Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment. METHODS: This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups. RESULTS: Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group. CONCLUSION: This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.

Behavioral toxicity and neurotoxic mechanisms of PLA-PBAT biodegradable microplastics in zebrafish.

Escalation of ecological concern due to biodegradable plastics has attracted the attention of many contemporary researchers. This study searched to investigate the acute and sub-chronic toxicity of polylactic acid (PLA) and polybutyleneadipate-co-terephthalate (PLA-PBAT) bio-microplastics on 3-month-old zebrafish to elucidate their potential toxic mechanisms. Acute toxicity assessments revealed 96 h-LC50 value of 12.69 mg/L for PLA-PBAT. Sub-chronic exposure of over 21 days revealed deviations in critical behavioral patterns and physiological indicators. In treated groups, weight gain and specific growth rates were significantly lower than those obtained for the control group, such that high doses induced significant reductions in total organ coefficient (p < 0.05). A positive correlation was observed between zebrafish mortality and increased doses. Detailed behavioral evaluations revealed a dose-dependent decrease in the speed and range of swimming, along with modifications in shoaling behavior, anxiety-like responses, and avoidance behaviors. Brain tissues transcriptomic analyses revealed the molecular responses underlying sub-chronic exposure to PLA-PBAT. Totally 702 DEGs and 5 KEGG pathways were significantly identified in low-dose group, with the top 2 significant pathways being ribosome pathway and cytokine-cytokine receptor interaction pathway. Totally 650 DEGs and 5 KEGG pathways were significantly identified in medium-dose group, with the top 2 significant pathways being Herpes simplex virus 1 infection pathway and complement and coagulation cascades pathway. Totally 1778 DEGs and 16 KEGG pathways were significantly identified in high-dose group, with the top 2 significant pathways being metabolism of xenobiotics by cytochrome P450 and drug metabolism - cytochrome P450 pathway. Most significantly enriched pathways are associated with immune responses. The validation of key gene in cytokine-cytokine receptor interaction pathway also confirmed its high correlation with behavioral indicators. These results indicate that PLA-PBAT is likely to cause behavioral abnormalities in zebrafish by triggering immune dysregulation in the brain.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.

BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Physiological and biochemical effects of polystyrene micro/nano plastics on Arabidopsis thaliana.

Microplastics have garnered global attention due to their potential ecological risks. Research shows micro/nano-plastics pollution has adverse effects on plant growth, development, and physiological characteristics. However, the mechanisms underlying these effects remain unclear. The study examined the effects of polystyrene micro/nano-plastics with varying sizes and concentrations on different physiological and biochemical markers of A. thaliana. The indicators assessed include seed viability, growth, chlorophyll content, accumulation of root reactive oxygen species, and root exudates. Using fluorescence labeling, we investigated the absorption and translocation processes of micro/nano-plastics in A. thaliana. We also performed transcriptomic analysis to better understand the particular mechanisms of micro/nano-plastics. It indicated that micro/nano-plastics had an adverse effect on seed germination, especially under high concentration and small particle size treatments. This effect diminished with prolonged exposure. High concentrations at 50 nm and 100 nm treatment groups significantly inhibited the growth. Conversely, low concentrations of 1000 nm had a promoting effect. Exposure to micro/nano-plastics potentially resulted in decreased chlorophyll content, the accumulation of H2O2 in roots, and stimulated root secretion of oxalic acid. Through transcriptomic analysis, the gene expression linked to micro/nano-plastic treatments of varying sizes enriched multiple metabolic pathways, impacting plant growth, development, environmental adaptation, metabolism, pigment synthesis, and stress response.

Development and evaluation of a human CD47/HER2 bispecific antibody for Trastuzumab-resistant breast cancer immunotherapy.

