Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Cancer drug indication approvals in China and the United States: a comparison of approval times and clinical benefit, 2001-2020.

Background: Perceived delays in cancer drug approvals have been a major concern for policymakers in China. Policies have been implemented to accelerate the launch of new cancer drugs and indications. This study aimed to assess similarities and differences between China and the United States in the approvals, timing, and clinical benefit evidence of cancer drug indications between 2001 and 2020. Methods: This study retrospectively identified all cancer drugs and indications approved in both China and the United States from January 1st, 2001 to December 31, 2020, and described differences in approval times as well as in submission and review times. Information on the availability of overall survival benefit evidence by December 31, 2020, was collected. Univariate and multiple logistic regression analyses were used to assess whether evidence of benefit and other factors affected the propensity and timing of approvals of cancer drug indications in China. Findings: Between 2001 and 2020, 229 indications corresponding to 145 cancer drugs approved in the United States were identified. Of those, 80 indications (34.9%) were also approved in China by the end of 2020. Cancer drug indications were approved in China at a median of 1273.5 days after approval in the United States. The median submission and review time differences for cancer drug indications in China were 1198.0 days and 180.0 days respectively. Submission time differences accounted for most of the approval time differences (p < 0.001). Indications supported by overall survival benefit evidence had shorter median review time differences (145.0 days) than those without such evidence (235.0 days, p = 0.008). Indications with overall survival benefit evidence were 3.94 times more likely to be approved in China compared to those without such evidence (p = 0.001), controlling for approval year, cancer type, and the prevalence of cancer by site. Interpretation: FDA-approved cancer drug indications demonstrating a survival benefit were more likely to receive approvals in China with shorter regulatory review times compared to indications without such evidence. Given that manufacturer submission times were the main driver of cancer drug approval times in China, factors influencing submission timing should be explored. Funding: No funding.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

Integrating molecular subtype and CD8+ T cells infiltration to predict treatment response and survival in muscle-invasive bladder cancer.

BACKGROUND: Luminal and Basal are the primary intrinsic subtypes of muscle-invasive bladder cancer (MIBC). The presence of CD8+ T cells infiltration holds significant immunological relevance, potentially influencing the efficacy of antitumor responses. This study aims to synergize the influence of molecular subtypes and CD8+ T cells infiltration in MIBC. METHODS: This study included 889 patients with MIBC from Zhongshan Hospital, The Cancer Genome Atlas, IMvigor210 and NCT03179943 cohorts. We classified the patients into four distinct groups, based on the interplay of molecular subtypes and CD8+ T cells and probed into the clinical implications of these subgroups in MIBC. RESULTS: Among patients with Luminal-CD8+Thigh tumors, the confluence of elevated tumor mutational burden and PD-L1 expression correlated with a heightened potential for positive responses to immunotherapy. In contrast, patients featured by Luminal-CD8+Tlow displayed a proclivity for deriving clinical advantages from innovative targeted interventions. The Basal-CD8+Tlow subgroup exhibited the least favorable three-year overall survival outcome, whereas their Basal-CD8+Thigh counterparts exhibited a heightened responsiveness to chemotherapy. CONCLUSIONS: We emphasized the significant role of immune-molecular subtypes in shaping therapeutic approaches for MIBC. This insight establishes a foundation to refine the process of selecting subtype-specific treatments, thereby advancing personalized interventions for patients.

POLQ identifies a better response subset to immunotherapy in muscle-invasive bladder cancer with high PD-L1.

BACKGROUND: Though programmed cell death-ligand 1 (PD-L1) has been used in predicting the efficacy of immune checkpoint blockade (ICB), it is insufficient as a single biomarker. As a key effector of an intrinsically mutagenic microhomology-mediated end joining (MMEJ) pathway, DNA polymerase theta (POLQ) was overexpressed in various malignancies, whose expression might have an influence on genomic stability, therefore altering the sensitivity to chemotherapy and immunotherapy. METHODS: A total of 1304 patients with muscle-invasive bladder cancer (MIBC) from six independent cohorts were included in this study. The Zhongshan Hospital (ZSHS) cohort (n = 134), The Cancer Genome Atlas (TCGA) cohort (n = 391), and the Neo-cohort (n = 148) were included for the investigation of chemotherapeutic response. The IMvigor210 cohort (n = 234) and the UNC-108 cohort (n = 89) were used for the assessment of immunotherapeutic response. In addition, the relationship between POLQ and the immune microenvironment was assessed, and GSE32894 (n = 308) was used only for the evaluation of the immune microenvironment. RESULTS: We identified POLQhigh PD-L1high patients could benefit more from immunotherapy and platinum-based chemotherapy. Further analysis revealed that high POLQ expression was linked to chromosome instability and higher tumor mutational burden (TMB), which might elicit the production of neoantigens. Further, high POLQ expression was associated with an active tumor immune microenvironment with abundant infiltration of immune effector cells and molecules. CONCLUSIONS: The study demonstrated that high POLQ expression was correlated with chromosome instability and antitumor immune microenvironment in MIBC, and the combination of POLQ and PD-L1 could be used as a superior companion biomarker for predicting the efficacy of immunotherapy.

A highly sensitive Lock-Cas12a biosensor for detection and imaging of miRNA-21 in breast cancer cells.

The expression levels of microRNA (miRNA) vary significantly in correlation with the occurrence and progression of cancer, making them valuable biomarkers for cancer diagnosis. However, their quantitative detection faces challenges due to the high sequence homology, low abundance and small size. In this work, we established a strand displacement amplification (SDA) approach based on miRNA-triggered structural "Lock" nucleic acid ("Lock" DNA), coupled with the CRISPR/Cas12a system, for detecting miRNA-21 in breast cancer cells. The "Lock" DNA freed the CRISPR-derived RNA (crRNA) from the dependence on the target sequence and greatly facilitated the extended detection of different miRNAs. Moreover, the CRISPR/Cas12a system provided excellent amplification ability and specificity. The designed biosensor achieved high sensitivity detection of miRNA-21 with a limit of detection (LOD) of 28.8 aM. In particular, the biosensor could distinguish breast cancer cells from other cancer cells through intracellular imaging. With its straightforward sequence design and ease of use, the Lock-Cas12a biosensor offers significant advantages for cell imaging and early clinical diagnosis.

Integration of CD4+ T cells and molecular subtype predicts benefit from PD-L1 blockade in muscle-invasive bladder cancer.

Muscle-invasive bladder cancer (MIBC) is a disease characterized by molecular and clinical heterogeneity, posing challenges in selecting the most appropriate treatment in clinical settings. Considering the significant role of CD4+ T cells, there is an emerging need to integrate CD4+ T cells with molecular subtypes to refine classification. We conducted a comprehensive study involving 895 MIBC patients from four independent cohorts. The Zhongshan Hospital (ZSHS) and The Cancer Genome Atlas (TCGA) cohorts were included to investigate chemotherapeutic response. The IMvigor210 cohort was included to assess the immunotherapeutic response. NCT03179943 was used to evaluate the clinical response to a combination of immune checkpoint blockade (ICB) and chemotherapy. Additionally, we evaluated genomic characteristics and the immune microenvironment to gain deeper insights into the distinctive features of each subtype. We unveiled four immune-molecular subtypes, each exhibiting distinct clinical outcomes and molecular characteristics. These subtypes include luminal CD4+ Thigh, which demonstrated benefits from both immunotherapy and chemotherapy; luminal CD4+ Tlow, characterized by the highest level of fibroblast growth factor receptor 3 (FGFR3) mutation, thus indicating potential responsiveness to FGFR inhibitors; basal CD4+ Thigh, which could benefit from a combination of ICB and chemotherapy; and basal CD4+ Tlow, characterized by an immune suppression microenvironment and likely to benefit from transforming growth factor-ß (TGF-ß) inhibition. This immune-molecular classification offers new possibilities for optimizing therapeutic interventions in MIBC.

TP53 disruptive mutation predicts platinum-based chemotherapy and PD-1/PD-L1 blockade response in urothelial carcinoma.

