Metal-Organic Framework-Based Composites for the Adsorption Removal of Per- and Polyfluoroalkyl Substances from Water.

The increasing health risks posed by per- and polyfluoroalkyl substances (PFASs) in the environment highlight the importance of implementing effective removal techniques. Conventional wastewater treatment processes are inadequate for removing persistent organic pollutants. Recent studies have increasingly demonstrated that metal-organic frameworks (MOFs) are capable of removing PFASs from water through adsorption techniques. However, there is still constructive discussion on the potential of MOFs in adsorbing and removing PFASs for large-scale engineering applications. This review systematically investigates the use of MOFs as adsorbents for the removal of PFAS in water treatment. This primarily involved a comprehensive analysis of existing literature to understand the adsorption mechanisms of MOFs and to identify factors that enhance their efficiency in removing PFASs. We also explore the critical aspects of regeneration and stability of MOFs, assessing their reusability and long-term performance, which are essential for large-scale water treatment applications. Finally, our study highlights the challenges of removing PFASs using MOFs. Especially, the efficient removal of short-chain PFASs with hydrophilicity is a major challenge, while medium- to long-chain PFASs are frequently susceptible to being captured from water by MOFs through multiple synergistic effects. The ion-exchange force may be the key to solving this difficulty, but its susceptibility to ion interference in water needs to be addressed in practical applications. We hope that this review can provide valuable insights into the effective removal and adsorption mechanisms of PFASs as well as advance the sustainable utilization of MOFs in the field of water treatment, thereby presenting a novel perspective.

Effects of foliar selenium, biochar, and pig manure on cadmium accumulation in rice grains and assessment of health risk.

Addressing cadmium (Cd) contamination in agricultural lands is crucial, given its health implications and accumulation in crops. This study used pot experiments to evaluate the impact of foliar selenium spray (Se) (0.40 mM), corn straw biochar (1%), and pig manure (1%) on the growth of rice plants, the accumulation of Cd in rice grain, and to examine their influence on health risk indices associated with Cd exposure. The treatments were designated as follows: a control group without any amendment (CK), biochar (T1), pig manure (T2), Se (T3), Se and biochar (T4), Se and pig manure (T5), and Se along with biochar and pig manure (T6). Our results indicated that the treatments affected soil pH and redox potential and improved growth and the nitrogen and phosphorus content in rice plants. The soil-plant analysis development (SPAD) meter readings of leaves during the tillering stage indicated a 5.27%-15.86% increase in treatments T2 to T6 compared to CK. The flag leaves of T2 exhibited increases of 12.06%-38.94% for electrolyte leakage and an 82.61%-91.60% decline in SOD compared to treatments T3 to T6. Treatments T1 to T6 increased protein content; however, amylose content was significantly reduced in T6. Treatment T6 recorded the lowest Cd concentration in rice grains (0.018 mg/kg), while T2 recorded the highest (0.051 mg/kg). The CK treatment group showed a grain Cd content reduction of 29.30% compared to T2. The assessment of acceptable daily intake, hazard quotient, and carcinogenic risk revealed an ascending order as follows: T6 < T3 < T5 < T4 < T1 < CK < T2. In conclusion, the application of treatment T6 demonstrates the potential to lower oxidative stress, enhance production, reduce cancer risk, and ensure the safe cultivation of rice in environments affected by Cd contamination.

The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.

