Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly.

OBJECTIVES: To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs. METHODS: Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA. CONCLUSIONS: Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs. CLINICAL RELEVANCE STATEMENT: It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations. KEY POINTS: Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Serum biomarkers in patients with drug-resistant epilepsy: a proteomics-based analysis.

Objective: To investigate the serum biomarkers in patients with drug-resistant epilepsy (DRE). Methods: A total of 9 DRE patients and 9 controls were enrolled. Serum from DRE patients was prospectively collected and analyzed for potential serum biomarkers using TMT18-labeled proteomics. After fine quality control, bioinformatics analysis was conducted to find differentially expressed proteins. Pathway enrichment analysis identified some biological features shared by differential proteins. Protein-protein interaction (PPI) network analysis was further performed to discover the core proteins. Results: A total of 117 serum differential proteins were found in our study, of which 44 were revised upwards and 73 downwards. The up-regulated proteins mainly include UGGT2, PDIA4, SEMG1, KIAA1191, CCT7 etc. and the down-regulated proteins mainly include ROR1, NIF3L1, ITIH4, CFP, COL11A2 etc. Pathway enrichment analysis identified that the upregulated proteins were mainly enriched in processes such as immune response, extracellular exosome, serine-type endopeptidase activity and complement and coagulation cascades, and the down-regulated proteins were enriched in signal transduction, extracellular exosome, zinc/calcium ion binding and metabolic pathways. PPI network analysis revealed that the core proteins nodes include PRDX6, CAT, PRDX2, SOD1, PARK7, GSR, TXN, ANXA1, HINT1, and S100A8 etc. Conclusion: The discovery of these differential proteins enriched our understanding of serum biomarkers in patients with DRE and potentially provides guidance for future targeted therapy.

RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study.

BACKGROUND: It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer. METHODS: A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models' performance was analyzed by the receiver operating characteristic analysis in the test cohort. RESULTS: Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774-0.906] and test cohort (AUC 0.767; 95% CI 0.613-0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821-0.938) and test cohort (AUC 0.844; 95% CI 0.701-0.936). CONCLUSION: The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.

Study of brain network alternations in non-lesional epilepsy patients by BOLD-fMRI.

Objective: To investigate the changes of brain network in epilepsy patients without intracranial lesions under resting conditions. Methods: Twenty-six non-lesional epileptic patients and 42 normal controls were enrolled for BOLD-fMRI examination. The differences in brain network topological characteristics and functional network connectivity between the epilepsy group and the healthy controls were compared using graph theory analysis and independent component analysis. Results: The area under the curve for local efficiency was significantly lower in the epilepsy patients compared with healthy controls, while there were no differences in global indicators. Patients with epilepsy had higher functional connectivity in 4 connected components than healthy controls (orbital superior frontal gyrus and medial superior frontal gyrus, medial superior frontal gyrus and angular gyrus, superior parietal gyrus and paracentral lobule, lingual gyrus, and thalamus). In addition, functional connectivity was enhanced in the default mode network, frontoparietal network, dorsal attention network, sensorimotor network, and auditory network in the epilepsy group. Conclusion: The topological characteristics and functional connectivity of brain networks are changed in in non-lesional epilepsy patients. Abnormal functional connectivity may suggest reduced brain efficiency in epilepsy patients and also may be a compensatory response to brain function early at earlier stages of the disease.

A tract-based spatial statistics study of white matter integrity in epilepsy.

OBJECTIVE: To explore the changes of cerebral white matter diffusion tensor in epilepsy. METHODS: This study was a retrospective study based on diffusion tensor imaging (DTI). Twenty-six epileptic patients and 42 normal controls matched for sex, age and handedness were enrolled in our research. Based on the method of tract-based spatial statistics (TBSS), we analyzed the changes of each relevant parameter index of DTI in white matter of the brain in all subjects, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). RESULTS: In comparison with the control group, epileptic patients had decreased FA and elevated MD, AD, and RD in the anterior thalamic radiation, corticospinal tract, forceps major, forceps minor, cingulum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and uncinate fasciculus (P < 0.05). CONCLUSION: Widespread white matter integrity was observed in epileptic patients, which may be the structural basis for the development of affective disorders, impaired cognition, and motor abnormalities.

