Excellent Specificity Research Articles

A sensitive and robust analytical method for the determination of enramycin residues in swine tissues using UHPLC-MS/MS.

Enramycin, a common growth promoter utilized in chickens and pigs, is sensitive against Gram-positive bacteria, and the maximum residue limit (MRL) of enramycin set up by is 30 μg/kg. However, the methods have been reported for detecting enramycin have failed to meet the accuracy requirements, with the required limit of quantification being higher than the MRL. To address this issue, we developed a high-sensitive and robust analytical method based on ultrahigh-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS), to determine enramycin residues in swine tissues, including liver, kidney, pork, and fat. The ENV cartridge was selected to cleanup and enrich analytes after being extracted using a mixture of 55% methanol containing 0.2 M hydrochloric acid. With comprehensively validation, this established method was found great linearity of enramycin in each tissue, with a coefficient of variation above 0.99. Satisfactory recoveries from four different spiking levels were acquired (70.99-101.40%) while the relative standard deviations were all below 9%. The limit of quantification of enramycin in the present study is 5 μg/kg in fat and 10 μg/kg in other tissues, meeting the requirements for conducting the corresponding safety evaluation study. This method was demonstrated with excellent specificity, stability, and high sensitivity. To conclude, this novel approach is sufficiently sensitive and robust for the safety evaluation of enramycin in food products.

A Novel Score to Predict Individual Risk for Future Alzheimer’s Disease: A Longitudinal Study of the ADNI Cohort

Background: Identifying high-risk individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer’s disease (AD) is crucial for early intervention. Objective: This study aimed to develop and validate a novel clinical score for personalized estimation of MCI-to-AD conversion. Methods: The data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study were analyzed. Two-thirds of the MCI patients were randomly assigned to a training cohort (n = 478), and the remaining one-third formed the validation cohort (n = 239). Multivariable logistic regression was performed to identify factors associated with MCI-to-AD progression within 4 years. A prediction score was developed based on the regression coefficients derived from the logistic model and tested in the validation cohort. Results: A lipidomics-signature was obtained that showed a significant association with disease progression. The MCI conversion scoring system (ranged from 0 to 14 points), consisting of the lipidomics-signature and five other significant variables (Apolipoprotein ɛ4, Rey Auditory Verbal Learning Test immediate and delayed recall, Alzheimer’s Disease Assessment Scale delayed recall test, Functional Activities Questionnaire, and cortical thickness of the AD signature), was constructed. Higher conversion scores were associated with a higher proportion of patients converting to AD. The scoring system demonstrated good discrimination and calibration in both the training cohort (AUC = 0.879, p of Hosmer-Lemeshow test = 0.597) and the validation cohort (AUC = 0.915, p of Hosmer-Lemeshow test = 0.991). The risk classification achieved excellent sensitivity (0.84) and specificity (0.75). Conclusions: The MCI-to-AD conversion score is a reliable tool for predicting the risk of disease progression in individuals with MCI.

Fabrication of albumin-Ti3C2 MXene quantum dots-based nanohybrids for breast cancer imaging and synergistic photo/chemotherapeutics

Advancement in the development of new materials with theranostic and phototherapeutic potential along with receptiveness to external stimuli has been persistently inspiring oncology research. Herein, titanium carbide-based MXene quantum dots (FHMQDs) have been synthesized and modified to take advantage of stimuli-responsive behavior and target specificity for breast cancer cells. With a size of around 3 nm, the developed FHMQDs demonstrate high fluorescent emission at around 460 nm. With ∼90 % encapsulation efficiency of doxorubicin (DOX), the developed system also offers rapid DOX release behavior when encountering an acidic pH (5.4). Further, the in vitro assessment of the developed FHMQDs on MDA-MB 231 breast cancer cells presents excellent target specificity to cancer cells which was reflected by its high cytotoxicity against cancer cells. Additionally, the outstanding photodynamic efficiency of FHMQDs due to excessive Reactive Oxygen Species (ROS) generating ability along with apoptosis promoting capability of FHMQDs in cancer cells demonstrates a synergistic approach in cancer theranostics. Encouragingly, the fabricated FHMQDs also exhibited fluorescent labelling and bioimaging capacity which makes it an incredible platform that ensures theranostic excellence in breast cancer research.