The treatment for trastuzumab-resistant breast cancer (BC) remains a challenge in clinical settings. It was known that CD47 is preferentially upregulated in HER2+ BC cells, which is correlated with drug resistance to trastuzumab. Here, we developed a novel anti-CD47/HER2 bispecific antibody (BsAb) against trastuzumab-resistant BC, named IMM2902. IMM2902 demonstrated high binding affinity, blocking activity, antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and internalization degradation effects against both trastuzumab-sensitive and trastuzumab-resistant BC cells in vitro. The in vivo experimental data indicated that IMM2902 was more effective than their respective controls in inhibiting tumor growth in a trastuzumab-sensitive BT474 mouse model, a trastuzumab-resistant HCC1954 mouse model, two trastuzumab-resistant patient-derived xenograft (PDX) mouse models and a cord blood (CB)-humanized HCC1954 mouse model. Through spatial transcriptome assays, multiplex immunofluorescence (mIFC) and in vitro assays, our findings provided evidence that IMM2902 effectively stimulates macrophages to generate C-X-C motif chemokine ligand (CXCL) 9 and CXCL10, thereby facilitating the recruitment of T cells and NK cells to the tumor site. Moreover, IMM2902 demonstrated a high safety profile regarding anemia and non-specific cytokines release. Collectively, our results highlighted a novel therapeutic approach for the treatment of HER2+ BCs and this approach exhibits significant anti-tumor efficacy without causing off-target toxicity in trastuzumab-resistant BC cells.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome.

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Mycophenolate mofetil reduces the risk of relapse in anti-leucine-rich glioma-inactivated protein 1 encephalitis: a prospective observational cohort study.

BACKGROUND: Mycophenolate mofetil (MMF) is frequently used in the treatment of neurological autoimmune disorders. However, its effect on the relapse risk in anti-leucine-rich glioma-inactivated protein 1 (anti-LGI1) encephalitis is not well studied. METHODS: In this prospective observational cohort study, anti-LGI1 encephalitis patients were grouped according to MMF treatment status (MMF and non-MMF groups). The primary outcome was relapse after disease onset. RESULTS: A total of 83 patients were included, with a median onset age of 60 years. Fifty-four patients were men (65.1%). The MMF group comprised 28 patients and the non-MMF group comprised 55. Median follow-up from symptom onset was 26 months. Relapse occurred in 43 patients (51.8%). Median modified Rankin scale (mRS) score at enrollment was significantly higher in the MMF group than the non-MMF group (3 vs. 2; p = 0.001). Median mRS score at last follow-up was comparable between groups (1 vs. zero; p = 0.184). Both MMF treatment (HR 0.463; 95% CI, 0.231-0.929; p = 0.030) and cognitive impairment at enrollment (HR 3.391; 95% CI, 1.041-11.044; p = 0.043) were independent predictors of relapse. Starting immunotherapy before development of cognitive impairment trended towards reducing relapse risk. Outcome at last follow-up was good (mRS score 0-2) in all patients except for one in the non-MMF group. Adverse events associated with MMF treatment were mild and transient. CONCLUSION: Although the outcome of anti-LGI1 encephalitis patients is generally favorable, relapse is common, especially in those with cognitive impairment. MMF treatment is well-tolerated and can significantly reduce the risk of relapse.

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics.

BACKGROUND: Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status. PURPOSE: To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG. STUDY TYPE: Prospective. POPULATION: Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients. FIELD STRENGTH/SEQUENCE: 3 T, multi-shell diffusion imaging. ASSESSMENT: Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients. STATISTICAL TESTS: Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant. RESULTS: 82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75). DATA CONCLUSION: Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration.

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs.

Nose to brain delivery of Astragaloside IV by ß-Asarone modified chitosan nanoparticles for multiple sclerosis therapy.

