Achieving a reliable and accurate biomedical image segmentation is a long-standing problem. In order to train or adapt the segmentation methods or measure their performance, reference segmentation masks are required. Usually gold-standard annotations, i.e. human-origin reference annotations, are used as reference although they are very hard to obtain. The increasing size of the acquired image data, large dimensionality such as 3D or 3D + time, limited human expert time, and annotator variability, typically result in sparsely annotated gold-standard datasets. Reliable silver-standard annotations, i.e. computer-origin reference annotations, are needed to provide dense segmentation annotations by fusing multiple computer-origin segmentation results. The produced dense silver-standard annotations can then be either used as reference annotations directly, or converted into gold-standard ones with much lighter manual curation, which saves experts' time significantly. We propose a novel full-resolution multi-rater fusion convolutional neural network (CNN) architecture for biomedical image segmentation masks, called DeepFuse, which lacks any down-sampling layers. Staying everywhere at the full resolution enables DeepFuse to fully benefit from the enormous feature extraction capabilities of CNNs. DeepFuse outperforms the popular and commonly used fusion methods, STAPLE, SIMPLE and other majority-voting-based approaches with statistical significance on a wide range of benchmark datasets as demonstrated on examples of a challenging task of 2D and 3D cell and cell nuclei instance segmentation for a wide range of microscopy modalities, magnifications, cell shapes and densities. A remarkable feature of the proposed method is that it can apply specialized post-processing to the segmentation masks of each rater separately and recover under-segmented object parts during the refinement phase even if the majority of inputs vote otherwise. Thus, DeepFuse takes a big step towards obtaining fast and reliable computer-origin segmentation annotations for biomedical images.

• MeSH
• lidé MeSH
• neuronové sítě * MeSH
• počítačové zpracování obrazu * metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Mitochondrial dysfunction stands as a pivotal feature in neurodegenerative disorders, spurring the quest for targeted therapeutic interventions. This review examines Ubiquitin-Specific Protease 30 (USP30) as a master regulator of mitophagy with therapeutic promise in Alzheimer's disease (AD) and Parkinson's disease (PD). USP30's orchestration of mitophagy pathways, encompassing PINK1-dependent and PINK1-independent mechanisms, forms the crux of this exploration. METHOD: A systematic literature search was conducted in PubMed, Scopus, and Web of Science, selecting studies that investigated USP's function, inhibitor design, or therapeutic efficacy in AD and PD. Inclusion criteria encompassed mechanistic and preclinical/clinical data, while irrelevant or duplicate references were excluded. Extracted findings were synthesized narratively. RESULTS: USP30 modulates interactions with translocase of outer mitochondrial membrane (TOM) 20, mitochondrial E3 ubiquitin protein ligase 1 (MUL1), and Parkin, thus harmonizing mitochondrial quality control. Emerging novel USP30 inhibitors, racemic phenylalanine derivatives, N-cyano pyrrolidine, and notably, benzosulphonamide class compounds, restore mitophagy, and reduce neurodegenerative phenotypes across diverse models with minimal off-target effects. Modulation of other USPs also influences neurodegenerative disease pathways, offering additional therapeutic avenues. CONCLUSIONS: In highlighting the nuanced regulation of mitophagy by USP30, this work heralds a shift toward more precise and effective treatments, paving the way for a new era in the clinical management of neurodegenerative disorders.

• MeSH
• individualizovaná medicína metody   MeSH
• lidé MeSH
• mitochondriální proteiny MeSH
• mitofagie * fyziologie   účinky léků   MeSH
• neurodegenerativní nemoci * farmakoterapie   metabolismus   MeSH
• specifické proteázy ubikvitinu metabolismus   antagonisté a inhibitory   MeSH
• thiolesterhydrolasy metabolismus   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