A biomimetic skin microtissue biosensor for the detection of fish parvalbumin

In this paper, a biomimetic skin microtissue biosensor was developed based on three-dimensional (3D) bioprinting to precisely and accurately determine fish parvalbumin (FV). Based on the principle that allergens stimulate cells to produce ONOO− (peroxynitrite anion), a screen-printed electrode for the detection nanomolar level ONOO− was innovatively prepared to indirectly detect FV based on the level of ONOO− release. Gelatin methacryloyl (GelMA), RBL-2H3 cells, and MS1 cells were used as bio-ink for 3D bioprinting. The high-throughput and standardized preparation of skin microtissue was achieved using stereolithography 3D bioprinting technology. The printed skin microtissues were put into the self-designed 3D platform that integrated cell culture and electrochemical detection. The experimental results showed that the sensor could effectively detect FV when the optimized ratio of RBL-2H3 to MS1 cells and allergen stimulation time were 2:8 and 2 h, respectively. The linear detection range was 0.125–3.0 μg/mL, and the calculated lowest detection limit was 0.122 μg/mL. In addition, the sensor had excellent selectivity, specificity, stability, and reliability. Thus, this study successfully constructed a biomimetic skin microtissue electrochemical sensor for PV detection.

A Phase 3 Biomarker Validation of GALAD for the Detection of Hepatocellular Carcinoma in Cirrhosis

Better surveillance tests for hepatocellular carcinoma (HCC) are needed. The GALAD score (gender, age, α-fetoprotein [AFP] L3, AFP, and des carboxyprothrombin) has been shown to have excellent sensitivity and specificity for HCC in phase 2 studies. We performed a phase 3 biomarker validation study to compare GALAD with AFP in detecting HCC. This is a prospective study of patients with cirrhosis enrolled at 7 centers. Surveillance for HCC was performed every 6 months at each site, and HCC diagnosis was confirmed per American Association for the Study of Liver Diseases guidelines. Blood for biomarker research was obtained at each follow-up visit and stored in a biorepository. Measurements of AFP, AFP-L3, and des-γ carboxyprothrombin) were performed in a FujiFilm laboratory by staff blinded to clinical data. The performance of GALAD in detecting HCC was retrospectively evaluated within 12 months before the clinical diagnosis. All analyses were conducted by an unblinded statistician in the EDRN data management and coordinating center. A total of 1,558 patients with cirrhosis were enrolled and followed for a median of 2.2 years. A total of 109 patients developed HCC (76 very early or early stage), with an annual incident rate of 2.4%. The areas under the curve for AFP and GALAD within 12 months before HCC were 0.66 and 0.78 (P < .001), respectively. Using a cutoff for GALAD of -1.36, the specificity was 82%, and the sensitivity at 12 months before HCC diagnosis was 62%. For comparison, performance of AFP at 82% specificity showed 41% sensitivity at 12 months before HCC diagnosis (P= .001). GALAD score, compared to AFP, improves the detection of HCC within 12 months before the actual diagnosis.

Lung Ultrasound Score for Prediction of Surfactant Administration in Preterm Infants with Respiratory Failure.

We investigated the predictive value of a lung ultrasound score (LUS) for surfactant administration in a United States Level 4 Neonatal Intensive Care Unit. Thirty infants born at <37 weeks gestational age with respiratory distress syndrome associated respiratory failure requiring continuous positive airway pressure were included. A LUS was obtained within six hours of life. Surfactant administration in the first five days of life was recorded. Receiver operating characteristic (ROC) analysis for LUS and surfactant administration was performed. Median completed gestational age was 33 weeks (31-34 weeks interquartile range) and median birth weight was 2.0 kg (1.5-2.3 kg). LUS for predicting an initial surfactant dose had an area under the ROC curve of 0.97. A score >9 provided 100% sensitivity and 91% specificity for predicting administration of an initial surfactant dose. A LUS > 9 provided excellent sensitivity and specificity for predicting which infants will receive surfactant for associated respiratory failure.