OBJECTIVES: To evaluate the diagnostic value of computer-aided diagnosis (CAD) software on ultrasound in distinguishing benign and malignant breast masses and avoiding unnecessary biopsy. METHODS: This prospective, multicenter study included patients who were scheduled for pathological diagnosis of breast masses between April 2019 and November 2020. Ultrasound images, videos, CAD analysis, and BI-RADS were obtained. The AUC, accuracy, sensitivity, specificity, PPV, and NPV were calculated and compared with radiologists. RESULTS: Overall, 901 breast masses in 901 patients were enrolled in this study. The accuracy, sensitivity, specificity, PPV and NPV of CAD software were 89.6%, 94.2%, 87.0%, 80.4%, and 96.3, respectively, in the long-axis section; 89.0%, 91.4%, 87.7%, 80.8%, and 94.7%, respectively, in the short-axis section. With BI-RADS 4a as the cut-off value, CAD software has a higher AUC (0.906 vs 0.734 vs 0.696, all p < 0.001) than both experienced and less experienced radiologists. With BI-RADS 4b as the cut-off value, CAD software showed better AUC than less experienced radiologists (0.906 vs 0.874, p < 0.001), but not superior to experienced radiologists (0.906 vs 0.883, p = 0.057). After the application of CAD software, the unnecessary biopsy rate of BI-RADS categories 4 and 5 was significantly decreased (33.0% vs 11.9%, 37.8% vs 14.5%), and the malignant rate of biopsy in category 4a was significantly increased (11.6% vs 40.7%, 7.4% vs 34.9%, all p < 0.001). CONCLUSIONS: CAD software on ultrasound can be used as an effective auxiliary diagnostic tool for differential diagnosis of benign and malignant breast masses and reducing unnecessary biopsy. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT03887598) KEY POINTS: • Prospective multicenter study showed that computer-aided diagnosis software provides greater diagnostic confidence for differentiating benign and malignant breast masses. • Computer-aided diagnosis software can help radiologists reduce unnecessary biopsy. • The management of patients with breast masses becomes more appropriate.

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.

Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

Cationic metal-organic frameworks as an efficient adsorbent for the removal of 2,4-dichlorophenoxyacetic acid from aqueous solutions.

Metal-organic frameworks (MOFs) material with high surface area, good chemical stability and multi-functionality, has become an emerging adsorbent for water treatment. A novel kind of quaternary amine anionic-exchange MOFs UiO-66 namely UiO-66-NMe3+ was firstly synthesized for adsorptive removal of a widely used toxic herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions. The well-prepared UiO-66-NMe3+ MOFs were fully characterized, and then the main parameters affecting the adsorption process including solution pH, adsorbent dosage and coexisting anions were systematically investigated. The maximum adsorption capacity of UiO-66-NMe3+ toward 2,4-D reached as high as 279 mg g-1, much higher than that of pristine UiO-66 and aminated UiO-66. The adsorption mechanism could be attributed to the electrostatic interactions efficiently enhanced by the functionalization of quaternary amine groups, combining with the π-π conjugations between the linkers in MOFs and 2,4-D molecules, leading to the better adsorption performance of UiO-66-NMe3+. Additionally, the UiO-66-NMe3+ could be well regenerated by simple solvent washing and exhibited a slight decline of adsorption capacity after seven successive recycle. Furthermore, satisfactory adsorption capacity and reusability of the MOFs in environmental water samples were attained. Comparing with reported activated carbon and resin materials, the UiO-66-NMe3+ MOFs possessed higher adsorption capacity and shorter equilibrium time, as well as good reusability and practicality. The developed ion-exchange functionalized MOFs provided an ideal alternative for efficient adsorptive-removal of 2,4-D from complicated aqueous environment.

AcousticRobots: Smart acoustically powered micro-/nanoswimmers for precise biomedical applications.

Although nanotechnology has evolutionarily progressed in biomedical field over the past decades, achieving satisfactory therapeutic effects remains difficult with limited delivery efficiency. Ultrasound could provide a deep penetration and maneuverable actuation to efficiently power micro-/nanoswimmers with little harm, offering an emerging and fascinating alternative to the active delivery platform. Recent advances in novel fabrication, controllable concepts like intelligent swarm and the integration of hybrid propulsions have promoted its function and potential for medical applications. In this review, we will summarize the mechanisms and types of ultrasonically propelled micro/nanorobots (termed here as "AcousticRobots"), including the interactions between AcousticRobots and acoustic field, practical design considerations (e.g., component, size, shape), the synthetic methods, surface modification, controllable behaviors, and the advantages when combined with other propulsion approaches. The representative biomedical applications of functional AcousticRobots are also highlighted, including drug delivery, invasive surgery, eradication on the surrounding bio-environment, cell manipulation, detection, and imaging, etc. We conclude by discussing the challenges and outlook of AcousticRobots in biomedical applications.