TP53 mutation is one of the most common genetic alterations in urothelial carcinoma (UrCa), and heterogeneity of TP53 mutants leads to heterogeneous clinical outcomes. This study aimed to investigate the clinical relevance of specific TP53 mutations in UrCa. In this study, a total of eight cohorts were enrolled, along with matched clinical annotation. TP53 mutations were classified as disruptive and nondisruptive according to the degree of disturbance of p53 protein function and structure. We evaluated the clinical significance of TP53 mutations in our local datasets and publicly available datasets. The co-occurring events of TP53 mutations in UrCa, along with their therapeutic indications, functional effects, and the tumor immune microenvironment, were also investigated. TP53 mutations were identified in 49.7% of the UrCa patients. Within this group, 25.1% of patients carried TP53Disruptive mutations, a genetic alteration correlated with a significantly poorer overall survival (OS) when compared to individuals with TP53Nondisruptive mutations and those with wild-type TP53. Significantly, patients with TP53Disruptive mutations exhibit an increased probability of responding favorably to PD-1/PD-L1 blockade and chemoimmunotherapy. Meanwhile, there was no noteworthy distinction in OS among patients with varying TP53 mutation status who underwent chemotherapy. Samples with TP53Disruptive mutations showed an enriched APOBEC- and POLE-related mutational signature, as well as an elevated tumor mutation burden. The sensitivity to immunotherapy in tumors carrying TP53Disruptive mutation may be attributed to the inflamed tumor microenvironment characterized by increased CD8+T cell infiltration and interferon-gamma signaling activation. In conclusion, UrCa patients with TP53Disruptive mutations have shown reduced survival rates, yet they may respond well to PD-1/PD-L1 blockade therapy and chemoimmunotherapy. By distinguishing specific TP53 mutations, we can improve risk stratification and offer personalized genomics-guided therapy to UrCa patients. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Integrative score based on CDK6, PD-L1 and TMB predicts response to platinum-based chemotherapy and PD-1/PD-L1 blockade in muscle-invasive bladder cancer.

BACKGROUND: Cyclin-dependent kinase 6 (CDK6) was proved to be an important regulator in the progression of cell cycle and has been a promising therapeutic target in cancer treatment. However, the clinical significance of CDK6 in muscle-invasive bladder cancer (MIBC) remains obscure. Herein, we attempt to explore the clinical relevance of CDK6 and assess the feasibility of the integrative model to predict immune checkpoint blockade (ICB) response. METHODS: This study enrolled 933 patients with muscle-invasive bladder cancer (MIBC) from Zhongshan Hospital (ZSHS), The Cancer Genome Atlas (TCGA), Chemo, IMvigor210 and UC-GENOME cohorts. Kaplan-Meier survival and Cox regression analyses were performed to assess clinical outcomes based on CDK6 expression. RESULTS: High CDK6 expression conferred poor prognosis and superior response to platinum-based chemotherapy but inferior response to ICB in MIBC. Furthermore, the integrative model named response score based on CDK6, PD-L1 and TMB could better predict the response to ICB and chemotherapy. Patients with higher response scores were characterised by inflamed immune microenvironment and genomic instability. CONCLUSIONS: CDK6 expression was correlated with prognosis and therapy response in MIBC. Integration of CDK6, PD-L1 and TMB could better identify patients who were most likely to benefit from ICB and chemotherapy.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Insights into the regulatory role of RNA methylation modifications in glioma.

Epitranscriptomic abnormalities, which are highly prevalent in primary central nervous system malignancies, have been identified as crucial contributors to the development and progression of gliomas. RNA epitranscriptomic modifications, particularly the reversible modification methylation, have been observed throughout the RNA cycle. Epitranscriptomic modifications, which regulate RNA transcription and translation, have profound biological implications. These modifications are associated with the development of several cancer types. Notably, three main protein types-writers, erasers, and readers, in conjunction with other related proteins, mediate these epitranscriptomic changes. This review primarily focuses on the role of recently identified RNA methylation modifications in gliomas, such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), and N1-methyladenosine (m1A). We delved into their corresponding writers, erasers, readers, and related binding proteins to propose new approaches and prognostic indicators for patients with glioma.

Circuit-Level Modeling and Simulation of Wireless Sensing and Energy Harvesting With Hybrid Magnetoelectric Antennas for Implantable Neural Devices.

