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Objectives: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. Methods: A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. Results: DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. Conclusions: DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. Clinical relevance statement: We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. Key Points: • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction. [ABSTRACT FROM AUTHOR]

A shift of El Niño onset location from eastern Pacific (EP) to western Pacific (WP) occurred around 1970. It was accompanied by a faster mean sea surface temperature (SST) warming in WP and a change of precursory SST and wind anomaly patterns. The eigenvalue analysis of a simple coupled model shows that an SST anomaly (SSTA) in WP may grow under the post-1970 mean condition but cannot under the pre-1970 mean condition. As a result, a warm SSTA appeared in WP accompanying to a preceding La Niña condition in EP after 1970, whereas such a warming was rarely seen before 1970. The preceding SSTA patterns led to distinctive zonal wind responses in EP, favoring El Niño onset in EP prior to 1970. For the post-1970 El Niño onset, an initial warming in WP was induced by anomalous downward solar radiation in association with atmospheric meridional overturning circulation or anomalous horizontal advection associated with thermocline induced eastward geostrophic currents. [ABSTRACT FROM AUTHOR]

Objectives: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. Methods: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). Results: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942–0.958), sensitivity of 0.833 (95% CI: 0.780–0.848), specificity of 0.929 (95% CI: 0.914–0.936), positive predictive value of 0.833 (95% CI: 0.811–0.860), and negative predictive value of 0.929 (95% CI: 0.914–0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. Conclusions: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. Clinical relevance statement: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. Key Points: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95. [ABSTRACT FROM AUTHOR]

Aim: Human stem cells from the apical papilla (SCAPs) are an appealing stem cell source for tissue regeneration engineering. Circular RNAs (circRNAs) are known to exert pivotal regulatory functions in various cell differentiation processes, including osteogenesis of mesenchymal stem cells. However, few studies have shown the potential mechanism of circRNAs in the odonto/osteogenic differentiation of SCAPs. Herein, we identified a novel circRNA, circ‐ZNF236 (hsa_circ_0000857) and found that it was remarkably upregulated during the SCAPs committed differentiation. Thus, in this study, we showed the significance of circ‐ZNF236 in the odonto/osteogenic differentiation of SCAPs and its underlying regulatory mechanisms. Methodology: The circular structure of circ‐ZNF236 was identified via Sanger sequencing, amplification of convergent and divergent primers. The proliferation of SCAPs was detected by CCK‐8, flow cytometry analysis and EdU incorporation assay. Western blotting, qRT‐PCR, Alkaline phosphatase (ALP) and Alizarin red staining (ARS) were performed to explore the regulatory effect of circ‐ZNF236/miR‐218‐5p/LGR4 axis in the odonto/osteogenic differentiation of SCAPs in vitro. Fluorescence in situ hybridization, as well as dual‐luciferase reporting assays, revealed that circ‐ZNF236 binds to miR‐218‐5p. Transmission electron microscopy (TEM) and mRFP‐GFP‐LC3 lentivirus were performed to detect the activation of autophagy. Results: Circ‐ZNF236 was identified as a highly stable circRNA with a covalent closed loop structure. Circ‐ZNF236 had no detectable influence on cell proliferation but positively regulated SCAPs odonto/osteogenic differentiation. Furthermore, circ‐ZNF236 was confirmed as a sponge of miR‐218‐5p in SCAPs, while miR‐218‐5p targets LGR4 mRNA at its 3′‐UTR. Subsequent rescue experiments revealed that circ‐ZNF236 regulates odonto/osteogenic differentiation by miR‐218‐5p/LGR4 in SCAPs. Importantly, circ‐ZNF236 activated autophagy, and the activation of autophagy strengthened the committed differentiation capability of SCAPs. Subsequently, in vivo experiments showed that SCAPs overexpressing circ‐ZNF236 promoted bone formation in a rat skull defect model. Conclusions: Circ‐ZNF236 could activate autophagy through increasing LGR4 expression, thus positively regulating SCAPs odonto/osteogenic differentiation. Our findings suggested that circ‐ZNF236 might represent a novel therapeutic target to prompt the odonto/osteogenic differentiation of SCAPs. [ABSTRACT FROM AUTHOR]

This study employs the regional Climate-Weather Research and Forecasting model (CWRF) to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin (YRB), and then enhance its predictive ability through an optimal multi-physics ensemble approach. The CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980–2015. Long-term average summer biases in YRB precipitation are remotely correlated with those of large-scale circulations. These teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations, despite minor shifts in their primary action centers. Increased YRB precipitation aligns with a southward shifted East Asian westerly jet, an intensified low-level southerly flow south of YRB, and a south-eastward shifted South Asian high, alongside higher moisture availability over YRB. Conversely, decreased YRB precipitation corresponds to an opposite circulation pattern. The CWRF control configuration using the ensemble cumulus parameterization (ECP), compared to other cumulus schemes, best captures the observed YRB precipitation characteristics and associated circulation patterns. Coupling ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF skills. Compared to the control CWRF, the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation's spatial distributions, interannual anomalies, and associated circulation patterns. The Bayesian Joint Probability calibration to these configurations improves the ensemble's spatial distributions but compromises its interannual anomalies and teleconnection patterns. Our findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation prediction. This is notably achieved by realistically coupling cumulus, microphysics, and radiation processes to accurately capture circulation teleconnections. Further enhancements can be achieved by optimizing the multi-physics ensemble among skill-enhanced configurations. [ABSTRACT FROM AUTHOR]

Background: Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC). Methods: DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS. Results: Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression. Conclusion: DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms. [ABSTRACT FROM AUTHOR]

The topography significantly influences local climate precipitation and the intensity of precipitation events, yet the specific differences in its elevational effects require further understanding. This study focuses on precipitation in Zhengzhou City, Henan Province, utilizing hourly data and a topographic elevation precipitation increment model to assess the impact of topography on local climate precipitation and extreme heavy rainfall events. The results indicate that the daily precipitation attributed to topographic elevation in Zhengzhou in July was 0.21 mm, accounting for 4.9% of the total precipitation. In the extreme heavy rainfall event on 20 July 2021 ("7.20" event), the precipitation due to topographic elevation reaches 48.7 mm, constituting 15.8% of the total precipitation. Additionally, numerical simulations using the Weather Research and Forecasting (WRF) model for the 20–21 July 2021 rainfall event in Zhengzhou show that the WRF model effectively reproduces the spatiotemporal characteristics of the precipitation process. The simulated topographic elevation precipitation intensity is 49.8 mm/day, accounting for 16.6% of daily precipitation, closely resembling observational data. Sensitivity experiments further reveal that reducing the heights of the Taihang Mountains and Funiu Mountains weakens the low-level easterly winds around Zhengzhou. Consequently, as the center of the heavy rainfall shifts northward or westward, the intensity of topographic elevation-induced precipitation decreases to 7.3 mm/day and 12.9 mm/day. [ABSTRACT FROM AUTHOR]