The Landscape of Biomimetic Nanovesicles in Brain Diseases.

Brain diseases, such as brain tumors, neurodegenerative diseases, cerebrovascular diseases, and brain injuries, are caused by various pathophysiological changes, which pose a serious health threat. Brain disorders are often difficult to treat due to the presence of the blood-brain barrier (BBB). Biomimetic nanovesicles (BNVs), including endogenous extracellular vesicles (EVs) derived from various cells and artificial nanovesicles, possess the ability to penetrate the BBB and thus can be utilized for drug delivery to the brain. BNVs, especially endogenous EVs, are widely distributed in body fluids and usually carry various disease-related signal molecules such as proteins, RNA, and DNA, and may also be analyzed to understand the etiology and pathogenesis of brain diseases. This review covers the exhaustive classification and characterization of BNVs and pathophysiological roles involved in various brain diseases, and emphatically focuses on nanotechnology-integrated BNVs for brain disease theranostics, including various diagnosis strategies and precise therapeutic regulations (e.g., immunity regulation, disordered protein clearance, anti-neuroinflammation, neuroregeneration, angiogenesis, and the gut-brain axis regulation). The remaining challenges and future perspectives regarding the nanotechnology-integrated BNVs for the diagnosis and treatment of brain diseases are also discussed and outlined.

Selenium enhancement strategy under precise fertilization in foxtail millet rhizosphere.

Enhancing selenium content in millet is a crucial strategy to address malnutrition due to selenium deficiency. Jingu 21 was used as the experimental material in this study. The effects of selenium fertilizer application amount, vertical position of fertilization, and horizontal position of fertilization on the selenium content in various millet organs were assessed using a three-factor, five-level quadratic rotation combination design. The results indicate that selenium fertilizer application amount, vertical fertilization position, and horizontal fertilization position significantly affected the selenium content in various millet organs. Analysis of the selenium accumulation for different millet organs show that the recommended optimal agronomic strategy for producing selenium-enriched millet comprises a selenium fertilizer application amount ranging from 100.65 to 120.15 kg/hm2, a vertical fertilization position of 10.28-11.76 cm, and a horizontal fertilization position of 6.74-7.29 cm. This study elucidates the patterns of selenium content accumulation under precise fertilization measures of millet and provides valuable insights for implementing selenium enhancement techniques in the production of selenium-enriched millet.

Selenium loss during boiling processes and its bioaccessibility in different crops: Estimated daily intake.

Food crops provide a good selenium (Se) source for Se-deficient populations. This study assessed how boiling affects Se concentration, speciation, and bioaccessibility in common food crops to determine human Se intake. Boiling rice resulted in an 11.9% decrease in minimum Se content, while sorghum experienced a maximum (34.9%) reduction. Boiled vegetables showed a 21% - 40% Se loss. Cereals showed notable decreases in selenomethionine (SeMet) and selenocysteine (SeCys2), while most vegetables exhibited a significant reduction in Se-methylselenocysteine (SeMeCys). Boiling significantly reduced the Se bioaccessibility in all food crops, except cabbage and potato. Cereal crops were more efficacious in meeting the recommended daily intake (RDI) of Se compared to vegetables. Rice exceeds other crops and provides up to 39.2% of the WHO/FAO-recommended target minimum daily intake of 60 µg/day. This study provides insight into a substantial dissonance between the estimated daily intake (EDI) of Se and the bioaccessible Se in both raw and boiled crops. Consequently, revising EDI standards is imperative.

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.

[Determination of five nonsteroidal anti-inflammatory drugs in water by dispersive solid phase extraction-ultra performance liquid chromatography-tandem mass spectrometry based on metal-organic framework composite aerogel].

