Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation.

Background: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI. Methods: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling. Results: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model. Conclusions: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

Histological Features of Uterine Myometrial Dysfunction: Possible Involvement of Localized Inflammation.

OBJECTIVE: The latest perspective suggests that elevated levels of inflammation and cytokines are implicated in atonic postpartum hemorrhage. Lipopolysaccharide (LPS) has been widely used to induce inflammation in animal models. Therefore, this study aimed to induce uterine inflammation using LPS to investigate whether local inflammation triggers dysfunction and atrophy in the myometrium, as well as the potential underlying molecular mechanisms involved. METHODS: In vivo, an animal model was established by intraperitoneal injection of 300 µg/ kg LPS in rats on gestational day 21. Hematoxylin-eosin (H&E) staining and Masson staining were employed to determine morphological changes in the rat uterine smooth muscle. Enzyme-linked immunosorbent assay (ELISA) was used to detect inflammatory cytokines. Immunohistochemistry, tissue fluorescence, and Western blotting were conducted to assess the expression levels of the uterine contraction-related proteins Toll-like receptor 4 (TLR4) and the nuclear factor kappa-B (NF-κB) signaling pathway. In vitro, human uterine smooth muscle cells (HUtSMCs) were exposed to 2 µg/mL LPS to further elucidate the involvement of the TLR4/NF-κB signaling pathway in LPS-mediated inflammation. RESULTS: In this study, LPS induced uterine myometrial dysfunction in rats, leading to a disorganized arrangement, a significant increase in collagen fiber deposition, and widespread infiltration of inflammatory cells. In both in vivo animal models and in vitro HUtSMCs, LPS elevated IL-6, IL-1ß, and TNF-α levels while concurrently suppressing the expression of connexin 43 (Cx43) and oxytocin receptor (OXTR). Mechanistically, the LPS-treated group exhibited TLR4 activation, and the phosphorylation levels of p65 and IκBα were notably increased. CONCLUSION: LPS triggered the TLR4/NF-κB signaling pathway, inducing an inflammatory response in the myometrium and leading to uterine myometrial dysfunction and uterine atony.

Combining transfer learning and hyperspectral imaging to identify bruises of pears across different bruise types.

Mechanical bruise is one of the most crucial factors affecting the quality of pears, which has a huge influence on postharvest transportation, storage, and sale of pears. To rapidly detect early bruises of pears across different bruise types, hyperspectral imaging technology coupled with transfer learning methods was performed in this study. Two transfer learning methods, that is, transfer component analysis (TCA) and manifold embedded distribution alignment (MEDA), were applied for two tasks (impact bruise â†’ crush bruise, crush bruise â†’ impact bruise). Supporting vector machine (SVM) was set as a baseline to conduct analysis and comparison of the transferability of the models. The result showed that, for task 1 (impact bruise â†’ crush bruise), MEDA and TCA-SVM model achieved a classification accuracy of 93.33% and 91.11% in target domain, individually. For task 2 (crush bruise â†’impact bruise), MEDA and TCA-SVM model achieved an accuracy of 88.89% and 85.19% in target domain, respectively. Both the two models improved the accuracy compared with SVM models (84.44% for task 1; 77.04% for task 2). Overall, the results indicated that transfer learning approaches could perform pear bruise detection across different bruise types. Hyperspectral imaging in combination with transfer learning methods is a promising possibility for the efficient and cost-saving field detection of fruit bruises among different bruise types. PRACTICAL APPLICATION: The production and export of pears are faced with problems of mechanical damage due to vibration, collision, impact, and other factors, which cause chemical changes in color, odor, and taste. Sometimes the bruise was too slight to be ignored which would infect with other fruits in the future. In this study, we used hyperspectral imaging combined with transfer learning method could detect these slight bruises caused by different factors. Distinguishing different types of damage can provide a reference for quick judgment of the process causing damage and take prompt measures to reduce economic losses.

Lignin organic molecule aggregate derived turbine-like nanocarbon with high nitrogen doping for potassium ion hybrid capacitors.

Potassium-ion hybrid capacitors (PIHCs) represent a burgeoning class of electrochemical energy storage devices characterized by their remarkable energy and power densities. Utilizing amorphous carbon derived from sustainable biomass presents an economical and environmentally friendly option for anode material in high-rate potassium-ion storage applications. Nevertheless, the potassium-ion storage capacity of most biomass-derived carbon materials remains modest. Addressing this challenge, nitrogen doping engineering and the design of distinctive nanostructures emerge as effective strategies for enhancing the electrochemical performance of amorphous carbon anodes. Developing highly nitrogen-doped nanocarbon materials is a challenging task because most lignocellulosic biomasses lack nitrogen functional groups. In this work, we propose a general strategy for directly carbonizing supermolecule-mediated lignin organic molecular aggregate (OMA) to prepare highly nitrogen-doped biomass-derived nanocarbon. We obtained lignin-derived, highly nitrogen-doped turbine-like carbon (LNTC). Featuring a three-dimensional turbine-like structure composed of amorphous, thin carbon nanosheets, LNTC demonstrated a capacity of 377 mAh g-1 when used as the anode for PIHCs. This work also provides a new synthesis method for preparing highly nitrogen-doped nanocarbon materials derived from biomass.

A medical image segmentation method for rectal tumors based on multi-scale feature retention and multiple attention mechanisms.

BACKGROUND: With the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features. PURPOSE: The efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high-level semantic features in deep networks. METHODS: In this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump-connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi-scale Feature Retention (MFR), Multi-branch Cross-channel Attention (MCA), and Coordinate Attention (CA). RESULTS: Compared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed. CONCLUSIONS: Our proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.

A Phase I Clinical Study of the Pharmacokinetics and Safety of Prusogliptin Tablets in Subjects with Mild to Moderate Hepatic Insufficiency and Normal Liver Function.

BACKGROUND: Prusogliptin is a potent and selective DPP-4 inhibitor. In different animal models, Prusogliptin showed potential efficacy in the treatment of type 2 diabetes. However, the knowledge of its pharmacokinetics and safety in patients with liver dysfunction is limited. OBJECTIVES: The present study evaluated the pharmacokinetics and safety of Prusogliptin in subjects with mild or moderate hepatic impairment compared with healthy subjects. METHODS: According to the liver function of the subjects, we divided them into a mild liver dysfunction group, a moderate liver dysfunction group and a normal liver function group. All subjects in three groups received a single oral dose of Prusogliptin 100-mg tablets. Pharmacokinetics and safety index collection was carried out before and after taking the drug. Plasma pharmacokinetics of Prusogliptin were evaluated, and geometric least- -squares mean (GLSM) and associated 90% confidence intervals for insufficient groups versus the control group were calculated for plasma exposures. RESULTS: After a single oral administration of 100 mg of Prusogliptin tablets, the exposure level of Prusogliptin in subjects with mild liver dysfunction was slightly higher than that in healthy subjects. The exposure level of Prusogliptin was significantly increased in subjects with moderate liver dysfunction. There were no adverse events in this study. CONCLUSION: The exposure level of Prusogliptin in subjects with liver dysfunction was higher than that in healthy subjects. No participant was observed of adverse events. Prusogliptin tablets were safe and well tolerated in Chinese subjects with mild to moderate liver dysfunction and normal liver function.

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica.

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments.

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.

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia.

OBJECTIVE: To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients. MATERIALS AND METHODS: This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed. RESULTS: 84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver. CONCLUSION: MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

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.

Value of Magnetic Resonance T1 Mapping in Evaluating the Early Response to Treatment for Rheumatoid Arthritis.

