Predicting efficacy of sub-anesthetic ketamine/esketamine i.v. dose during course of cesarean section for PPD prevention, utilizing traditional logistic regression and machine learning models.

OBJECTIVE: Increasing researches supported that intravenous ketamine/esketamine during the perioperative period of cesarean section could prevent postpartum depression(PPD). With the effective rate ranging from 87.2 % to 95.5 % in PPD, ketamine/esketamine's responsiveness was individualized. To optimize ketamine dose/form based on puerpera prenatal characteristics, reducing adverse events and improving the total efficacy rate, prediction models were developed to predict ketamine/esketamine's efficacy. METHOD: Based on two randomized controlled trials, 12 prenatal features of 507 women administered the ketamine/esketamine intervention were collected. Traditional logistics regression, SVM, random forest, KNN and XGBoost prediction models were established with prenatal features and dosage regimen as predictors. RESULTS: According to the logistic regression model (ain = 0.10, aout = 0.15, area under the receiver operating characteristic curve, AUC = 0.728), prenatal Edinburgh Postnatal Depression Scale (EPDS) score ≥ 10, thoughts of self-injury and bad mood during pregnancy were associated with poorer ketamine efficacy in PPD prevention, whilst a high dose of esketamine (0.25 mg/kg loading dose+2 mg/kg PCIA) was the most effective dosage regimen and esketamine was more recommended rather than ketamine in PPD. The AUCvalidation set of KNN and XGBoost model were 0.815 and 0.651, respectively. CONCLUSION: Logistic regression and machine learning algorithm, especially the KNN model, could predict the effectiveness of ketamine/esketamine iv. during the course of cesarean section for PPD prevention. An individualized preventative strategy could be developed after entering puerpera clinical features into the model, possessing great clinical practice value in reducing PPD incidence.

Multi-scale dense selective network based on border modeling for lung nodule segmentation.

PURPOSE: Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules. METHODS: In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder-decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules. RESULTS: We evaluate the BorDenNet rigorously on the lung public dataset LIDC-IDRI. For the segmentation of the target nodules, the average Dice score is 92.78[Formula: see text], the average sensitivity is 91.37[Formula: see text], and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People's Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules. CONCLUSION: Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency.

What difficulties did the college students encountered in information seeking during the COVID-19 pandemic?

To better promote information service and fight the infodemic, this paper investigated the difficulties that Chinese college students encountered in information seeking during the COVID-19 pandemic. We collected data in two stages. In the first stage in November 2020, we collected data from the Foundation of Information Science course. 54 college students who took the course completed an assignment to illustrate their information needs and difficulties during the pandemic. In the second stage in March 2021, trough convenience sampling we conducted an online survey by WenJuanXing. The participants were required to answer the same question as the question in the first stage. We collected 204 valid responses. Then, based on the search task difficulty reason scheme proposed by Liu et al. (2015) (denoted LKC15), we used content analysis to code the responses to analyze the difficulties that Chinese students encountered. LKC15's difficulty reasons were classified from three aspects: user, task, and user-task interaction. The findings indicated that 14 of the 21 difficulty reasons in LKC15 were identified in this study. Moreover, we added 17 new Difficulty reasons to revise the scheme. The difficulty reasons of user-task interaction were mentioned most frequently. In terms of user-task interaction, the difficulty reasons related to document features were mentioned most frequently, followed by the search results. Finally, it provided some suggestions and discussed the directions for future study.

MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data.

PURPOSE: Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS: In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS: The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION: In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the high-risk group for sudden death of AMI. AMI occurs quickly and does not show obvious symptoms before onset. In addition, postonset clinical testing is also a complex and invasive test, which may cause some postoperative complications. Therefore, it is necessary to propose a noninvasive and convenient auxiliary diagnostic method. In traditional Chinese medicine (TCM), it is an effective auxiliary diagnostic strategy to complete the disease diagnosis through some body surface features. It is helpful to observe whether the palmar thenar undergoes hypertrophy and whether the metacarpophalangeal joint is swelling in detecting acute myocardial infarction. Combined with deep learning, we propose a depth model based on traditional palm image (MTIALM), which can help doctors of traditional Chinese medicine to predict myocardial infarction. By building the shared network, the model learns information that covers all the tasks. In addition, task-specific attention branch networks are built to simultaneously detect the symptoms of different parts of the palm. The information interaction module (IIM) is proposed to further integrate the information between task branches to ensure that the model learns as many features as possible. Experimental results show that the accuracy of our model in the detection of metacarpophalangeal joints and palmar thenar is 83.16% and 84.15%, respectively, which are significantly improved compared with the traditional classification methods.

Correlations Between SIRT Genetic Polymorphisms and Postpartum Depressive Symptoms in Chinese Parturients Who Had Undergone Cesarean Section.

