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Background: Since the 2016 WHO guidelines, glioma diagnosis has entered an era of integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has focused on promoting the application of molecular diagnosis in the classification of central nervous system tumors. Genetic information such as IDH1 and 1p/19q are important molecular markers, and pathological grading is also a key clinical indicator. However, obtaining genetic pathology labels is more costly than conventional MRI images, resulting in a large number of missing labels in realistic modeling., Method: We propose a training strategy based on label encoding and a corresponding loss function to enable the model to effectively utilize data with missing labels. Additionally, we integrate a graph model with genes and pathology-related clinical prior knowledge into the ResNet backbone to further improve the efficacy of diagnosis. Ten-fold cross-validation experiments were conducted on a large dataset of 1072 patients., Results: The classification area under the curve (AUC) values are 0.93, 0.91, and 0.90 for IDH1, 1p/19q status, and grade (LGG/HGG), respectively. When the label miss rate reached 59.3 %, the method improved the AUC by 0.09, 0.10, and 0.04 for IDH1, 1p/19q, and pathological grade, respectively, compared to the same backbone without the missing label strategy., Conclusions: Our method effectively utilizes data with missing labels and integrates clinical prior knowledge, resulting in improved diagnostic performance for glioma genetic and pathological markers, even with high rates of missing labels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Posthepatectomy liver failure (PHLF) is a major cause of postoperative mortality in hepatocellular carcinoma (HCC) patients. The study aimed to develop a method based on the two-dimensional shear wave elastography and clinical data to evaluate the risk of PHLF in HCC patients with chronic hepatitis B., Methods: This multicenter study proposed a deep learning model (PHLF-Net) incorporating dual-modal ultrasound features and clinical indicators to predict the PHLF risk. The datasets were divided into a training cohort, an internal validation cohort, an internal independent testing cohort, and three external independent testing cohorts. Based on ResNet50 pretrained on ImageNet, PHLF-Net used a progressive training strategy with images of varying granularity and incorporated conventional B-mode and elastography images and clinical indicators related to liver reserve function., Results: In total, 532 HCC patients who underwent hepatectomy at five hospitals were enrolled. PHLF occurred in 147 patients (27.6%, 147/532). The PHLF-Net combining dual-modal ultrasound and clinical indicators demonstrated high effectiveness for predicting PHLF, with AUCs of 0.957 and 0.923 in the internal validation and testing sets, and AUCs of 0.950, 0.860, and 1.000 in the other three independent external testing sets. The performance of PHLF-Net outperformed models of single- and dual-modal US., Conclusions: Preoperative ultrasound imaging combining clinical indicators can effectively predict the PHLF probability in patients with HCC. In the internal and external validation sets, PHLF-Net demonstrated its usefulness in predicting PHLF., (© 2024 American Institute of Ultrasound in Medicine.)

Invasive fungal diseases (IFDs) are becoming increasingly acknowledged as a significant concern linked to heightened rates of morbidity and mortality. Regrettably, the available antifungal therapies for managing IFDs are constrained. Emerging evidence indicates that enolase holds promise as a potential target protein for combating IFDs; however, there is currently a deficiency in antifungal medications specifically targeting enolase. This study establishes that isobavachalcone (IBC) exhibits noteworthy antifungal efficacy both in vitro and in vivo. Moreover, our study has demonstrated that IBC effectively targets Eno1 in Candida albicans (CaEno1), resulting in the suppression of the glycolytic pathway. Additionally, our research has indicated that IBC exhibits a higher affinity for CaEno1 compared to human Eno1 (hEno1), with the presence of isoprenoid in the side chain of IBC playing a crucial role in its ability to inhibit enolase activity. These findings contribute to the comprehension of antifungal approaches that target Eno1, identifying IBC as a potential inhibitor of Eno1 in human pathogenic fungi.

Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.

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., (© 2024. The Author(s).)