Nonsteroidal anti-inflammatory drugs (NSAIDs) are a class of synthetic drugs that do not contain glucocorticoids. NSAIDs are widely used for their analgesic, antipyretic, and anti-inflammatory effects. Due to their low adsorption coefficients and recalcitrance to biodegradation, NSAIDs readily enter environmental water through sewage discharge and exist stably for long periods. The long-term presence of trace amounts of NSAIDs in environmental water has adverse health effects on humans and animals. Therefore, it is important to establish an appropriately sensitive and reliable method for the determination of NSAIDs in environmental water, where their concentrations are low. Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) is highly selective and sensitive, and so is especially suitable for detection of NSAIDs. Solid phase extraction is one of the most commonly used pretreatment methods. The extraction efficiency depends mainly on the adsorbents used. Metal-organic framework (MOF) aerogel SPE materials combine the attributes of highly selective adsorption property and high affinity. Moreover, the monolithic structure of the MOF aerogel composite simplifies the solid-liquid separation process. In this work, a novel MOF/chitosan (CS) composite designated Co-UiO-67(bpy)/CS, was prepared as the adsorbent material to enrich ketoprofen (KPF), naproxen (NPX), flurbiprofen (FPN), diclofenac (DCF), and ibuprofen (IBF) in water. This facilitated the detection of these compounds by UPLC-MS/MS. Co-UiO-67(bpy) was synthesized by a solvothermal method by mixing zirconium chloride, cobalt chloride, and the organic ligand 2,2-bipyridine-5,5 dicarboxylic acid. A CS suspension was used to prepared the hydrogel, which was freeze-dried to obtain the Co-UiO-67(bpy)/CS aerogel. The prepared material was characterized by Fourier transform-infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). Co-UiO-67 (bpy) was embedded into chitosan. A layered porous MOF composite aerogel was observed. The extraction efficiency of the five NSAIDs was investigated and optimized by assessing type of extraction material, MOF amount, extraction time, sample pH, ionic strength, formic acid concentration in eluent, elution time, and elution volume. The optimized results showed that the target compounds could be completely adsorbed within 5 min. In the UPLC-MS/MS experiment, NSAIDs were analyzed in the negative ionization multiple radiation monitoring (MRM) mode. Gradient elution was carried out with 0.01% formic acid aqueous solution and methanol as the mobile phases. The analytical method was established in the optimized extraction conditions. The five NSAIDs displayed good linearity with linear correlation coefficients greater than 0.9937. The limits of detection (LODs) and limits of quantification (LOQs) of this developed method were 0.32-2.06 ng/L and 1.05-6.78 ng/L, respectively. Satisfactory recoveries of the five analytes were achieved within 74.5%-114.1% at three spiked concentrations of 40, 250, and 1500 ng/L, as well as good precision with relative standard deviations of 1.3%-12.3% (intra-day) and 1.3%-11.5% (inter-day). The method was then used to test real-world water samples. Trace amounts of ketoprofen and flurbiprofen were detected in municipal wastewater (14.52 ng/L and 10.05 ng/L, respectively). The method exhibited good sensitivity, accuracy, and precision, and the operation process was convenient. The present study thus presents a novel method for the detection of the trace NSAIDs in environmental waters.

Magnetic covalent-organic frameworks for the simultaneous extraction of eleven emerging aromatic disinfection byproducts in water samples coupled with UHPLC-MS/MS determination.

A simple method based on magnetic solid-phase extraction (MSPE) was developed for the simultaneous extraction of eleven emerging aromatic disinfection byproducts (DBPs) in water samples coupled with ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) determination. A magnetic covalent-organic framework (COF) material, namely, Fe3O4 @TpBD, was facilely synthesized and fully characterized, followed by an MSPE process. Several important MSPE parameters, such as the magnetic ratio, Fe3O4 @TpBD amount and sample pH, were systematically investigated. Under optimal conditions, the limits of detection and quantification of this COF-MSPE-UHPLC-MS/MS method were as low as 0.07-1.81 ng/L and 0.24-5.99 ng/L, respectively. Good precision was obtained with relative standard deviations (RSDs) of 1.3-10.9% (intraday) and 4.3-15.9% (interday). Furthermore, the validated method was proven applicable to real water samples; for example, the recoveries were 86.8-115.1% for the secondary effluent, and several DBPs in swimming pool water were detected. Notably, the MSPE process required only 7 min, ensuring that the DBPs were relatively stable during the whole analysis process and that Fe3O4 @TpBD demonstrated excellent reusability. The COF-based MSPE method with simplicity, rapidity and efficiency provided an ideal sample pretreatment alternative to determine trace DBPs in complex matrices.