BACKGROUND: Duchenne muscular dystrophy (DMD) patients are monitored periodically for cardiac involvement, including cardiac MRI with gadolinium-based contrast agents (GBCA). Texture analysis (TA) offers an alternative approach to assess late gadolinium enhancement (LGE) without relying on GBCA administration, impacting DMD patients' care. The study aimed to evaluate the prognostic value of selected TA features in the LGE assessment of DMD patients. RESULTS: We developed a pipeline to extract TA features of native T1 parametric mapping and evaluated their prognostic value in assessing LGE in DMD patients. For this evaluation, five independent TA features were selected using Boruta to identify relevant features based on their importance, least absolute shrinkage and selection operator (LASSO) to reduce the number of features, and hierarchical clustering to target multicollinearity and identify independent features. Afterward, logistic regression was used to determine the features with better discrimination ability. The independent feature inverse difference moment normalized (IDMN), which measures the pixel values homogeneity in the myocardium, achieved the highest accuracy in classifying LGE (0.857 (0.572-0.982)) and also was significantly associated with changes in the likelihood of LGE in a subgroup of patients with three yearly examinations (estimate: 23.35 (8.7), p-value = 0.008). Data are presented as mean (SD) or median (IQR) for normally and non-normally distributed continuous variables and numbers (percentages) for categorical ones. Variables were compared with the Welch t-test, Wilcoxon rank-sum, and Chi-square tests. A P-value < 0.05 was considered statistically significant. CONCLUSION: IDMN leverages the information native T1 parametric mapping provides, as it can detect changes in the pixel values of LGE images of DMD patients that may reflect myocardial alterations, serving as a supporting tool to reduce GBCA use in their cardiac MRI examinations.

• MeSH
• dítě MeSH
• Duchennova muskulární dystrofie * diagnostické zobrazování   patologie   MeSH
• gadolinium MeSH
• kontrastní látky MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mladiství MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.

• MeSH
• dospělí MeSH
• elastografie * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory štítné žlázy diagnostické zobrazování   klasifikace   patologie   diagnóza   MeSH
• prospektivní studie MeSH
• radiomika MeSH
• senioři MeSH
• štítná žláza diagnostické zobrazování   patologie   MeSH
• strojové učení * MeSH
• support vector machine MeSH
• tenkojehlová biopsie MeSH
• uzly štítné žlázy * diagnostické zobrazování   patologie   klasifikace   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Diagnostic cortical stimulation (CS) in intracranial electroencephalography (iEEG) is an established epilepsy presurgical assessment tool to delineate relevant brain functions and elicit habitual epileptic seizures. Currently, no consensus exists as to whether CS should be routinely performed in pediatric patients. A significant challenge is their limited ability to cooperate during the procedure or to describe non-observable seizure semiology features. Our goal was to identify the spectrum of CS practices in Canada, for both eloquent cortex mapping and seizure stimulation. METHODS: An online survey, answered by all 8 Canadian pediatric epilepsy centers, enquired about implantation, stimulation methods, and use of standardized protocols. A systematic literature review extracted detailed stimulation parameters. RESULTS: Most of the institutions (n = 7/8) reported performing CS during presurgical evaluation. Four institutions indicated they perform stimulation in all implanted patients for the purpose of eloquent cortex mapping and seizure stimulation. The majority of physicians had their individual approach to CS. A largely variable approach to CS, mainly in the choice of stimulation parameters (i.e., train and pulse duration), was observed, with the highest variance concerning the purpose of seizure stimulation. The literature review highlighted an overall small sample size and minimal number of publications. Even though there is a rising trend towards stereotactic iEEG implantation, more data were available on subdural EEGs. CONCLUSION: This study shows individual and sparsely validated approach to CS in pediatric epilepsy. The literature review underscores the urgent need to harmonize pediatric intracranial EEG practices. More multicenter studies are needed to identify safe stimulation thresholds and allow implementation of evidence-based guidelines.

