Current evidence of synaptic dysfunction after stroke: Cellular and molecular mechanisms.

BACKGROUND: Stroke is an acute cerebrovascular disease in which brain tissue is damaged due to sudden obstruction of blood flow to the brain or the rupture of blood vessels in the brain, which can prompt ischemic or hemorrhagic stroke. After stroke onset, ischemia, hypoxia, infiltration of blood components into the brain parenchyma, and lysed cell fragments, among other factors, invariably increase blood-brain barrier (BBB) permeability, the inflammatory response, and brain edema. These changes lead to neuronal cell death and synaptic dysfunction, the latter of which poses a significant challenge to stroke treatment. RESULTS: Synaptic dysfunction occurs in various ways after stroke and includes the following: damage to neuronal structures, accumulation of pathologic proteins in the cell body, decreased fluidity and release of synaptic vesicles, disruption of mitochondrial transport in synapses, activation of synaptic phagocytosis by microglia/macrophages and astrocytes, and a reduction in synapse formation. CONCLUSIONS: This review summarizes the cellular and molecular mechanisms related to synapses and the protective effects of drugs or compounds and rehabilitation therapy on synapses in stroke according to recent research. Such an exploration will help to elucidate the relationship between stroke and synaptic damage and provide new insights into protecting synapses and restoring neurologic function.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

OBJECTIVE: To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer.

Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model.

Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value.

Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

High-resolution magnetic resonance vessel wall imaging provides new insights into Moyamoya disease.

Moyamoya disease (MMD) is a rare condition that affects the blood vessels of the central nervous system. This cerebrovascular disease is characterized by progressive narrowing and blockage of the internal carotid, middle cerebral, and anterior cerebral arteries, which results in the formation of a compensatory fragile vascular network. Currently, digital subtraction angiography (DSA) is considered the gold standard in diagnosing MMD. However, this diagnostic technique is invasive and may not be suitable for all patients. Hence, non-invasive imaging methods such as computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are often used. However, these methods may have less reliable diagnostic results. Therefore, High-Resolution Magnetic Resonance Vessel Wall Imaging (HR-VWI) has emerged as the most accurate method for observing and analyzing arterial wall structure. It enhances the resolution of arterial walls and enables quantitative and qualitative analysis of plaque, facilitating the identification of atherosclerotic lesions, vascular entrapment, myofibrillar dysplasia, moyamoya vasculopathy, and other related conditions. Consequently, HR-VWI provides a new and more reliable evaluation criterion for diagnosing vascular lesions in patients with Moyamoya disease.

Moyamoya syndrome may result from psoriasis: Four case reports.

BACKGROUND: Moyamoya syndrome (MMS) is a group of diseases that involves more than one underlying disease and is accompanied by moyamoya vascular phenomena. Psoriasis is a chronic immune skin disease closely linked to high blood pressure and heart disease. However, psoriasis-related MMS has not been reported. CASE SUMMARY: We collected data on patients with stroke due to MMS between January 2017 and December 2019 and identified four cases of psoriasis. Case histories, imaging, and hematological data were collected. The average age of the initial stroke onset was 58.25 ± 11.52 years; three cases of hemorrhagic and one case of ischemic stroke were included. The average duration from psoriasis confirmation to the initial MMS-mediated stroke onset was 17 ± 3.56 years. All MMS-related stenoses involved the bilateral cerebral arteries: Suzuki grade III in one case, grade IV in two cases, and grade V in one case. Abnormally elevated plasma interleukin-6 levels were observed in four patients. Two patients had abnormally elevated immunoglobulin E levels, and two had thrombocytosis. All four patients received medication instead of surgery. With an average follow-up time of 2 years, two causing transient ischemic attacks occurred in two patients, and no hemorrhagic events occurred. CONCLUSION: Psoriasis may be a potential risk factor for MMS. Patients with psoriasis should be screened for MMS when they present with neurological symptoms.

Targeting dysregulated lipid metabolism for the treatment of Alzheimer's disease and Parkinson's disease: Current advancements and future prospects.