Determination of anionic perfluorinated compounds in water samples using cationic fluorinated metal organic framework membrane coupled with UHPLC-MS/MS.

Global concerns stem from the environmental crisis have compelled researchers to develop selective and sensitive methods for the identification and measurement of emerging pollutants in the environmental matrices. The cationic F-TMU-66+Cl-/polyvinylidene fluoride metal-organic frameworks (MOFs) mixed matrix membrane (F-TMU-66+Cl-/PVDF MMM) was synthesized and used as a versatile adsorbent with multiple binding sites for the simultaneous extraction of twelve anionic perfluorinated compounds (PFCs) from reservoir water samples. The physical and chemical characteristics of the materials, as well as adsorption mechanism were fully surveyed by various instrumental techniques. Important extraction parameters, including amount of MOFs, pH, desorption conditions, and salinity were systematically investigated and optimized. The combination of dispersive membrane solid extraction based on F-TMU-66+Cl-/PVDF MMM with ultra-high performance liquid chromatography-tandem mass spectrometry provided ultra-low limit of detections within the range of 0.03-0.48 ng/L. By virtue of the simplicity and robustness of the extraction procedure, high sensitivity of detection scheme, good stability and selectivity of the F-TMU-66+Cl-/PVDF MMM, the developed method exhibits excellent practicability for ultra-trace analysis of anionic PFCs in water samples.

Determination of Neonicotinoid Insecticides in Environmental Water by the Enrichment of MIL-53 Mixed Matrix Membrane Coupled with High Performance Liquid Chromatography.

Metal organic framework based mixed matrix membranes (MOF-MMMs) were synthesized and applied for dispersive membrane extraction (DME) of four neonicotinoid insecticides (nitenpyram, thiacloprid, imidacloprid, and acetamiprid) in environmental water, combined with high performance liquid chromatography (HPLC) for determination. Several experimental conditions were optimized in detail, involving dosage percentage of MOF, extraction time, sample pH, salinity, type and volume of eluent, and elution time. High sensitivity with limits of detection and quantification were achieved as 0.013-0.064 µg L-1 and 0.038-0.190 µg L-1, respectively, and good precision with relative standard deviations were obtained as 3.07-12.78%. The proposed method has been successfully applied to determine four neonicotinoid insecticides in tap water, surface water, and seawater, satisfactory recoveries of spiked water samples were between 72.50 and 117.98%. Additionally, the MOF-MMMs showed good reusability with the extraction efficiencies almost remaining stable after 14 cycles. The MOF-MMMs based DME followed by the HPLC method can be a promising utility for the determination of neonicotinoid insecticides in environmental water samples, with high sensitivity and convenient operation.

Ultrasound elastography.

Physicians have used palpation as a diagnostic examination to understand the elastic properties of pathology for a long time since they realized that tissue stiffness is closely related to its biological characteristics. US elastography provided new diagnostic information about elasticity comparing with the morphological feathers of traditional US, and thus expanded the scope of the application in clinic. US elastography is now widely used in the field of diagnosis and differential diagnosis of abnormality, evaluating the degree of fibrosis and assessment of treatment response for a range of diseases. The World Federation of Ultrasound Medicine and Biology divided elastographic techniques into strain elastography (SE), transient elastography and acoustic radiation force impulse (ARFI). The ARFI techniques can be further classified into point shear wave elastography (SWE), 2D SWE, and 3D SWE techniques. The SE measures the strain, while the shear wave-based techniques (including TE and ARFI techniques) measure the speed of shear waves in tissues. In this review, we discuss the various techniques separately based on their basic principles, clinical applications in various organs, and advantages and limitations and which might be most appropriate given that the majority of doctors have access to only one kind of machine.

[Determination of seven phenoxy acid herbicides in water by dispersive solid phase extraction-ultra performance liquid chromatography-tandem mass spectrometry based on cationic metal-organic framework mixed matrix membrane].