A magnetoelectric antenna (ME) can exhibit the dual capabilities of wireless energy harvesting and sensing at different frequencies. In this article, a behavioral circuit model for hybrid ME antennas is described to emulate the radio frequency (RF) energy harvesting and sensing operations during circuit simulations. The ME antenna of this work is interfaced with a CMOS energy harvester chip towards the goal of developing a wireless communication link for fully integrated implantable devices. One role of the integrated system is to receive pulse-modulated power from a nearby transmitter, and another role is to sense and transmit low-magnitude neural signals. The measurements reported in this paper are the first results that demonstrate simultaneous low-frequency wireless magnetic sensing and high-frequency wireless energy harvesting at two different frequencies with one dual-mode ME antenna. The proposed behavioral ME antenna model can be utilized during design optimizations of energy harvesting circuits. Measurements were performed to validate the wireless power transfer link with an ME antenna having a 2.57 GHz resonance frequency connected to an energy harvester chip designed in 65nm CMOS technology. Furthermore, this dual-mode ME antenna enables concurrent sensing using a carrier signal with a frequency that matches the second 63.63 MHz resonance mode. A wireless test platform has been developed for evaluation of ME antennas as a tool for neural implant design, and this prototype system was utilized to provide first experimental results with the transmission of magnetically modulated action potential waveforms.

Improving Tumor Classification by Reusing Self-predicted Segmentation of Medical Images as Guiding Knowledge.

Differential diagnosis of tumors is important for computer-aided diagnosis. In computer-aided diagnosis systems, expert knowledge of lesion segmentation masks is limited as it is only used during preprocessing or as supervision to guide feature extraction. To improve the utilization of lesion segmentation masks, this study proposes a simple and effective multitask learning network that improves medical image classification using self-predicted segmentation as guiding knowledge; we call this network RS 2-net. In RS 2-net, the predicted segmentation probability map obtained from the initial segmentation inference is added to the original image to form a new input, which is then reinput to the network for the final classification inference. We validated the proposed RS 2-net using three datasets: the pNENs-Grade dataset, which tested the prediction of pancreatic neuroendocrine neoplasm grading, and the HCC-MVI dataset, which tested the prediction of microvascular invasion of hepatocellular carcinoma, and ISIC 2017 public skin lesion dataset. The experimental results indicate that the proposed strategy of reusing self-predicted segmentation is effective, and RS 2-net outperforms other popular networks and existing state-of-the-art studies. Interpretive analytics based on feature visualization demonstrates that the improved classification performance of our reuse strategy is due to the semantic information that can be acquired in advance in a shallow network.

Chemical Composition, In Vitro Antioxidant Activities, and Inhibitory Effects of the Acetylcholinesterase of Liparis nervosa (Thunb.) Lindl. Essential Oil.

The present study aimed to investigate the essential oil composition of Liparis nervosa (Thunb.) Lindl., grown in China, and to determine its antioxidant and inhibitory effects on acetylcholinesterase. The essential oil was obtained by hydrodistillation, and the chemical compounds were analyzed by GC-MS and GC-FID. We used 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and ferric reducing assay power (FRAP) to evaluate the antioxidant activity. The anti-acetylcholinesterase activity of the essential oil was also examined. Sixty-seven compounds were identified, representing 98.50 % of the total essential oil, which was shown to be rich in methyl (9E,11E)-octadeca-9,11-dienoate (31.69%), n-hexadecanoic acid (15.08%), isopropyl palmitate (12.44%), propyl tetradecanoate (7.20%), tetradecanoic acid (4.01%), 17-octadecynoic acid (3.71%), and pentacosane (2.24%). Its antioxidant ability was analyzed via ABTS (IC50 = 721.95 ± 9.93 µg/mL), DPPH scavenging capacity (IC50 > 10,000 µg/mL), and the FRAP method (Trolox equivalent antioxidant concentration 39.64 ± 3.38 µM/g). Acetylcholinesterase inhibition effects were evaluated and had an IC50 value of 51.96 ± 14.26 µg/mL. The results show that this essential oil has interesting biological potential, encouraging further investigations, especially regarding the mechanisms of action of its antioxidant and anti-acetylcholinesterase activity. This is the first time that the chemical composition, antioxidant activity, and acetylcholinesterase inhibition effects of essential oil from L. nervosa have been studied.