Alzheimer's and Parkinson's diseases are two of the most frequent neurological diseases. The clinical features of AD are memory decline and cognitive dysfunction, while PD mainly manifests as motor dysfunction such as limb tremors, muscle rigidity abnormalities, and slow gait. Abnormalities in cholesterol, sphingolipid, and glycerophospholipid metabolism have been demonstrated to directly exacerbate the progression of AD by stimulating Aß deposition and tau protein tangles. Indirectly, abnormal lipids can increase the burden on brain vasculature, induce insulin resistance, and affect the structure of neuronal cell membranes. Abnormal lipid metabolism leads to PD through inducing accumulation of α-syn, dysfunction of mitochondria and endoplasmic reticulum, and ferroptosis. Great progress has been made in targeting lipid metabolism abnormalities for the treatment of AD and PD in recent years, like metformin, insulin, peroxisome proliferator-activated receptors (PPARs) agonists, and monoclonal antibodies targeting apolipoprotein E (ApoE). This review comprehensively summarizes the involvement of dysregulated lipid metabolism in the pathogenesis of AD and PD, the application of Lipid Monitoring, and emerging lipid regulatory drug targets. A better understanding of the lipidological bases of AD and PD may pave the way for developing effective prevention and treatment methods for neurodegenerative disorders.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions.

PURPOSE: This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation. METHODS: This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy. RESULTS: The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89). CONCLUSION: Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

Unraveling the mystery: congenital absence of the portal vein and its cardiac implications.

A 25-year-old male with tachycardia and right heart enlargement was diagnosed with atrial-ventricular nodal reentrant tachycardia (AVNRT) and successfully treated with radiofrequency ablation. Cardiac MRI confirmed right heart enlargement but ruled out cardiomyopathy. Further imaging revealed a rare congenital absence of the portal vein (CAPV) with an associated portosystemic shunt. This case highlights the importance of considering CAPV in unexplained cardiac symptoms and structural heart changes.

Delayed cardiotoxicity following osimertinib therapy in non-small cell lung cancer: a unique case report.

This case report features a 62-year-old male with stage IB lung adenocarcinoma harboring an epidermal growth factor receptor exon 19 deletion, who underwent treatment with osimertinib following a left upper lobectomy and lymph node dissection. Despite a history of smoking and well-managed type 2 diabetes, the patient developed heart failure 18 months post-initiation of osimertinib therapy, marking one of the latest occurrences of heart failure following osimertinib treatment documented in limited literature. Cardiac MRI revealed significant left ventricular enlargement, lateral wall myocardial thinning, and localized myocardial fibrosis without perfusion defects, a finding not previously reported in literature. This case underscores the severe and unusual cardiac effects of osimertinib in patients with latent risk factors, highlighting the importance of vigilant cardiac monitoring and a multidisciplinary management approach.

Normobaric hyperoxia alleviates complement C3-mediated synaptic pruning and brain injury after intracerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is a common cerebrovascular disease, and the complement cascade exacerbates brain injury after ICH. As the most abundant component of the complement system, complement component 3 (C3) plays essential roles in all three complement pathways. However, the effects of C3 on neurological impairment and brain injury in ICH patients and the related mechanism have not been fully elucidated. Normobaric hyperoxia (NBO) is regarded as a treatment for ICH patients, and recent clinical studies also have confirmed the neuroprotective role of NBO against acute ICH-mediated brain damage, but the underlying mechanism still remains elusive. AIMS: In the present study, we investigated the effects of complement C3 on NBO-treated ICH patients and model mice, and the underlying mechanism of NBO therapy in ICH-mediated brain injury. RESULTS: Hemorrhagic injury resulted in the high plasma C3 levels in ICH patients, and the plasma C3 levels were closely related to hemorrhagic severity and clinical outcomes after ICH. BO treatment alleviated neurologic impairments and rescued the hemorrhagic-induced increase in plasma C3 levels in ICH patients and model mice. Moreover, the results indicated that NBO exerted its protective effects of on brain injury after ICH by downregulating the expression of C3 in microglia and alleviating microglia-mediated synaptic pruning. CONCLUSIONS: Our results revealed that NBO exerts its neuroprotective effects by reducing C3-mediated synaptic pruning, which suggested that NBO therapy could be used for the clinical treatment of ICH.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model.

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases.

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.

Mechanisms and therapeutic targets of mitophagy after intracerebral hemorrhage.

Mitochondria are dynamic organelles responsible for cellular energy production. In addition to regulating energy homeostasis, mitochondria are responsible for calcium homeostasis, clearance of damaged organelles, signaling, and cell survival in the context of injury and pathology. In stroke, the mechanisms underlying brain injury secondary to intracerebral hemorrhage are complex and involve cellular hypoxia, oxidative stress, inflammatory responses, and apoptosis. Recent studies have shown that mitochondrial damage and autophagy are essential for neuronal metabolism and functional recovery after intracerebral hemorrhage, and are closely related to inflammatory responses, oxidative stress, apoptosis, and other pathological processes. Because hypoxia and inflammatory responses can cause secondary damage after intracerebral hemorrhage, the restoration of mitochondrial function and timely clearance of damaged mitochondria have neuroprotective effects. Based on studies on mitochondrial autophagy (mitophagy), cellular inflammation, apoptosis, ferroptosis, the BNIP3 autophagy gene, pharmacological and other regulatory approaches, and normobaric oxygen (NBO) therapy, this article further explores the neuroprotective role of mitophagy after intracerebral hemorrhage.

Normobaric hyperoxia therapy in acute ischemic stroke: A literature review.

Background: Ischemic stroke is one of the most severe cerebrovascular diseases that leads to disability and death and seriously endangers health and quality of life. Insufficient oxygen supply is a critical factor leading to ischemic brain injury. However, effective therapies for ischemic stroke are lacking. Oxygen therapy has been shown to increase oxygen supply to ischemic tissues and improve prognosis after cerebral ischemia/reperfusion. Normobaric hyperoxia (NBHO) has been shown to have neuroprotective effects during ischemic stroke and is considered an appropriate neuroprotective therapy for ischemic stroke. Evidence indicates that NBHO plays a neuroprotective role through different mechanisms in acute ischemic stroke. Recent studies have also reported that combinations with other drug therapies can enhance the efficacy of NBHO in ischemic stroke. Here, we aimed to provide a summary of the potential mechanisms underlying the use of NBHO in ischemic stroke and an overview of the benefits of NBHO in ischemic stroke. Methods: We screened 83 articles on PubMed and other websites. A quick review was conducted, including clinical trials, animal trials, and reviews of studies in the field of NBHO treatment published before July 1, 2023. The results were described and synthesized, and the bias risk and evidence quality of all included studies were assessed. Results: The results were divided into four categories: the mechanism of NBHO, animal and clinical trials of NBHO, the clinical application and prospects of NBHO, and adverse reactions of NBHO. Conclusion: NBHO is a simple, non-invasive therapy that may be delivered early after stroke onset, with promising potential for the treatment of acute ischemic stroke. However, the optimal therapeutic regimen remains uncertain. Further studies are needed to confirm its efficacy and safety.

Total cholesterol mediates the association between history of gestational diabetes mellitus and bone mineral density in US women aged 20-49 years.

OBJECTIVE: The aim of this study is to investigate the potential association between a history of gestational diabetes mellitus (GDM) and lumbar bone mineral density (BMD) among premenopausal women, with an additional examination of the mediating role of serum total cholesterol (TC). METHODS: In this cross-sectional study, 1809 women aged 20-49 years with at least one live birth between 2011 and 2018, drawn from the NHANES dataset, were analyzed. GDM history was identified through questionnaires. Using weighted multiple linear regression, we assessed the relationship between GDM history and lumbar BMD. Additionally, mediation analysis was performed to investigate the potential mediating role of TC. RESULTS: The fully adjusted linear regression model revealed a negative association between a history of GDM and lumbar BMD, indicating a reduction in lumbar BMD (ß = -0.023, 95% CI: -0.043, -0.003, P = 0.0275). Subgroup analysis highlighted a more pronounced trend in individuals aged ≥ 35 years and with a body mass index ≥ 30 kg/m². Furthermore, mediation analysis demonstrated a significant direct effect of a history of GDM on lumbar BMD (P < 0.0001), with serum TC playing a partial mediating role in this interaction (5.33%, P = 0.028). CONCLUSIONS: In women aged 20-49 years within the United States, a history of GDM was associated with diminished lumbar BMD, potentially mediated through serum TC.