Candidiasis, which presents a substantial risk to human well-being, is frequently treated with azoles. However, drug-drug interactions caused by azoles inhibiting the human CYP3A4 enzyme, together with increasing resistance of Candida species to azoles, represent serious issues with this class of drug, making it imperative to develop innovative antifungal drugs to tackle this growing clinical challenge. A drug repurposing approach is used to examine a library of Food and Drug Administration (FDA)-approved drugs, ultimately identifying otilonium bromide (OTB) as an exceptionally encouraging antifungal agent. Mechanistically, OTB impairs vesicle-mediated trafficking by targeting Sec31, thereby impeding the plasma membrane (PM) localization of the ergosterol transporters, such as Sip3. Consequently, OTB obstructs the movement of ergosterol across membranes and triggers cytotoxic autophagy. It is noteworthy that C. albicans encounters challenges in developing resistance to OTB because it is not a substrate for drug transporters. This study opens a new door for antifungal therapy, wherein OTB disrupts ergosterol subcellular distribution and induces cytotoxic autophagy. Additionally, it circumvents the hepatotoxicity associated with azole-mediated liver enzyme inhibition and avoids export-mediated drug resistance in C. albicans., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Aims: Azoles have been widely employed for the treatment of invasive fungal diseases; however, their efficacy is diminished as pathogenic fungi tolerate them due to their fungistatic properties. Geldanamycin (GdA) can render azoles fungicidal by inhibiting the ATPase and molecular chaperone activities of heat shock protein 90 (Hsp90). Nonetheless, the clinical applicability of GdA is restricted due to its cytotoxic ansamycin scaffold structure, its induction of cytoprotective heat shock responses, and the conservative nature of Hsp90. Hence, it is imperative to elucidate the mechanism of action of GdA to confer fungicidal properties to azoles and mitigate the toxic adverse effects associated with GdA., Materials and Methods: Through various experimental methods, including the construction of gene-deleted Candida albicans mutants, in vitro drug sensitivity experiments, Western blot analysis, reactive oxygen species (ROS) assays, and succinate dehydrogenase activity assays, we identified Hsp90 client proteins associated with the tolerance of C. albicans to azoles., Key Findings: It was observed that GdA effectively hindered the entry of Hsp90 into mitochondria, resulting in the alleviation of inhibitory effect of Hsp90 on succinate dehydrogenase. Consequently, the activation of succinate dehydrogenase led to an increased production of ROS. within the mitochondria, thereby facilitating the antifungal effects of azoles against C. albicans., Significance: This research presents a novel approach for conferring fungicidal properties to azoles, which involves specifically disrupting the interaction of between Hsp90 and succinate dehydrogenase rather than employing a non-specific inhibition of ATPase activity of Hsp90., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

Methyltransferase-like 3 (METTL3) is a crucial element of N6-methyladenosine (m 6 A) modifications and has been extensively studied for its involvement in diverse biological and pathological processes. In this study, we explored how METTL3 affects the differentiation of stem cells from the apical papilla (SCAPs) into odonto/osteoblastic lineages through gain- and loss-of-function experiments. The m 6 A modification levels were assessed using m 6 A dot blot and activity quantification experiments. In addition, we employed Me-RIP microarray experiments to identify specific targets modified by METTL3. Furthermore, we elucidated the molecular mechanism underlying METTL3 function through dual-luciferase reporter gene experiments and rescue experiments. Our findings indicated that METTL3 +/- mice exhibited significant root dysplasia and increased bone loss. The m 6 A level and odonto/osteoblastic differentiation capacity were affected by the overexpression or inhibition of METTL3. This effect was attributed to the acceleration of pre-miR-665 degradation by METTL3-mediated m 6 A methylation in cooperation with the "reader" protein YTHDF2. Additionally, the targeting of distal-less homeobox 3 (DLX3) by miR-665 and the potential direct regulation of DLX3 expression by METTL3, mediated by the "reader" protein YTHDF1, were demonstrated. Overall, the METTL3/pre-miR-665/DLX3 pathway might provide a new target for SCAP-based tooth root/maxillofacial bone tissue regeneration., (© 2024. The Author(s).)

Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R 2 ) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

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&#95;circ&#95;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., (© 2024 British Endodontic Society. Published by John Wiley & Sons Ltd.)

Background and Aims: The clinical management of small gastric submucosal tumors (SMTs) (<2 cm) faces a non-negligible challenge because of the lack of guideline consensus and effective diagnostic tools. This article develops an automatically optimized radiomics modeling system (AORMS) based on EUS images to diagnose and evaluate SMTs., Methods: A total of 205 patients with EUS images of small gastric SMTs (<2 cm) were retrospectively enrolled in the development phase of AORMS for the diagnosis and the risk stratification of GI stromal tumor (GIST). A total of 178 patients with images from different centers were prospectively enrolled in the independent testing phase. The performance of AORMS was compared to that of endoscopists in the development set and evaluated in the independent testing set., Results: AORMS demonstrated an area under the curve (AUC) of 0.762 for the diagnosis of GIST and 0.734 for the risk stratification of GIST, respectively. In the independent testing set, AORMS achieved an AUC of 0.770 and 0.750 for the diagnosis and risk stratification of small GISTs, respectively. In comparison, the AUCs of 5 experienced endoscopists ranged from 0.501 to 0.608 for diagnosing GIST and from 0.562 to 0.748 for risk stratification. AORMS outperformed experienced endoscopists by more than 20% in diagnosing GIST., Conclusions: AORMS implements automatic parameter selection, which enhances its robustness and clinical applicability. It has demonstrated good performance in the diagnosis and risk stratification of GISTs, which could aid endoscopists in the diagnosis of small gastric SMTs (<2 cm)., Competing Interests: Disclosure All authors disclosed no financial relationships., (Copyright © 2024. Published by Elsevier Inc.)

Chemical cleaning and disinfection are crucial steps for eliminating infection in root canal treatment. However, irrigant selection or irrigation procedures are far from clear. The vapor lock effect in the apical region has yet to be solved, impeding irrigation efficacy and resulting in residual infections and compromised treatment outcomes. Additionally, ambiguous clinical indications for root canal medication and non-standardized dressing protocols must be clarified. Inappropriate intracanal medication may present side effects and jeopardize the therapeutic outcomes. Indeed, clinicians have been aware of these concerns for years. Based on the current evidence of studies, this article reviews the properties of various irrigants and intracanal medicaments and elucidates their effectiveness and interactions. The evolution of different kinetic irrigation methods, their effects, limitations, the paradigm shift, current indications, and effective operational procedures regarding intracanal medication are also discussed. This expert consensus aims to establish the clinical operation guidelines for root canal irrigation and a position statement on intracanal medication, thus facilitating a better understanding of infection control, standardizing clinical practice, and ultimately improving the success of endodontic therapy., (© 2024. The Author(s).)

Endodontic diseases are a kind of chronic infectious oral disease. Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha. However, it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy (RCT). Recent research, encompassing bacterial etiology and advanced imaging techniques, contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT. Success in RCT hinges on factors like patients, infection severity, root canal anatomy, and treatment techniques. Therefore, improving disease management is a key issue to combat endodontic diseases and cure periapical lesions. The clinical difficulty assessment system of RCT is established based on patient conditions, tooth conditions, root canal configuration, and root canal needing retreatment, and emphasizes pre-treatment risk assessment for optimal outcomes. The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT. These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

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 H 2 O 2 ) 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.

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., (© 2024. The Author(s).)

