Multimodal neuroimaging exploration of the mechanisms of sleep quality deterioration after SARS-CoV-2 Omicron infection.

BACKGROUND: To evaluate the neurological alterations induced by Omicron infection, to compare brain changes in chronic insomnia with those in exacerbated chronic insomnia in Omicron patients, and to examine individuals without insomnia alongside those with new-onset insomnia. METHODS: In this study, a total of 135 participants were recruited between January 11 and May 4, 2023, including 26 patients with chronic insomnia without exacerbation, 24 patients with chronic insomnia with exacerbation, 40 patients with no sleep disorder, and 30 patients with new-onset insomnia after infection with Omicron (a total of 120 participants with different sleep statuses after infection), as well as 15 healthy controls who were never infected with Omicron. Neuropsychiatric data, clinical symptoms, and multimodal magnetic resonance imaging data were collected. The gray matter thickness and T1, T2, proton density, and perivascular space values were analyzed. Associations between changes in multimodal magnetic resonance imaging findings and neuropsychiatric data were evaluated with correlation analyses. RESULTS: Compared with healthy controls, gray matter thickness changes were similar in the patients who have and do not have a history of chronic insomnia groups after infection, including an increase in cortical thickness near the parietal lobe and a reduction in cortical thickness in the frontal, occipital, and medial brain regions. Analyses showed a reduced gray matter thickness in patients with chronic insomnia compared with those with an aggravation of chronic insomnia post-Omicron infection, and a reduction was found in the right medial orbitofrontal region (mean [SD], 2.38 [0.17] vs. 2.67 [0.29] mm; P < 0.001). In the subgroups of Omicron patients experiencing sleep deterioration, patients with a history of chronic insomnia whose insomnia symptoms worsened after infection displayed heightened medial orbitofrontal cortical thickness and increased proton density values in various brain regions. Conversely, patients with good sleep quality who experienced a new onset of insomnia after infection exhibited reduced cortical thickness in pericalcarine regions and decreased proton density values. In new-onset insomnia patients post-Omicron infection, the thickness in the right pericalcarine was negatively correlated with the Self-rating Anxiety Scale (r = - 0.538, P = 0.002, PFDR = 0.004) and Self-rating Depression Scale (r = - 0.406, P = 0.026, PFDR = 0.026) scores. CONCLUSIONS: These findings help us understand the pathophysiological mechanisms involved when Omicron invades the nervous system and induces various forms of insomnia after infection. In the future, we will continue to pay attention to the dynamic changes in the brain related to insomnia caused by Omicron infection.

The role of chemerin in the regulation of cGAS-STING pathway in gestational diabetes mellitus placenta.

Recent studies already confirmed that placenta mitochondrial dysfunction is associated with the progression of gestational diabetes mellitus (GDM). Besides, a possible relationship between adipokine chemerin and disulfide-bond A oxidoreductase-like protein (DsbA-L) had been revealed, whereas the potential interaction remains unclear. In addition, very little is still known about the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway and its mechanisms of action in the context of GDM. The present study aims to investigate the underlying mechanism of cGAS-STING pathway and its regulatory relationship with chemerin in GDM. A total of 50 participants, including 25 cases of GDM patients and 25 pregnant women with normal glucose tolerance, were enrolled, and their placenta tissues at term labor were collected. Besides, an insulin resistance cell model was established on the human trophoblastic cell line to explore the molecular mechanism of chemerin on cGAS-STING pathway. Results showed that there were mitochondrial pathological changes in GDM placenta, accompanied by the decreased expression of DsbA-L, increased level of chemerin, and the activation of cGAS-STING pathway. In the insulin resistant cell model, overexpression of chemerin upregulated protein expression of DsbA-L, and recombinant chemerin presented time-dependent inhibition on the cGAS-STING pathway, but this effect was not dependent on DsbA-L. In conclusion, elevated chemerin is probably a protective mechanism, which may be a potential therapeutic strategy for GDM.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics.

OBJECTIVES: To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS: In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS: The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS: • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Analysis of Distribution of Intracranial and Extracranial Atherosclerotic Lesions and Risk Factors for Recurrence in First-Ever and Recurrent Ischemic Stroke Patients in Northeast China.

Objective: The purpose of this study is to analyze the distribution characteristics of atherosclerotic lesions and the risk factors of recurrence in patients with ischemic stroke. Methods: A total of 505 patients diagnosed with ischemic stroke from October 2016 to October 2022 were included. Divide 505 patients with ischemic stroke into old stroke group and new stroke group. Patients without old cerebral infarction were included in the first ischemic stroke group (first group), while patients with old cerebral infarction were included in the recurrent ischemic stroke group (recurrence group).Carotid artery color Doppler ultrasonography and transcranial Doppler ultrasonography were performed on all patients. Results: We compared the distribution and risk factors of atherosclerotic lesions between the first and recurrent groups (378 cases) (127 cases). Mild, moderate, and severe stenosis of the middle cerebral artery (MCA) and occlusion of the intracranial vertebral artery (VA) were the most common in both groups. Intracranial artery stenosis is significantly higher than extracranial artery stenosis, and the anterior circulation artery is more affected than the posterior circulation artery. In the initial and recurrent groups, the proportion of patients with intracranial artery stenosis was significantly higher than that of patients with extracranial artery stenosis (43.4% vs. 22.5% and 53.4% vs. 22.5%), and the number of patients with anterior circulation stenosis was higher than that of other groups. Compared with the first group, the recurrence group had a higher incidence of hypertension, dyslipidemia, and insufficient physical exercise. There is a significant difference in the levels of triglycerides (TG) and platelets (PLT) between the two groups in biochemical indicators. In the first group, infarction was most common in 284 cases (75.1%) of the frontal lobe, followed by 232 cases (61.4%) of the basal ganglia, and 147 cases (38.9%) of the parietal lobe. In the recurrence group, the proportion of frontal lobe infarction [284 (74.0%)], basal ganglia infarction [232 (70.1%)], and parietal lobe infarction [147 (37.0%)] was the highest. It can be observed that the recurrence group had a higher incidence of basal ganglia infarction (70.1% vs. 61.4%), but a lower incidence of occipital lobe infarction (0.8% vs. 4.2%). Conclusions: Our study found no significant difference in the distribution of intracranial and extracranial atherosclerotic lesions between first-ever and recurrent ischemic stroke patients in China. Notably, hypertension, years of dyslipidemia, insufficient physical exercise, elevated triglyceride (TG) levels, and increased platelet (PLT) counts were identified as significant risk factors for stroke recurrence. These findings may have implications for the management and prevention of recurrent ischemic strokes in clinical practice.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

DHA supplementation and pregnancy complications.

Docosahexaenoic acid (DHA) supplementation is recommended for women during pregnancy because of its neurological, visual, and cognitive effects. Previous studies have suggested that DHA supplementation during pregnancy may prevent and treat certain pregnancy complications. However, there are contradictions in the current related studies, and the specific mechanism by which DHA acts remains unclear. This review summarizes the research on the relationship between DHA intake during pregnancy and preeclampsia, gestational diabetes mellitus, preterm birth, intrauterine growth restriction, and postpartum depression. Furthermore, we explore the impact of DHA intake during pregnancy on the prediction, prevention, and treatment of pregnancy complications as well as its impact on offspring neurodevelopment. Our results suggest that there is limited and controversial evidence for the protective effect of DHA intake on pregnancy complications, with the exception of preterm birth and gestational diabetes mellitus. However, additional DHA supplementation may improve long-term neurodevelopmental outcomes in the offspring of women with pregnancy complications.

Long-term outcomes of offspring from multiple gestations: a two-sample Mendelian randomization study on multi-system diseases using UK Biobank and FinnGen databases.

BACKGROUND: Assisted reproductive technologies (ART) have increased the incidence of multiple births, which can have a negative impact on maternal and offspring health. The study aimed to investigate the association between genetically predicted multiple birth and the risk of 42 common diseases of the nervous, psychiatric, cardiovascular, respiratory, digestive, and endocrine systems. METHODS: The study utilized two-sample Mendelian randomization (MR) analysis to explore the potential causal relationship between genetically predicted multiple birth and the genetically predicted risk of diseases. The study used the FinnGen and UK Biobank datasets for analysis. RESULTS: The study found no significant causal relationship between multiple birth and psychiatric disorders. However, the lower limits of the 95% confidence intervals for bipolar affective disorder and anxiety disorders were not robust, indicating a need for further investigation. The study found that multiple birth may be a strong risk factor for infantile cerebral palsy, and caution is necessary in both natural and ART multiple births. The study revealed a potential causal relationship between multiple birth and coronary heart disease, ischemic heart disease, and deep vein thrombosis, which may be related to abnormal intrauterine environments in multiple pregnancies. Surprisingly, multiple birth appears to have a protective effect against some respiratory diseases, such as chronic obstructive pulmonary disease and asthma. CONCLUSIONS: The study highlights the need for caution regarding the risk of infantile cerebral palsy, cardiovascular diseases, and psychiatric disorders in multiple birth. Our study can lead to the development of preventive strategies and improved clinical management for affected infants.

Differential and correlational tractography as tract-based biomarkers in mild traumatic brain injury: A longitudinal MRI study.

We evaluated the fiber bundles in mild traumatic brain injury (mTBI) patients using differential and correlational tractography in a longitudinal analysis. Diffusion MRI data were acquired in 34 mTBI patients at 7 days (acute stage) and 3 months or longer (chronic stage) after mTBI. Trail Making Test A (TMT-A) and Digital Symbol Substitution Test changes were used to evaluate the cognitive performance. Longitudinal correlational tractography showed decreased anisotropy in the corpus callosum during the chronic mTBI stage. The changes in anisotropy in the corpus callosum were significantly correlated with the changes in TMT-A (false discovery rate [FDR] = 0.000094). Individual longitudinal differential tractography found that anisotropy decreased in the corpus callosum in 30 mTBI patients. Group cross-sectional differential tractography found that anisotropy increased (FDR = 0.02) in white matter in the acute mTBI patients, while no changes occurred in the chronic mTBI patients. Our study confirms the feasibility of using correlational and differential tractography as tract-based monitoring biomarkers to evaluate the disease progress of mTBI, and indicates that normalized quantitative anisotropy could be used as a biomarker to monitor the injury and/or repairs of white matter in individual mTBI patients.

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.

BACKGROUND: We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). METHODS: We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. RESULTS: NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. CONCLUSION: Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements.

BACKGROUND: Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making. PURPOSE: To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements. STUDY TYPE: Retrospective. POPULATION: In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled. FIELD STRENGTH/SEQUENCE: A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI. ASSESSMENT: ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively. STATISTICAL TESTS: Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test. RESULTS: For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs. DATA CONCLUSION: The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal cancer diagnosis. However, accurate segmentation of rectal cancerous regions remains a challenging task due to the shape of rectal tumor has significant variations and the tumor and surrounding tissue are indistinguishable. In addition, in the early research on rectal tumor segmentation, most deep learning methods were based on convolutional neural networks (CNNs), and traditional CNN have small receptive field, which can only capture local information while ignoring the global information of the image. Nevertheless, the global information plays a crucial role in rectal tumor segmentation, so traditional CNN-based methods usually cannot achieve satisfactory segmentation results. In this paper, we propose an encoder-decoder network named Dual Parallel Net (DuPNet), which fuses transformer and classical CNN for capturing both global and local information. Meanwhile, as for capture features at different scales as well as to avoid accuracy loss and parameters reduction, we design a feature adaptive block (FAB) in skip connection between encoder and decoder. Furthermore, in order to utilize the apriori information of rectal tumor shape effectively, we design a Gaussian Mixture prior and embed it in self-attention mechanism of the transformer, leading to robust feature representation and accurate segmentation results. We have performed extensive ablation experiments to verify the effectiveness of our proposed dual parallel encoder, FAB and Gaussian Mixture prior on the dataset from the Shanxi Cancer Hospital. In the experimental comparison with the state-of-the-art methods, our method achieved a Mean Intersection over Union (MIoU) of 89.34% on the test set. In addition to that, we evaluated the generalizability of our method on the dataset from Xinhua Hospital, the promising results verify the superiority of our method.

In Vitro Investigation on the Effect of Dendrobine on the Activity of Cytochrome P450 Enzymes.

