搜索结果：fatty

Non-alcoholic fatty pancreas disease (NAFPD) is an underdiagnosed condition associated with metabolic syndrome, insulin resistance, and increased risk of pancreatic cancer. Diagnosis typically relies on subjective visual assessment of ultrasound images by clinicians. We propose an end-to-end framework for automatically classifying normal versus fatty pancreas from abdominal ultrasound images. Our method employs a TransUNet-based segmentation architecture with a ResNet encoder and transformer bottleneck to delineate the pancreas and the splenic vein, followed by anatomically-guided patch extraction and patient-level classification through pairwise texture comparison. The feature engineering mimics clinical reasoning by comparing the echogenicity of peri-venous fat to the pancreatic parenchyma, providing an interpretable signal for classification. The segmentation models are initialized via domain-specific transfer learning from a liver segmentation task. We validate the full pipeline on a clinical dataset of 214 abdominal ultrasound images with 107 expert-labeled cases using 5-fold cross-validation. SVM with RBF kernel achieves a mean cross-validated accuracy of 89.7\%\,$\pm$\,1.8\%

Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-level screening tools remain inadequate. We present Method, a machine-learning framework for NAFLD risk prediction coupling gradient-boosted decision trees with conformal prediction to yield calibrated, distribution-free coverage guarantees on individual risk estimates. It integrates a mutual-information-based stability selection procedure to identify a compact, clinically interpretable feature subset via bootstrap resampling, constructing prediction sets whose marginal coverage provably exceeds a user-specified confidence level. We evaluated Method on a multicenter cohort from Guangzhou, China (primary n=2,187; external validation n=412) using 78 candidate features across demographics, metabolic biomarkers, and lifestyle factors. Method achieves an AUROC of 0.912 internally and 0.891 externally, outperforming deep neural networks, TabNet, support vector machines, and logistic regression. Conformal prediction sets achieve 91.3% empirical coverage at the 90% nominal level. A three-tier risk stratification derived from these scores separ

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3,766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3,766 AfAm and 3,766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI ris

Current medical image classification efforts mainly aim for higher average performance, often neglecting the balance between different classes. This can lead to significant differences in recognition accuracy between classes and obvious recognition weaknesses. Without the support of massive data, deep learning faces challenges in fine-grained classification of fatty liver. In this paper, we propose an innovative deep learning framework that combines feature decoupling and adaptive adversarial training. Firstly, we employ two iteratively compressed decouplers to supervised decouple common features and specific features related to fatty liver in abdominal ultrasound images. Subsequently, the decoupled features are concatenated with the original image after transforming the color space and are fed into the classifier. During adversarial training, we adaptively adjust the perturbation and balance the adversarial strength by the accuracy of each class. The model will eliminate recognition weaknesses by correctly classifying adversarial samples, thus improving recognition robustness. Finally, the accuracy of our method improved by 4.16%, achieving 82.95%. As demonstrated by extensive exp

In this study, the use of X-Ray reflectometry technique signifies the types of rough and smooth surfaces of hematite film prepared from different fatty acid derivatives of the iron salt. Followed by this, the film morphology and crystallographic properties concerning different fatty acid chain length have been discussed.

The origin of fatty acids on the prebiotic Earth is important as they likely formed the encapsulating membranes of the first protocells. Carbon-rich meteorites (i.e., carbonaceous chondrites) such as Murchison and Tagish Lake are well known to contain these molecules, and their delivery to the early planet by intense early meteorite bombardments constitutes a key prebiotic source. We collect the fatty acid abundances measured in various carbonaceous chondrites from the literature and analyze them for patterns and correlations. Fatty acids in meteorites include straight-chain and branched-chain monocarboxylic and dicarboxylic acids up to 12 carbons in length---fatty acids with at least 8 carbons are required to form vesicles, and modern cell membranes employ lipids with ~12--20 carbons. To understand the origin of meteoritic fatty acids, we search the literature for abiotic fatty acid reaction pathways and create a candidate list of 11 reactions that could potentially produce these fatty acids in meteorite parent bodies. Straight-chain monocarboxylic acids (SCMA) are the dominant fatty acids in meteorites, followed by branched-chain monocarboxylic acids (BCMA). SCMA are most abundan

Saturated fatty acids are abundant organic compounds in oceans and sea sprays. Their photochemical reactions induced by solar radiation have recently been discovered as an abiotic source of volatile organic compounds, which serve as precursors of secondary organic aerosols. However, photoabsorption of wavelengths longer than 250 nm in liquid saturated fatty acids remains unexplained, despite being first reported in 1931. Here we demonstrate that the previously reported absorption of wavelengths longer than 250 nm by liquid nonanoic acid [CH3(CH2)7COOH)] originates from traces of impurities (0.1% at most) intrinsically contained in nonanoic acid reagents. Absorption cross sections of nonanoic acid newly obtained here indicate that the upper limit of its photolysis rate is three-to-five orders of magnitude smaller than those for atmospherically relevant carbonyl compounds.

I present a theory of schizophrenia (SZ) that mechanistically explains its etiology, symptoms, pathophysiology, and treatment. SZ involves the chronic release of membrane polyunsaturated fatty acids (PUFAs) and their utilization for the synthesis of stress-induced plasticity agents such as endocannabinoids (ECBs). The causal event in SZ is prolonged stress during a sensitive period, which can induce prolonged and heritable changes. The physiological effect of the released PUFAs and their products is to disconnect neurons from their inputs and promote intrinsic excitability. I show that these effects can explain the positive, negative, cognitive, and mood symptoms of SZ, as well as the mechanisms of many known triggers of psychosis. The theory is supported by overwhelming evidence addressing lipids, immunity, ECBs, neuromodulators, hormones, neurotransmitters, and cortical parameters in SZ. It explains why antipsychotic drugs are effective against positive symptoms, and why they do not affect the other symptoms. Finally, I present promising treatment directions implied by the theory, including some that are immediately available.

Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challenges due to its non-specific symptoms and the invasive nature of liver biopsies. Our research introduces a novel artificial intelligence cascade model employing ensemble learning and feature fusion techniques. We developed a non-invasive, robust, and reliable diagnostic artificial intelligence tool that utilizes anthropometric and laboratory parameters, facilitating early detection and intervention in NAFLD progression. Our novel artificial intelligence achieved an 86% accuracy rate for the NASH steatosis staging task (non-NASH, steatosis grade 1, steatosis grade 2, and steatosis grade 3) and an impressive 96% AUC-ROC for distinguishing between NASH (steatosis grade 1, grade 2, and grade3) and non-NASH cases, outperforming current state-of-the-art models. This notable improvement in diagnostic performance underscores the potential application of artificial intelligence

Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation.

Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of $1.90$ compared to $1.67$ for GANs, and a minimum FID score of $69.45$ compared to $100.05$ for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of $0.904$ on a NAFLD prediction task.

Background: The epidemic of nonalcoholic fatty liver disease (NAFLD) and its metabolic effects present a serious public health concern. We hypothesized that the Ramadan fasting model (RFM), which involves fasting from dawn to dusk for a month, could provide potential therapeutic benefits and mitigate NAFLD. Accordingly, we aimed to validate this hypothesis using obese male rats. Methods: Rats were split into two groups (n = 24 per group), and they were given either a standard (S) or high-fat diet (HFD) for 12 weeks. During the last four weeks of the study period, both S- and HFD-fed rats were subdivided into eight groups to assess the effect of RFM with/without training (T) or glucose administration (G) on the lipid profile, liver enzymes, and liver structure (n=6/group). Results: The HFD+RFM groups exhibited a significantly lower final body weight than that the HFDC group. Serum cholesterol, low-density lipoprotein, and triglyceride levels were significantly lower in the HFD+RFM, HFD+RFM+T, and HFD+RFM+G groups than those in the HFDC group. Compared with the HFD-fed group, all groups had improved serum high-density lipoprotein levels. Furthermore, HFD groups subjected to RFM had r