• MeSH
• dítě MeSH
• elektroencefalografie metody   MeSH
• elektrokortikografie metody   MeSH
• epilepsie chirurgie   patofyziologie   diagnóza   MeSH
• lidé MeSH
• mapování mozku * metody   MeSH
• mozková kůra patofyziologie   MeSH
• pediatrie metody   MeSH
• průzkumy a dotazníky MeSH
• záchvaty * patofyziologie   diagnóza   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH
• Geografické názvy
• Kanada MeSH

Dictyosphaerium chlorelloides is a green microalga from the Chlorella clade that produces highly viscous exocellular polysaccharides. The cell wall polysaccharides of this alga have not been studied in detail. In this article, water-soluble polysaccharides from D. chlorelloides biomass were extracted with hot water and purified by preparative chromatography. The composition, structural features and molecular masses of subsequently eluted fractions F1, F2, F3, F4 and F5 (minor) were determined. Three high-yield products F1, F3 and F4 consisted mainly of galactopyranosyl, 2-O-methyl-galactopyranosyl, rhamnopyranosyl and mannopyranosyl units at different proportions, while F2 was rich in glucose. Immunoactivity of these fractions was evidenced in a mixed population of immune cells derived from mice spleens after incubation with polysaccharides by flow cytometry, MTT and Immunospot assays. These fractions, except F2, demonstrated selective immunostimulant activity, and the F1 fraction induced the most potent effect, closely followed by the F3 and F4 fractions. The in vivo mechanism of their action is associated with the activation of innate immunity and shapes the immune response to the Th1 type.

• MeSH
• adjuvancia imunologická farmakologie   chemie   izolace a purifikace   MeSH
• buněčná stěna * chemie   MeSH
• Chlorophyta chemie   MeSH
• mikrořasy * chemie   MeSH
• myši inbrední BALB C MeSH
• myši MeSH
• polysacharidy * farmakologie   chemie   izolace a purifikace   MeSH
• slezina cytologie   účinky léků   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Acute tubulointerstitial nephritis (ATIN) is a well-recognized cause of acute kidney injury (AKI) due to the tubulointerstitial inflammation. The aim of this study was to explore the clinical features, outcomes, and responses to corticosteroid treatment in patients with ATIN. METHODS: Patients with biopsy-proven ATIN, who were diagnosed between 1994 and 2016 at the Department of Nephrology, Charles University, First Faculty of Medicine, and General University Hospital in Prague, were included in the study. Patient demographics, the aetiological and clinical features, the treatment given, and the outcome at 1 year of follow-up were extracted from patient records. RESULTS: A total of 103 ATIN patients were analysed, of which 68 had been treated with corticosteroids. There was no significant difference in the median serum creatinine 280 (169-569) μmol/L in the conservatively managed group versus 374 (249-558) μmol/L in the corticosteroid-treated group, p = 0.18, and dependence on dialysis treatment at baseline at the time of biopsy (10.3 vs. 8.6%). During the 1 year of follow-up, those ATIN patients who had been treated with corticosteroids did better and showed greater improvement in kidney function, determined as serum creatinine difference from baseline and from 1 month over 1-year period (p = 0.001). CONCLUSIONS: This single-centre retrospective cohort study supports the beneficial role of the administration of corticosteroid therapy in the management of ATIN.

• MeSH
• dialýza ledvin * škodlivé účinky   MeSH
• hormony kůry nadledvin terapeutické užití   MeSH
• intersticiální nefritida * farmakoterapie   diagnóza   MeSH
• kreatinin MeSH
• ledviny patologie   MeSH
• lidé MeSH
• retrospektivní studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.

• MeSH
• časové faktory MeSH
• elektrokardiografie * MeSH
• fenotyp * MeSH
• hodnocení rizik MeSH
• kardiovaskulární nemoci diagnóza   mortalita   genetika   patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• neuronové sítě * MeSH
• prediktivní hodnota testů * MeSH
• prognóza MeSH
• reprodukovatelnost výsledků MeSH
• rizikové faktory MeSH
• senioři MeSH
• srdeční frekvence MeSH
• strojové učení bez učitele MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• Geografické názvy
• Spojené státy americké MeSH

OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor. METHODS: We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE). RESULTS: The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.

• MeSH
• acetabulum * diagnostické zobrazování   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• lineární modely MeSH
• mladý dospělý MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• software * MeSH
• soudní antropologie * metody   MeSH
• strojové učení * MeSH
• určení kostního věku * metody   MeSH
• zobrazování trojrozměrné * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

• MeSH
• algoritmy * MeSH
• artefakty MeSH
• elektroencefalografie * metody   MeSH
• lidé MeSH
• mozek * fyziologie   MeSH
• rozhraní mozek-počítač MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH