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Obesity Facts Jun 2024Non-alcoholic fatty liver disease (NAFLD), now termed metabolic dysfunction-associated steatotic liver disease (MASLD), is an escalating health concern linked to obesity...
INTRODUCTION
Non-alcoholic fatty liver disease (NAFLD), now termed metabolic dysfunction-associated steatotic liver disease (MASLD), is an escalating health concern linked to obesity and type 2 diabetes. Despite liver biopsy being the gold standard, its invasiveness underscores the need for non-invasive diagnostic methods.
METHODS
A cross-sectional study was performed to assess MASLD using the non-invasive OWLiver® serum lipidomics test in a cohort of 117 patients with severe obesity undergoing bariatric surgery, comparing outcomes with liver biopsy. Exclusions (n = 24) included insufficient data, liver disease etiology other than MASLD, corticosteroid treatment, excessive alcohol consumption, low glomerular filtration rate and declination to participate. Comprehensive laboratory tests, demographic assessments and liver biopsies were performed. Serum metabolites were analyzed using OWLiver®, a serum lipidomic test that discriminates between healthy liver, steatosis, metabolic dysfunction-associated steatohepatitis (MASH) and MASH with fibrosis ≥2 by means of three algorithms run sequentially.
RESULTS
Liver biopsy revealed a MASLD prevalence of 95.7%, with MASH present in 28.2% of cases. OWLiver® demonstrated a tendency to diagnose more severe cases. Body mass index (BMI), rather than the presence of type 2 diabetes, emerged as the sole independent factor linked to the probability of concordance. Therefore, the all-population concordance of 63.2% between OWLiver® and liver biopsy notably raised to 77.1% in patients with a BMI <40 kg/m². These findings suggest a potential correlation between lower BMI and enhanced concordance between OWLiver® and biopsy.
CONCLUSION
This study yields valuable insights into the concordance between liver biopsy and the non-invasive serum lipidomic test, OWLiver®, in severe obesity. OWLiver® demonstrated a tendency to amplify MASLD severity, with BMI values influencing concordance. Patients with BMI < 40 kg/m² may derive optimal benefits from this non-invasive diagnostic approach.
PubMed: 38934179
DOI: 10.1159/000538765 -
Clinical and Molecular Hepatology Jun 2024In managing metabolic dysfunction-associated steatotic liver disease, which affects over 30% of the general population, effective noninvasive biomarkers for assessing... (Review)
Review
In managing metabolic dysfunction-associated steatotic liver disease, which affects over 30% of the general population, effective noninvasive biomarkers for assessing disease severity, monitoring disease progression, predicting the development of liver-related complications, and assessing treatment response are crucial. The advantage of simple fibrosis scores lies in their widespread accessibility through routinely performed blood tests and extensive validation in different clinical settings. They have shown reasonable accuracy in diagnosing advanced fibrosis and good performance in excluding the majority of patients with a low risk of liver-related complications. Among patients with elevated serum fibrosis scores, a more specific fibrosis and imaging biomarker has proved useful to accurately identify patients at risk of liver-related complications. Among specific fibrosis blood biomarkers, enhanced liver fibrosis is the most widely utilized and has been approved in the United States as a prognostic biomarker. For imaging biomarkers, the availability of vibration-controlled transient elastography has been largely improved over the past years, enabling the use of liver stiffness measurement (LSM) for accurate assessment of significant and advanced fibrosis, and cirrhosis. Combining LSM with other routinely available blood tests enhances the ability to diagnose at-risk metabolic dysfunction-associated steatohepatitis; and predict liver-related complications, some reaching an accuracy comparable to that of liver biopsy. Magnetic resonance imaging-based modalities provide the most accurate quantification of liver fibrosis, though the current utilization is limited to research settings. Expanding their future use in clinical practice depends on factors such as cost and facility availability.
PubMed: 38934108
DOI: 10.3350/cmh.2024.0246 -
Kidney Research and Clinical Practice Jun 2024Acute kidney injury (AKI) is a significant challenge in healthcare, imposing a significant social burden. While there are considerable researches dedicated to AKI and...
BACKGROUND
Acute kidney injury (AKI) is a significant challenge in healthcare, imposing a significant social burden. While there are considerable researches dedicated to AKI and the recovery of AKI patients, a crucial factor in their prognosis, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning-based approach to predict restoration of kidney function in patients with AKI.
METHODS
Our study encompassed data from 350,345 cases, derived from two hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset or reduction to values lower than the baseline, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis.
RESULTS
Our analysis contained 7,041 and 2,929 patients' data from internal cohort and external cohort respectively. The Categorical Boosting model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic curve (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations values were employed to explain key factors impacting recovery of renal function in AKI patients, highlighting factors such as elevated urine specific gravity, body temperature, and phosphorus levels.
CONCLUSION
This study presented a novel machine learning framework for predicting renal function recovery in patients with AKI, offering a deeper understanding of the key variables affecting recovery. The clinical applicability of the model was assessed across distinct hospital settings, which revealed variations in its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real-world scenarios.
PubMed: 38934029
DOI: 10.23876/j.krcp.23.330 -
Frontiers in Genetics 2024Short stature is one of the most prevalent endocrine disorders in children, and its genetic basis is a complex and actively researched subject. Currently, there is...
BACKGROUND
Short stature is one of the most prevalent endocrine disorders in children, and its genetic basis is a complex and actively researched subject. Currently, there is limited genetic research on exome sequencing for short stature, and more large-scale studies are necessary for further exploration.
METHODS
The retrospective study entailed investigation of 98 Chinese children with short statures (height SDS ≤ -2.5) of unknown etiologies recruited between 2017 and 2021. Whole-exome sequencing (WES) was performed on these patients to identify the potential genetic etiologies. The clinical data were reviewed retrospectively to assess the pathogenicity of the identified mutations. Additionally, 31 patients consented to and received recombinant human growth hormone (rhGH) therapy for 12 months. The short-term effects of rhGH treatment were evaluated across different etiologies of patients with short statures.
RESULTS
The WES results were used to identify 31 different variants in 18 genes among 24 (24.5%) patients. Individuals with more severe short statures were more likely to have underlying genetic etiologies. Short stature accompanied by other phenotypes had significantly higher diagnostic yields than simple severe short stature. The rhGH therapy demonstrated efficacy in most children. Nevertheless, the treatment response was suboptimal in a boy diagnosed with 3M syndrome.
CONCLUSION
WES is an important approach for confirming genetic disorders in patients with severe short statures of unknown etiologies, suggesting that it could be used as a primary diagnostic strategy. The administration of rhGH may not be suitable for all children with short statures, and the identification of the genetic cause of short stature by WES has significant guidance value for rhGH treatment.
PubMed: 38933926
DOI: 10.3389/fgene.2024.1364441 -
JGH Open : An Open Access Journal of... Jun 2024Atrophic gastritis (AG) and gastric intestinal metaplasia (GIM) are early changes in the stepwise progression to gastric adenocarcinoma. There is heterogeneity in...
BACKGROUND AND AIM
Atrophic gastritis (AG) and gastric intestinal metaplasia (GIM) are early changes in the stepwise progression to gastric adenocarcinoma. There is heterogeneity in international guidelines regarding the endoscopic diagnosis and surveillance of AG and GIM. This study aims to determine the prevalence of GIM in an Australian center and assess the approach of Australian endoscopists for these two conditions.
METHODS
We conducted a single-center retrospective study of adult patients between January 2015 and December 2020 diagnosed with GIM on gastric biopsy following upper gastric endoscopy. A web-based, 25-question, investigator-designed, multiple-choice survey was distributed among all registered endoscopists in Australia.
RESULTS
The overall prevalence of GIM within a single Australian center was 11.7% over 5 years. Of the 1026 patients identified, only 58.7% underwent mapping biopsies using the modified Sydney protocol. Among the cohort, 1.6% had low-grade dysplasia, 0.9% had high-grade dysplasia, and 1.8% had malignancy on initial gastroscopy. Two hundred and sixty-seven (7.2%) endoscopists completed the survey, 44.2% indicated they would perform mapping for all patients, and 36% only for high-risk patients. Only 1.5% ( = 4) of respondents were able to correctly identify all six endoscopic photos of GIM/AG.
CONCLUSION
This study demonstrates that in a large tertiary center, GIM is a prevalent endoscopic finding, but the associated rates of dysplasia and cancer were low. Additionally, among a small proportion of surveyed Australian endoscopists, there is notable variability in the endoscopic approach for AG and GIM and significant knowledge gaps. More training is required to increase the recognition of GIM and compliance with histological mapping.
PubMed: 38933895
DOI: 10.1002/jgh3.13115 -
Nanoscale Advances Jun 2024Tumors pose a significant threat to human health, and their occurrence and fatality rates are on the rise each year. Accurate tumor diagnosis is crucial in preventing...
Tumors pose a significant threat to human health, and their occurrence and fatality rates are on the rise each year. Accurate tumor diagnosis is crucial in preventing untimely treatment and late-stage metastasis, thereby reducing mortality. To address this, we have developed a novel type of hybrid nanogel called γ-FeO@PNIPAM/PAm/CTS, which contains iron oxide nanoparticles and poly(-isopropyl acrylamide)/polyacrylamide/chitosan. The rationale for this study relies on the concept that thermosensitive PNIPAM has the ability to contract when exposed to elevated temperature conditions found within tumors. This contraction leads to a dense clustering of the high-loading γ-FeO nanoparticles within the nanogel, thus greatly enhancing the capabilities of MRI. Additionally, the amino groups in chitosan on the particle surface can be converted into ammonium salts under mildly acidic conditions, allowing for an increase in the charge of the nanogel specifically at the slightly acidic tumor site. Consequently, it promotes the phagocytosis of tumor cells and effectively enhances the accumulation and retention of nanogels at the tumor site. The synthesis of the hybrid nanogels involves a surfactant-free emulsion copolymerization process, where vinyl-modified γ-FeO superparamagnetic nanoparticles are copolymerized with the monomers in the presence of chitosan. We have optimized various reaction parameters to achieve a high loading content of the superparamagnetic nanoparticles, reaching up to 60%. The achieved value of 517.74 mM S significantly surpasses that of the clinical imaging contrast agent Resovist (approximately 151 mM S). To assess the performance of these magnetic nanogels, we conducted experiments using Cal27 oral tumors and 4T1 breast tumors in animal models. The nanogels exhibited temperature- and pH-sensitivity, enabling magnetic targeting and enhancing diagnosis through MRI. The results demonstrated the potential of these hybrid nanogels as contrast agents for magnetic targeting in biomedical applications.
PubMed: 38933853
DOI: 10.1039/d4na00014e -
Frontiers in Endocrinology 2024Radiofrequency ablation (RFA) is an emerging non-surgical treatment for benign thyroid nodules (BTN). Despite its proven safety profile, data on the learning curve (LC)...
OBJECTIVE
Radiofrequency ablation (RFA) is an emerging non-surgical treatment for benign thyroid nodules (BTN). Despite its proven safety profile, data on the learning curve (LC) required to achieve proficiency are still lacking.
MATERIALS AND METHODS
The first 179 RFA procedures performed by a single operator in patients with non-functioning BTN were retrospectively analyzed. Six-month nodule volume reduction rate (VRR) ≥ 50% was regarded as reflection of proficiency. Multiple linear regression analysis has been performed to determine the relationship between the VRR and clinical variables. Cumulative sum (CUSUM) charts were plotted to assess LCs for all consecutive procedures and in relation to basal nodule size. In details, Group 1 (G1): 57 patients with small nodules (<10 ml); Group 2 (G2): 87 patients with intermediate nodules (10 - 25 ml); Group 3 (G3): 35 patients with large size (> 25 ml).
RESULTS
LC of all 179 procedures showed 3 phases: initial learning (1-39 procedures); consolidation (40-145 procedures); and experienced period (146-179 procedures). For G1 and G2 proficiency is achieved starting from the 10th procedure within the group (or 37th considering consecutively all procedures) and from the 59th procedure within the group (or 116th considering consecutively all procedures), respectively. LC of G3 did not detect operator proficiency.
CONCLUSION
Specific LCs exist concerning the basal size of the nodule treated with RFA. In nodules with baseline volume > 25 ml suboptimal VRR has to be expected. Previously achieved experience on small-intermediate nodules does not seem to provide advantages in terms of higher VRR in the treatment of large nodules. Other potential and non-modifiable factors likely play a key role in the final volume reduction independently from the increased skill of the operator.
Topics: Humans; Thyroid Nodule; Female; Male; Radiofrequency Ablation; Retrospective Studies; Middle Aged; Adult; Learning Curve; Clinical Competence; Aged; Treatment Outcome
PubMed: 38933827
DOI: 10.3389/fendo.2024.1399912 -
Frontiers in Endocrinology 2024The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective...
OBJECTIVES
The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).
METHODS
Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule's largest diameter.
RESULTS
The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I treatment.
CONCLUSION
This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.
Topics: Humans; Thyroid Neoplasms; Iodine Radioisotopes; Lung Neoplasms; Female; Male; Middle Aged; Adult; Aged; Deep Learning; Retrospective Studies; Tomography, Emission-Computed, Single-Photon; Cohort Studies
PubMed: 38933823
DOI: 10.3389/fendo.2024.1429115 -
Frontiers in Neuroscience 2024Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory...
PURPOSE
Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).
METHODS
We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.
RESULTS
SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.
CONCLUSION
Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
PubMed: 38933814
DOI: 10.3389/fnins.2024.1402039 -
Frontiers in Bioengineering and... 2024This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis....
This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis. Clinical and magnetic resonance imaging (MRI) data of 302 patients with and without osteoarthritis were retrospectively collected from April 2022 to October 2023 at Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine. The participants were randomly divided into two groups (a training group, n = 211 and a testing group, n = 91). We used logistic regression to analyze clinical characteristics and established a clinical model. Radiomics signatures were developed by extracting radiomic features from the bone marrow edema area using MRI. A nomogram was developed based on the rad-score and clinical characteristics. The diagnostic performance of the three models was compared using the receiver operating characteristic curve and Delong's test. The accuracy and clinical application value of the nomogram were evaluated using calibration curve and decision curve analysis. Clinical characteristics such as age, radiographic grading, Western Ontario and McMaster Universities Arthritis Index score, and radiological features were significantly correlated with the diagnosis of osteoarthritis. The Rad score was constructed from 11 radiological features. A clinical model was developed to diagnose osteoarthritis (training group: area under the curve [AUC], 0.819; testing group: AUC, 0.815). Radiomics models were used to effectively diagnose osteoarthritis (training group,: AUC, 0.901; testing group: AUC, 0.841). The nomogram model composed of Rad score and clinical characteristics had better diagnostic performance than a simple clinical model (training group: AUC, 0.906; testing group: AUC, 0.845; < 0.01). Based on DCA, the nomogram model can provide better diagnostic performance in most cases. In conclusion, the MRI-bone marrow edema-based radiomics-clinical nomogram model showed good performance in diagnosing early osteoarthritis.
PubMed: 38933540
DOI: 10.3389/fbioe.2024.1368188