Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting.

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Designing Peptide-Based Nanoinhibitors of Programmed Cell Death Ligand 1 (PD-L1) for Enhanced Chemo-immunotherapy.

The combination of immune checkpoint blockade (ICB) and chemotherapy has shown significant potential in the clinical treatment of various cancers. However, circulating regeneration of PD-L1 within tumor cells greatly limits the efficiency of chemo-immunotherapy and consequent patient response rates. Herein, we report the synthesis of a nanoparticle-based PD-L1 inhibitor (FRS) with a rational design for effective endogenous PD-L1 suppression. The nanoinhibitor is achieved through self-assembly of fluoroalkylated competitive peptides that target PD-L1 palmitoylation. The FRS nanoparticles provide efficient protection and delivery of functional peptides to the cytoplasm of tumors, showing greater inhibition of PD-L1 than nonfluorinated peptidic inhibitors. Moreover, we demonstrate that FRS synergizes with chemotherapeutic doxorubicin (DOX) to boost the antitumor activities via simultaneous reduction of PD-L1 abundance and induction of immunogenic cell death in murine colon tumor models. The nano strategy of PD-L1 regulation present in this study is expected to advance the development of ICB inhibitors and overcome the limitations of conventional ICB-assisted chemo-immunotherapy.

Identification of Breast Cancer Immune-related Prognostic Characteristics in Tumor Microenvironment.

BACKGROUND: Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects. OBJECTIVE: This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value. METHODS: RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan-Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes. RESULTS: In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased. CONCLUSION: Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

Association of molecular subtypes and treatment with survival in invasive micropapillary breast cancer: an analysis of the Surveillance, Epidemiology, and End Results database.

BACKGROUND: This study aims to examine the features, treatments, and survival of invasive micropapillary carcinoma (IMPC) according to different molecular subtypes. METHODS: In this cohort study, data between 2010 and 2018 were retrospectively reviewed from the Surveillance, Epidemiology, and End Results database. Molecular subtypes were categorized into four varieties: hormone receptor (HR)+/HER2- (Luminal A), HR+/HER2+ (Luminal B), HR-/HER2- [triple-negative (TN)], and HR-/HER2+ (HER2 enriched). RESULTS: In this study, 1,180 IMPC patients were included, with 99 patients (8.39%) of the 1,180 patients having an overall mortality, and 53 patients (53.54%) of the 99 patients having a breast cancer-specific mortality. The follow-up duration was 40.00 (18.50, 61.00) months. TN molecular subtype was associated with worse OS and BCSS in IMPC patients. Treatment of chemotherapy, radiation, and combination therapy were associated with better survival in HR+/HER2+ molecular subtype and HR+/HER2- molecular subtype. However, in HR-/HER2- molecular subtype, treatment of chemotherapy was associated with a poor BCSS, and treatment of radiation was not associated with OS and BCSS. Surgery treatment was not associated with survival in HR+/HER2+ molecular subtype and HR+/HER2- molecular subtype. However, surgery treatment of mastectomy was associated with better OS in HR-/HER2- molecular subtype (P < 0.05). CONCLUSION: The prognosis of IMPC was significantly influenced by different molecular subtypes. Chemotherapy and radiotherapy are beneficial in HR+/HER2+ and HR+/HER2- patients. However, they should be used with caution in HR-/HER2- (TN) patients.

Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery.

Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 Orbita hyperspectral (OHS) imagery in the Southern Sichuan Bamboo Sea and its surrounding areas in Sichuan Province, China. The random forest and Spearman's rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. The main findings were: (1) The XGBoost model achieved accurate and reliable classification results. The XGBoost model had a higher overall accuracy (80.6%), kappa coefficient (0.708), and mean F1-score (0.805) than the spectral angle mapper (SAM) method; (2) The optimal feature variables that were important and uncorrelated for classification accuracy included the blue band (B1, 464-468 nm), near-infrared band (B27, 861-871 nm), green band (B5, 534-539 nm), elevation, texture feature mean, green band (B4, 517-523 nm), and red edge band (B17, 711-720 nm); and (3) the XGBoost model based on the optimal feature variable selection showed good adaptability to land classification and had better classification performance. Moreover, the mean F1-score indicated that the model could well balance the user's and producer's accuracy. Additionally, our study demonstrated that OHS imagery has great potential for land cover classification and that combining multiple features to enhance classification is an approach worth exploring. Our study provides a methodological reference for the application of OHS images for plant species identification.

Spatial-Temporal Evolution and Driving Forces of Drying Trends on the Qinghai-Tibet Plateau Based on Geomorphological Division.

The Qinghai-Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration.

Spatial-Temporal Evolution and Driving Forces of NDVI in China's Giant Panda National Park.

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan-Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake.

Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009-2012, 2013, and 2014-2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods.