Dynamic liver volume change in predicting hepatic decompensation and long-term effects of stereotactic body radiation therapy.

BACKGROUND AND AIM: This study aimed to investigate the association between liver volume change and hepatic decompensation and compare the risk of hepatic decompensation in patients with liver cirrhosis (LC) and hepatocellular carcinoma (HCC) who underwent stereotactic body radiation therapy (SBRT). METHODS: A retrospective review of SBRT-treated HCC and compensated LC without HCC patients was conducted. Liver volume was measured using auto-segmentation software on liver dynamic computed tomography scans. The decompensation event was defined as the first occurrence of refractory ascites, esophageal variceal bleeding, hepatic encephalopathy, or spontaneous bacterial peritonitis. We evaluated the association between the rate of liver volume decrease and hepatic decompensation and compared decompensation events between the SBRT and LC cohorts using propensity score matching. RESULTS: A total of 138 patients from the SBRT cohort and 488 from the LC cohort were analyzed. The rate of liver volume decrease was associated with the risk of decompensation events in both cohorts. The 3-year rate of decompensation events was significantly higher in the group with a liver volume decreasing rate > 7%/year compared with the group with a rate < 7%/year. In the propensity score-matched cohort, the 3-year rate of decompensation events after a single session of SBRT was not significantly different from that in the LC cohort. CONCLUSIONS: The rate of liver volume decrease was significantly associated with the risk of hepatic decompensation in both HCC patients who received SBRT and LC patients. A single session of SBRT for HCC did not result in a higher decompensation rate compared with LC.

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus.

BACKGROUND AND OBJECTIVE: The cone-beam computed tomography (CBCT) provides three-dimensional volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. We introduce the development of AI-based computer-aided diagnosis for CBCT considering the intrinsic imaging noise and evaluate its efficacy and implications. METHODS: We propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The proposed method is model agnostic and compatible with various existing and future AI-based denoising or diagnosis models. RESULTS: The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average area under the curve, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. In addition, the physician's ability to evaluate the AI-derived diagnosis results may be enhanced compared with existing solutions. CONCLUSION: This pioneering study on AI-based diagnosis using CBCT indicates that denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions. Furthermore, we believe that the performance enhancement will expedite the adoption of automated diagnostic solutions using CBCT, especially in locations with a shortage of skilled clinicians and limited access to high-dose scanning.

Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of worse prognosis in multiple system atrophy. Recording sounds generated during sleep by video-polysomnography is recommended for detecting stridor, but the analysis is labor intensive and time consuming. A method for automatic stridor detection should be developed using technologies such as artificial intelligence (AI) or machine learning. However, the rarity of stridor hinders the collection of sufficient data from diverse patients. Therefore, an AI method with high diagnostic performance should be devised to address this limitation. We propose an AI method for detecting patients with stridor by combining audio splitting and reintegration with few-shot learning for diagnosis. We used video-polysomnography data from patients with stridor (19 patients with multiple system atrophy) and without stridor (28 patients with parkinsonism and 18 patients with sleep disorders). To the best of our knowledge, this is the first study to propose a method for stridor detection and attempt the validation of few-shot learning to process medical audio signals. Even with a small training set, a substantial improvement was achieved for stridor detection, confirming the clinical utility of our method compared with similar developments. The proposed method achieved a detection accuracy above 96% using data from only eight patients with stridor for training. Performance improvements of 4%-13% were achieved compared with a state-of-the-art AI baseline. Moreover, our method determined whether a patient had stridor and performed real-time localization of the corresponding audio patches, thus providing physicians with support for interpreting and efficiently employing the results of this method.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes.

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

Immune Responses to Plant-Derived Recombinant Colorectal Cancer Glycoprotein EpCAM-FcK Fusion Protein in Mice.

Epidermal cell adhesion molecule (EpCAM) is a tumor-associated antigen (TAA), which has been considered as a cancer vaccine candidate. The EpCAM protein fused to the fragment crystallizable region of immunoglobulin G (IgG) tagged with KDEL endoplasmic reticulum (ER) retention signal (EpCAM-FcK) has been successfully expressed in transgenic tobacco (Nicotiana tabacum cv. Xanthi) and purified from the plant leaf. In this study, we investigated the ability of the plant-derived EpCAM-FcK (EpCAM-FcKP) to elicit an immune response in vivo. The animal group injected with the EpCAM-FcKP showed a higher differentiated germinal center (GC) B cell population (~9%) compared with the animal group injected with the recombinant rhEpCAM-Fc chimera (EpCAM-FcM). The animal group injected with EpCAM-FcKP (~42%) had more differentiated T follicular helper cells (Tfh) than the animal group injected with EpCAM-FcM (~7%). This study demonstrated that the plant-derived EpCAM-FcK fusion antigenic protein induced a humoral immune response in mice.