Prediction models for postoperative pulmonary complications in intensive care unit patients after noncardiac thoracic surgery

BackgroundPostoperative pulmonary complication (PPC) is a leading cause of mortality and poor outcomes in postoperative patients. No studies have enrolled intensive care unit (ICU) patients after noncardiac thoracic surgery, and effective prediction models for PPC have not been developed. This study aimed to explore the incidence and risk factors and construct prediction models for PPC in these patients.MethodsThis study retrospectively recruited patients admitted to the ICU after noncardiac thoracic surgery at West China Hospital, Sichuan University, from July 2019 to December 2022. The patients were randomly divided into a development cohort and a validation cohort at a 70% versus 30% ratio. The preoperative, intraoperative and postoperative variables during the ICU stay were compared. Univariate and multivariate logistic regression analyses were applied to identify candidate predictors, establish prediction models, and compare the accuracy of the models with that of reported risk models.ResultsA total of 475 ICU patients were enrolled after noncardiac thoracic surgery (median age, 58; 72% male). At least one PPC occurred in 171 patients (36.0%), and the most common PPC was pneumonia (153/475, 32.21%). PPC significantly increased the duration of mechanical ventilation (p < 0.001), length of ICU stay (p < 0.001), length of hospital stay (LOS) (p < 0.001), and rate of reintubation (p = 0.047) in ICU patients. Seven risk factors were identified, and then the prediction nomograms for PPC were constructed. At ICU admission, the area under the curve (AUC) was 0.766, with a sensitivity of 0.71 and specificity of 0.60; after extubation, the AUC was 0.841, with a sensitivity of 0.75 and specificity of 0.83. The models showed robust discrimination in both the development cohort and the validation cohort, and they were well calibrated and more accurate than reported risk models.ConclusionsICU patients who underwent noncardiac thoracic surgery were at high risk of developing PPCs. Prediction nomograms were constructed and they were more accurate than reported risk models, with excellent sensitivity and specificity. Moreover, these findings could help assess individual PPC risk and enhance postoperative management of patients.

Review on Analytical Method Validation for Trithioparamethoxy Phenylpropene &amp; Chlorpheniramine Maleate in Pharmaceutical Dosage Form

Analytical method validation is a crucial step in ensuring the reliability and accuracy of analytical procedures employed for the determination of active pharmaceutical ingredients (APIs) in pharmaceutical dosage forms. This study focuses on the validation of analytical methods for the quantification of trithioparamethoxy phenylpropene and chlorpheniramine maleate in pharmaceutical dosage forms. The methods were developed using high- performance liquid chromatography (HPLC) or using ultra performance liquid chromatography (UPLC) with appropriate detection techniques. Parameters such as specificity, linearity, precision, accuracy, robustness, ruggedness, range, stability, LOD, LOQ and system suitability were evaluated according to International Conference on Harmonization (ICH) guidelines. The validated methods demonstrated excellent specificity, linearity over a wide concentration range, precise and accurate results, robustness against variations in method parameters, and suitable system suitability. The validated methods are suitable for routine quality control analysis of pharmaceutical formulations containing trithioparamethoxy phenylpropene and chlorpheniramine maleate, ensuring the reliability and consistency of drug products.

Preparation of monoclonal antibodies recognizing pharmacologically active metabolites of metamizole based on rational hapten design and their application in the detection of animal-derived food

Metamizole (MET) is an antipyretic and analgesic drug, the illegal use of which can result in residues of MET metabolites in edible tissues of animals. In this study, a computational chemistry-assisted hapten screening strategy was used to screen for the optimal immunogenic hapten-A and the optimal coating antigen hapten-G-OVA. A monoclonal antibody capable of recognizing two pharmacologically active metabolites of MET, 4-methylamidinoantipyrine (MAA) and 4-aminoantipyrine (AA), was prepared from the hapten-A. The antibody showed excellent specificity for MAA and AA and almost no cross-reactivity with the pharmacologically inactive metabolites 4-formamidinoantipyrine (FAA) and 4-acetamidinoantipyrine (AAA). An ic-ELISA was developed for the simultaneous detection of MAA and AA in animal-derived food, the limits of detection for MAA ranged from 0.93 to 1.18 μg/kg, while those for AA ranged from 1.74 to 4.61 μg/kg. The recovery rate fell within the range of 82 %–110 %, with a coefficient of variation less than 16.39 %.

Exploring and identifying the imaging biomarkers for predicting anti-VEGF treatment response in polypoidal choroidal vasculopathy: aprospective multicenter study.