The model simulation focuses on an extreme rainfall event that triggered a flood hazard in the Lake Victoria basin region of East Africa from June 24th to 26th, 2022. This study investigates the impacts of its urban canopy on the extreme rainfall events over the Lake Victoria basin in East Africa, employing the Weather Research and Forecasting (WRF) model at a convective-permitting resolution. The rapid urbanization of the region has given rise to an urban canopy, which has notable effects on local weather patterns, including the intensity and distribution of rainfall. The model incorporates high-resolution land use and urban canopy parameters to accurately capture the influences of urbanization on local weather patterns. This research comprises three sets of experiments, two with urban areas and one without, using the WRF model; the experiments focus on three days of an extreme rainfall event in the Lake Victoria basin. Satellite-based precipitation products and reanalysis datasets are employed for a synoptic analysis and model evaluation. The results demonstrate the model's effectiveness in capturing meteorological variables during an extreme event compared to observed data. The synoptic patterns reveal that, during the extreme event, the Mascarene and St. Helena influenced rainfall conditions over the Lake Victoria Basin by directing moist air toward the northwest. This led to increased moisture convergence from the urban–rural interface toward urban areas, enhancing convection and processes that result in extreme rainfall. Moreover, this study indicates that the urban canopy, specifically the building effect parameterization, significantly amplifies the intensity and duration of rainfall in the urban areas of the region. This research also indicates a general increase in air temperature, relative humidity, latent heat flux, and surface sensible heat flux due to the urban canopy. These findings highlight the substantial influence of urbanization on rainfall patterns in the urban environment. [ABSTRACT FROM AUTHOR]

Candida albicans, a prominent opportunistic pathogenic fungus in the human population, possesses the capacity to induce life-threatening invasive candidiasis in individuals with compromised immune systems despite the existence of antifungal medications. When faced with macrophages or neutrophils, C. albicans demonstrates its capability to endure oxidative stress through the utilization of antioxidant enzymes. Therefore, the enhancement of oxidative stress in innate immune cells against C. albicans presents a promising therapeutic approach for the treatment of invasive candidiasis. In this study, we conducted a comprehensive analysis of a library of drugs approved by the Food and Drug Administration (FDA). We discovered that halofantrine hydrochloride (HAL) can augment the antifungal properties of oxidative damage agents (plumbagin, menadione, and H2O2) by suppressing the response of C. albicans to reactive oxygen species (ROS). Furthermore, our investigation revealed that the inhibitory mechanism of HAL on the oxidative response is dependent on Cap1. In addition, the antifungal activity of HAL has been observed in the Galleria mellonella infection model. These findings provide evidence that targeting the oxidative stress response of C. albicans and augmenting the fungicidal capacity of oxidative damage agents hold promise as effective antifungal strategies. [ABSTRACT FROM AUTHOR]

Background: The tumor immune microenvironment can influence the prognosis and treatment response to immunotherapy. We aimed to develop a non-invasive radiomic signature in high-grade glioma (HGG) to predict the absolute density of tumor-associated macrophages (TAMs), the preponderant immune cells in the microenvironment of HGG. We also aimed to evaluate the association between the signature, and tumor immune phenotype as well as response to immunotherapy. Methods: In this retrospective setting, total of 379 patients with HGG from three independent cohorts were included to construct a radiomic model named Radiomics Immunological Biomarker (RIB) for predicting the absolute density of M2-like TAM using the mRMR feature ranking method and LASSO classifier. Among them, 145 patients from the TCGA microarray cohort were randomly allocated into a training set (N=101) and an internal validation set (N=44), while the immune-phenotype cohort (N=203) and the immunotherapy-treated cohort (N=31, patients from a prospective clinical trial treated with DC vaccine) recruited from Huashan Hospital were used as two external validation sets. The immunotherapy-treated cohort was also used to evaluate the relationship between RIB and immunotherapy response. Radiogenomic analysis was performed to find functional annotations using RNA sequencing data from TAM cells. Results: An 11-feature radiomic model for M2-like TAM was developed and validated in four datasets of HGG patients (area under the curve = 0.849, 0.719, 0.674, and 0.671) using MRI images of post contrast enhanced T1-weighted (T1CE). Patients with high RIB scores had a strong inflammatory response. Four hub-genes (SLC7A7, RNASE6, HLA-DRB1 and CD300A) expressed by TAM were identified to be closely related to the RIB, providing important evidence for biological interpretation. Only individuals with a high RIB score were shown to have survival benefits from DC vaccine [DC vaccine vs. Placebo: median progression-free survival (mPFS), 10.0 mos vs. 4.5 mos, HR=0.17, P=0.0056, 95%CI=0.041-0.68; median overall survival (mOS), 15.0 mos vs. 7.0 mos, HR=0.17, P =0.0076, 95%CI=0.04-0.68]. Multivariate analyses also confirmed that treatment by DC vaccine was an independent factor for improved survival in the high RIB score group. However, in the low RIB score group, DC vaccine was not associated with improved survival. Furthermore, a radiomic nomogram based on the RIB score and clinical factors could efficiently predict the 1-, 2-, and 3-year survival rates, as confirmed by ROC curve analysis (AUC for 1-, 2- and 3-year survival: 0.705, 0.729 and 0.684, respectively). Conclusions: The radiomic model could allow for non-invasive assessment of the absolute density of TAM from MRI images in HGG patients. Of note, our RIB model is the first immunological radiomic model confirmed to have the ability to predict survival benefits from DC vaccine in gliomas, thereby providing a novel tool to inform treatment decisions and monitor patient treatment course by radiomics. [ABSTRACT FROM AUTHOR]

Optimal plant growth in many species is achieved when the two major forms of N are supplied at a particular ratio. This study investigated optimal nitrogen forms and ratios for tomato growth using the 'Jingfan 502' tomato variety. Thirteen treatments were applied with varying proportions of nitrate nitrogen (NN), ammonium nitrogen (AN), and urea nitrogen (UN). Results revealed that the combination of AN and UN inhibited tomato growth and photosynthetic capacity. Conversely, the joint application of NN and UN or NN and AN led to a significant enhancement in tomato plant growth. Notably, the T12 (75%UN:25%NN) and T4 (75%NN:25%AN) treatments significantly increased the gas exchange and chlorophyll fluorescence parameters, thereby promoting the accumulation of photosynthetic products. The contents of fructose, glucose, and sucrose were significantly increased by 121.07%, 206.26%, and 94.64% and by 104.39%, 156.42%, and 61.40%, respectively, compared with those in the control. Additionally, AN favored starch accumulation, while NN and UN favored fructose, sucrose, and glucose accumulation. Gene expression related to nitrogen and sugar metabolism increased significantly in T12 and T4, with T12 showing greater upregulation. Key enzyme activity in metabolism also increased notably. In summary, T12 enhanced tomato growth by upregulating gene expression, increasing enzyme activity, and boosting photosynthesis and sugar accumulation. Growers should consider using NN and UN to reduce AN application in tomato fertilization. [ABSTRACT FROM AUTHOR]

Purpose: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. Methods: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. Results: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. Conclusion: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well. [ABSTRACT FROM AUTHOR]

Candida albicans filamentation plays a significant role in developing both mucosal and invasive candidiasis, making it a crucial virulence factor. Consequently, exploring and identifying inhibitors that impede fungal hyphal formation presents an intriguing approach toward antifungal strategies. In line with this anti-filamentation strategy, we conducted a comprehensive screening of a library of FDA-approved drugs to identify compounds that possess inhibitory properties against hyphal growth. The compound octenidine dihydrochloride (OCT) exhibits potent inhibition of hyphal growth in C. albicans across different hyphae-inducing media at concentrations below or equal to 3.125 μM. This remarkable inhibitory effect extends to biofilm formation and the disruption of mature biofilm. The mechanism underlying OCT's inhibition of hyphal growth is likely attributed to its capacity to impede ergosterol biosynthesis and induce the generation of reactive oxygen species (ROS), compromising the integrity of the cell membrane. Furthermore, it has been observed that OCT demonstrates protective attributes against invasive candidiasis in Galleria mellonella larvae through its proficient eradication of C. albicans colonization in infected G. mellonella larvae by impeding hyphal formation. Although additional investigation is required to mitigate the toxicity of OCT in mammals, it possesses considerable promise as a potent filamentation inhibitor against invasive candidiasis. [ABSTRACT FROM AUTHOR]

Background: Microglial activation in the spinal trigeminal nucleus (STN) plays a crucial role in the development of trigeminal neuralgia (TN). The involvement of adenosine monophosphate-activated protein kinase (AMPK) and N-methyl-D-aspartate receptor 1 (NMDAR1, NR1) in TN has been established. Initial evidence suggests that stem cells from human exfoliated deciduous teeth (SHED) have a potential therapeutic effect in attenuating TN. In this study, we propose that SHED-derived exosomes (SHED-Exos) may alleviate TN by inhibiting microglial activation. This study sought to assess the curative effect of SHED-Exos administrated through the tail vein on a unilateral infraorbital nerve chronic constriction injury (CCI-ION) model in mice to reveal the role of SHED-Exos in TN and further clarify the potential mechanism. Results: Animals subjected to CCI-ION were administered SHED-Exos extracted by differential ultracentrifugation. SHED-Exos significantly alleviated TN in CCI mice (increasing the mechanical threshold and reducing p-NR1) and suppressed microglial activation (indicated by the levels of TNF-α, IL-1β and IBA-1, as well as p-AMPK) in vivo and in vitro. Notably, SHED-Exos worked in a concentration dependent manner. Mechanistically, miR-24-3p-upregulated SHED-Exos exerted a more significant effect, while miR-24-3p-inhibited SHED-Exos had a weakened effect. Bioinformatics analysis and luciferase reporter assays were utilized for target gene prediction and verification between miR-24-3p and IL1R1. Moreover, miR-24-3p targeted the IL1R1/p-p38 MAPK pathway in microglia was increased in CCI mice, and participated in microglial activation in the STN. Conclusions: miR-24-3p-encapsulated SHED-Exos attenuated TN by suppressing microglial activation in the STN of CCI mice. Mechanistically, miR-24-3p blocked p-p38 MAPK signaling by targeting IL1R1. Theoretically, targeted delivery of miR-24-3p may offer a potential strategy for TN. [ABSTRACT FROM AUTHOR]

Aim: Recently, miR‐27b‐5p was shown to be abundantly expressed in extracellular vehicles (EVs) from the inflammatory microenvironment. This study determined the role of miR‐27b‐5p in regulating osteogenic and odontogenic differentiation of stem cells from human exfoliated deciduous teeth (SHEDs) and further examined the regulatory mechanism of bone morphogenetic protein receptor type‐1A (BMPR1A). Methodology: Characteristics of SHEDs and SHEDs‐EVs derived from SHEDs were evaluated respectively. The expression of miR‐27b‐5p in SHEDs and EVs was detected during osteo‐induction. Mechanically, SHEDs were treated with miR‐27b‐5p mimics or an inhibitor, and the osteogenic/odontogenic differentiation and proliferation were assessed. Bioinformatic analysis and luciferase reporter were utilized for target gene prediction and verification. Finally, BMPR1A‐overexpressed plasmids were transfected into SHEDs to investigate the participation of the BMPR1A/SMAD4 pathway. Data were analysed using Student's t‐test, one‐way analysis of variance and Chi‐square test. Results: MiR‐27b‐5p was expressed in both SHEDs and EVs and was significantly increased at the initial stage of differentiation and then decreased in a time‐dependent manner (p <.01). Upregulation of miR‐27b‐5p significantly suppressed osteogenic/odontogenic differentiation of SHEDs and inhibited proliferation (p <.05), whereas inhibition of miR‐27b‐5p enhanced the differentiation (p <.05). Dual‐luciferase reporter assay and pull‐down assay confirmed the binding site between miR‐27b‐5p and BMPR1A (p <.05). The overexpression of BMPR1A rescued the effect of miR‐27b‐5p, while contributed to the decrease of pluripotency (p <.05). Additionally, miR‐27b‐5p maintained pluripotency in BMPR1A‐overexpressed SHEDs (p <.05). Conclusions: MiR‐27b‐5p in SHEDs/EVs was inversely associated with differentiation and suppressed the osteogenic and odontogenic differentiation of SHEDs and maintained the pluripotency of SHEDs partly by shuttering BMPR1A‐targeting BMP signalling. Theoretically, inhibition of miR‐27b‐5p represents a potential strategy to promote osteanagenesis and dentinogenesis. However, miR‐27b‐5p capsuled EVs might maintain cell pluripotency and self‐renewal for non‐cell‐targeted therapy. [ABSTRACT FROM AUTHOR]

