Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.

INTRODUCTION: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. MATERIALS AND METHODS: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes. RESULTS: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). CONCLUSIONS: The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

Fabrication and characterization of hybrid eco-friendly high methoxyl pectin/gelatin/TiO2/curcumin (PGTC) nanocomposite biofilms for salmon fillet packaging.

Hybrid eco-friendly nanocomposite films were fabricated by blending high-methoxyl pectin, gelatin, TiO2, and curcumin through the solution casting method. Various concentrations (0-5 wt%) of TiO2 nanoparticles (TNPs) and curcumin as an organic filler were added to the blend solutions. A high TNP concentration affected the surface morphology, roughness, and compactness of the films. Additionally, 3D mapping revealed the nanoparticle distribution in the film layers. Moisture content, water solubility, and light transmittance reduced dramatically with increasing TNP content, in accordance with the water vapor and oxygen permeabilities. X-ray diffraction revealed that the films were semicrystalline nanocomposites, and the thermal properties of the films increased when 5 wt% of TNPs was incorporated into the blend solution. Fourier-transform infrared and Raman analyses revealed interactions among biopolymers, nanoparticles, and organic fillers through hydrogen bonding. The shelf life of fresh salmon fillets was prolonged to six days for all groups, revealed by total viable counts and psychrotrophic bacteria counts, and the pH of the salmon fillets could be extended until the sixth day for all groups. Biodegradation assays demonstrated a significant weight loss in the nanocomposite films. Therefore, a nanocomposite film with 5 wt% TNPs could potentially be cytotoxic to NIH 3T3 cells.

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning.

BACKGROUND: Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. AIM: To develop a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. METHODS: Using multi-view ultrasound data from 3310 patients, 19513 studies, and 228075 images from a retrospective cohort of patients received elastography, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from clinical ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis. RESULTS: The DL algorithm demonstrated repeatable measurements with a moderate number of images (three for each viewpoint) and high agreement across three premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: Areas under the curve of the ROC to classify mild, moderate, and severe steatosis grades were 0.85, 0.91, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter (CAP) with statistically significant improvements for all levels on the unblinded histology-proven cohort and for "= severe" steatosis on the blinded histology-proven cohort. CONCLUSION: The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than the CAP.

Cutoff Values of Acoustic Radiation Force Impulse Two-Location Measurements in Different Etiologies of Liver Fibrosis.

BACKGROUND: Acoustic radiation force impulse (ARFI) imaging is a popular modality to measure liver fibrosis. ARFI selects optimal locations for measurement under imaging guiding. However, there are concerns on study locations and observers bias. To decrease the variations, ARFI at two locations was measured with standardized protocol. This study attempted to establish its cutoff values according to Metavir fibrosis score in different etiologies. METHODS: A consecutive series of patients who received liver histology study were prospectively enrolled. All cases had hemogram, liver biochemistry, viral markers, and ARFI two-location measurements within 4 weeks of histology study. A standardized protocol was performed by single technologist. We excluded patients with alanine aminotransferase >5x upper limit normal. RESULTS: Five hundred and ten patients that included 153 seronegative for both HBsAg and anti-HCV Non-B non-C (NBNC), 33 autoimmune liver diseases (AILD), 261 chronic hepatitis B (CHB), and 63 chronic hepatitis C (CHC) were enrolled. About 83% of NBNC patients had fat cell >5%. For diagnosis of liver cirrhosis, the area under receiver operating characteristic curve of NBNC, AILD, CHB, and CHC groups was 0.937, 0.929, 0.784, and 0.937; the cutoff values for mean ARFI were 1.788, 2.095, 1.455, and 1.710 m/s, respectively. The sensitivity and specificity are both over 0.818 for patients with nonalcoholic fatty liver diseases, CHC, and AILD, but the corresponding data are only 0.727-0.756 in CHB. The Fibrosis-4 Score is as good as ARFI on fibrosis assessment in NBNC. CONCLUSION: The performance of ARFI two-location measurement is excellent in NBNC, AILD, and CHC, but is only satisfactory in CHB.

Protein sensing in living cells by molecular rotor-based fluorescence-switchable chemical probes.

In this paper, we introduce a general design to construct fluorescence-switching probes by using conjugates of a fluorescent molecular rotor and protein specific ligands for the selective protein detection and real-time tracking of protein degradation in living cells. Upon the interaction of the ligand with the protein ligand-binding domain, the crowded surroundings restrict the bond rotation of the fluorescent molecular rotor to trigger the emission of a strong fluorescence signal, which is reduced upon the addition of a competitive ligand or after protein degradation. With this probe design, two fluorescent probes for MGMT and hCAII proteins were constructed and applied for detecting the endogenous proteins in living cells. In addition, real-time degradation kinetics of the alkylated-MGMT at the single living cell level were revealed for the first time. We believe that this fluorescence-switching probe design can possibly be extended for the analysis of other proteins, for which there are still no effective tools to visualize them in living cells.

Steric-dependent label-free and washing-free enzyme amplified protein detection with dual-functional synthetic probes.

Enzyme-catalyzed signal amplification with an antibody-enzyme conjugate is commonly employed in many bioanalytical methods to increase assay sensitivity. However, covalent labeling of the enzyme to the antibody, laborious operating procedures, and extensive washing steps are necessary for protein recognition and signal amplification. Herein, we describe a novel label-free and washing-free enzyme-amplified protein detection method by using dual-functional synthetic molecules to impose steric effects upon protein binding. In our approach, protein recognition and signal amplification are modulated by a simple dual-functional synthetic probe which consists of a protein ligand and an inhibitor. In the absence of the target protein, the inhibitor from the dual-functional probe would inhibit the enzyme activity. In contrast, binding of the target protein to the ligand perturbs this enzyme-inhibitor affinity due to the generation of steric effects caused by the close proximity between the target protein and the enzyme, thereby activating the enzyme to initiate signal amplification. With this strategy, the fluorescence signal can be amplified to as high as 70-fold. The generality and versatility of this strategy are demonstrated by the rapid, selective, and sensitive detection of four different proteins, avidin, O6-methylguanine DNA methyltransferase (MGMT), SNAP-tag, and lactoferrin, with four different probes.

A selective and sensitive fluorescent albumin probe for the determination of urinary albumin.

In this communication, we report a simple albumin probe based on a fluorescent molecular rotor for the detection of trace albumin levels in urine. In the presence of albumin, the probe exhibits remarkable 400-fold fluorescence enhancement with high selectivity and sensitivity. The probe was successfully applied in the quantitative detection of urinary albumin.