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.

OBJECTIVE: To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS: A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS: The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION: The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer.

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

The metagenomic and metabolomic profile of the gut microbes in Chinese full-term and late preterm infants treated with Clostridium butyricum.

The present study investigated the composition, abundance, and diversity of gut microbes in full-term and late-preterm infants from a medical center in eastern China. A total of 144 genomes of stool samples were captured for 16S rRNA metagenomic analyses. A high abundance of commensal intestinal bacteria was detected in these samples such as Phocaeicola vulgatus, Escherichia coli, and Faecalibacterium prausnitzii, indicating a relatively consistent diversity of gut microbes in the present full-term infants aged 38-40 weeks. However, late preterm infants (n = 50) with mandatory antimicrobials feeding exhibited lower diversity but a higher composition of opportunistic pathogens such as Enterococcus species. Centralized on the situation, we explored the regulatory effect of Clostridium butyricum as probiotics on these late preterm infants. The consumption of C. butyricum did not restore the composition of gut microbes altered by antimicrobials to normal levels, although several opportunistic pathogens decreased significantly after probiotic therapy including Staphylococcus aureus, Sphingomonas echinoides, and Pseudomonas putida. We also compared the effects of day-fed versus night-fed probiotics. Intriguingly, the nighttime feeding showed a higher proportion of C. butyricum compared with probiotic day-feeding. Finally, fecal metabolome and metabolites were analyzed in late preterm infants with (n = 20) or without probiotic therapy (n = 20). The KEGG enrichment analysis demonstrated that vitamin digestion and absorption, synaptic vesicle cycle, and biotin metabolism were significantly increased in the probiotic-treated group, while MSEA indicated that a series of metabolism were significantly enriched in probiotic-treated infants including glycerolipid, biotin, and lysine, indicating the complex effects of probiotic therapy on glutathione metabolism and nutrients digestion and absorption in late preterm infants. Overall, this study provided metagenomic and metabolomic profile of the gut microbes in full-term newborns and late preterm infants in eastern China. Further studies are needed to support and elucidate the role of probiotic feeding in late preterm infants with mandatory antimicrobial treatment.

Value of Multimodal Diffusion-weighted Imaging in Preoperative Evaluation of Ki-67 Expression in Endometrial Carcinoma.

PURPOSE: To investigate the value of multimodal diffusion weighted imaging (DWI) in preoperative evaluation of Ki-67 expression of endometrial carcinoma (EC). MATERIALS AND METHODS: Patients who had undergone pelvic DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) sequence MRI scan before surgery were retrospectively enrolled. Single index model, double index model, and DKI were used for post-processing of the DWI data, and the apparent diffusion coefficient (ADC), real diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), non-Gaussian mean diffusion kurtosis (MK), mean diffusion coefficient (MD) and anisotropy fraction (FA) were calculated and compared between the Ki-67 high (≥50%) and low (<50%) expression groups. RESULTS: Forty-two patients with a median age of 56 (range 37 - 75) years were enrolled, including 15 patients with a high Ki-67 (≥50%) expression and 27 with a low Ki-67 (<50%) expression. The MK (0.91 ± 0.12 vs. 0.76 ± 0.12) was significantly (P<0.05) higher while MD (0.99 ± 0.17 vs. 1.16 ± 0.22), D (0.55 ± 0.06 vs. 0.62 ± 0.08), and f (0.21 vs. 0.28) were significantly (P<0.05) lower in the high than in the low expression group. The combined model of MK, MD, D, and f-values had the largest area under the curve (AUC) value of 0.869 (95% CI: 0.764-0.974), sensitivity 0.733 and specificity 0.852, followed by the MK value with an AUC value 0.827 (95% CI: 0.700-0.954), sensitivity 0.733 and specificity 0.815. CONCLUSIONS: IVIM and DKI have certain diagnostic values for preoperative evaluation of the EC Ki-67 expression, and the combined model has the highest diagnostic efficiency.