A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial.

Social media analyses have become increasingly popular among health care researchers. Social media continues to grow its user base and, when analyzed, offers unique insight into health problems. The process of obtaining data for social media analyses varies greatly and involves ethical considerations. Data extraction is often facilitated by software tools, some of which are open source, while others are costly and therefore not accessible to all researchers. The use of software for data extraction is accompanied by additional challenges related to the uniqueness of social media data. Thus, this paper serves as a tutorial for a simple method of extracting social media data that is accessible to novice health care researchers and public health professionals who are interested in pursuing social media research. The discussed methods were used to extract data from Facebook for a study of maternal perspectives on sudden unexpected infant death.

Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method.

To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

Using clotted, pelleted blood samples for direct molecular detection of Bartonella spp. in small mammal wildlife surveillance studies.

OBJECTIVE: Bartonella are emerging bacterial zoonotic pathogens. Utilization of clotted blood samples for surveillance of these bacteria in wildlife has begun to supersede the use of tissues; however, the efficacy of these samples has not been fully investigated. Our objective was to compare the efficacy of spleen and blood samples for DNA extraction and direct detection of Bartonella spp. via qPCR. In addition, we present a protocol for improved DNA extraction from clotted, pelleted (i.e., centrifuged) blood samples obtained from wild small mammals. RESULTS: DNA concentrations from kit-extracted blood clot samples were low and A260/A280 absorbance ratios indicated high impurity. Kit-based DNA extraction of spleen samples was efficient and produced ample DNA concentrations of good quality. We developed an in-house extraction method for the blood clots which resulted in apposite DNA quality when compared to spleen samples extracted via MagMAX DNA Ultra 2.0 kit. We detected Bartonella in 9/30 (30.0%) kit-extracted spleen DNA samples and 11/30 (36.7%) in-house-extracted blood clot samples using PCR. Our results suggest that kit-based methods may be less suitable for DNA extraction from blood clots, and that blood clot samples may be superior to tissues for Bartonella detection.

Mutual learning with memory for semi-supervised pest detection.

Effectively monitoring pest-infested areas by computer vision is essential in precision agriculture in order to minimize yield losses and create early scientific preventative solutions. However, the scale variation, complex background, and dense distribution of pests bring challenges to accurate detection when utilizing vision technology. Simultaneously, supervised learning-based object detection heavily depends on abundant labeled data, which poses practical difficulties. To overcome these obstacles, in this paper, we put forward innovative semi-supervised pest detection, PestTeacher. The framework effectively mitigates the issues of confirmation bias and instability among detection results across different iterations. To address the issue of leakage caused by the weak features of pests, we propose the Spatial-aware Multi-Resolution Feature Extraction (SMFE) module. Furthermore, we introduce a Region Proposal Network (RPN) module with a cascading architecture. This module is specifically designed to generate higher-quality anchors, which are crucial for accurate object detection. We evaluated the performance of our method on two datasets: the corn borer dataset and the Pest24 dataset. The corn borer dataset encompasses data from various corn growth cycles, while the Pest24 dataset is a large-scale, multi-pest image dataset consisting of 24 classes and 25k images. Experimental results demonstrate that the enhanced model achieves approximately 80% effectiveness with only 20% of the training set supervised in both the corn borer dataset and Pest24 dataset. Compared to the baseline model SoftTeacher, our model improves mAP @0.5 (mean Average Precision) at 7.3 compared to that of SoftTeacher at 4.6. This method offers theoretical research and technical references for automated pest identification and management.

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

Validation of LC-MS/MS method for opioid monitoring in Valencia City wastewater: Assessment of synthetic wastewater as potential aid.

In this study, a comprehensive and sensitive method for the simultaneous detection of 17 opioids (OPs) and their human metabolites in wastewater using high-performance liquid chromatography coupled to tandem mass spectrometry was validated. The chromatographic separations of opioids were carried out on a Kinetex® Biphenyl column (1.7 µm, 100 Å, 50 × 2.1 mm). A synthetic wastewater approach was used for recovery studies to mimic a contaminant-free matrix. Two solid-phase extraction (SPE) sorbents (hydrophilic-lipophilic balance and mixed mode with the previous phase and a weak cationic exchange) were studied to optimize sample treatment and obtain higher recoveries. The mixed mode was chosen because the recoveries of 17 target analytes at three spiked concentrations (25, 50, and 100 ng mL-1) were > 80 % for 75 % of the analytes in a simulated wastewater. The intra- and inter-day relative standard deviations (RSDs) were between ±1 % and ±20 %. The method limits of quantification ranged from 5 to 25 ng L-1, the only exceptions being heroin (275 ng L-1) and morphine-3ß-glucuronide (250 ng L-1). Suppression/enhancement is comparable between the synthetic and the influent wastewater. The analytical method was applied to the OPs analysis in twenty-one influent samples collected from the treatment plants treating the wastewater of Valencia City (Spain). Twelve OPs were detected with total daily concentrations ranging from 1 ng L-1 to 2135 ng L-1. The widespread presence of these compounds in water suggests potential widespread exposure, highlighting the need for increased environmental awareness. Furthermore, the estimated daily intake results raise concerns about opioid use as a potential future health and social issue.

Formic acid sandwich method is well-suited for filamentous fungi identification and improves turn around time using Zybio EXS2600 mass spectrometry.

OBJECTIVES: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is extensively employed for the identification of filamentous fungi on MALDI Biotyper (Bruker Daltonics) and Vitek MS (biomerieux), but the performance of fungi identification on new EXS2600 (Zybio) is still unknow. Our study aims to evaluate the new EXS2600 system's (Zybio) ability to rapidly identify filamentous fungi and determine its effect on turnaround time (TAT) in our laboratory. METHODS: We tested 117 filamentous fungi using two pretreatment methods: the formic acid sandwich (FA-sandwich) and a commercial mold extraction kit (MEK, Zybio). All isolates were confirmed via sequence analysis. Laboratory data were extracted from our laboratory information system over two 9-month periods: pre-EXS (April to December 2022) and post-EXS (April to December 2023), respectively. RESULTS: The total correct identification (at the species, genus, or complex/group level) rate of fungi was high, FA-sandwich (95.73%, 112/117), followed by MEK (94.02%, 110/117). Excluding 6 isolates not in the database, species-level identification accuracy was 92.79% (103/111) for FA-sandwich and 91.89% (102/111) for MEK; genus-level accuracy was 97.29% (108/111) and 96.39% (107/111), respectively. Both methods attained a 100% correct identification rate for Aspergillus, Lichtheimia, Rhizopus Mucor and Talaromyces species, and were able to differentiate between Fusarium verticillioides and Fusarium proliferatum within the Fusarium fujikuroi species complex. Notably, high confidence was observed in the species-level identification of uncommon fungi such as Trichothecium roseum and Geotrichum candidum. The TAT for all positive cultures decreased from pre EXS2600 to post (108.379 VS 102.438, P < 0.05), and the TAT for tissue decreased most (451.538 VS 222.304, P < 0.001). CONCLUSIONS: The FA-sandwich method is more efficient and accurate for identifying filamentous fungi with EXS2600 than the MEK. Our study firstly evaluated the performance of fungi identification on EXS2600 and showed it is suitable for clinical microbiology laboratories use.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Introduction: Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. Methods: This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Results: Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. Discussion: We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Managing SVC Syndrome in Patients with Cardiac Implantable Electronic Device Leads: Strategies and Considerations.

BACKGROUND: Data on Transvenous (TV) lead-associated superior vena cava (SVC) syndrome are limited. The management of this problem might require a multidisciplinary approach, often involving TV lead extraction (TLE) followed by angioplasty and stenting. OBJECTIVE: To describe the management and outcome of TV lead-associated SVC syndrome METHODS: We retrospectively identified patients with a diagnosis of SVC syndrome and TV leads at Emory Healthcare between 2015 and 2023. RESULTS: 15 patients with lead-related SVC syndrome were identified. The cohort average age was 50 years. Symptoms included swelling in the face, neck, and upper extremity (67%), shortness of breath (53%) and lightheadedness (40%). Patients had on average 2 ± 0.7 leads crossing the SVC with a lead dwell time of 9.8 ± 7.5 years. Thirteen patients were managed with transvenous lead extraction (TLE), followed by SVC stenting and angioplasty (10), angioplasty alone (2), while one patient had no intervention after TLE. One patient was managed with anticoagulation, and another had angioplasty and stenting with lead jailing. One patient experienced SVC perforation and cardiac tamponade during SVC stenting managed successfully with a covered stent and pericardiocentesis. Among the 12 patients with TLE and angioplasty ± stenting, 7 patients underwent reimplantation of a transvenous lead. Two of those patients had symptoms recurrence and none of the 5 patients without lead reimplantation had recurrence of symptoms. CONCLUSION: Lead-related SVC syndrome management requires a multidisciplinary approach often including TLE followed by angioplasty and stenting. Avoiding TV lead reimplantation might help reduce symptoms recurrence.