Theories on the origins of life propose that early cell membranes were synthesized from amphiphilic molecules simpler than phospholipids such as fatty alcohols. The discovery in the interstellar medium (ISM) of ethanolamine, the simplest phospholipid head group, raises the question whether simple amphiphilic molecules are also synthesized in space. We investigate whether precursors of fatty alcohols are present in the ISM. For this, we have carried out a spectral survey at 7, 3, 2 and 1 mm toward the Giant Molecular Cloud G+0.693-0.027 located in the Galactic Center using the IRAM 30m and Yebes 40m telescopes. Here, we report the detection in the ISM of the primary alcohol n-propanol (in both conformers Ga-n-C3H7OH and Aa-n-C3H7OH), a precursor of fatty alcohols. The derived column densities of n-propanol are (5.5+-0.4)x10^13 cm^-2 for the Ga conformer and (3.4+-0.3)x10^13 cm^-2 for the Aa conformer, which imply molecular abundances of (4.1+-0.3)x10^-10 for Ga-n-C3H7OH and of (2.5+-0.2)x10^-10 for Aa-n-C3H7OH. We also searched for the AGa conformer of n-butanol (AGa-n-C4H9OH) without success yielding an upper limit to its abundance of <4.1x10^-11. The inferred CH3OH:C2H5OH:C3H7O

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disorder characterized by the excessive accumulation of fat in the liver in individuals who do not consume significant amounts of alcohol, including risk factors like obesity, insulin resistance, type 2 diabetes, etc. We aim to identify subgroups of NAFLD patients based on demographic, clinical, and genetic characteristics for precision medicine. The genomic and phenotypic data (3,408 cases and 4,739 controls) for this study were gathered from participants in Mayo Clinic Tapestry Study (IRB#19-000001) and their electric health records, including their demographic, clinical, and comorbidity data, and the genotype information through whole exome sequencing performed at Helix using the Exome+$^\circledR$ Assay according to standard procedure (www$.$helix$.$com). Factors highly relevant to NAFLD were determined by the chi-square test and stepwise backward-forward regression model. Latent class analysis (LCA) was performed on NAFLD cases using significant indicator variables to identify subgroups. The optimal clustering revealed 5 latent subgroups from 2,013 NAFLD patients (mean age 60.6 years and 62.1% women), while

Superhydrophobcity is a well-known wetting phenomenon found in numerous plants and insects. It is achieved by the combination of the surfaces chemical properties and its surface roughness. Inspired by nature, numerous synthetic superhydrophobic surfaces have been developed for various applications. Designated surface coating is one of the fabrication routes to achieve the superhydrophobicity. Yet, many of these coatings, such as fluorine-based formulations, may pose severe health and environmental risks, limiting the applicability. Herein, we present a new family of superhydrophobic coatings comprised of natural saturated fatty acids, which are not only a part of our daily diet, but can be produced from renewable feedstock, providing a safe and sustainable alternative to existing state-of-the-art. These crystalline coatings are readily fabricated via single-step deposition routes, thermal deposition or spray-coating. The fatty acids self-assemble into highly hierarchical crystalline structures exhibiting a water contact angle of about 165 degrees and contact angle hysteresis lower than 6 degrees, while their properties and morphology depend on the specific fatty acid used as well a

Several short-chain fatty acids and their corresponding potential existing hydrated forms are important molecules in interstellar space. Their structures were optimized with twelve different computational methods. The dipole moments and the spectral constants, including rotational constants and centrifugal distortion constants were obtained. According to the benchmark study, revDSD-PBEP86-D3(BJ) is the most suitable method that was selected for rotational calculation. Symmetry-adapted perturbation theory was used to study the strength and composition of the interaction between acids and water in clusters. The possibility of its existing under the low-temperature and low-pressure conditions was confirmed by calculating of binding free energy. Furthermore, ab initio molecular dynamics simulations were used to investigate whether the internal rotations of acids could be observed. The 3-fold splitting from the predicted high-resolution microwave rotational spectra of the acetic acid monohydrate at different temperatures perfectly proved the accuracy of the simulations.

The famous series of Fibonacci numbers is defined by a recursive equation saying that each number is the sum of its two predecessors, with the initial condition that the first two numbers are equal to unity. Here, we show that the numbers of fatty acids (straight-chain aliphatic monocarboxylic acids) with n carbon atoms is exactly given by the Fibonacci numbers. Thus, by investing one more carbon atom into extending a fatty acid, an organism can increase the variability of the fatty acids approximately by the factor of the Golden section, 1.618. As the Fibonacci series grows asymptotically exponentially, our results are in line with combinatorial complexity found generally in biology. We also outline potential extensions of the calculations to modified (e.g., hydroxylated) fatty acids. The presented enumeration method may be of interest for lipidomics, combinatorial chemistry, synthetic biology and the theory of evolution (including prebiotic evolution).