BACKGROUND: Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) is usually used for the semi-quantitative evaluation of joint changes in Rheumatoid Arthritis (RA). However, this method cannot evaluate early changes in bone marrow edema (BME). OBJECTIVE: To determine whether T1 mapping of wrist BME predicts early treatment response in RA. METHODS: This study prospectively enrolled 48 RA patients administered oral anti-rheumatic drugs. MRI of the most severely affected wrist was performed before and after 4 (48 patients) and 8 weeks of treatment (38 patients). Mean T1 values of BME in the lunate, triangular, and capitate bones; RAMRIS for each wrist; Erythrocyte-Sedimentation Rate (ESR); and 28-joint Disease Activity Score (DAS28)-ESR score were analyzed. Patients were divided into responders (4 weeks, 30 patients; 8 weeks, 32 patients) and non-responders (4 weeks, 18 patients; 8 weeks, 6 patients), according to EULAR response criteria. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of T1 values. RESULTS: ESR and DAS28-ESR were not correlated with T1 value and RAMRIS at each examination (P > 0.05). Changes in T1 value and DAS28-ESR relative to the baseline were moderately positively correlated with each other at 4 and 8 weeks (r = 0.555 and 0.527, respectively; P < 0.05). At 4 weeks, the change and rate of change in T1 value significantly differed between responders and non-responders (-85.63 vs. -19.92 ms; -12.89% vs. -2.81%; P < 0.05). The optimal threshold of the rate of change in T1 value at 4 weeks for predicting treatment response was -5.32% (area under the ROC curve, 0.833; sensitivity, 0.900; specificity, 0.667). CONCLUSION: T1 mapping provides a new imaging method for monitoring RA lesions; changes in wrist BME T1 values reflect early treatment response.

MiR-155-5p improves the insulin sensitivity of trophoblasts by targeting CEBPB in gestational diabetes mellitus.

INTRODUCTION: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication featuring impaired insulin sensitivity. MiR-155-5p is associated with various metabolic diseases. However, its specific role in GDM remains unclear. CCAAT enhancer binding protein beta (CEBPB), a critical role in regulating glucolipid metabolism, has been identified as a potential target of miR-155-5p. This study aims to investigate the impact of miR-155-5p and CEBPB on insulin sensitivity of trophoblasts in GDM. METHODS: Placental tissues were obtained from GDM and normal pregnant women; miR-155-5p expression was then evaluated by RT‒qPCR and CEBPB expression by western blot and immunohistochemical staining. To investigate the impact of miR-155-5p on insulin sensitivity and CEBPB expression, HTR-8/SVneo cells were transfected with either miR-155-5p mimic or inhibitor under basal and insulin-stimulated conditions. Cellular glucose uptake consumption was quantified using a glucose assay kit. Furthermore, the targeting relationship between miR-155-5p and CEBPB was validated using a dual luciferase reporter assay. RESULTS: Reduced miR-155-5p expression and elevated CEBPB expression were observed in GDM placentas and high glucose treated HTR8/SVneo cells. The overexpression of miR-155-5p significantly enhanced insulin signaling and glucose uptake in trophoblasts. Conversely, inhibiting miR-155-5p induced the opposite effects. Additionally, CEBPB was directly targeted and negatively regulated by miR-155-5p in HTR8/SVneo cells. Silencing CEBPB effectively restored the inhibitory effect of miR-155-5p downregulation on insulin sensitivity in trophoblasts. DISCUSSION: These findings suggest that miR-155-5p could enhance insulin sensitivity in trophoblasts by targeting CEBPB, highlighting the potential of miR-155-5p as a therapeutic target for improving the intrauterine hyperglycemic environment in GDM.

A chromosome-scale assembly for 'd'Anjou' pear.

Cultivated pear consists of several Pyrus species with Pyrus communis (European pear) representing a large fraction of worldwide production. As a relatively recently domesticated crop and perennial tree, pear can benefit from genome-assisted breeding. Additionally, comparative genomics within Rosaceae promises greater understanding of evolution within this economically important family. Here, we generate a fully phased chromosome-scale genome assembly of P. communis 'd'Anjou.' Using PacBio HiFi and Dovetail Omni-C reads, the genome is resolved into the expected 17 chromosomes, with each haplotype totaling nearly 540 Megabases and a contig N50 of nearly 14 Mb. Both haplotypes are highly syntenic to each other and to the Malus domestica 'Honeycrisp' apple genome. Nearly 45,000 genes were annotated in each haplotype, over 90% of which have direct RNA-seq expression evidence. We detect signatures of the known whole-genome duplication shared between apple and pear, and we estimate 57% of d'Anjou genes are retained in duplicate derived from this event. This genome highlights the value of generating phased diploid assemblies for recovering the full allelic complement in highly heterozygous crop species.

Radiomics Analysis of Diffusion Kurtosis Imaging: Distinguishing Between Glioblastoma and Single Brain Metastasis.