Chemical nature evolution of solid supports used in electromembrane extraction procedures: A comparative analysis based on metric tools.

BACKGROUND: In recent decades, green chemistry has been focusing on the adaptation of different chemical methods towards environmental friendliness. Sample preparation procedures, which constitute a fundamental step in analytical methodology, have also been modified and implemented in this direction. In particular, electromembrane extraction (EME) procedures, which have traditionally used plastic supports, have been optimized towards greener approaches through the emergence of alternative materials. In this regard, biopolymer-based membranes (such as agarose or chitosan) have become versatile and very promising substitutes to perform these processes. RESULTS: Different green metric tools (Analytical Eco-Scale, ComplexGAPI and AGREEprep have been applied to study the evolution of solid supports used in EME from nanostructured tissues and polymer inclusion membranes to agar films and chitosan flat membranes. The main goal is to evaluate the usage of these new biomaterials in the analytical procedure to quantify their environmental impact in the frame of Green Analytical Chemistry (GAC). In addition, both RGB model and BAGI metrics have been employed to study the sustainability of the whole procedure, including not only greenness, but also analytical performance and feasibility aspects. Results obtained after the performance of the mentioned metrics have demonstrated that the most efficient and environmentally friendly analytical methods are based on the use of chitosan supports. This improvement is mainly due to the chemical nature of this biopolymer as well as to the removal of organic solvents. SIGNIFICANCE: This work highlights the advantages of biodegradable materials employment in EME procedures to achieve green analytical methodologies. These materials also contribute to raise the figure of merits regarding to the quantification parameters in a wide range of applications compared to classical supports employed in EME, thus enhancing sustainability of procedures.

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

Deep learning-driven dyslexia detection model using multi-modality data.

Background: Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task. Specifically, AMCF employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models, aiming to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of AMCF, it is applied to seven public visual classification datasets commonly used in transfer learning, and compared with the most widely used single-source domain fine-tuning methods. Experimental results demonstrate that, in comparison with the existing fine-tuning methods, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, thereby significantly improving the fine-tuning performance.

Perioperative outcomes of transvaginal specimen extraction laparoscopic total gastrectomy and conventional laparoscopic-assisted total gastrectomy.

BACKGROUND: Natural orifice specimen extraction surgery (NOSES) has emerged as a promising alternative compared to conventional laparoscopic-assisted total gastrectomy (LATG) for treating gastric cancer (GC). However, evidence regarding the efficacy and safety of NOSES for GC surgery is limited. This study aimed to compare the safety and feasibility, in addition to postoperative complications of NOSES and LATG. AIM: To discuss the postoperative effects of two different surgical methods in patients with GC. METHODS: Dual circular staplers were used in Roux-en-Y digestive tract reconstruction for transvaginal specimen extraction LATG, and its outcomes were compared with LATG in a cohort of 51 GC patients with tumor size ≤ 5 cm. The study was conducted from May 2018 to September 2020, and patients were categorized into the NOSES group (n = 22) and LATG group (n = 29). Perioperative parameters were compared and analyzed, including patient and tumor characteristics, postoperative outcomes, and anastomosis-related complications, postoperative hospital stay, the length of abdominal incision, difference in tumor type, postoperative complications, and postoperative survival. RESULTS: Postoperative exhaust time, operation duration, mean postoperative hospital stay, length of abdominal incision, number of specific staplers used, and Brief Illness Perception Questionnaire score were significant in both groups (P < 0.01). In the NOSES group, the postoperative time to first flatus, mean postoperative hospital stay, and length of abdominal incision were significantly shorter than those in the LATG group. Patients in the NOSES group had faster postoperative recovery, and achieved abdominal minimally invasive incision that met aesthetic requirements. There were no significant differences in gender, age, tumor type, postoperative complications, and postoperative survival between the two groups. CONCLUSION: The application of dual circular staplers in Roux-en-Y digestive tract reconstruction combined with NOSES gastrectomy is safe and convenient. This approach offers better short-term outcomes compared to LATG, while long-term survival rates are comparable to those of conventional laparoscopic surgery.

Incidental seminoma in nonobstructive azoospermia: a case report.