Head and neck squamous cell carcinoma (HNSCC) remains a formidable clinical challenge due to its high recurrence rate and limited targeted therapeutic options. This study aims to elucidate the role of tensin 4 (TNS4) in the pathogenesis of HNSCC across clinical, cellular, and animal levels. We found a significant upregulation of TNS4 expression in HNSCC tissues compared to normal controls. Elevated levels of TNS4 were associated with adverse clinical outcomes, including diminished overall survival. Functional assays revealed that TNS4 knockdown attenuated, and its overexpression augmented, the oncogenic capabilities of HNSCC cells both in vitro and in vivo . Mechanistic studies revealed that TNS4 overexpression promotes the interaction between integrin α5 and integrin β1, thereby activating focal adhesion kinase (FAK). This TNS4-mediated FAK activation simultaneously enhanced the PI3K/Akt signaling pathway and facilitated the interaction between TGFβRI and TGFβRII, leading to the activation of the TGFβ signaling pathway. Both of these activated pathways contributed to HNSCC tumorigenesis. Additionally, we found that hypoxia-inducible factor 1α (HIF-1α) transcriptionally regulated TNS4 expression. In conclusion, our findings provide the basis for innovative TNS4-targeted therapeutic strategies, which could potentially improve prognosis and survival rates for patients with HNSCC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

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.

Background: Ultrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection., Methods: In this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance., Results: The experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study., Conclusion: The combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Jia, Luo, Yu and Xia.)

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., (© 2023. CARS.)

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.

Background: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals., Methods: We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053)., Findings: In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average., Interpretation: STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes., Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section., Competing Interests: Declaration of interests The authors declare that they have no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s).)

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.

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., (© 2023 British Endodontic Society. Published by John Wiley & Sons Ltd.)

Macrophage performs multiple functions such as pathogen phagocytosis, antigen presentation, and tissue remodeling by polarizing toward a spectrum of phenotypes. Dynamic imaging of macrophage phenotypes is critical for evaluating disease progression and the therapeutic response of drug candidates. However, current technologies cannot identify macrophage phenotypes in vivo. Herein, a surface-enhanced Raman scattering nanoprobe, AH1, which enables the accurate determination of physiological pH with high sensitivity and tissue penetration depth through ratiometric Raman signals is developed. Due to the phenotype-dependent metabolic reprogramming, AH1 can effectively identify macrophage subpopulations by measuring the acidity levels in phagosomes. After intravenous administration, AH1 not only visualizes the spatial distribution of macrophage phenotypes in brain tumors and epileptic regions of mouse models, but also reveals the repolarization of macrophages in brain lesions after drug intervention. This work provides a new tool for dynamically monitoring the disease-associated immune microenvironment and evaluating the efficacy of immune-therapeutics in vivo., (© 2023 Wiley-VCH GmbH.)

Purpose: The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status., Methods: This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status., Results: Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas., Conclusion: Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDHwildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas.