OBJECTIVE: To investigate the association of genetic polymorphisms of SIRT with postpartum depressive symptoms and analyze the risk factors for postpartum depressive symptoms in women following cesarean section. METHODS: A total of 368 Chinese woman undergoing cesarean section were enrolled in this study. A cutoff of ≥10 for the Edinburgh Postnatal Depression Scale identified postpartum depressive symptoms. Genotypes of SIRT1, SIRT 2, and SIRT 6 were determined using Sequenom MassArray single-nucleotide polymorphism (SNP) analysis. We analyzed the contribution of genetic factors (SNPs, linkage disequilibrium, and haplotype) to postpartum depressive symptoms and performed logistic regression analysis to identify all potential risk factors for postpartum depressive symptoms and define interactions between genetic and environmental factors. RESULTS: The incidence of postpartum depressive symptoms was 18.7% in this cohort. Univariate analysis suggested that SIRT2 polymorphism at rs2873703 (TT genotype) and rs4801933 ((TT genotype) and SIRT6 polymorphism at rs350846 (CC genotype) and rs107251 (TT genotype) were significantly correlated with the occurrence of postpartum depressive symptoms (p<0.05). Linkage disequilibrium was identified between SIRT6 polymorphisms rs350846 and rs107251. Incidence of postpartum depressive symptoms in cesarean-section parturients with SIRT2 haplotype CCC was decreased (OR 0.407, 95% CI 0.191-0.867; p=0.016). SIRT2 polymorphisms rs2873703 and rs4801933 were multiply collinear. Logistic regression analysis showed that SIRT2 polymorphism at rs2873703 (TT genotype) and rs4801933 (TT genotype), domestic violence, stress during pregnancy, and depressive prenatal mood were risk factors for postpartum depressive symptoms (p<0.05). CONCLUSION: Pregnant women with SIRT2 genotypes rs2873703 TT and rs4801933 TT and experiencing domestic violence, stress during pregnancy, and prenatal depression are more likely to suffer from postpartum depressive symptoms.

Dexmedetomidine Alleviates Postpartum Depressive Symptoms following Cesarean Section in Chinese Women: A Randomized Placebo-Controlled Study.

OBJECTIVES: Few studies have investigated the prophylactic efficacy of dexmedetomidine (DEX) in postpartum depressive symptoms (PDS). A randomized double-blind placebo-controlled trial was conducted to investigate whether the administration of DEX, immediately after delivery and for patient-controlled intravenous analgesia (PCIA), can attenuate PDS. METHODS: A total of 600 parturients scheduled for elective cesarean delivery under spinal anesthesia were randomly allocated into the control group (infusion with 0.9% normal saline after delivery and PCIA with sufentanil) and the DEX group (DEX infusion 0.5 µg/kg after delivery and PCIA with DEX plus sufentanil). The prevalence of postpartum depressive disorders was indicated by the Edinburgh Postnatal Depression Scale (EPDS). Postoperative analgesia, sedation, and sleep quality of parturients were also assessed. RESULTS: Postpartum blues and PDS prevalence in the DEX, versus control, group were significantly lower (5.0% vs 14.1%, p<0.001; 5.7% vs 16.3%, p<0.001, respectively), especially in parturients with antenatal depression or moderate stress during pregnancy. Compared with the control group, the EPDS score at postpartum days 7 and 42 in the DEX group was significantly lower (4.23 ± 4.37 vs 1.93 ± 3.36, p<0.001; 4.68 ± 4.78 vs 1.99 ± 3.18, p<0.001, respectively), as was the incidence of postpartum self-harm ideation at postpartum days 7 and 42 in the DEX group versus the control group (1.1% vs 4.0%, p=0.03; 0.4% vs 2.9%, p=0.04, respectively). The pain score and the sleep quality in the DEX group were better than that in the control group (p<0.001). CONCLUSION: The application of DEX in the early postpartum period can significantly attenuate the incidence of postpartum depressive disorders.

Prophylactic use of ketamine reduces postpartum depression in Chinese women undergoing cesarean section✰.

This study aimed to explore the effect of prophylactic ketamine administration on postpartum depression in Chinese woman undergoing cesarean section. This randomized controlled study included 654 Chinese women undergoing cesarean section. At 10 min after child birth, patients in the ketamine group were given 0.5 mg/kg ketamine, whereas patients in the control group received standard postpartum care. At the end of operation, all patients were armed with a patient-controlled intravenous analgesia device. The primary outcome was the prevalence of postpartum depression (PPD), as assessed by the Edinburgh Postnatal Depression Scale (EPDS), and the secondary outcomes included the safety assessment and the Numerical Rating Scale (NRS) of postoperative pain. The prevalence of postpartum blues and postpartum depression were significantly lower in the ketamine group than in the control group. Logistic analysis showed that ketamine administration protected against postpartum depression, and PPD-associated risk factors included stress during pregnancy, antenatal depressive symptom and antenatal suicidal ideation. In addition, the antidepressive effect of prophylactic ketamine was stronger in mothers with a history of moderate stress during pregnancy, antenatal depressive symptom and antenatal suicidal ideation. Our findings suggest that ketamine functions as a prophylactic agent against PPD.