RATIONALE AND OBJECTIVES: This study aimed to assess the value of diffusion kurtosis imaging (DKI)-based radiomics models in differentiating glioblastoma (GB) from single brain metastasis (SBM) and compare their diagnostic performance with that of routine magnetic resonance imaging (MRI) models. MATERIALS AND METHODS: A total of 110 patients who underwent DKI and were pathologically diagnosed with GB (n = 58) or SBM (n = 52) were enrolled in this study. Radiomics features were extracted from the manually delineated region of interest of the lesion. A training set for model development was constructed from the images of 88 random patients, and 22 patients were reserved for independent validation. Seven single-DKI-parametric models and a multi-DKI-parametric model were constructed using six classifiers, whereas four single-routine-sequence models (based on T2 weighted imaging, apparent diffusion coefficient, T2-dark-fluid, and contrast-enhanced T1 magnetization prepared rapid gradient echo) and a multisequence routine MRI model were constructed for comparison. Receiver operating characteristic curve analysis was conducted to assess the diagnostic performance. The areas under the curve (AUCs) of different models were compared using the DeLong test. RESULTS: The AUCs of the single-DKI-parametric models ranged from 0.800 to 0.933 (mean kurtosis [MK] model). The multi-DKI-parametric model had a slightly higher AUC (0.958) than the MK model; however, the difference was not statistically significant (P = 0.688). In comparison, the AUCs of the routine MRI models ranged from 0.633 to 0.733 (multisequence routine MRI model). The AUC of the multi-DKI-parametric model was significantly higher than that of the multisequence routine MRI model (P = 0.042). CONCLUSION: The multi-DKI-parametric radiomics model exhibited better performance than that of the single-DKI-parametric models and routine MRI models in distinguishing GB from SBM.

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.

Multilayer network analysis of dynamic network reconfiguration in patients with moderate-to-severe obstructive sleep apnea and its association with neurocognitive function.

BACKGROUND: Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically. PURPOSE: To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function. METHODS: Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed. RESULTS: At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function. CONCLUSION: Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients.

Gray Matter Thickness and Subcortical Nuclear Volume in Men After SARS-CoV-2 Omicron Infection.

Importance: The clinical manifestations and effects on the brain of the SARS-CoV-2 Omicron variant in the acute postinfection phase remain unclear. Objective: To investigate the pathophysiological mechanisms underlying clinical symptoms and changes to gray matter and subcortical nuclei among male patients after Omicron infection and to provide an imaging basis for early detection and intervention. Design, Setting, and Participants: In this cohort study, a total of 207 men underwent health screening magnetic resonance imaging scans between August 28 and September 18, 2022; among them, 98 provided complete imaging and neuropsychiatric data. Sixty-one participants with Omicron infection were reevaluated after infection (January 6 to 14, 2023). Neuropsychiatric data, clinical symptoms, and magnetic resonance imaging data were collected in the acute post-Omicron period, and their clinical symptoms were followed up after 3 months. Gray matter indexes and subcortical nuclear volumes were analyzed. Associations between changes in gray matter and neuropsychiatric data were evaluated with correlation analyses. Exposures: Gray matter thickness and subcortical nuclear volume change data were compared before and after Omicron infection. Main Outcomes and Measures: The gray matter indexes and subcutaneous nuclear volume were generated from the 3-dimensional magnetization-prepared rapid acquisition gradient echo and were calculated with imaging software. Results: Ninety-eight men underwent complete baseline data collection; of these, 61 (mean [SD] age, 43.1 [9.9] years) voluntarily enrolled in post-Omicron follow-up and 17 (mean [SD] age, 43.5 [10.0] years) voluntarily enrolled in 3-month follow-up. Compared with pre-Omicron measures, Beck Anxiety Inventory scores were significantly increased (median, 4.50 [IQR, 1.00-7.00] to 4.00 [IQR, 2.00-9.75]; P = .006) and depressive distress scores were significantly decreased (median, 18.00 [IQR, 16.00-20.22] to 16.00 [IQR, 15.00-19.00]; P = .003) at the acute post-Omicron follow-up. Fever, headache, fatigue, myalgia, cough, and dyspnea were the main symptoms during the post-Omicron follow-up; among the participants in the 3-month follow-up, fever (11 [64.7%] vs 2 [11.8%]; P = .01), myalgia (10 [58.8%] vs 3 (17.6%]; P = .04), and cough (12 [70.6%] vs 4 [23.5%]; P = .02) were significantly improved. The gray matter thickness in the left precuneus (mean [SD], 2.7 [0.3] to 2.6 [0.2] mm; P < .001) and right lateral occipital region (mean [SD], 2.8 [0.2] to 2.7 [0.2] and 2.5 [0.2] to 2.5 [0.2] mm; P < .001 for both) and the ratio of the right hippocampus volume to the total intracranial volume (mean [SD]. 0.003 [0.0003] to 0.003 [0.0002]; P = .04) were significantly reduced in the post-Omicron follow-up. The febrile group had reduced sulcus depth of the right inferior parietal region compared with the nonfebrile group (mean [SD], 3.9 [2.3] to 4.8 [1.1]; P = .048. In the post-Omicron period, the thickness of the left precuneus was negatively correlated with the Beck Anxiety Inventory scores (r = -0.39; P = .002; false discovery rate P = .02), and the ratio of the right hippocampus to the total intracranial volume was positively correlated with the Word Fluency Test scores (r = 0.34; P = .007). Conclusions and Relevance: In this cohort study of male patients infected with the Omicron variant, the duration of symptoms in multiple systems after infection was short. Changes in gray matter thickness and subcortical nuclear volume injury were observed in the post-Omicron period. These findings provide new insights into the emotional and cognitive mechanisms of an Omicron infection, demonstrate its association with alterations to the nervous system, and verify an imaging basis for early detection and intervention of neurological sequelae.