Polypoidal choroidal vasculopathy (PCV) is a hemorrhagic fundus disease that can lead to permanent vision loss. Predicting the treatment response to anti-VEGF monotherapy in PCV is consistently challenging. We aimed to conduct a prospective multicenter study to explore and identify the imaging biomarkers for predicting the anti-VEGF treatment response in PCV patients, establish predictive model, and undergo multicenter validation. This prospective multicenter study utilized clinical characteristics and images of treatment naïve PCV patients from 15 ophthalmic centers nationwide to screen biomarkers, develop model, and validate its performance. Patients from Peking Union Medical College Hospital were randomly divided into a training set and an internal validation set. A nomogram was established by univariate, LASSO regression, and multivariate regression analysis. Patients from the other 14 centers served as an external test set. Area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Decision curve analysis (DCA) and clinical impact curve (CIC) were utilized to evaluate the practical utility in clinical decision-making. The eye distribution for the training set, internal validation set, and external test set were 66, 31, and 71, respectively. The 'Good responder' exhibited a thinner subfoveal choroidal thickness (SFCT) (230.67 ± 61.96 vs. 314.42 ± 88.00 μm, p < 0.001), lower choroidal vascularity index (CVI) (0.31 ± 0.08 vs. 0.36 ± 0.05, p = 0.006), fewer choroidal vascular hyperpermeability (CVH) (31.0 vs. 62.2%, p = 0.012), and more intraretinal fluid (IRF) (58.6 vs. 29.7%, p = 0.018). SFCT (OR 0.990; 95% CI 0.981-0.999; p = 0.033) and CVI (OR 0.844; 95% CI 0.732-0.971; p = 0.018) were ultimately included as the optimal predictive biomarkers and presented in the form of a nomogram. The model demonstrated AUC of 0.837 (95% CI 0.738-0.936), 0.891 (95% CI 0.765-1.000), and 0.901 (95% CI 0.824-0.978) for predicting 'Good responder' in the training set, internal validation set, and external test set, respectively, with excellent sensitivity, specificity, and practical utility. Thinner SFCT and lower CVI can serve as imaging biomarkers for predicting good treatment response to anti-VEGF monotherapy in PCV patients. The nomogram based on these biomarkers exhibited satisfactory performances.

Systematic literature review to inform the EULAR recommendations for the use of imaging in crystal-induced arthropathies in clinical practice

ObjectiveTo summarise current data regarding the use of imaging in crystal-induced arthropathies (CiAs) informing a European Alliance of Associations for Rheumatology task force.MethodsWe performed four systematic searches in Embase, Medline...

Quantitative analysis of selected alkaloids of Mitragyna speciosa using 1H quantitative nuclear magnetic resonance spectroscopy.

Mitragyna speciosa is a perennial plant native to Asia, well known for its psychoactive properties. Its major alkaloid mitragynine is known to have sedative and euphoric effects. Hence, the plant has been a subject of abuse, leading to addiction, necessitating efficient analytical methods to detect its psychoactive constituents. However, current chromatography-based methods for detecting the alkaloids are time consuming and costly. Quantitative nuclear magnetic resonance (qNMR) spectroscopy emerges as a promising alternative due to its nondestructive nature, structural insights, and short analysis time. Hence, a rapid and precise qNMR method was developed to quantify selected major psychoactive alkaloids in various parts of M.speciosa. Mitragynine, specioliatine, and speciogynine were quantified in relation to the integral value of the -OCH3 groups of the alkaloids and the internal standard 1,4-dinitrobenzene. The precision and reproducibility of the method gave a relative standard deviation (RSD) of 2%, demonstrating the reliability of the method. In addition, the method showed excellent specificity, sensitivity, high linearity range (R2 = 0.999), and limits of detection (LOD) and quantification (LOQ) values. The analysis revealed that the red-veined M.speciosa leaves contained higher levels of mitragynine (32.34 mg/g), specioliatine (16.84 mg/g) and speciogynine (7.69 mg/g) compared to the green-veined leaves, stem bark, or fruits.

Research on injury risk of elite male athletes in racing ice sports based on blood indexes.

This study aims to explore the relationship between blood biochemical indexes and injury risk of elite male athletes in racing ice sports. The male athletes compared the demographic indexes, monthly injuries, and longitudinal tracking data. The non-linear relationship was analyzed using an unrestricted cubic spline. Generalized estimating equations estimated the relative risk (OR) of injury occurrence. Receiver operating characteristics and the area under the curve determined diagnostic accuracy. In the snow sledding group, when creatine kinase rises to 489.46 u/L or Testosterone decreases to 41.32 ng/ml, the risk increases by 1.70 times (OR=1.70, p<0.001) and 1.69 times(OR=1.69, p<0.001) with statistical significance. the Creatine kinase (OR=1.01, P=0.007) and Testosterone (OR=1.00, P＜0.001) were included in the injury prediction model. The model exhibits excellent discrimination, with sensitivity and specificity of 82.8% and 86.5%, respectively. In the ice skating group, when Creatine kinase rise to 467.00 u/L, the risk increases by 2.56 times with statistical significance (OR=2.56, p<0.001). Creatine kinase (OR=1.01, P＜0.001) was included in the predictive model. The model demonstrates good discrimination, with sensitivity and specificity of 90.5% and 66.7%, respectively. Creatine kinase and Testosterone are the risk predictors of injury in elite snowmobile male athletes. Creatine kinase is an independent risk factor for injury in elite speed skaters.

CRISPR-based dual-aptamer proximity ligation coupled hybridization chain reaction for precise detection of tumor extracellular vesicles and cancer diagnosis

Tumor cell-derived extracellular vesicles (TEVs) contain numerous cellular molecules and are considered potential biomarkers for non-invasive liquid biopsy. However, due to the low abundance of TEVs secreted by tumor cells and their phenotypic heterogeneity, there is a lack of sensitive and specific methods to quantify TEVs. Here, we developed a dual-aptamer proximity ligation-coupled hybridization chain reaction (HCR) method for tracing TEVs, exploiting CRISPR to achieve highly sensitive detection. Taking advantage of the high binding affinity of aptamers, the two aptamers (AptEpCAM, AptHER2) exhibited the high selectivity for TEVs recognition. HCR generated long-repeated sequence containing multiple crRNA targetable barcodes, and the signals were further amplified by CRISPR upon recognizing the HCR sequences, thereby enhancing the sensitivity. Under optimal conditions, the developed method demonstrated a favorable linear relationship in the range of 2 × 103−107 particles/μL, with a limit of detection (LOD) of 3.3 × 102 particles/μL. We directly applied our assay to clinical plasma analysis, achieving 100 % accuracy in cancer diagnosis, thus demonstrating the potential clinical applications of TEVs. Due to its simplicity and rapidity, excellent sensitivity and specificity, this method has broad applications in clinical medicine.

A novel electrochemical sensor with NiSx@MoS2 composite for efficient NO2− sensing

Excess nitrites are potentially threatening to human health, so it is urgent to develop accurate and sensitive methods. The development of sensors can provide early warning of possible hazards and alert people to protect public health. This work presents an NiSx@MoS2-composite with excellent electrochemical activity, representing a key finding for highly sensitive NO2− detection and sensor development. With the assistance of NiSx@MoS2, this electrochemical sensor has excellent quantitative detection performance. It has a wide detection range (0.0001–0.0020 mg/mL) and a low detection limit (1.863*10−5 mg/mL) for NO2−. This electrochemical sensor maintains excellent specificity among numerous interferences, and it completes the accurate detection of different real food samples. Pleasingly, the electrochemical sensor has satisfactory repeatability stability, and potential for practical applications. It would demonstrate tremendous potential in scientific dietary guidance, food safety detection and other fields.

Sortase-Mediated Site-Specific Conjugation to Prepare Fluorine-18-Labeled Nanobodies.

Single-domain antibodies, or nanobodies (Nbs), are promising biomolecules for use in molecular imaging due to their excellent affinity, specificity, and fast clearance from the blood. Given their short blood half-life, pairing Nbs with short-lived imaging radioisotopes is desirable. Because fluorine-18 (18F) is routinely used for clinical imaging, it is an attractive radioisotope for Nbs. We report a novel sortase-based, site-specific 18F-labeling method applied to three nanobodies. Labeled nanobodies were synthesized either by a two-step indirect radiolabeling method in one pot or by a one-step direct labeling method using a sortase-mediated conjugation of either the radiolabeled chelator (H-GGGK((±)-Al[18F]FH3RESCA)-NH2) or the unlabeled chelator (H-GGGK((±)-H3RESCA)-NH2) followed by labeling with Al[18F]F, respectively. The overall radiochemical yields were 15-43% (n = 22, decay-corrected) in 70 min (indirect labeling) and 23-58% (n = 12, decay-corrected) in 50 min (direct labeling). The radiochemical purities of the labeled nanobodies prepared by both methods were >98% with a specific activity of 400-600 Ci/mmol (n = 22) for each of the three Nbs tested and exhibited excellent stability profiles under physiological conditions. This simple, site-specific, reproducible, and generalizable 18F-labeling method to prepare nanobodies (Nb-Al[18F]F-RESCA) or other low molecular weight biomolecules can easily be adopted in various settings for preclinical and clinical studies.