Dendrobine is the major active ingredient of Dendrobium nobile, Dendrobium chrysotoxum, and Dendrobium fimbriatum, all of which are used in traditional Chinese medicine owing to their antitumor and anti-inflammation activities. Hence, investigation on the interaction of dendrobine with cytochrome P450 enzymes could provide a reference for the clinical application of Dendrobium. The effects of dendrobine on cytochrome P450 enzymes activities were investigated in the presence of 0, 2.5, 5, 10, 25, 50, and 100 µM dendrobine in pooled human liver microsomes. The specific inhibitors were employed as the positive control and the blank groups were set as the negative control. The Lineweaver-Burk plots were plotted to characterize the specific inhibition model and obtain the kinetic parameters. The study reveals that dendrobine significantly inhibited the activity of CYP3A4, 2C19, and 2D6 with IC50 values of 12.72, 10.84, and 15.47 µM, respectively. Moreover, the inhibition of CYP3A4 was found to be noncompetitive (Ki = 6.41 µM) and time dependent (KI = 2.541 µM-1, Kinact = 0.0452 min-1), while the inhibition of CYP2C19 and 2D6 was found to be competitive with the Ki values of 5.22 and 7.78 µM, respectively, and showed no time-dependent trends. The in vitro inhibitory effect of dendrobine implies the potential drug-drug interaction between dendrobine and CYP3A4-, 2C9-, and 2D6-metabolized drugs. Nonetheless, these findings need further in vivo validation.

Multi-echo Dixon and breath-hold T2-corrected multi-echo single-voxel MRS for quantifying hepatic iron overload in rabbits.

BACKGROUND: Three-dimensional (3D) multi-echo-Dixon (ME-Dixon) and breath-hold T2-corrected multi-echo single-voxel MR spectroscopy (HISTO) can simultaneously quantify liver fat and liver iron. However, their diagnostic efficacy and application scope for quantitative iron in co-existing fatty liver have not been adequately evaluated. PURPOSE: To evaluate the accuracy of ME-Dixon and HISTO for quantitative analysis of hepatic iron in rabbits with iron deposition and fatty liver using liver-iron concentration (LIC) as a reference standard. MATERIAL AND METHODS: ME-Dixon, HISTO, and conventional two-dimensional multi-echo gradient echo (GRE) sequences were performed on 42 rabbits. The following parameters were calculated: R2* from ME-Dixon and GRE; proton density fat fraction (PDFF) from the ME-Dixon, HISTO (normal TE range), and HISTO-H (extended TE range); and R2_water from HISTO and HISTO-H. The LIC and liver-fat concentration (LFC) were measured through chemical analysis, and their relationship with the MRI parameters were assessed. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the diagnostic efficiency. RESULTS: LIC was significantly correlated with R2_HISTO-H, R2*_Dixon, and R2*_GRE (r = 0.858, 0.910, 0.931, respectively; P < 0.001) and weakly with R2_HISTO (r = 0.424; P = 0.008). A strong correlation was also observed between the LFC and PDFF obtained from HISTO, HISTO-H, and ME-Dixon (r = 0.776, 0.811, 0.888, respectively; P < 0.001). ME-Dixon showed the best performance with moderate iron overload (AUC = 0.983). CONCLUSION: 3D ME-Dixon is useful for quantifying the LIC, especially with co-existing fatty liver. Its diagnostic performance is also superior to that of the HISTO sequence.

Preimplantation genetic testing for aneuploidy helps to achieve a live birth with fewer transfer cycles for the blastocyst FET patients with unexplained recurrent implantation failure.

PURPOSE: This retrospective cohort study aimed to investigate the value of preimplantation genetic testing for aneuploidy (PGT-A) as a screening test for patients suffering from unexplained recurrent implantation failure (RIF). METHODS: After screening patients in one reproductive medicine center, twenty-nine, forty-nine and thirty-eight women (< 40 years old) who had suffered unexplained RIF with PGT-A, or RIF without PGT-A, or no RIF with PGT-A were included. The clinical pregnancy rate and live birth rate per transfer, the conservative and optimal cumulative clinical pregnancy rates (CCPR) and live birth rates (CLBR) after three blastocyst FETs were analyzed. RESULTS: The live birth rate per transfer was significantly higher in the RIF + PGT-A group than that in the RIF + NO PGT-A group (47.6% vs. 24.6%, p = 0.014). After 3 cycles of FET, RIF + PGT-A group had significantly higher conservative CLBR and optimal CLBR compared to the RIF + NO PGT-A group (69.0% vs. 32.7%, p = 0.002 and 73.7% vs. 57.5%, p = 0.016), but had similar conservative and optimal CLBRs compared to the NO RIF + PGT-A group. The number of FET cycles required when half women achieved a live birth was 1 in the PGT-A group and 3 in RIF + NO PGT-A group. The miscarriage rates were not different between the RIF + PGT-A and RIF + NO PGT-A, RIF + PGT-A and NO RIF + PGT-A groups. CONCLUSION: PGT-A did be superior in reducing the number of transfer cycles required to achieve a similar live birth rate. Further studies to identify the RIF patients who would benefit most from PGT-A are necessary.

