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.

The Landscape of Smart Biomaterial-Based Hydrogen Therapy.

Hydrogen (H2) therapy is an emerging, novel, and safe therapeutic modality that uses molecular hydrogen for effective treatment. However, the impact of H2 therapy is limited because hydrogen molecules predominantly depend on the systemic administration of H2 gas, which cannot accumulate at the lesion site with high concentration, thus leading to limited targeting and utilization. Biomaterials are developed to specifically deliver H2 and control its release. In this review, the development process, stimuli-responsive release strategies, and potential therapeutic mechanisms of biomaterial-based H2 therapy are summarized. H2 therapy. Specifically, the produced H2 from biomaterials not only can scavenge free radicals, such as reactive oxygen species (ROS) and lipid peroxidation (LPO), but also can inhibit the danger factors of initiating diseases, including pro-inflammatory cytokines, adenosine triphosphate (ATP), and heat shock protein (HSP). In addition, the released H2 can further act as signal molecules to regulate key pathways for disease treatment. The current opportunities and challenges of H2-based therapy are discussed, and the future research directions of biomaterial-based H2 therapy for clinical applications are emphasized.

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.

[Determination of seven phenoxy carboxylic acid herbicides in water using dispersive solid phase extraction coupled with ultra-high performance liquid chromatography-tandem mass spectrometry based on metal-organic framework composite aerogel].

Phenoxy carboxylic acid herbicides (PCAs) are difficult to degrade and, thus, pose significant threats to the environment and human health. The limit for 2,4-dichlorophenoxyacetic acid is 30 µg/L in China's standards for drinking water quality, 70 µg/L in the United States' drinking water standards, and 30 µg/L in the World Health Organization's guidelines for drinking water quality. Therefore, the development of an effective detection method for trace PCAs in water is a crucial endeavor. Metal-organic frameworks (MOFs) are novel porous materials that possess advantages such as a large specific surface area, adjustable pore size, and abundant active sites. They exhibit excellent adsorption capability for various compounds. However, the applications of MOFs as adsorbents are limited. For example, the process of isolating powdered MOFs from aqueous solutions is laborious, and microporous MOFs exhibit limited surface affinity, which decreases their mass transfer efficiency in the liquid phase. MOF crystals can be embedded in a substrate to overcome these limitations. Aerogels are obtained by drying hydrogels, which are hydrophilic polymers with a three-dimensional crosslinked network structure. Spongy aerogel materials exhibit unique structural properties such as high porosity, large pore volume, ultralow density, and easy tailorability. When MOFs are combined with an aerogel, their efficient and selective adsorption properties are preserved. In addition, MOF aerogels exhibit a hierarchical porous structure, which enhances the affinity and mass transfer efficiency of the MOF for target molecules. At present, MOF aerogels are primarily prepared by freeze-drying or using supercritical carbon dioxide. These drying processes require significant amounts of energy and time. Hence, the development of greener and more efficient methods to prepare skeleton aerogels is urgently needed. In this study, we prepared an environment-friendly aerogel at ambient temperature and pressure without the use of specialized drying equipment. This ambient-dried MOF composite aerogel was then used for the dispersive solid phase extraction (DSPE) of seven PCAs from environmental water, followed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The key parameters affecting the efficiency of DSPE, including the extraction conditions, ratio of MIL-101(Fe)-NH2 to sodium alginate, pH of the aqueous samples, extraction time, ionic strength (salinity), and elution conditions, such as the elution solvent ratio, elution time, and elution volume, were investigated to obtain optimal extraction efficiency. The adsorbent could adsorb the target contaminants within 12 min, and the analytes could be completely desorbed within 30 s by elution with 4 mL of 1.5% (v/v) formic acid in methanol solution. The water samples could be analyzed without pH adjustment. The main adsorption mechanisms were electrostatic interactions and π-π conjugation. Thus, a new method based on MOF aerogels coupled with UHPLC-MS/MS was developed for the determination of the seven PCA residues in water. The calibration curves for the seven PCAs showed good linearity (r2≥0.9986), with limits of detection (LODs) and quantification (LOQs) ranging from 0.30 to 1.52 ng/L and from 1.00 to 5.00 ng/L, respectively. Good intra- and inter-day precision values of 6.5%-17.1% and 7.4%-19.4%, respectively, were achieved under low (8 ng/L), medium (80 ng/L), and high (800 ng/L) spiking levels. The developed method was applied to the detection of PCAs in surface water, seawater, and waste leachate, and the detected mass concentrations ranged from 0.6 to 19.3 ng/L. Spiked recovery experiments were conducted at mass concentrations of 8, 80, and 800 ng/L, and the recoveries ranged from 61.7% to 120.3%. The proposed method demonstrates good sensitivity, precision, and accuracy, and has potential applications in the detection of trace PCAs in environmental water.

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.

Beta-blocker drives the conjugative transfer of multidrug resistance genes in pure and complex biological systems.

Drug resistance poses a high risk to human health. Extensive use of non-antibiotic drugs contributes to antibiotic resistance genes (ARGs) transfer. However, how they affect the spread of broad-host plasmids in complex biological systems remains unknown. This study investigated the effect of metoprolol on the transfer frequency and host range of ARGs in both intrageneric and intergeneric pure culture systems, as well as in anammox microbiome. The results showed that environmental concentrations of metoprolol significantly promoted the intrageneric and intergeneric conjugative transfer. Initially, metoprolol induced excessive oxidative stress, resulting in high cell membrane permeability and bacterial SOS response. Meanwhile, more pili formation increased the adhesion and contact between bacteria, and the abundance of conjugation-related genes also increased significantly. Activation of the electron transport chain provided more ATP for this energy-consuming process. The underlying mechanism was further verified in the complex anammox conjugative system. Metoprolol induced the enrichment of ARGs and mobile genetic elements. The enhanced bacterial interaction and energy generation facilitated the high conjugative transfer frequency of ARGs. In addition, plasmid-borne ARGs tended to transfer to opportunistic pathogens. This work raises public concerns about the health and ecological risks of non-antibiotic drugs.

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 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.

[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.

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.