An audit of autopsy-confirmed diagnostic errors in perinatal deaths: What are the most common major missed diagnoses.

Perinatal autopsies are essential to establish the cause of stillbirth or neonatal death and improve clinical practice. Limited studies have provided detailed major missed diagnoses of perinatal deaths in current clinical practice. In this retrospective audit of 177 perinatal autopsies including 99 stillbirths and 78 neonatal deaths with complete pathologic evaluation, 66 cases (21 Class I and 45 Class II diagnostic errors) were revealed as major discrepancies (37.3%), with complete agreements in 80 cases (45.2%). The difference in major discrepancies between stillbirth and neonatal death groups was significant (P < 0.001), with neonatal deaths being more prone to Class I errors. Various respiratory diseases (25/66, 37.9%) and congenital malformations (16/66, 24.2%) accounted for the majority of missed diagnoses (41/66, 62.1%). More importantly, neonatal respiratory distress syndrome (NRDS) was the most common type I missed diagnosis (7/8, 87.5%), markedly higher than the average 11.9% of all Class I errors. Our findings suggest that there are high disparities between clinical diagnoses and autopsy findings in perinatal deaths, and that various respiratory diseases are mostly inclined to cause major diagnostic errors. We first demonstrated that NRDS is the most common type I missed diagnosis in perinatal deaths, which clinicians should pay special attention to in practice.

Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers. Methods: We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models. Results: The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164). Conclusions: The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.

[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection.

Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is [Formula: see text], which are higher than when using the feature representation in the concrete-or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike.

Multiview Feature Fusion Representation for Interictal Epileptiform Spikes Detection.

Interictal epileptiform spikes (IES) of scalp electroencephalogram (EEG) signals have a strong relation with the epileptogenic region. Since IES are highly unlikely to be detected in scalp EEG signals, the primary diagnosis depends heavily on the visual evaluation of IES. However, visual inspection of EEG signals, the standard IES detection procedure is time-consuming, highly subjective, and error-prone. Furthermore, the highly complex, nonlinear, and nonstationary characteristics of EEG signals lead to the incomplete representation of EEG signals in existing computer-aided methods and consequently unsatisfactory detection performance. Therefore, a novel multiview feature fusion representation (MVFFR) method was developed and combined with a robustness classifier to detect EEG signals with/without IES. MVFFR comprises two steps: First, temporal, frequency, temporal-frequency, spatial, and nonlinear domain features are transformed by the IES to express the latent information effectively. Second, the unsupervised infinite feature-selection method determines the most distinct feature fusion representations. Experimental results using a balanced dataset of six patients showed that MVFFR achieved the optimal detection performance (accuracy: 89.27%, sensitivity: 89.01%, specificity: 89.54%, and precision: 89.82%) compared with other feature ranking methods, and the MVFFR-related method were complementary and indispensable. Additionally, in an independent test, MVFFR maintained excellent generalization capacity with a false detection rate per minute of 0.15 on the unbalanced dataset of one patient.

ZNF191 alters DNA methylation and activates the PI3K-AKT pathway in hepatoma cells via transcriptional regulation of DNMT1.

BACKGROUND: Alteration of DNA methylation is an important event in pathogenesis and progression of hepatocellular carcinoma (HCC). DNA methyltransferase (DNMT) 1, the foremost contributor in DNA methylation machinery, was revealed elevated in HCC and significantly correlates with poor prognosis. However, the transcriptional regulation of DNMT1 in HCC remains unknown. METHODS: Real-time PCR and immunohistochemistry were performed to detect DNMT1 and zinc finger transcription factor 191 (ZNF191) expressions in HCCs. Transcription activity of DNMT1promoter was analyzed with Luciferase reporter activity assay. The binding capacity of ZNF191 protein to DNMT1 promoter was examined with chromatin immunoprecipitation-qPCR (ChIP-qPCR) and electrophoretic mobility shift assay (EMSA). DNA methylation level of hepatoma cells was detected with Methylation array. RESULTS: ZNF191 can regulate DNMT1 mRNA and protein expression positively, and increase the transcription activity of the DNMT1 promoter. ChIP-qPCR and EMSA revealed that ZNF191 protein directly binds to the DNMT1 promoter at nt-240 AT(TCAT)3 TC. Moreover, DNMT1 and ZNF191 expression correlate positively in human HCCs. With methylation array, DNA methylation alteration was observed in hepatoma cells with ZNF191 knockdown, and the differential methylation sites are enriched in the PI3K-AKT pathway. Furthermore, we proved DNMT1 contributes the effect of ZNF191 on hepatoma cell growth via the PI3K-AKT pathway. CONCLUSION: ZNF191 is a novel transcription regulator for DNMT1, and the pro-proliferation effect of ZNF191/DNMT1/p-AKT axis in hepatoma cells implies that ZNF191 status in HCCs may affect the therapeutic effect of DNMTs inhibitors and PI3K inhibitors for precise treatment of the disease.

[Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network].

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.

Antitumor effects and mechanisms of CpG ODN combined with attenuated Salmonella-delivered siRNAs against PD-1.

Numerous studies have focused on the treatment of melanoma, but the current therapies for melanoma have limited therapeutic effects. To find a more effective therapy for melanoma, we combined artificially designed CpG ODN (cytosine-phosphate-guanine oligodeoxynucleotides) and siRNAs (small-interfering ribonucleic acids) targeting PD-1 (programmed cell death protein 1), which were delivered by attenuated Salmonella to treat melanoma in mice, and explored the underlying antitumor mechanisms. We found that mice receiving the combination therapy had the smallest tumor size and the longest survival time. The possible mechanisms underlying this phenomenon include pathways mediated by cyclin D1, p-STAT3 (phosphorylated signal transducers and activators of transcription protein 3), MMP2 (matrix metallopeptidase 2) and cleaved caspase 3, since after treatment, the expression of cyclin D1, p-STAT3, and MMP2 decreased but that of cleaved caspase 3 increased; additional mechanisms include increases in the recruitment of immune cells to tumor sites and in the number of immune cells in mouse spleens and the upregulation of TNF-α (tumor necrosis factor) and IL-6 (interleukin 6). We demonstrated that the combination therapy composed of CpG ODN and PD-1-siRNA delivered by attenuated Salmonella exhibited a strong ability to inhibit melanoma and improve the antitumor immune responses of tumor-bearing mice.