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to identify good surgical candidates. This study aims to establish a radiomics system for the automatic diagnosis of hippocampal sclerosis with the help of machine learning. A total of 240 cases were analysed to develop a diagnostic model. First, an automatic hippocampal segmentation process was established that exploits a priori knowledge of the relatively fixed location of the hippocampus in brain partitions, as well as a deep-learning segmentation network based on an Attention U-net. Then, we extracted 527 radiomics features from each side of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on the left or right side. When the automatic diagnosis model identified HS and NC, the sensitivity and specificity reached 0.941 and 0.917 in the 10-fold cross-validation set and 0.920 and 0.909 in the independent testing set. When the diagnostic model detected HS lateralization, the sensitivity and specificity reached 0.923 and 0.920 in cross-validation and 0.909 and 0.929 in independent testing. Our results show that the developed radiomics model can help detect TLE patients with hippocampal sclerosis and has the potential to simplify preoperative evaluations and select surgical candidates. [ABSTRACT FROM AUTHOR]

Cisplatin resistance poses a substantial hurdle in effectively treating head and neck squamous cell carcinoma (HNSCC). Utilizing multiple tumor models and examining an internal HNSCC cohort, squalene epoxidase (SQLE) is pinpointed as a key driver of chemoresistance and tumorigenesis, operating through a cholesterol‐dependent pathway. Comprehensive transcriptomic analysis reveals that SQLE is essential for maintaining c‐Myc transcriptional activity by stabilizing the c‐Myc protein and averting its ubiquitin‐mediated degradation. Mechanistic investigation demonstrates that SQLE inhibition diminishes Akt's binding affinity to lipid rafts via a cholesterol‐dependent process, subsequently deactivating lipid raft‐localized Akt, reducing GSK‐3β phosphorylation at S9, and increasing c‐Myc phosphorylation at T58, ultimately leading to c‐Myc destabilization. Importantly, employing an Sqle conditional knockout mouse model, SQLE's critical role in HNSCC initiation and progression is established. The preclinical findings demonstrate the potent synergistic effects of combining terbinafine and cisplatin in arresting tumor growth. These discoveries not only provide novel insights into the underlying mechanisms of SQLE‐mediated cisplatin resistance and tumorigenesis in HNSCC but also propose a promising therapeutic avenue for HNSCC patients unresponsive to conventional cisplatin‐based chemotherapy. [ABSTRACT FROM AUTHOR]

Model simulations show that drought may become more severe and widespread in the 21st century due to human-induced global warming. However, the contributions from the key factors to the model-projected drought changes in China have not yet been examined in detail. We used the self-calibrated Palmer Drought Severity Index with Penman–Monteith potential evapotranspiration (scPDSIpm) based on 10 model simulations selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We investigated the contributions from precipitation (P), near-surface air temperature and specific humidity [Δ(T + q)], net surface longwave radiation (LW), net surface shortwave radiation (SW), and wind speed (WS) to the future changes in scPDSIpm, including the long-term mean, drying area, probability distribution function (PDF), drought frequency, and drought duration based on the scPDSIpm over China. Our results show that model-projected drying mainly occurs over southern China, whereas the dry areas under drought conditions increase from 20% to about 23%/30% under the two scenarios of the shared socioeconomic pathway (SSP2-4.5/SSP5-8.5) from 1985 to 2100, despite large uncertainties in individual projections partly due to internal variability. Drought frequency is projected to increases by about 10%–54% (15%–88%) under the SSP2-4.5 (SSP5-8.5) scenario by the late 21st century, along with increases in drought duration. These changes are accompanied by a decrease in the mean scPDSIpm and flattening of the PDFs. The changes in drying over southern China are mainly attributed to surface warming and the increased surface vapor pressure deficit (VPD), with small contributions from changes in the surface net radiation. The changes in wetting over northern China mostly result from increased precipitation along with a small wetting effect from the changes in wind speed. [ABSTRACT FROM AUTHOR]

Catalytic hydrodechlorination (HDC) is considered as one of the most promising strategies for the treatment of chlorinated environmental pollutants. Here we proposed a strategy for rapid complete dechlorination by exploiting a B–N co-doped hierarchically porous carbon material (BNC) to immobilize Pd NPs, which makes use of not only the micro/meso porous structure to accelerate the mass transport and to provide enormous active sites, but also the synergistic effect for codoping of B–N to enhance the interaction between Pd NPs and support and thus to stabilize and disperse the small size of the Pd particle. Ascribed to these combined advantages, Pd/BNC exhibited more distinctive catalytic dehalogenation activity (k = −0.14 min−1) and better stability (about 90.0% activity retention after 5 cycles) than Pd/C (k = −0.0070 min−1). Additionally, density functional theory (DFT) calculation indicates that the incorporation of N and B atoms in the carbon skeleton will reduce the adsorption energy of Cl− and thus Pd/BNC exhibits more remarkable stability than Pd/C during long-term HDC reactions. As a result, ultrafine Pd particles and high Pd2+/(Pd2+ + Pd0) ratios were found in Pd/BNC. [ABSTRACT FROM AUTHOR]

Moyamoya disease (MMD) is a serious intracranial cerebrovascular disease. Cerebral hemorrhage caused by MMD will bring life risk to patients. Therefore, MMD detection is of great significance in the prevention of cerebral hemorrhage. In order to improve the accuracy of digital subtraction angiography (DSA) in the diagnosis of ischemic MMD, in this paper, a deep network architecture combined with 3D convolutional neural network (3D CNN) and bidirectional convolutional gated recurrent unit (BiConvGRU) is proposed to learn the spatiotemporal features for ischemic MMD detection. Firstly, 2D convolutional neural network (2D CNN) is utilized to extract spatial features for each frame of DSA. Secondly, the long-term spatiotemporal features of DSA sequence are extracted by BiConvGRU. Thirdly, the short-term spatiotemporal features of DSA are further extracted by 3D convolutional neural network (3D CNN). In addition, different features are extracted when gray images and optical flow images pass through the network, and multiple features are extracted by features fusion. Finally, the fused features are utilized to classify. The proposed method was quantitatively evaluated on a data sets of 630 cases. The experimental results showed a detection accuracy of 0.9788, sensitivity and specificity were 0.9780 and 0.9796, respectively, and area under curve (AUC) was 0.9856. Compared with other methods, we can get the highest accuracy and AUC. The experimental results show that the proposed method is stable and reliable for ischemic MMD detection, which provides an option for doctors to accurately diagnose ischemic MMD. [ABSTRACT FROM AUTHOR]

