-III-R criteria narrowed but did not eliminate the discrepancy between patients' and families' assessments of the patients' depression. Uniform procedures for gathering and integrating data from the family that are relevant to diagnosis in this group are indicated.

Aged↗

PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗

PubMed1986

Computed tomographic evaluation of morphological and functional condition of left ventricle in heart aneurysm.

Computed tomography is a valuable method for the diagnosis of post-infarction aneurysm of the left ventricle of the heart. It gives additional information concerning the morphological and functional condition of the left ventricle. The suggested method of layer-by-layer scanning improves the diagnostic efficiency of CT during examination of patients with heart aneurysm and is noninvasive. Precise individual calculation of the peak concentration of contrast medium, defined by dynamic scanning, optimises the process of gated CT. CT makes it possible to study changes in cavity configuration, left ventricle wall thinning, induration and calcification of the myocardium, changes in left ventricle wall mobility, decreased thickening of the myocardium at systole and left ventricle cavity thrombosis--all changes characteristic of left ventricular aneurysm. CT provides important additional information about the condition of the inter-ventricular septum. EDV, ESV and EF data obtained using CT produce important information about the functional state of the left ventricle. Computed tomography can be used as an independent method of left ventricle aneurysm detection, especially in those institutions where more complicated investigation methods are not used and interventional cardiac procedures are not practised. Complex use of computed tomography and left ventriculography in cardiosurgical institutions makes it possible to improve significantly the diagnosis of cardiac aneurysm. Calculation of integral data about left ventricle pumping function based on CT and LVG data gives proper evaluation of the indications for operative intervention in left ventricular aneurysm.

Adult↗

PubMed2026

Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.

Single-cell and spatial omics (SPOs) technologies have advanced how healthcare physicians characterise brain tumours by enabling detailed understanding of their cellular architecture, functional states, and microenvironmental dynamics. These approaches provide high-resolution detection of tumour heterogeneity and allow precise analysis of the brain tumour microenvironment. Their application has also led to the discovery of novel biomarkers used for early brain tumour detection, prognosis, and improved tumour stratification. Furthermore, integrative multi-omic analyses have revealed new therapeutic targets, clarified mechanisms of drug resistance, and uncovered molecular pathways underpinning treatment failure. By bridging cellular-level insights with spatial context, SPOs hold significant promise for advancing personalised diagnostics, predicting therapeutic response, and guiding the development of targeted interventions for brain tumours. Despite these advances, several limitations constrain the full translational potential of SPOs, including high experimental costs, substantial computational demands, lack of standardised protocols, and challenges in data integration and reproducibility. Addressing these barriers through scalable bioinformatic pipelines, consensus experimental frameworks, and cost-effective platforms remains critical for broadening accessibility and enabling clinical adoption.

Brain Neoplasms↗