Multiple sclerosis (MS), an autoimmune disease, has been considered an inflammatory disorder of the central nervous system (CNS) with demyelination and axonal damage. Although there are certain first-line therapies to treat MS, their unsatisfactory efficacy is partly due to the limited CNS access after systemic administration. Besides, there is an urgent need to treat MS by enhancing remyelination or neuroprotection, or dampen the activity of microglia. Astragaloside IV (ASI) bears anti-inflammatory, antioxidant, remyelination and neuroprotective activity. While its poor permeability, relatively high molecular weight and low lipophilicity restrict it to reach the brain. Therefore, ß-asarone modified ASI loaded chitosan nanoparticles (ASI-ßCS-NP) were prepared to enhance the nose-to-brain delivery and therapeutic effects of ASI on EAE mice. The prepared ASI-ßCS-NP showed mean size of about 120 nm, and zeta potential from +19 to +25 mV. DiR-ßCS-NP was confirmed with good nose-to-brain targeting ability. After intranasal administration, the ASI-ßCS-NP significantly reduced behavioral scores, decreased weight loss, suppressed inflammatory infiltration and astrocyte/microglial activation, reduced demyelination and increased remyelination on a mice EAE model. Our findings indicate that ASI-ßCS-NP may be a potent treatment for MS after nose-to-brain drug delivery.

Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.

Drug sensitivity prediction plays a crucial role in precision cancer therapy. Collaboration among medical institutions can lead to better performance in drug sensitivity prediction. However, patient privacy and data protection regulation remain a severe impediment to centralized prediction studies. For the first time, we proposed a federated drug sensitivity prediction model with high generalization, combining distributed data sources while protecting private data. Cell lines are first classified into three categories using the waterfall method. Focal loss for solving class imbalance is then embedded into the horizontal federated deep learning framework, i.e., HFDL-fl is presented. Applying HFDL-fl to homogeneous and heterogeneous data, we obtained HFDL-Cross and HFDL-Within. Our comprehensive experiments demonstrated that (i) collaboration by HFDL-fl outperforms private model on local data, (ii) focal loss function can effectively improve model performance to classify cell lines in sensitive and resistant categories, and (iii) HFDL-fl is not significantly affected by data heterogeneity. To summarize, HFDL-fl provides a valuable solution to break down the barriers between medical institutions for privacy-preserving drug sensitivity prediction and therefore facilitates the development of cancer precision medicine and other privacy-related biomedical research.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

The reference value of anti-Müllerian hormone to diagnose polycystic ovary syndrome is inversely associated with BMI: a retrospective study.

BACKGROUND: This study aimed to evaluate the cut-off value of anti-Müllerian hormone (AMH) combined with body mass index (BMI) in the diagnosis of polycystic ovary syndrome (PCOS) and polycystic ovary morphology (PCOM). METHODS: This retrospective study included 15,970 patients: 3775 women with PCOS, 2879 women with PCOM, and 9316 patients as controls. Multivariate logistic regression analysis was used to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for AMH. We randomly divided the patients into two data sets. In dataset 1, a receiver operating characteristic (ROC) curve was generated to analyze the accuracy of basic AMH levels in diagnosing PCOS and PCOM. The optimal cut-off value was calculated in dataset 1 and validated in dataset 2, expressed as sensitivity and specificity. RESULTS: In the PCOS group, obese patients had the lowest AMH levels, while underweight patients had the highest AMH level (P < 0.001). After adjusting for age, the ratio of luteinizing hormone (LH) and follicle stimulating hormone (FSH), serum testosterone level, and BMI, AMH was an independent predictor of PCOS and PCOM. In the group with BMI < 18.5 kg/m2, the optimistic AMH cut-off value was 5.145 ng/mL with a sensitivity of 84.3% and specificity of 89.1%, whereas in the BMI ≥ 28 kg/m2 group, the optimistic AMH cut-off value was 3.165 ng/mL with a sensitivity of 88.7% and specificity of 74.6%. For the BMI range categories of 18.5-24, 24.0-28 kg/m2, the optimistic AMH cut-off values were 4.345 ng/mL and 4.115 ng/mL, respectively. The tendency that the group with lower weight corresponded to higher AMH cut-off values was also applicable to PCOM. In the same BMI category, patients with PCOM had a lower AMH diagnosis threshold than those with PCOS (< 18.5 kg/m2, 5.145 vs. 4.3 ng/mL; 18.5-24 kg/m2, 4.345 vs. 3.635 ng/mL; 24.0-28 kg/m2, 4.115 vs. 3.73 ng/mL; ≥ 28 kg /m2, 3.165 vs. 3.155 ng/mL). These cut-off values had a good diagnostic efficacy in the validation dataset. Based on different phenotypes and severity of ovulation disorders, the distribution of AMH in PCOS were also significantly different (P < 0.001). CONCLUSIONS: AMH is a potential diagnostic indicator of PCOS and is adversely associated with BMI. The AMH cut-off value for diagnosing PCOS was significantly higher than that for PCOM.

Analysis of the Relationship between Economic Development and Water Resources-Ecological Management Capacity in China Based on Nighttime Lighting Data.

Water resources are important factors limiting social and economic development, so how to ensure the coordination between economic development and water resources-ecological management capacity has become a key issue that needs to be addressed urgently for China's high-quality economic development. This paper used nighttime light data as proxy variables of economic development to calculate the coupling coordination degree between provincial economic development and water resources-ecological management capacity in China from 2004 to 2019 based on the coupling coordination degree model; w constructed a spatial econometric model to explore the spatial correlation and influencing factors between economic development and water resources-ecological management capacity. The results are shown in the following: (1) The overall level of China's economic development is on an upward trend, but the regional development is unbalanced, showing a decreasing spatial pattern distribution of the eastern coastal region-mid-western region-far-western region. (2) The level of water resources-ecological management capacity is low, and the spatial distribution shows a decreasing trend in the far west-central and western-eastern coastal regions. (3) The level of coupling and coordination between economic development and water resources-ecological management capacity rises from a mild imbalance level to a little imbalance level, and the spatial distribution is consistent with the spatial distribution of economic development. (4) The factors influencing the level of coupling and coordination of inter-provincial economic development and water resources-ecological management capacity in China mainly involve the population scale, technological progress, affluence, and foreign direct investment. Each province and city should take into account its own actual situation and develop targeted measures to promote the coordinated development of economic development and the water resources-ecological management capacity.

Brain age gap in neuromyelitis optica spectrum disorders and multiple sclerosis.

OBJECTIVE: To evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS). METHODS: This cohort study used data retrospectively collected from 6 tertiary neurological centres in China between 2009 and 2018. In total, 199 patients with NMOSD and 200 patients with RRMS were studied alongside 269 healthy controls. Clinical follow-up was available in 85 patients with NMOSD and 124 patients with RRMS (mean duration NMOSD=5.8±1.9 (1.9-9.9) years, RRMS=5.2±1.7 (1.5-9.2) years). Deep learning was used to learn 'brain age' from MRI scans in the healthy controls and estimate the brain age gap (BAG) in patients. RESULTS: A significantly higher BAG was found in the NMOSD (5.4±8.2 years) and RRMS (13.0±14.7 years) groups compared with healthy controls. A higher baseline disability score and advanced brain volume loss were associated with increased BAG in both patient groups. A longer disease duration was associated with increased BAG in RRMS. BAG significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD and RRMS. CONCLUSIONS: There is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS.

Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas.

BACKGROUND: Prognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a fully automated deep learning (DL) pipeline for stratified overall survival (OS) prediction based on preoperative MR images. METHODS: A total of 587 patients diagnosed with intramedullary tumors were retrospectively enrolled in our hospital to develop an automated pipeline for tumor segmentation and OS prediction. The automated pipeline included a T2WI-based tumor segmentation model and 3 cascaded binary OS prediction models (1-year, 3-year, and 5-year models). For the tumor segmentation model, 439 cases of intramedullary tumors were used to model training and testing using a transfer learning strategy. A total of 138 patients diagnosed with astrocytomas were included to train and test the OS prediction models via 10 × 10-fold cross-validation using CNNs. RESULTS: The dice of the tumor segmentation model with the test set was 0.852. The results indicated that the best input of OS prediction models was a combination of T2W and T1C images and the tumor mask. The 1-year, 3-year, and 5-year automated OS prediction models achieved accuracies of 86.0%, 84.0%, and 88.0% and AUCs of 0.881 (95% CI 0.839-0.918), 0.862 (95% CI 0.827-0.901), and 0.905 (95% CI 0.867-0.942), respectively. The automated DL pipeline achieved 4-class OS prediction (<1 year, 1-3 years, 3-5 years, and >5 years) with 75.3% accuracy. CONCLUSIONS: We proposed an automated DL pipeline for segmenting spinal cord astrocytomas and stratifying OS based on preoperative MR images.