As a common but critical dynamic crossover in glass-forming liquids (GFLs), the discovery of fragile-to-strong (F-S) transition promises a novel route for understanding supercooled liquid and glass transition. The present work, for the first time, successfully realizes the quantitative prediction of the F-S transition in nine metallic glass-forming liquids, by a counter-intuitive approach that focuses on local atomic activation events, rather than relaxation, upon cooling. The dynamic crossover originates from a disorder-to-order transition by self-regulating behavior of atomic position within a cage controlled by finite atomic activation events, due to the appearance of local cooperative motion of nearest neighborhood atoms. Moreover, the dominant role of entropy in this anomaly has been discovered, and the correspondence between the crossover of configuration entropy involved in activation events and the occurrence of F-S transition has been found. Our work implies that the feature of atomic energy fluctuations reflected by atomic activation events has a close linkage to complex dynamic behaviors of disordered systems. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the regulatory effect of hypoxia on human periodontal ligament stem cells (PDLSCs) through RNA sequencing (RNA SEQ). Human PDLSCs were cultured in normoxia (20% O2) or hypoxia (2% O2). Material and Methods: Total RNA was extracted and sequenced. The expression profiles of circRNAs, lncRNAs, and miRNAs were determined, and the lncRNA/circRNA‐miRNA‐mRNA networks were analyzed. Results: In total, 15 miRNAs, 449 lncRNAs, and 53 circRNAs were differentially expressed. Among them, 21 circRNAs, 262 lncRNAs, 5 miRNAs, and 5 mRNAs were selected to construct competing endogenous RNA (ceRNA) networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to explore potential related pathways and regulatory functions. Several ceRNA axes (lncRNA‐FTX/circRNA‐FAT1‐hsa‐miR‐4781‐3p‐SMAD5 and circRNA LPAR1‐hsa‐miR‐342‐3p‐ADAR) may provide a theoretical basis on the study of osteogenic differentiation of PDLSCs under hypoxia. Conclusion: This study revealed that the expression profiles of circRNAs, lncRNAs, and miRNAs had changed significantly in PDLSCs cultured in 2% O2; specific circRNAs/lncRNAs may play a competitive role in the differentiation of PDLSCs. [ABSTRACT FROM AUTHOR]

In the presence of an MMC-HVDC system, current differential protection (CDP) has the risk of failure in operation under an internal fault. In addition, CDP may also incur security issues in the presence of current transformer (CT) saturation and outliers. In this paper, a current trajectory image-based protection algorithm is proposed for AC lines connected to MMC-HVDC stations using a convolution neural network improved by a channel attention mechanism (CA-CNN). Taking the dual differential currents as two-dimensional coordinates of the moving point, the moving-point trajectories formed by differential currents have significant differences under internal and external faults. Therefore, internal faults can be identified using image recognition based on CA-CNN. This is improved by a channel attention mechanism, data augmentation, and adaptive learning rate. In comparison with other machine learning algorithms, the feature extraction ability and accuracy of CA-CNN are greatly improved. Various fault conditions like different network structures, operation modes, fault resistances, outliers, and current transformer saturation, are fully considered to verify the superiority of the proposed protection algorithm. The results confirm that the proposed current trajectory image-based protection algorithm has strong learning and generalizability, and can identify internal faults reliably. [ABSTRACT FROM AUTHOR]

The dynamic crossover in supercooled liquids initially predicted by model coupling theory has been widely accepted, but its underlying structural origin is still an open issue for glass-forming liquids. By molecular dynamics simulations of binary CuZr liquids, the present work verifies that high pressure could enhance this crossover, facilitating the studies on the structural features at the crossover temperature Tc. We discover that the topological connectivity of icosahedral clusters is responsible for this dynamic crossover, rather than all clusters. Tc is the temperature at which the connectivity degree between these clusters reaches a maximum and the dynamic heterogeneity begins to keep stable. Below Tc, the fractal topological structures appear in the medium-range order scale. The icosahedral clusters with a certain connectivity pattern can be regarded as a fractal structural unit. By employing the established fractal analysis method, the fractal dimension D of the icosahedral network is calculated. Our results indicate that the D value increases monotonically with increasing pressure and the fractal behavior of the icosahedral network is an inherent feature of metallic glasses. We also find similar fractal behavior in clusters with high local five-fold symmetry. Our findings shed light on the origin of a dynamic crossover in the deep supercooled region of metallic glasses and also demonstrate the important role of icosahedral clusters in uncovering the fractal behavior of metallic glass. [ABSTRACT FROM AUTHOR]

Conflicting evidence exists regarding the effectiveness of magnesium supplementation during pregnancy in gestational diabetes mellitus (GDM) patients. This meta‐analysis examines the effect of magnesium on glycemic indices and metabolic status in GDM. We searched databases for randomized controlled trials (RCTs) conducted, and after applying inclusion and exclusion criteria, a total of four RCTs were considered eligible for the analysis. Outcome parameters included markers for glycemic control and metabolic status. A total of four RCTs with 198 participants (control = 99; magnesium supplemented = 99) were selected for the analysis. Magnesium supplementation resulted in a significant reduction in markers of glycemic control—fasting plasma glucose (standard mean difference (μ^) = −0.83; 95% CI: [−1.13, –0.54]; p‐value <.0001), and insulin levels (μ^ = −0.95; 95% CI: [−1.38, −0.52]; p‐value <.0001). Also, Mg intake resulted in altered oxidative stress markers TAC (μ^ = 1.09; 95% CI: [0.10, 2.07]; p‐value =.03) of the pregnant women. No significant effect on GSH and CRP levels was observed. This study provides evidence of the positive effects of magnesium intervention on insulin sensitivity and oxidative stress in GDM patients. [ABSTRACT FROM AUTHOR]

Artificial intelligence approaches to analyze pathological images (pathomic) for outcome prediction have not been sufficiently considered in the field of pituitary research. A total of 5,504 hematoxylin & eosin‐stained pathology image tiles from 58 acromegalic patients with a good or poor outcome were integrated with other clinical and genetic information to train a low‐rank fusion convolutional neural network (LFCNN). The model was externally validated in 1,536 patches from an external cohort. The primary outcome was the time to the first endocrine remission after stereotactic radiosurgery (SRS). The median time of initial endocrine remission was 43 months (interquartile range [IQR]: 13–60 months) after SRS, and the 24‐month initial cumulative remission rate was 57.9% (IQR: 46.4–72.3%). The patient‐wise accuracy of the LFCNN model in predicting the primary outcome was 92.9% in the internal test dataset, and the sensitivity and specificity were 87.5 and 100.0%, respectively. The LFCNN model was a strong predictor of initial cumulative remission in the training cohort (hazard ratio [HR] 9.58, 95% confidence interval [CI] 3.89–23.59; p < 0.001) and was higher than that of established prognostic markers. The predictive value of the LFCNN model was further validated in an external cohort (HR 9.06, 95% CI 1.14–72.25; p = 0.012). In this proof‐of‐concept study, clinically and genetically useful prognostic markers were integrated with digital images to predict endocrine outcomes after SRS in patients with active acromegaly. The model considerably outperformed established prognostic markers and can potentially be used by clinicians to improve decision‐making regarding adjuvant treatment choices. © 2022 The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Stem cells from the apical papilla (SCAPs) are important for tooth root development and regeneration of root dentin. Here, we examined the expression of programmed cell death protein-1 (PD-1) in SCAPs and investigated the effects of PD-1 on odontogenic and osteogenic differentiation, as well as the relationship between PD-1 and SHP2/NF-κB signals. SCAPs were obtained and cultured in the related medium. The proliferation ability was evaluated by the cell counting kit 8 (CCK-8) and the 5-ethynyl-20-deoxyuridine (EdU) assay. Alkaline phosphatase (ALP) activity assay, ALP staining, Western blot, real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR), Alizarin Red S (ARS) staining, and immunofluorescence (IF) staining were performed to explore the osteo/odontogenic potential and the involvement of SHP2/NF-κB pathways. Besides, we transplanted SCAPs components into mouse calvaria defects to evaluate osteogenesis in vivo. We found that human SCAPs expressed PD-1 for the first time. PD-1 knockdown enhanced the osteo/odontogenic differentiation of SCAPs by suppressing the SHP2 pathway and activating the NF-κB pathway. Overexpression of PD-1 inhibited the osteogenesis and odontogenesis of SCAPs via activation of SHP2 signal and inhibition of the NF-κB pathway. PD-1 activated SHP2 signal to block NF-κB signal and then played a vital role in osteo/odontogenic differentiation of SCAPs. Schematic diagram for PD-1/ SHP2/NF-κB axis. Schematic diagram for the activation of SHP2 and NF-κB pathways by PD-1 overexpression and inhibition. PD-1 can trigger the phosphorylation of SHP2, and suppress the phosphorylation of IκBα and inhibited P65 phosphorylation and transportation into nuclei, which finally inhibited the activation of NF-κB pathway [ABSTRACT FROM AUTHOR]

Background: Extracellular vesicles (EVs) play a key role in constructing a microenvironment that favors the differentiation of stem cells. The present work aimed to determine the molecular mechanisms by which EV derived from inflammatory dental pulp stem cell (iDPSC-EV) influence periodontal ligament stem cells (PDLSCs) and provide a potential strategy for bone and dental pulp regeneration.Methods: The osteogenic and odontogenic differentiation was assessed by quantitative real-time polymerase chain reaction (qRT-PCR), western blot, alkaline phosphatase (ALP) activity assay, ALP staining, alizarin red S (ARS) staining, and immunofluorescence staining. To detect proliferation, the Cell Counting Kit-8 (CCK-8) assay, and flow cytometry analysis were used. EVs were isolated by the Exoperfect kit and ultrafiltration and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and western blot. The expression profile of miRNAs in EVs was studied using miRNA sequence and bioinformatics, and one of the upregulated miRNAs was evaluated on PDLSCs.Results: The inflammatory microenvironment stimulated osteogenic and odontogenic differentiation of DPSCs and iDPSC-EV behaved alike on PDLSCs. MiR-758-5p was upregulated in iDPSC-EV and was demonstrated to play a significant role in the osteogenic and odontogenic commitment of PDLSCs. A dual-luciferase reporter assay confirmed the binding site between miR-758-5p and limb development membrane protein 1 (LMBR1). The knockdown of LMBR1 also enhanced the above potential. Mechanically, bone morphogenetic protein (BMP) signaling was activated.Conclusions: EVs from the inflammatory microenvironment enhanced the osteogenic and odontogenic differentiation of PDLSCs partly by shuttering LMBR1-targeting miR-758-5p via BMP signaling. [ABSTRACT FROM AUTHOR]

Background: An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. Methods: A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. Results: The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. Conclusion: The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice. [ABSTRACT FROM AUTHOR]

Background: This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC).Methods: The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction.Results and Conclusion: Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC. [ABSTRACT FROM AUTHOR]

Understanding the impact of anthropogenic climate change on drought is of great significance to the prevention of its adverse effects. Two drought indices, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are used here for the detection and attribution of autumn droughts in China, and for the exploration of the role played by the anthropogenic climate change. SPI is only related to precipitation, but SPEI involves both precipitation and potential evapotranspiration. For their trend's spatial patterns, the historical simulations (including all forcings, noted as ALL) from 11 models of the Coupled Model Intercomparison Project phase 6, as an ensemble, are able to reproduce their observational counterpart. SPI shows wetting trend in the north of 35°N and drying trend in the south. SPEI shows drying trend in almost whole China. The drying trend in historical simulations ALL is significantly stronger, compared with the counterpart from the accompanying simulations (called NAT) with only natural forcings implemented. This result clearly indicates that anthropogenic climate change plays a dominant role in the enhancement of autumn drought in China. A more rigorous detection work is also performed with the signal's fingerprint of ALL (and NAT) projected onto the observation and assessed with the background noise from no external-forcing control simulations. The trend pattern signal in ALL is significantly detected in observation for both SPI and SPEI, with a more pronounced signal in SPEI than in SPI, while the signal of NAT is not detected for neither SPI nor SPEI. Finally, extreme droughts (with indices beyond −2) are assessed in terms of probability ratio between ALL and NAT. It is shown that the anthropogenic precipitation change plays a leading role in the south of 35°N, while the anthropogenic temperature change leads in the north. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury is a common and devastating injury that is the leading cause of neurological disability and death worldwide. Patients with cerebral lobe contusion received conservative treatment because of their mild manifestations, but delayed intracranial hematoma may increase and even become life-threatening. We explored the noninvasive method to predict the prognosis of progression and Glasgow Outcome Scale (GOS) by using a quantitative radiomics approach and statistical analysis. Methods: Eighty-eight patients who were pathologically diagnosed were retrospectively studied. The radiomics method developed in this work included image segmentation, feature extraction, and feature selection. The nomograms were established based on statistical analysis and a radiomics method. We conducted a comparative study of hematoma progression and GOS between the clinical factor alone and fusion radiomics features. Results: Nineteen clinical factors, 513 radiomics features, and 116 locational features were considered. Among clinical factors, international normalized ratio, prothrombin time, and fibrinogen were enrolled for hematoma progression. As for GOS, treatment strategy, age, Glasgow Coma Scale score, and blood platelet were associated factors. Eight features for GOS and five features for hematoma progression were filtered by using sparse representation and locality preserving projection-combined method. Four nomograms were constructed. After fusion radiomics features, area under the curve of hematoma progression prediction increased from 0.832 to 0.899, whereas GOS prediction went from 0.794 to 0.844. Conclusions: A radiomic-based model that merges radiomics and clinical features is a noninvasive approach to predict hematoma progression and clinical outcomes of cerebral contusions in traumatic brain injury. [ABSTRACT FROM AUTHOR]