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., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Early screening and frequent monitoring effectively decrease the risk of severe scoliosis, but radiation exposure is a consequence of traditional radiograph examinations. Additionally, traditional X-ray images on the coronal or sagittal plane have difficulty providing three-dimensional (3-D) information on spinal deformities. The Scolioscan system provides an innovative 3-D spine imaging approach via ultrasonic scanning, and its feasibility has been demonstrated in numerous studies. In this paper, to further examine the potential of spinal ultrasonic data for describing 3-D spinal deformities, we propose a novel deep-learning tracker named Si-MSPDNet for extracting widely employed landmarks (spinous process (SP)) in ultrasonic images of spines and establish a 3-D spinal profile to measure 3-D spinal deformities. Si-MSPDNet has a Siamese architecture. First, we employ two efficient two-stage encoders to extract features from the uncropped ultrasonic image and the patch centered on the SP cut from the image. Then, a fusion block is designed to strengthen the communication between encoded features and further refine them from channel and spatial perspectives. The SP is a very small target in ultrasonic images, so its representation is weak in the highest-level feature maps. To overcome this, we ignore the highest-level feature maps and introduce parallel partial decoders to localize the SP. The correlation evaluation in the traditional Siamese network is also expanded to multiple scales to enhance cooperation. Furthermore, we propose a binary guided mask based on vertebral anatomical prior knowledge, which can further improve the performance of our tracker by highlighting the potential region with SP. The binary-guided mask is also utilized for fully automatic initialization in tracking. We collected spinal ultrasonic data and corresponding radiographs on the coronal and sagittal planes from 150 patients to evaluate the tracking precision of Si-MSPDNet and the performance of the generated 3-D spinal profile. Experimental results revealed that our tracker achieved a tracking success rate of 100% and a mean IoU of 0.882, outperforming some commonly used tracking and real-time detection models. Furthermore, a high correlation existed on both the coronal and sagittal planes between our projected spinal curve and that extracted from the spinal annotation in X-ray images. The correlation between the tracking results of the SP and their ground truths on other projected planes was also satisfactory. More importantly, the difference in mean curvatures was slight on all projected planes between tracking results and ground truths. Thus, this study effectively demonstrates the promising potential of our 3-D spinal profile extraction method for the 3-D measurement of spinal deformities using 3-D ultrasound data., Competing Interests: Declaration of Competing Interest The authors declare that there is not financial interests and personal relationships which may affect the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides., Methods: This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort., Findings: The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort., Interpretation: The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers., Funding: Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631)., Competing Interests: Declaration of interests All authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Bone defects caused by bone trauma, infection, surgery, or other systemic diseases remain a severe challenge for the medical field. To address this clinical problem, different hydrogels were exploited to promote bone tissue regrowth and regeneration. Keratins are natural fibrous proteins found in wool, hair, horns, nails, and feather. Due to their unique characteristics of outstanding biocompatibility, great biodegradability, and hydrophilic, keratins have been widely applicated in different fields. In our study, the feather keratin-montmorillonite nanocomposite hydrogels that consist of keratin hydrogels serving as the scaffold support to accommodate endogenous stem cells and montmorillonite is synthesized. The introduction of montmorillonite greatly improves the osteogenic effect of the keratin hydrogels via bone morphogenetic protein 2 (BMP-2)/phosphorylated small mothers against decapentaplegic homolog 1/5/8 (p-SMAD 1/5/8)/runt-related transcription factor 2 (RUNX2) expression. Moreover, the incorporation of montmorillonite into hydrogels can improve the mechanical properties and bioactivity of the hydrogels. The morphology of feather keratin-montmorillonite nanocomposite hydrogels was shown by scanning electron microscopy (SEM) to have an interconnected porous structure. The incorporation of montmorillonite into the keratin hydrogels was confirmed by the energy dispersive spectrum (EDS). We prove that the feather keratin-montmorillonite nanocomposite hydrogels enhance the osteogenic differentiation of BMSCs. Furthermore, micro-CT and histological analysis of rat cranial bone defect demonstrated that feather keratin-montmorillonite nanocomposite hydrogels dramatically stimulated bone regeneration in vivo. Collectively, feather keratin-montmorillonite nanocomposite hydrogels can regulate BMP/SMAD signaling pathway to stimulate osteogenic differentiation of endogenous stem cells and promote bone defect healing, indicating their promising candidate in bone tissue engineering., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Epilepsy is one of the most common complications after craniotomy, which happens suddenly and does great harm. There still lacks of effective prediction method during the operation. The main purpose of this paper is to explore the correlation between the characteristics of intraoperative electrocorticogram (ECoG) and postoperative epilepsy, and select effective features to establish a prediction model. This retrospective study uses intraoperative ECoG recordings of 144 patients with cerebrovascular diseases undergoing cerebral revascularization surgeries. The cases are divided into subtypes of ischemic and hemorrhagic. Nine types of ECoG features are designed on different frequency bands indicating clinical information, power spectrum, complexity, sequence change, and information quantity, while their changes in different surgical stages are also considered. Then statistical analysis is used to obtain features significantly related to postoperative epilepsy (p<0.05). The sparse representation method is used on these features to further screen and reduce the redundancy, and then machine learning methods are used to establish a prediction model for postoperative epilepsy. The accuracy, sensitivity and specificity of the best prediction model can achieve 0.817, 0.800 and 0.833 respectively under 5-fold cross validation.Clinical Relevance-This study explores the correlation between the characteristics of intraoperative ECoG and postoperative epilepsy, investigates the possibility to use the ECoG features and machine learning algorithms to assess the risk of postoperative epilepsy during the surgery. Further results are expected to provide reference for preventive measures to reduce the occurrence of postoperative epilepsy.