Mori Cortex Radicis extract inhibits human norovirus surrogate in simulated digestive conditions.

Norovirus is a major cause of acute gastroenteritis globally, resulting in enormous health and societal costs. In this study, the antiviral activities of Mori Cortex Radicis (MCR) extract and its bioactive flavonoids, morusin and kuwanon G, were tested against murine norovirus (MNV), a human norovirus surrogate, using plaque assay. The antiviral activity was confirmed in simulated digestive conditions, including simulated saliva fluid (SSF), simulated gastric fluid (SGF), and simulated intestinal fluid (SIF). Pre-treatment of MNV with MCR extract at 1000 µg/mL showed antiviral activity with a 1.1-log reduction. Morusin and kuwanon G also demonstrated a 1.0-log and 0.6-log reductions of MNV titers, respectively, at 100 µM. MCR extract at a concentration of 2 mg/mL in SSF, SGF, and SIF markedly reduced MNV titers by 1.8, 1.9, and 1.5 logs, respectively. Therefore, these data suggest that MCR extract can be used to control norovirus infectivity.

Magnolia officinalis and Its Honokiol and Magnolol Constituents Inhibit Human Norovirus Surrogates.

Norovirus is a major cause of foodborne disease and nonbacterial gastroenteritis globally. This study evaluated the antiviral effects of Magnolia officinalis extract and its honokiol and magnolol constituents against human norovirus surrogates, murine norovirus (MNV) and feline calicivirus (FCV) in vitro, and in model food systems. Pretreatment or cotreatment of M. officinalis extract at 1 mg/mL reduced MNV and FCV titers by 0.6-1.8 log. Honokiol and magnolol, which are the major polyphenols in the extract, showed significant antiviral effects against MNV and FCV. The virus-infected cells that were treated with M. officinalis extract exhibited significantly increased glutathione levels (p < 0.05). The extract, honokiol, and magnolol revealed ferric ion-reducing and 2,2-diphenyl-1-picrylhydrazyl radical scavenging activities in a dose-dependent manner. Furthermore, MNV and FCV titers were reduced by >1.6 log or to undetectable levels in apple, orange, and plum juices and by 0.9 and 1.6 log in milk, respectively, when they were treated with the extract at 5 mg/mL. Therefore, the present study suggests that M. officinalis extract can be used as an antiviral food material to control norovirus foodborne diseases.

Expression of Colorectal Cancer Antigenic Protein Fused to IgM Fc in Chinese Cabbage (Brassica rapa).

The epithelial cell adhesion molecule (EpCAM) is a tumor-associated antigen and a potential target for tumor vaccine. The EpCAM is a cell-surface glycoprotein highly expressed in colorectal carcinomas. The objective of the present study is to develop an edible vaccine system through Agrobacterium-mediated transformation in Chinese cabbage (Brassica rapa). For the transformation, two plant expression vectors containing genes encoding for the EpCAM recombinant protein along with the fragment crystallizable (Fc) region of immunoglobulin M (IgM) and Joining (J)-chain tagged with the KDEL endoplasmic reticulum retention motif (J-chain K) were constructed. The vectors were successfully transformed and expressed in the Chinese cabbage individually using Agrobacterium. The transgenic Chinese cabbages were screened using genomic polymerase chain reaction (PCR) in T0 transgenic plant lines generated from both transformants. Similarly, the immunoblot analysis revealed the expression of recombinant proteins in the transformants. Further, the T1 transgenic plants were generated by selfing the transgenic plants (T0) carrying EpCAM-IgM Fc and J-chain K proteins, respectively. Subsequently, the T1 plants generated from EpCAM-IgM Fc and J-chain K transformants were crossed to generate F1 plants carrying both transgenes. The presence of both transgenes was validated using PCR in the F1 plants. In addition, the expression of Chinese cabbage-derived EpCAM-IgM Fc × J-chain K was evaluated using immunoblot and ELISA analyses in the F1 plants. The outcomes of the present study can be utilized for the development of a potential anti-cancer vaccine candidate using Chinese cabbage.