Surgeons face challenges in intraoperatively defining margin of brain tumors due to its infiltrative nature. Extracellular acidosis caused by metabolic reprogramming of cancer cells is a reliable marker for tumor infiltrative regions. Although the acidic margin‐guided surgery shows promise in improving surgical prognosis, its clinical transition is delayed by having the exogenous probes approved by the drug supervision authority. Here, an intelligent surface‐enhanced Raman scattering (SERS) navigation system delineating glioma acidic margins without administration of exogenous probes is reported. With assistance of this system, the metabolites at the tumor cutting edges can be nondestructively transferred within a water droplet to a SERS chip with pH sensitivity. Homemade deep learning model automatically processes the Raman spectra collected from the SERS chip and delineates the pH map of tumor resection bed with increased speed. Acidity correlated cancer cell density and proliferation level are demonstrated in tumor cutting edges of animal models and excised tissues from glioma patients. The overall survival of animal models post the SERS system guided surgery is significantly increased in comparison to the conventional strategy used in clinical practice. This SERS system holds the promise in accelerating clinical transition of acidic margin‐guided surgery for solid tumors with infiltrative nature. [ABSTRACT FROM AUTHOR]

The ability of human periodontal ligament stem cells (PDLSCs) to differentiate into osteoblasts is significant in periodontal regeneration tissue engineering. In this study, we explored the role and mechanism of circRNA FAT1 (circFAT1) in the osteogenic differentiation of human PDLSCs. The proliferation capacity of PDLSCs was evaluated by EdU and CCK-8 assay. The abilities of circFAT1 and miR-4781-3p in regulating PDLSC differentiation were analyzed by western blot, reverse transcription-polymerase chain reaction (RT-PCR), alkaline phosphatase (ALP), and Alizarin red staining (ARS). A nucleocytoplasmic separation experiment was utilized for circFAT1 localization. A dual-luciferase reporter assay confirmed the binding relationship between miR-4781-3p and circFAT1. It was showed that circFAT1 does not affect the proliferation of PDLSCs. The osteogenic differentiation of PDLSCs was benefited from circFAT1, which serves as a miRNA sponge for miR-4781-3p targeting SMAD5. Both knockdown of circFAT1 and overexpression of miR-4781-3p suppressed the osteogenic differentiation of PDLSCs. Thus, circFAT1 might be considered as a potential target of PDLSCs mediated periodontal bone regeneration. [ABSTRACT FROM AUTHOR]

Let-7 miRNA family has been proved as a key regulator of mesenchymal stem cells' (MSCs') biological features. However, whether let-7b could affect the differentiation or proliferation of periodontal ligament stem cells (PDLSCs) is still unknown. Here, we found that the expression of hsa-let-7b was visibly downregulated after mineralization induction of PDLSCs. After transfected with hsa-let-7b mimics or inhibitor reagent, the proliferation ability of PDLSCs was detected by cell counting kit-8 (CCK-8), flow cytometry, and 5-ethynyl-2-deoxyuridine (EdU) assay. On the other hand, the osteogenic differentiation capacity was detected by alkaline phosphatase (ALP) staining and activity, alizarin red staining, Western blot, and quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR). We verified that hsa-let-7b did not significantly impact the proliferation ability of PDLSCs, but it could curb the osteogenic differentiation of PDLSCs. Besides, we predicted CTHRC1 acts as the downstream gene of hsa-let-7b to affect this process. Moreover, the combination of CTHRC1 and hsa-let-7b was verified by dual luciferase reporter assay. Our results demonstrated that the osteogenic differentiation of PDLSCs was enhanced after inhibiting hsa-let-7b, while was weakened after cotransfection with Si-CTHRC1. Collectively, hsa-let-7b can repress the osteogenic differentiation of PDLSCs by regulating CTHRC1. [ABSTRACT FROM AUTHOR]

Climate change has and will continue to affect food security in different dimensions. This study aims at comparing current and future maize and sorghum climate suitability and yield using five state-of-art climate models in the Representative Concentration Pathways (RCP8.5) in Couple Intercomparison Project Phase 5 (CMIP5). The study considered 1970–2000 as the baseline and 2040–2060 (the 2050s) and 2061–2080 (2070s) as the projection periods. The mechanist FAO Eco-crop model and statistical crop model were used. The results show that rainfall and temperature are projected to increase by 2050s and 2070s with certain (uncertainty) in temperature (rainfall) projection. The central and western highlands are the optimum suitable zones for maize. Gradual reduction of maize suitable zone is anticipated leading to a yield loss of 10.8% (23.7%) by 2050s (2070s). The sorghum climate suitable areas are expected to increase in the future leading to a yield gain of 114% (80.7%) by 2050s (2070s) from the baseline yield. Currently, temperature creates more uncertainties in crop yield than rainfall. The findings of this research enlighten the policymakers and farmers on the probable susceptibilities of the agricultural system in future climate and the viable strategic measures to curb food insecurity over the country. [ABSTRACT FROM AUTHOR]

It is essential to assess flash drought risk based on a reliable flash drought intensity (severity) index incorporating comprehensive information of the rapid decline ("flash") in soil moisture toward drought conditions and soil moisture thresholds belonging to the "drought" category. In this study, we used the Gan River basin as an example to define a flash drought intensity index that can be calculated for individual time steps (pentads) during a flash drought period over a given grid (or station). The severity of a complete flash drought event is the sum of the intensity values during the flash drought. We explored the spatial and temporal characteristics of flash droughts with different grades based on their respective severities. The results show that decreases in total cloud cover, precipitation, and relative humidity, as well as increases in 500-hPa geopotential height, convective inhibition, temperature, vapor pressure deficit, and wind speed can create favorable conditions for the occurrence of flash droughts. Although flash droughts are relatively frequent in the central and southern parts of the basin, the severity is relatively high in the northern part of the basin due to longer duration. Flash drought severity shows a slightly downward trend due to decreases in frequency, duration, and intensity from 1961 to 2018. Extreme and exceptional flash droughts decrease significantly while moderate and severe flash droughts trend slightly upward. Flash drought severity appears to be more affected by the interaction between duration and intensity as the grade increases from mild to severe. The frequency and duration of flash droughts are higher in July–October. The southern part of the basin is more prone to moderate and severe flash droughts, while the northern parts of the basin are more vulnerable to extreme and exceptional flash droughts due to longer durations and greater severities than other parts. Moderate, severe, extreme, and exceptional flash droughts occurred at approximately 3–6-, 5–15-, 10–50-, and 30–200-yr intervals, respectively, based on the copula analysis. [ABSTRACT FROM AUTHOR]

