Multi-residue analysis of 206 pesticides in grass forage by the one-step quick, easy, cheap, effective, rugged, and safe method combined with ultrahigh-performance liquid chromatography quadrupole orbitrap mass spectrometry.

A novel method for detecting pesticide multi-residue in grass forage (alfalfa and oat) was established based on the one-step automatic extraction and purification technology of quick, easy, cheap, effective, rugged, and safe combined with ultrahigh-performance liquid chromatography quadrupole Orbitrap high-resolution mass spectrometry. The crushed sample was extracted with acetonitrile with 1% acetate, followed by a cleanup step with a primary-secondary amine, octadecylsilane, and graphitized carbon black. The extraction and purification were carried out using the one-step automatic pretreatment equipment. The target pesticides were acquired in positive ion electrospray ionization mode and full scan/data dependent secondary scan mode. The calibration curve shows good linearity over the corresponding concentration range, with the coefficient of determination greater than 0.99. The screening detection limits were 0.5-50 µg/kg, and the limit of quantification for the 206 pesticides was set at 1-50 µg/kg. At the spiking levels of one, two, and 10 times of limit of quantification, more than 95% of pesticides had recovery between 70-120%, with a relative standard deviation ≤20%. The method was proved to be simple, rapid, high-sensitivity, and could be routinely used for the high throughput screening and quantitative analysis of pesticide residues in alfalfa and oat.

The Conceptual Modeling of Interoperability Framework of Heart Sound Monitor in the Context of an Interoperable End-to-End Architecture.

Background: Heart sound monitor (HSM), a device suitable for home-use, can be used to acquire heart sounds. It enables the telemonitoring of cardiac function, which has been largely evolved and widely used in recent years. Nevertheless, the designers paid little attention to the consistency of information model and data interaction of HSM, thus the data could not be shared and aggregated among healthcare systems. Consequently, the device's development and its application in person-centered telehealth are hindered. Objective: To solve this problem and to build interoperability for HSM, this article proposes a HSM interoperability framework that is constructed by using standardized modeling methods. Methods: The authors collected the common device-output information of HSM involved in telemonitoring, leveraged the standardized interoperability framework defined in ISO/IEEE 11073 Personal Health Device (11073-PHD) standards to model the static data structure and dynamic interaction behaviors of HSM. Results: Via a meta-analysis, the HSM device-output information includes collected data (heart sound measurement), and derived data (e.g., device status). Based on such information, an 11073-PHD-compliant domain information model has been successfully created. This enables the interoperability between HSM and aggregation device, allowing inter-device plug-and-play using the service model and communication model. A prototype of this design has been implemented and validated via Continua Enabling Software Library. Conclusions: The ISO/IEEE 11073-PHD standard framework has the potential to accommodate the HSM, which implicate HSM can be integrated into the interoperable ecosystem to achieve holistic health solution. Findings in this article may be taken as a reference for standard developing organizations to establish a standardized interoperability framework for HSM.

Cross-Subject Seizure Detection via Unsupervised Domain-Adaptation.

Automatic seizure detection from Electroencephalography (EEG) is of great importance in aiding the diagnosis and treatment of epilepsy due to the advantages of convenience and economy. Existing seizure detection methods are usually patient-specific, the training and testing are carried out on the same patient, limiting their scalability to other patients. To address this issue, we propose a cross-subject seizure detection method via unsupervised domain adaptation. The proposed method aims to obtain seizure specific information through shallow and deep feature alignments. For shallow feature alignment, we use convolutional neural network (CNN) to extract seizure-related features. The distribution gap of the shallow features between different patients is minimized by multi-kernel maximum mean discrepancies (MK-MMD). For deep feature alignment, adversarial learning is utilized. The feature extractor tries to learn feature representations that try to confuse the domain classifier, making the extracted deep features more generalizable to new patients. The performance of our method is evaluated on the CHB-MIT and Siena databases in epoch-based experiments. Additionally, event-based experiments are also conducted on the CHB-MIT dataset. The results validate the feasibility of our method in diminishing the domain disparities among different patients.

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data.

Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN based on Swin transformer (ST) and 2D convolutional neural network (2DCNN) is proposed. Time-frequency features extracted by short-term Fourier transform (STFT) are taken as the input of STCNN. ST blocks are used in STCNN to capture the global information and long-term dependencies of EEGs. Meanwhile, the 2DCNN blocks are adopted to capture the local information and short-term dependent features. The combination of the two blocks can fully exploit the seizure-related information thus improve the prediction performance. Comprehensive experiments are performed on the CHB-MIT scalp EEG dataset. The average seizure prediction sensitivity, the area under the ROC curve (AUC) and the false positive rate (FPR) are 92.94%, 95.56% and 0.073, respectively.

Relationship between positive margin and residual/recurrence after excision of cervical intraepithelial neoplasia: a systematic review and meta-analysis.