Background: Human dental stem cells (DSCs) are excellent sources of cells for treating dental and craniofacial diseases. However, the mechanisms regulating DSC osteogenic differentiation are still unclear. In this study, we aimed to determine the role of Krüppel-like factor 9 (KLF9) in regulating the biological functions of DSCs and explore the underlying molecular mechanisms., Methods: Bioinformatic analyses, quantitative real-time polymerase chain reaction (qRT‒PCR) and Western blotting were performed to determine the KLF9 level during osteogenic differentiation of DSCs. The effects of KLF9 depletion or overexpression on DSC osteogenic differentiation were then evaluated. The osteogenic potential and associated mineralized nodule-forming activities of DSCs were monitored via Alizarin red S staining and quantitative analyses of osteogenic markers. The regulatory effect of KLF9 on the Notch1 signaling pathway was analyzed by luciferase reporter assays., Results: KLF9 mRNA expression was consistently increased during mesenchymal stem cell osteogenic differentiation in multiple public datasets, and our qRT‒PCR and Western blotting data further validated this finding. In addition, KLF9 depletion promoted proliferation and suppressed osteogenic differentiation of DSCs, while enforced expression of KLF9 promoted the DSC osteogenic potential. Mechanistically, KLF9 negatively regulated the Notch1-mediated signaling pathway by directly binding to the Notch1 promoter. More importantly, Notch1 inhibition/overexpression partially rescued the suppressive/enhancing effects of KLF9 depletion/overexpression on the osteogenic differentiation of DSCs, indicating that Notch1 is a functional downstream target of KLF9., Conclusions: In summary, our results strongly demonstrate that KLF9 is a crucial transcription factor that controls the osteogenic differentiation of DSCs by negatively regulating the Notch1 signaling pathway., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s). Published by IMR Press.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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.

Introduction: White matter injury (WMI) is now the major disease that seriously affects the quality of life of preterm infants and causes cerebral palsy of children, which also causes periventricular leuko-malacia (PVL) in severe cases. The study aimed to develop a method based on cranial ultrasound images to evaluate the risk of WMI., Methods: This study proposed an ultrasound radiomics diagnostic system to predict the WMI risk. A multi-task deep learning model was used to segment white matter and predict the WMI risk simultaneously. In total, 158 preterm infants with 807 cranial ultrasound images were enrolled. WMI occurred in 32preterm infants (20.3%, 32/158)., Results: Ultrasound radiomics diagnostic system implemented a great result with AUC of 0.845 in the testing set. Meanwhile, multi-task deep learning model preformed a promising result both in segmentation of white matter with a Dice coefficient of 0.78 and prediction of WMI risk with AUC of 0.863 in the testing cohort., Discussion: In this study, we presented a data-driven diagnostic system for white matter injury in preterm infants. The system combined multi-task deep learning and traditional radiomics features to achieve automatic detection of white matter regions on the one hand, and design a fusion strategy of deep learning features and manual radiomics features on the other hand to obtain stable and efficient diagnostic performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Zhu, Yao, Yao, Yu, Qian and Chen.)

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% O 2 ) or hypoxia (2% O 2 )., 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% O 2 ; specific circRNAs/lncRNAs may play a competitive role in the differentiation of PDLSCs., (© 2021 Wiley Periodicals LLC.)