Analysis, validation, and discussion of key genes in placenta of patients with gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is a common complication during pregnancy, which can have harmful health consequences for both the mother and the fetus. Given the placenta's crucial role as an endocrine organ during pregnancy, exploring and validating key genes in the placenta hold significant potential in the realm of GDM prevention and treatment. In this study, differentially expressed genes (DEGs) were identified from two databases, GSE70493 and PRJNA646212, and verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in placenta tissues. DEGs expression was detected in normal or high-glucose-treated HTR8/SVneo cells. We also investigated the relationship between DEGs and glucose levels in GDM patients. By selecting the intersection of the two databases, we screened 20 DEGs, which were validated in GDM patients. We observed an up-regulation of SLAMF, ALDH1A2, and CHI3L2, and a down-regulation of HLA-E, MYH11, HLA-DRB5, ITGAX, GZMB, NAIP, TMEM74B, RANBP3L, PAEP, WT-1, and CEP170. We conducted further investigations into the expression of DEGs in HTR8/SVneo cells exposed to high glucose, revealing a significant upregulation in the expression of SERPINA3, while the expressions of HLA-E, BCL6, NAIP, PAEP, MUC16, WT-1, and CEP170 were decreased. Moreover, some DEGs were confirmed to have a positive or negative correlation with blood glucose levels of GDM patients through correlation analysis. The identified DEGs are anticipated to exert potential implications in the prevention and management of GDM, thereby offering potential benefits for improving pregnancy outcomes and long-term prognosis of fetuses among individuals affected by GDM.

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.

The alleviating effect of ellagic acid on DSS-induced colitis via regulating gut microbiomes and gene expression of colonic epithelial cells.

The anti-inflammatory effect of ellagic acid (EA) and its possible underlying mechanism in dextran sulfate sodium (DSS)-induced mouse chronic colonic inflammation were studied. It was observed that EA administration significantly alleviated the colonic inflammation phenotypes, including decreasing the disease activity index (DAI), enhancing the body weight loss, and improving the shortened length of the colon and pathological damage of colon tissue. Additionally, EA reshaped the constitution of the gut microbiota by elevating the ratio of Bacteroidetes along with Bacteroides and Muribaculaceae, while decreasing the proportion of Firmicutes. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) revealed that the metabolic function of the gut microbiota was also changed. Furthermore, mouse colon transcriptome analysis showed that the tight junction and peroxisome proliferator-activated receptor (PPAR) signaling pathways were activated and the expressions of related genes were upregulated after EA intervention. These results showed that EA could remodel the gut bacterial composition, change the intestinal epithelial cell gene expressions in mice, and consequently improve the colonic inflammatory symptoms.