Integrated diagnosis of glioma based on magnetic resonance images with incomplete ground truth labels.

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.

Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

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%.

Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma.

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.

Otilonium Bromide Exhibits Potent Antifungal Effects by Blocking Ergosterol Plasma Membrane Localization and Triggering Cytotoxic Autophagy in Candida Albicans.

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.

Isobavachalcone Exhibits Potent Antifungal Efficacy by Inhibiting Enolase Activity and Glycolysis in Candida albicans.

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.

METTL3-mediated pre-miR-665/DLX3 m6A methylation facilitates the committed differentiation of stem cells from apical papilla.

Methyltransferase-like 3 (METTL3) is a crucial element of N6-methyladenosine (m6A) 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 m6A modification levels were assessed using m6A 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 m6A 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 m6A 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.

Geldanamycin confers fungicidal properties to azole by triggering the activation of succinate dehydrogenase.

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.

Expert consensus on difficulty assessment of endodontic therapy.

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.

Expert consensus on irrigation and intracanal medication in root canal therapy.

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.

Halofantrine Hydrochloride Acts as an Antioxidant Ability Inhibitor That Enhances Oxidative Stress Damage to Candida albicans.

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.

TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy.

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 (R2) 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.

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images.

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.

Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.

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.

Hypoxia-driven TNS4 fosters HNSCC tumorigenesis by stabilizing integrin α5ß1 complex and triggering FAK-mediated Akt and TGFß signaling pathways.

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 ß1, thereby activating focal adhesion kinase (FAK). This TNS4-mediated FAK activation simultaneously enhanced the PI3K/Akt signaling pathway and facilitated the interaction between TGFßRI and TGFßRII, leading to the activation of the TGFß 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.

Circ-ZNF236 mediates stem cells from apical papilla differentiation by regulating LGR4-induced autophagy.

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.

MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.

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.

Automatically optimized radiomics modeling system for small gastric submucosal tumor (<2 cm) discrimination based on EUS images.

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).

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

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.

Analysis on Intraoperative Electrocorticogram Characteristics for Evaluating the Risk of Postoperative Epilepsy.

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.

Effects of Different Forms and Proportions of Nitrogen on the Growth, Photosynthetic Characteristics, and Carbon and Nitrogen Metabolism in Tomato.

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.