Objective: To report on the incidental finding of invasive seminoma in a patient with nonobstructive azoospermia during microdissection testicular sperm extraction. Design: Case report. Patients: A single patient diagnosed with nonobstructive azoospermia underwent microdissection testicular sperm extraction, and an incidental finding of invasive seminoma was made upon histopathological analysis. Results: An incidental discovery of invasive seminoma was observed in the sample pathology obtained during the microdissection testicular sperm extraction. Consequently, the patient underwent further diagnostic workup and a radical orchiectomy. Conclusions: Men with male factor infertility are at increased risk of testicular cancer. As such, it is imperative to incorporate a thorough physical examination and relevant imaging into their diagnostic process. Additionally, it is advisable to include histopathological analysis for all individuals undergoing microdissection testicular sperm extraction.

Development of a rapid and reliable method to simultaneously detect seven food allergens in processed foods using LC-MS/MS.

Rapid analysis of multiple food allergens is required to confirm the appropriateness of food allergen labelling in processed foods. This study aimed to develop a rapid and reliable method to simultaneously detect trace amounts of seven food allergenic proteins (wheat, buckwheat, milk, egg, crustacean, peanut, and walnut) in processed foods using LC-MS/MS. Suspension-trapping (S-Trap) columns and on-line automated solid-phase extraction were used to improve the complex and time-consuming pretreatment process previously required for allergen analysis using LC-MS/MS. The developed method enabled the simultaneous detection of selected marker peptides for specific proteins derived from seven food ingredients in five types of incurred samples amended with trace amounts of allergenic proteins. The limit of detection values of the method for each protein were estimated to be <1 mg/kg. The developed analytical approach is considered an effective screening method for confirming food allergen labelling on a wide range of processed foods.

Application of Natural Antibacterial Plants in the Extraction of Uranium from Seawater.

Marine biofouling directly affects the performance and efficiency of uranium (U(VI)) extraction from seawater. Compared to traditional chemical methods, natural plant extracts are generally biodegradable and nontoxic, making them an environmentally friendly alternative to synthetic chemicals in solving the marine biofouling problem. The effectiveness of natural antibacterial plants (i.e., pine needle, peppermint, Acorus gramineus Soland, Cacumen platycladi, and wormwood) in solving the marine biofouling problem was evaluated in this work. Experimental results showed that natural antibacterial plants could kill Vibrio alginolyticus in solution and effectively solve the marine biofouling problem of U(VI) extraction. To improve the adsorption capacity of natural plants for U(VI) in seawater, poly(vinylphosphonic acid) (PVPA) was modified on natural antibacterial plant surfaces by irradiation grafting technology. PVPA and natural antibacterial plants work as active sites and base materials for the U(VI) extraction material, respectively. The recovery performance of PVPA/pine needle for U(VI) was preliminarily studied. Results show that the adsorption of U(VI) on PVPA/pine needle follows pseudo-second-order and Langmuir models, and the maximum adsorption capacity is 111 mg/g at 298 K and pH 8.2.

Fabrication of sea urchin shaped polyaniline-modified magnetic microporous organic network for efficient extraction of non-steroidal anti-inflammatory drugs from animal-derived food samples.

In this work, a novel polyaniline-modified magnetic microporous organic network (MMON-PANI) composite was fabricated for effective magnetic solid phase extraction (MSPE) of five typical nonsteroidal anti-inflammatory drugs (NSAIDs) from animal-derived food samples before high performance liquid chromatography (HPLC) detection. The core-shell sea urchin shaped MMON-PANI integrates the merits of Fe3O4, MON, and PANI, exhibiting large specific surface area, rapid magnetic responsiveness, good stability, and multiple binding sites to NSAIDs. Convenient and effective extraction of trace NSAIDs from chicken, beef and pork samples is realized on MMON-PANI via the synergetic π-π, hydrogen bonding, hydrophobic, and electrostatic interactions. Under optimal conditions, the MMON-PANI-MSPE-HPLC-UV method exhibits wide linear ranges (0.2-1000 µg L-1), low limits of detection (0.07-1.7 µg L-1), good precisions (intraday and inter-day RSDs < 5.4 %, n = 3), large enrichment factors (98.6-99.9), and less adsorbent consumption (3 mg). The extraction mechanism and selectivity of MMON-PANI are also evaluated in detail. This work proves the incorporation of PANI onto MMON is an efficient way to promote NSAIDs enrichment and provides a new strategy to synthesize multifunctional MON-based composites in sample pretreatment.