Bone defects that result from trauma, infection, surgery, or congenital malformation can severely affect the quality of life. To address this clinical problem, a phosphoserine-loaded chitosan membrane that consists of chitosan membranes serving as the scaffold support to accommodate endogenous stem cells and phosphoserine is synthesized. The introduction of phosphoserine greatly improves the osteogenic effect of the chitosan membranes via mutual crosslinking using a crosslinker (EDC, 1-ethyl-3-(3-dimethyl aminopropyl)-carbodiimide). The morphology of PS-CS membranes was shown by scanning electron microscopy (SEM) to have an interconnected porous structure. The incorporation of phosphoserine into chitosan membranes was confirmed by energy dispersive spectrum (EDS), Fourier Transforms Infrared (FTIR), and X-ray diffraction (XRD) spectrum. The CCK8 assay and Live/Dead staining, Hemolysis analysis, and cell adhesion assay demonstrated that PS-CS membranes had good biocompatibility. The osteogenesis-related gene expression of BMSCs was higher in PS-CS membranes than in CS membranes, which was verified by alkaline phosphatase (ALP) activity, immunofluorescence staining, and real-time quantitative PCR (RT-qPCR). Furthermore, micro-CT and histological analysis of rat cranial bone defect demonstrated that PS-CS membranes dramatically stimulated bone regeneration in vivo . Moreover, H&E staining of the main organs (heart, liver, spleen, lung, or kidney) showed no obvious histological abnormalities, revealing that PS-CS membranes were no additional systemic toxicity in vivo . Collectively, PS-CS membranes may be a promising candidate for bone tissue engineering., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ke, Ye, Wu, Ma, Fang, Jiang and Yu.)

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped., (© 2023. The Author(s).)

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 T c . We discover that the topological connectivity of icosahedral clusters is responsible for this dynamic crossover, rather than all clusters. T c is the temperature at which the connectivity degree between these clusters reaches a maximum and the dynamic heterogeneity begins to keep stable. Below T c , 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.

Objective: The diagnosis of prostate cancer has been a challenging task. Compared with traditional diagnosis methods, the radiofrequency (RF) signal is not only non-invasive but also rich in microscopic lesion information. This paper proposes a novel and accurate method for detecting prostate cancer based on the ultrasound RF signal., Method: Our approach is based on low-dimensional features in the frequency domain and high-throughput features in the spatial domain. The whole process could be divided into two parts: first, we calculate three feature maps from the ultrasound original RF signal, and 1,050 radiomics features are extracted from the three feature maps; second, we extracted 37 spectral features from the normalized frequency spectrum after Fourier transform., Results: We use LASSO regression as the method for feature selection; moreover, we use support vector machine (SVM) for classification 10-fold cross-validation for examining the classification performance of the SVM. An AUC (area under the receiver operating characteristic curve) of 0.84 was obtained on 71 subjects., Conclusions: Our method is feasible to detect prostate cancer based on the ultrasound RF signal with superior classification performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xiao, Shen, Wang, Wu, Yu and Cui.)

Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Raman spectroscopy can be analyzed more efficiently and thus provide more accurate molecular information. However, no general neural network is designed for one-dimensional Raman spectral data so far. Furthermore, different combinations of hyperparameters of neural networks lead to results with significant differences, so the optimization of hyperparameters is a crucial issue in deep learning modeling. In this work, we propose a deep learning model designed for Raman spectral data and a hyperparameter optimization method to achieve its best performance, i.e., a method based on the simulated annealing algorithm to optimize the hyperparameters of the model. The proposed model and optimization method have been fully validated in a glioma Raman spectroscopy dataset. Compared with other published methods including linear regression, support vector regression, long short-term memory, VGG and ResNet, the mean squared error is reduced by 0.1557 while the coefficient determination is increased by 0.1195 on average., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare that there is no conflict of interest., (© 2022 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)

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., (© 2022 The Pathological Society of Great Britain and Ireland.)

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., (© Crown copyright 2022.)

Background and Aims: Microvascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC., Methods: A total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training ( n = 301), validation ( n = 102), and test ( n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI., Results: Compared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1-7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7-21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23-8.91], p = 0.011) in the test group., Conclusions: The proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Wei, Huang, Yao, Yan, Zou, Han, Li, Mao, Liao, Cao, Lin, Zhou, Tang, Hu, Li, Wang, Yu and Zhou.)

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)