Phenoxy acid herbicides are widely used because of their excellent efficiency and low cost. However, owing to their strong polarity and water solubility, these herbicides do not degrade easily in a water environment and persist for a long time in water bodies. These herbicides readily enter water bodies via surface runoff, infiltration, and other migration routes, thus affecting water quality safety. Therefore, it is of great significance to establish a sensitive and simple method for the quantitative analysis of phenoxy acid herbicides in environmental water. Given the low concentration of such contaminants in environmental water, appropriate detection methods are important. Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) has high sensitivity and accuracy, thus being well suited for the phenoxy acid herbicides analysis. Sample preparation techniques are also important for the extraction and enrichment of contaminants in environmental water. Dispersive solid phase extraction (DSPE) has attracted considerable attention owing to its low cost, ease of operation, and low solvent consumption. In general, the selectivity and efficiency of solid phase extraction are largely dependent on the characteristics of the solid adsorbent materials. Ionic metal-organic frameworks (iMOFs) have excellent ion-exchange properties and show selective absorptivity to ionic compounds. In this work, a metal-organic framework (MOF) MIL-101-NH2 was synthesized by a facile hydrothermal method. Then, a cationic MOF mixed matrix membrane (MMM) was fabricated by soaking the MOFs in a polyvinylidene fluoride (PVDF) solution and further functionalization with quaternary amine groups. A method was developed for the determination of seven phenoxy acid herbicides in water by UPLC-MS/MS based on DSPE. The prepared material was characterized by Fourier transform infrared spectroscopy and scanning electron microscopy to determine its functional groups and morphology. The results showed that there were quaternary amine groups in the material, and that the functionalization did not have any obvious effect on the chemical and crystal structures of MIL-101-NH2. The prepared MIL-101-$NMe_{3}^{+}$-PVDF MMM was used as an adsorbent for DSPE to enrich the seven phenoxy acid herbicides in water. It is well known that the key factors influencing extraction efficiency are the adsorption and elution conditions. To establish the optimum extraction conditions, the influence of some important factors, including the adsorbent amount, sample pH, extraction time, elution solvent, elution volume, and elution time, was investigated in detail. Gradient elution was carried out with 0.01% formic acid aqueous solution and acetonitrile as the mobile phase. The target analytes were separated on an ACQUITY UPLC BEH C18 column (100 mm×2.1 mm, 1.7 µm), and multiple reaction monitoring (MRM) was conducted in the negative electrospray ionization mode. The external standard method was used for quantitative analysis. The established method was verified in terms of the linear ranges, limits of detection (LODs), limits of quantification (LOQs), recoveries, and precision. Under the optimal conditions, the seven phenoxy acid herbicides showed good linear relationships in their respective concentration ranges, and the correlation coefficients were all higher than 0.997. The LODs and LOQs were 0.00010-0.00090 µg/L and 0.00033-0.00300 µg/L, respectively. The recoveries were tested at three spiked levels of 0.005, 0.05, and 0.2 µg/L. The average recoveries of the seven compounds were in the range of 80% to 102%. The intra-day and inter-day relative standard deviations were within 1.4% to 9.4% and 4.2% to 12.6%, respectively. The established method was applied to the analysis of the phenoxy acid herbicides in tap water and reservoir water. Three levels of spiked samples were adopted to investigate the accuracy of the method. The results demonstrated that our method is applicable to the detection of trace phenoxy acid herbicides in water samples. In summary, this method has the advantages of simplicity, rapidity, and sensitivity, and it is suitable for the detection of the seven phenoxy acid herbicides in environmental water.

Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound.

PURPOSE: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness. RESULTS: Multivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC. CONCLUSION: The CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules.

OBJECTIVE: The purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR). DESIGN AND METHODS: From June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms. RESULTS: In the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (P=0.108), 0.766 (P=0.101), 0.677 (P=0.211), 0.750 (P=0.128), and 0.680 (P=0.023), 0.761 (P=0.530), respectively. CONCLUSIONS: The study demonstrated that DL could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.