Overwork Among ICU Nurses: Identification of Risk Factors.

OBJECTIVE: This study aimed to describe the current situation and explore overwork predictors among ICU nurses in China. BACKGROUND: Overwork is a comprehensive condition of labor where employees work for extended periods with high intensity and high pressure, which can negatively affect their health. Limited literature exists regarding the prevalence, characteristics, professional identity, and environment of overwork among ICU nurses. METHODS: A cross-sectional design study was conducted. The Professional Identification Scale for Nurses, the Practice Environment Scale of the Nursing Work Index, and the Overwork Related Fatigue Scale (ORFS) were used. To explore relationships between variables, univariate analysis or bivariate correlations were used. Multiple regression was used to identify predictors of overwork. RESULTS: Almost 85% of nurses were categorized as overworked, of which, 30% were moderately to severely overworked. Gender, form of employment, stress related to ICU nursing technology and equipment updates, nurses' professional identity, and nurse working environment accounted for 36.6% in the ORFS. CONCLUSIONS: Overwork is common among ICU nurses. Nurse managers need to develop and implement strategies to better support nurses to prevent overwork.

Value for combination of T1WI star-VIBE with TWIST-VIBE dynamic contrast-enhanced MRI in distinguishing lung nodules. / T1WI star-VIBE增强结合TWIST-VIBE动态增强MRI对肺结节诊断的应用价值（英文）.

OBJECTIVES: With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T1 weighted free-breathing star-volumetric interpolated breath hold examination (T1WI star-VIBE) in identifying benign and malignant lung nodules. METHODS: We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T1WI-VIBE, the contrast-enhanced T1WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. RESULTS: There were significant differences in unenhanced T1WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T1WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). CONCLUSIONS: T1WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping.

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on December 14, 2021.

Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models.

BACKGROUND: The focus of neuro-oncology research has changed from histopathologic grading to molecular characteristics, and medical imaging routinely follows this change. PURPOSE: To compare the diagnostic performance of amide proton transfer (APT) and four diffusion models in gliomas grading and isocitrate dehydrogenase (IDH) genotype. STUDY TYPE: Prospective. POPULATION: A total of 62 participants (37 males, 25 females; mean age, 52 ± 13 years) whose IDH genotypes were mutant in 6 of 14 grade II gliomas, 8 of 20 of grade III gliomas, and 4 of 28 grade IV gliomas. FIELD STRENGTH/SEQUENCE: APT imaging using sampling perfection with application optimized contrasts by using different flip angle evolutions (SPACE) and DWI with q-space Cartesian grid sampling were acquired at 3 T. ASSESSMENT: The ability of diffusion kurtosis imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging (NODDI), mean apparent propagator (MAP), and APT imaging for glioma grade and IDH status were assessed, with histopathological grade and genetic testing used as a reference standard. Regions of interest (ROIs) were drawn by two neuroradiologists after consensus. STATISTICAL TESTS: T-test and Mann-Whitney U test; one-way analysis of variance (ANOVA); receiver operating curve (ROC) and area under the curve (AUC); DeLong test. P value < 0.05 was considered statistically significant. RESULTS: Compared with IDH-mutant gliomas, IDH-wildtype gliomas showed a significantly higher mean, 5th-percentile (APT5 ), and 95th-percentile from APTw, the 95th-percentile value of axial, mean, and radial diffusivity from DKI, and 95th-percentile value of isotropic volume fraction from NODDI, and no significantly different parameters from DTI and MAP (P = 0.075-0.998). The combined APT model showed a significantly wider area under the curve (AUC 0.870) for IDH status, when compared with DKI and NODDI. APT5 was significantly different between two of the three groups (glioma II vs. glioma III vs. glioma IV: 1.35 ± 0.75 vs. 2.09 ± 0.93 vs. 2.71 ± 0.81). DATA CONCLUSION: APT has higher diagnostic accuracy than DTI, DKI, MAP, and NODDI in glioma IDH genotype. APT5 can effectively identify both tumor grading and IDH genotyping, making it a promising biomarker for glioma classification. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm.

The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

Multi-UAV Path Planning Algorithm Based on BINN-HHO.

Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm's multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).