An electrochemical biosensor for sensitive detection of live Salmonella in food via MXene amplified methylene blue signals and electrostatic immobilization of bacteriophages.

Anovel bacteriophage-targeted electrochemical biosensor designed for accurate and quantitative detection of live Salmonella in food samplesis presented. The biosensor is simply constructed by electrostatic immobilizing bacteriophages on MXene-nanostructured electrodes. MXene, renowned for its high surface area, biocompatibility, and conductivity, serves as an ideal platform for bacteriophage immobilization. This allows for a high-density immobilization of bacteriophage particles, achieving approximately 71 pcs μm-2. Remarkably, the bacteriophages immobilized MXene nanostructured electrodes still maintain their viability and functionality, ensuring their effectiveness in pathogen detection. Therefore, the proposed biosensor exhibited enhanced sensitivity with a low limit of detection (LOD) of 5CFUmL-1. Notably, the biosensor shows excellent specificity in the presence of other bacteria that commonly contaminate food and can distinguish live Salmonella from a mixed population. Furthermore, it is applicable in detecting live Salmonella in food samples, which highlights its potential in food safety monitoring. This biosensor offers simplicity, convenience, and suitability for resource-limited environments, making it a promising tool for on-site monitoring of foodborne pathogenic bacteria.

Fluorescence immunochromatographic detection of antibodies to the p72 protein of African swine fever virus

The African swine fever virus (ASFV), a highly contagious pathogen responsible for African swine fever (ASF), causes significant economic losses in the global pork industry. Due to its large and complex structure, ASFV remains refractory to commercial vaccine development, necessitating the creation of rapid, sensitive, and specific diagnostic tools for disease control. In this study, quantum dots were conjugated to ASFV p72 protein to establish a fluorescent immunochromatographic assay for detecting ASFV-specific antibodies. The assay test strips contained four adjacent pads arranged sequentially: a sample-application pad, a pad containing mobile antigen-probe conjugate, a nitrocellulose readout pad featuring a test line containing immobilised staphylococcal protein A and a control line containing immobilised monoclonal antibodies against the ASFV p72 protein, and an absorbent pad driving the directional flow of liquid via capillary action. The resulting fluorescence immunochromatographic assay demonstrated highly sensitive and specific ASFV antibody detection in under 15 min. Specificity testing showed no cross-reactivity with serum antibodies against other viruses and sensitivity surpassing that of commercial ASFV antibody colloidal gold immunochromatographic test strips. This novel approach offers rapid detection, excellent specificity, and high sensitivity, and supports the future development of fluorescent immunochromatographic test strips for ASFV antibody detection.

Development of betabodies: The next generation of phosphatidylserine targeting agents

Externalized phosphatidylserine (PS) is a phospholipid and a selective marker of the tumor microenvironment (TME). It is exposed on the outer leaflet of the plasma membrane of tumor-associated endothelial cells, apoptotic tumor cells, and some viable tumor cells, where it functions in part to suppress immune responses by binding to PS receptors expressed on tumor-infiltrating myeloid cells. PS has been targeted with antibodies, such as bavituximab, that bind the phospholipid via a cofactor, β2-glycoprotein 1 (β2GP1); these antibodies showed excellent specificity for tumor vasculature and induce an immune stimulatory environment. We have advanced this concept by developing the next generation of PS targeting agent, a fusion protein (betabody) constructed by linking PS-binding domain V of β2GP1 to the Fc of an IgG2a. Betabodies bind to externalized PS with high affinity (∼1nM), without the requirement of a co-factor and localize robustly to the TME. We demonstrate that betabodies are a direct PS-targeting agent that has the potential to be used as anti-tumor therapy, drug delivery vehicles, and tools for imaging the TME.

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen’s Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.