Synergistic Effects of 9,10-Dihydro-9-oxa-10-phosphaphenanthrene-10-oxide-Based Derivative and Modified Sepiolite on Flame-Retarded Poly (Ethylene Oxide)-Poly (Butylene Adipate-Co-Terephthalate) Composites.

A 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO)-based derivative (PN-DOPO) combined with aluminium phosphates-coated sepiolite (Sep@AlPO4) was used to improve the flame retardance, thermal stability and mechanical performances of poly (ethylene oxide) (PEO)/poly (butylene adipate-co-terephthalate) (PBAT) blends. The synergistic effects of PN-DOPO and Sep@AlPO4 on flame-retarded PEO/PBAT composites were systematically discussed. Results indicated that introducing 5 wt% Sep@AlPO4 with 10 wt% PN-DOPO into PEO/PBAT achieved a V-1 rating for the UL-94 test and increased the limiting oxygen index value to 23.7%. Moreover, the peak heat release rate (p-HRR), average HRR and total heat release values of PEO/PBAT/PN10%/Sep5% composites decreased by 35.6%, 11.0% and 23.0% compared with those of PEO/PBAT, respectively. Thermogravimetric analysis (TGA) results confirmed that PN-DOPO/Sep@AlPO4 enhanced the initial thermal stability and char yield of PEO/PBAT matrix, and TGA/Fourier transform infrared spectrometry results revealed that the composites exhibited the characteristic absorption peaks of phosphorous-containing groups and an increase in gas-phase volatiles during thermal degradation. The morphological structures of the residues indicated that PN-DOPO and Sep@AlPO4 mixtures produced a more dense and continuous char layer on the composite surface during burning. Rheological behaviour revealed that higher complex viscosity and modulus values of PEO/PBAT/PN-DOPO/Sep@AlPO4 sample could also promote the crosslinking network structure of condensed phases during combustion. Furthermore, the PEO/PBAT/PN-DOPO/Sep@AlPO4 composites exhibited superior elongation at break and flexural performance than the PEO/PBAT system. All results demonstrated that the PEO/PBAT system modified with PN-DOPO/Sep@AlPO4 showed remarkable flame retardance, and improved thermal stability and mechanical properties, indicating its potential application in areas requiring fire safety.

Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning.

Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning.

Transient receptor potential vanilloid type-1 regulates periodontal disease damage via the PI3K/AKT signaling pathway.

Objectives: This study aimed to investigate the function of transient receptor potential vanilloid 1 (TRPV1) in regulating periodontal lesions. In addition, we explored the underlying mechanism of the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway. Materials and Methods: Lipopolysaccharide (LPS) stimulation of human periodontal ligament cells (HPDLCs) was used to construct a periodontitis cell model, and experimental periodontitis (EP) rats were established by ligation. The mechanism by which TRPV1 regulates periodontitis was further verified by injecting the TRPV1 agonist capsaicin (CPS) and antagonist capsazepine (CPZ) into the gingiva of rats; the alveolar bone losses in each group were measured by stereomicroscopy. Real-time quantitative polymerase chain reaction (qRT-PCR) and Western blotting (WB) were used to research the expression of TRPV1 and proinflammatory cytokines, and WB was performed to test the phosphorylation of PI3K and AKT. Results: In vitro experiments showed that LPS induced the upregulation of TRPV1 and proinflammatory cytokines and promoted the phosphorylation of PI3K and AKT proteins in HPDLCs, which was consistent with their expression in the rat periodontitis model. Moreover, in vivo studies indicated that CPZ had anti-inflammatory effects through the PI3K/AKT pathway and inhibited bone loss induced by periodontal ligation in rats, while CPS had the opposite effect. Conclusion: TRPV1 was involved in the process of alveolar bone defects and the inflammatory response in rats with periodontitis induced by ligation. Its mechanism might be related to the phosphorylation of related proteins in the PI3K/AKT signaling pathway.