Inhibitory Effects of Laminaria japonica Fucoidans Against Noroviruses.

Norovirus is the leading cause of nonbacterial foodborne disease outbreaks. Human noroviruses (HuNoVs) bind to histo-blood group antigens as the host receptor for infection. In this study, the inhibitory effects of fucoidans from brown algae, Laminaria japonica (LJ), Undaria pinnatifida and Undaria pinnatifida sporophyll, were evaluated against murine norovirus (MNoV), feline calicivirus (FCV) and HuNoV. Pretreatment of MNoV or FCV with the fucoidans at 1 mg/mL showed high antiviral activities, with 1.1 average log reductions of viral titers in plaque assays. They also showed significant inhibition on the binding of the P domains of HuNoV GII.4 and GII.17 to A- or O-type saliva and the LJ fucoidan was the most effective, reaching 54-72% inhibition at 1 mg/mL. In STAT1-/- mice infected with MNoV, oral administration of the LJ fucoidan, composed of mainly sulfated fucose and minor amounts of glucose and galactose, improved the survival rates of mice and significantly reduced the viral titers in their feces. Overall, these results provide the LJ fucoidan can be used to reduce NoV outbreaks.

Optimization of Ammonium Sulfate Concentration for Purification of Colorectal Cancer Vaccine Candidate Recombinant Protein GA733-FcK Isolated from Plants.

A protein purification procedure is required to obtain high-value recombinant injectable vaccine proteins produced in plants as a bioreactor. However, existing purification procedures for plant-derived recombinant proteins are often not optimized and are inefficient, with low recovery rates. In our previous study, we used 25-30% ammonium sulfate to precipitate total soluble proteins (TSPs) in purification process for recombinant proteins from plant leaf biomass which has not been optimized. Thus, the objective in this study is to optimize the conditions for plant-derived protein purification procedures. Various ammonium sulfate concentrations (15-80%) were compared to determine their effects on TSPs yield. With 50% ammonium sulfate, the yield of precipitated TSP was the highest, and that of the plant-derived colorectal cancer-specific surface glycoprotein GA733 fused to the Fc fragment of human IgG tagged with endoplasmic reticulum retention signal KDEL (GA733(P)-FcK) protein significantly increased 1.8-fold. SDS-PAGE analysis showed that the purity of GA733(P)-FcK protein band appeared to be similar to that of an equal dose of mammalian-derived GA733-Fc (GA733(M)-Fc). The binding activity of purified GA733(P)-FcK to anti-GA733 mAb was as efficient as the native GA733(M)-Fc. Thus, the purification process was effectively optimized for obtaining a high yield of plant-derived antigenic protein with good quality. In conclusion, the purification recovery rate of large quantities of recombinant protein from plant expression systems can be enhanced via optimization of ammonium sulfate concentration during downstream processes, thereby offering a promising solution for production of recombinant GA733-Fc protein in plants.

Effect of the developmental stage and tissue position on the expression and glycosylation of recombinant glycoprotein GA733-FcK in transgenic plants.

The influence of developmental stage and position (top, middle, and base) of leaves and stem tissues on the expression and glycosylation pattern of a recombinant therapeutic protein -GA733-FcK- was observed in transgenic seedlings during a 16-week growth period. RNA expression gradually increased with age in the middle and basal leaves and decreased in top leaves after 14 weeks. The protein expression level at all leaf positions increased until 14 weeks and slightly decreased at 16 weeks; it was lower in yellow leaves than in green leaves. In stem, protein expression gradually decreased from the top to the base. The glycosylation patterns of GA733-FcK were analyzed from 10 to 16 weeks. The plant-specific glycans increased in the top leaves at 14 weeks, but only slightly changed in the middle and basal leaves. The structure of glycans varied with tissue position. The glycosylation level in the top and middle leaves increased until 12 and 14 weeks, respectively, and decreased thereafter, whereas it decreased in basal leaves until 14 weeks and increased at 16 weeks. In stem, all three sections showed high-mannose type glycan structures. The area size of the glycans was significantly higher in the top stem than in both the middle and basal stems, and it was smaller in yellow leaves than in green leaves. The glycan profiles were similar between green and yellow leaves until 16 weeks. Thus, biomass-harvesting time should be optimized to obtain recombinant therapeutic proteins with ideal glycan structure profiles.