Functional Localized High-Concentration Ether-Based Electrolyte for Stabilizing High-Voltage Lithium-Metal Battery.

Localized high-concentration electrolytes have attracted much attention of researchers due to their low viscosity, low cost, and relatively higher electrochemical performance than their low-concentration counterparts. In our work, 1.5 M (mol L-1) locally concentrated ether-based electrolyte has been obtained by adding 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (HFE) into a 4 M LiFSI concentrated dimethoxyethane (DME)-based electrolyte. The optimal ratio is determined by density functional theory (DFT) calculation and experimental combination, and finally, DH(3/5)-1.5M-LiFSI (DME/HFE = 3:5 by volume) is obtained. The electrolyte not only has relatively good physical properties such as low viscosity and high conductivity but also shows decent electrochemical performance. Li∥Cu half-cells can maintain a coulombic efficiency of no less than 99% after circulating for 250 cycles under the condition of 1 mA cm-2 current density and 1 mAh cm-2 lithium deposition for each cycle, and the stable battery polarization voltage was about 50 mV. Furthermore, 0.15 M lithium trifluoromethyl acetate (LiCO2CF3) has been added as an additive to enhance the oxidation stability. The new electrolyte DH(3/5)-1.65M-LiFC (LiFC/LiFSI + LiCO2CF3) makes Li||NCM523 batteries maintain about 83% capacity after cycling for 250 times with a 0.5 C charge current density and a 1 C discharge current density of 160 mAh g-1 when charged to 4.3 V. Furthermore, this new additive has a little negative effect on the Li||Cu half-cell performance under the same condition as before, indicating this new type of localized high-concentration DME-based electrolyte benefits both high-voltage cathode and lithium-metal anode.

The association between insulin therapy and depression in patients with type 2 diabetes mellitus: a meta-analysis.

OBJECTIVES: Several patients with type 2 diabetes mellitus (T2DM) have depressive disorders. Whether insulin treatment was associated with increased risk of depression remains controversial. We performed a meta-analysis to evaluate the association of insulin therapy and depression. DESIGN: A meta-analysis. METHODS: We conducted a systematic search of PubMed, PsycINFO, Embase and the Cochrane Library from their inception to April 2016. Epidemiological studies comparing the prevalence of depression between insulin users and non-insulin users were included. A random-effects model was used for meta-analysis. The adjusted and crude data were analysed. RESULTS: Twenty-eight studies were included. Of these, 12 studies presented with adjusted ORs. Insulin therapy was significantly associated with increased risk of depression (OR=1.41, 95% CI 1.13 to 1.76, p=0.003). Twenty-four studies provided crude data. Insulin therapy was also associated with an odds for developing depression (OR=1.59, 95% CI 1.41 to 1.80, p<0.001). When comparing insulin therapy with oral antidiabetic drugs, significant association was observed for adjusted (OR=1.42, 95% CI 1.08 to 1.86, p=0.008) and crude (OR=1.61, 95% CI 1.35 to 1.93, p<0.001) data. CONCLUSIONS: Our meta-analysis confirmed that patients on insulin therapy were significantly associated with the risk of depressive symptoms.

Two nanocage-based metal-organic frameworks with mixed-cluster SBUs and CO2 sorption selectivity.

Two nanocage-based metal-organic frameworks respectively built on two and three types of clusters with diisophthalate ligand were constructed, displaying unprecedented (3,4,6)-connected nets, unusual nanocages, and new bipaddle-wheel tetranuclear cluster, as well as highly selective CO2 capture.

Double-step CO2 sorption and guest-induced single-crystal-to-single-crystal transformation in a flexible porous framework.

A new 2D highly flexible and breathing porous framework [CuL(Me2NH)]·DMF·H2O () (H3L = 5-(4'-carboxylphenoxy)nicotinic acid) has been synthesized using a tritopic linker with a flexible joint. The desolvated framework, [CuL(Me2NH)] (), undergoes structural contraction, and exhibits selective and double-step hysteretic adsorption for CO2. Furthermore, on exposure to CH2Cl2 at room temperature, a unique single-crystal-to-single-crystal transformation occurred between and [Cu2L2(Me2NH)2(H2O)2]·5H2O ().