Background: Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results: Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion: We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors. [ABSTRACT FROM AUTHOR]

Future changes in the frequency of extreme drought events are of vital importance for risk assessment and relevant policy making. But a reliable estimation of their probability is intrinsically challenging due to limited available observations or simulations. Here, we use two large ensemble simulations, 50 members from CanESM2 and 40 members from CESM1 under the future RCP8.5 scenario, to elaborate a reliable projection of the 100-yr drought events (once in a century) under different warming levels. It is however necessary to first remove systematic biases for the simulated temperature and precipitation through a bias-correction method based on quantile mapping. Droughts are diagnosed with the standardized precipitation evapotranspiration index (SPEI), which considers both precipitation and potential evapotranspiration (PET, involving temperature). The results show that the frequency of extreme droughts increases with the continued global warming. Some differences between the two ensembles are also observed, especially for high warming levels. The China-averaged probability of 100-yr droughts that occur once in a century in the current climate increase by a factor of 1.52 (1.44) and 1.90 (2.02) under 1.5°C and 2°C warming levels in CanESM2-LE (CESM1-LE), respectively. A simple statistical scheme shows that the increasing future risk of extreme droughts is mainly due to the increasing effect of PET on the occurrence of extreme drought events, while the effect of precipitation almost keeps constant with global warming. [ABSTRACT FROM AUTHOR]

An extreme warm winter (ranked first during the past 30‐years) occurred in Shanghai in 2019, accompanied with an extreme rainy winter (ranked thirdly in the same period). An observational diagnosis shows that the extreme warm and rainy winter arose from southerly anomalies associated with an anticyclone east of Shanghai. The cause of the anticyclone was attributed to V‐shaped upper‐tropospheric Rossby wave activity fluxes, originated from the North Atlantic and tropical Indian Ocean. Numerical model experiments indicate that the anomalous heat source in the tropical Indian Ocean played a dominant role (~65%) in causing the local anomalous circulation, while the heating in North Atlantic also played a role (~35%). A further analysis of the past 30 year data revealed that an extreme warm winter in the past did not coincide with an extreme wet winter. While the warm winter composite shows a large‐scale anticyclone anomaly over Central and East Asia, the rainy winter composite exhibits a circulation dipole pattern with an anomalous cyclone (anticyclone) west (east) of Shanghai. Numerical experiments confirm that the former was forced by the combined effect of precipitation anomalies over the North Atlantic and tropical Indian Ocean (IO) and Maritime Continent (MC) through Rossby wave energy dispersion, whereas the latter was caused by a dipole heating pattern over the tropical IO/MC sector. [ABSTRACT FROM AUTHOR]

Background: Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors. Methods: In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis. Results: The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with 'Rectlike' and 'deviation ratio of tumor tissue and normal thyroid gland' which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature 'minimum calcification area' which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature 'Rectlike' and 'deviation ratio of tumor and normal thyroid gland' (p<0.001; p<0.001); THBS1 was correlated with 'minimum calcification area' (p<0.001). Conclusions: The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management. [ABSTRACT FROM AUTHOR]

The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. (https://github.com/Haifengtao/AUCseg). [ABSTRACT FROM AUTHOR]

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model. Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model. Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set. Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM. [ABSTRACT FROM AUTHOR]

Objective: The conditioned medium of calcined tooth powder (CTP‐CM) is a type of biomimetic mineralized material and well contributing to bone healing and bone formation in vivo. However, little is known about the effect of CTP‐CM on human periodontal ligament stem cells (hPDLSCs) as well as the underlying mechanisms. Methods: ALP activity assay was conducted to select the concentration with the highest ALP level, which was used for the following experiments. Cell proliferation was measured by cell counting kit‐8 assay and flow cytometry analysis. Expression levels of osteogenic markers in CTP‐CM‐induced hPDLSCs were evaluated with real‐time quantitative reverse transcription polymerase chain reaction (qRT‐PCR), immunofluorescence staining, and Western blot. Mineralization of CTP‐CM‐induced hPDLSCs was evaluated by alizarin red staining. Furthermore, the involvement of NF‐κB pathway was examined by immunofluorescence staining and Western blot. Results: 20 μg/ml was selected for the further experiments. Functional studies demonstrated that CTP‐CM exerted almost no influence on the proliferation of hPDLSCs and CTP‐CM increased the osteogenic differentiation of hPDLSCs. Mechanistically, CTP‐CM leads to activation of NF‐κB signaling pathway. When treated with BMS345541, the osteogenic differentiation of CTP‐CM‐treated hPDLSCs was significantly attenuated. Conclusion: CTP‐CM can promote the osteogenic differentiation of hPDLSCs via activating NF‐κB pathway. [ABSTRACT FROM AUTHOR]

Gastric cancer is the second most lethal type of malignant tumor in the world. Early diagnosis of gastric cancer can reduce the transformation to advanced cancer and improve the early treatment rate. As a cheap, real-time, non-invasive examination method, oral contrast-enhanced ultrasonography (OCUS) is a more acceptable way to diagnose gastric cancer than interventional diagnostic methods such as gastroscopy. In this paper, we proposed a new method for the diagnosis of gastric diseases by automatically analyzing the hierarchical structure of gastric wall in gastric ultrasound images, which is helpful to quantify the diagnosis information of gastric diseases and is a useful attempt for early screening of gastric cancer. We designed a gastric wall detection network based on U-net. On this basis, anisotropic diffusion technology was used to extract the layered structure of the gastric wall. A simple and useful gastric cancer screening model was obtained by calculating and counting the thickness of the five-layer structure of the gastric wall. The experimental results showed that our model can accurately identify the gastric wall, and it was found that the layered parameters of abnormal gastric wall is significantly different from that of normal gastric wall. For the screening of gastric disease, a statistical model based on gastric wall stratification can give a screening accuracy of 95% with AUC of 0.92. [ABSTRACT FROM AUTHOR]

Background: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement.Methods: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation.Results and Conclusion: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively. [ABSTRACT FROM AUTHOR]

Purpose: Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients' recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper‐acute phase of stroke. In this paper, a two‐stage convolutional neural network‐based method was proposed to identify the invisible ischemic stroke from ncCT. Methods: In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end‐to‐end U‐net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet‐based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information. Results: Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method. Conclusions: There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two‐stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke. [ABSTRACT FROM AUTHOR]