Background: The relationship between endocervical and ectocervical margin status and residual or recurrence after cervical intraepithelial neoplasia (CIN) resection has been controversial. We investigated the relationship between the excision margins and residual/recurrence to assess indicators for the scope of resection and the risk of treatment failure by using meta-analysis. Methods: Literature searches were performed in PubMed, Medline, Embase, Central, Wangfang and CNKI databases. Patients after CIN resection were grouped according to whether there was residual or recurrence, and the differences in exposure factors between the two groups were compared. Or they were grouped by exposure factor, and compare the differences in residual and recurrence rates under different grouping conditions. The observed outcome was postoperative residual or recurrence. The risk of bias in the literature was assessed using the Newcastle-Ottawa Scale (NOS). The chi-square test were used for heterogeneity. Subgroup explored the sources of heterogeneity. Publication bias was assessed using funnel plots and Egger's test. Results: A total of 11 studies were included in this study, 8 studies were at low risk of bias and 3 studies were at high risk of bias. The 11 studies included 3065 patients, 774 patients with positive margins and 2,291 patients with negative margins. The rate of residual/recurrence after excision of CIN in patients with positive margins was significantly higher than in patients with negative margins [odds ratio (OR) =3.99, P<0.00001]. There was no heterogeneity among the studies (P=0.16), with publication bias (P<0.05). The residual/recurrence rate was significantly higher in patients with positive endocervical margins than in patients with negative endocervical margins (OR =2.59, P<0.00001). There was no heterogeneity among studies (P=0.78) and no publication bias (P<0.05). There was no significant difference in residual/recurrence rate between positive and negative ectocervical margins (OR =1.14, P=0.36). There was no heterogeneity among studies (P=0.32) and no publication bias (P<0.05). Conclusions: Positive endocervical margins, but not external cervical margins, are risk factors for residual/recurrence of CIN after resection. Close attention to the status of the endocervical margins is recommended. More aggressive treatment and frequent follow-up are needed for patients with positive endocervical margins.

A Hazard Analysis of Class I Recalls of Infusion Pumps.

BACKGROUND: The adverse event report of medical devices is one of the postmarket surveillance tools used by regulators to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of these products. However, with the development of the related technologies and market, the number of adverse events has also been on the rise, which in turn results in the need to develop efficient tools that help to analyze adverse events monitoring data and to identify risk signals. OBJECTIVE: This study aimed to establish a hazard classification framework of medical devices and to apply it over practical adverse event data on infusion pumps. Subsequently, it aimed to analyze the risks of infusion pumps and to provide a reference for the risk management of this type of device. METHODS: The authors define a general hierarchical classification of medical device hazards. This classification is combined with the Trace Intersecting Theory to form a human-machine-environment interaction model. Such a model was applied to the dataset of 2001 to 2017 class I infusion pump recalls extracted from the Food and Drug Administration (FDA) website. This dataset does not include cases involving illegal factors. RESULTS: The proposed model was used for conducting hazard analysis on 70 cases of class I infusion pump recalls by the FDA. According to the analytical results, an important source of product technical risk was that the infusion pumps did not infuse accurate dosage (ie, over- or underdelivery of fluid). In addition, energy hazard and product component failure were identified as the major hazard form associated with infusion pump use and as the main direct cause for adverse events in the studied cases, respectively. CONCLUSIONS: The proposed human-machine-environment interaction model, when applied to adverse event data, can help to identify the hazard forms and direct causes of adverse events associated with medical device use.

Study on clinical characteristics and follow-up visit of acquired aplastic anemia associated with parvovirus B19 infection.

OBJECTIVE: To investigate clinical characteristics of parvovirus (B19) related aplastic anemia (AA). METHODS: Of the 28 children with AA included in this study, 24 were treated routinely and received planned follow-up; 15 were subject to B19-DNA re-examination during the treatment and 8 underwent examination for B19-IgM and B19-IgG. Another 39 initially identified AA children were enrolled as the controls and received the treatment same as the above-mentioned group. RESULTS: There were more patients aged 5-8 y in the B19 infection group than the control group (P < 0.05). The course of AA in the B19 infection group was less than 2 mo and the serious aplastic anemia (SAA) and very serious aplastic anemia (VSAA) were more frequently observed in this group than the controls (P < 0.05). The overall efficacy of the treatments in the B19 infection group was more dismal than that in the controls (P < 0.05). Among 15 patients who were subjected to B19-DNA re-examination, negative findings were found in 6 patients with chronic aplastic anemia (CAA); the B19-DNA was persistently positive in 2 of the SAA and 5 VSAA patients. IgM and IgG were respectively detected in 3 and 2 patients out of the 8 children who received antibody examination. CONCLUSIONS: Parvovirus B19 infection contributes to the generation of AA, particularly in children aged 5-8 y. The AA induced may be mainly classified as serious and very serious type, with a course of disease less than 2 mo. Patients can be saved if B19-DNA is eliminated and the antibody is produced.