Identification, and Experimental and Bioinformatics Validation of an Immune-Related Prognosis Gene Signature for Low-Grade Glioma Based on mRNAsi.

BACKGROUND: Low-grade gliomas (LGGs), which are the second most common intracranial tumor, are diagnosed in seven out of one million people, tending to develop in younger people. Tumor stem cells and immune cells are important in the development of tumorigenesis. However, research on prognostic factors linked to the immune microenvironment and stem cells in LGG patients is limited. We critically need accurate related tools for assessing the risk of LGG patients. METHODS: In this study, we aimed to identify immune-related genes (IRGs) in LGG based on the mRNAsi score. We employed differentially expressed gene (DEG) methods and weighted correlation network analysis (WGCNA). The risk signature was then further established using a lasso Cox regression analysis and a multivariate Cox analysis. Next, we used immunohistochemical sections (HPA) and a survival analysis to identify the hub genes. A nomogram was built to assess the prognosis of patients based on their clinical information and risk scores and was validated using a DCA curve, among other methods. RESULTS: Four hub genes were obtained: C3AR1 (HR = 0.98, p < 0.001), MSR1 (HR = 1.02, p < 0.001), SLC11A1 (HR = 1.01, p < 0.01), and IL-10 (HR = 1.01, p < 0.001). For LGG patients, we created an immune-related prognostic signature (IPS) based on mRNAsi for estimating risk scores; different risk groups showed significantly different survival rates (p = 3.3 × 10-16). Then, via an evaluation of the IRG-related signature, we created a nomogram for predicting LGG survival probability. CONCLUSION: The outcome suggests that, when predicting the prognosis of LGG patients, our nomogram was more effective than the IPS. In this study, four immune-related predictive biomarkers for LGG were identified and proven to be IRGs. Therefore, the development of efficient immunotherapy techniques can be facilitated by the creation of the IPS.

The role of the gut microbiome and its metabolites in cerebrovascular diseases.

The gut microbiome is critically involved in maintaining normal physiological function in the host. Recent studies have revealed that alterations in the gut microbiome contribute to the development and progression of cerebrovascular disease via the microbiota-gut-brain axis (MGBA). As a broad communication network in the human body, MGBA has been demonstrated to have significant interactions with various factors, such as brain structure and function, nervous system diseases, etc. It is also believed that the species and composition of gut microbiota and its metabolites are intrinsically linked to vascular inflammation and immune responses. In fact, in fecal microbiota transplantation (FMT) research, specific gut microbiota and downstream-related metabolites have been proven to not only participate in various physiological processes of human body, but also affect the occurrence and development of cerebrovascular diseases directly or indirectly through systemic inflammatory immune response. Due to the high mortality and disability rate of cerebrovascular diseases, new treatments to improve intestinal dysbacteriosis have gradually attracted widespread attention to better ameliorate the poor prognosis of cerebrovascular diseases in a non-invasive way. This review summarizes the latest advances in the gut microbiome and cerebrovascular disease research and reveals the profound impact of gut microbiota dysbiosis and its metabolites on cerebrovascular diseases. At the same time, we elucidated molecular mechanisms whereby gut microbial metabolites regulate the expression of specific interleukins in inflammatory immune responses. Moreover, we further discuss the feasibility of novel therapeutic strategies targeting the gut microbiota to improve the outcome of patients with cerebrovascular diseases. Finally, we provide new insights for standardized diagnosis and treatment of cerebrovascular diseases.

Chemical Composition and In Vitro Antioxidant Activity and Anti-Acetylcholinesterase Activity of Essential Oils from Tadehagi triquetrum (L.) Ohashi.

The present study aimed to determine the chemical compositions of essential oils (EOs) from Tadehagi triquetrum (L.) Ohashi and evaluate their antioxidant and anti-cholinesterase activity under the comprehensive influence of chemical components. The essential oils were extracted from T. triquetrum (L.) Ohashi by hydrodistillation. A total of 58 organic compounds were identified by GC-FID and GC-MS analysis. The major components of T. triquetrum (L.) Ohashi EOs were identified as palmitic acid (22.46%), 1-Octen-3-ol (14.07%), Caryophyllene (7.20%), (Z)-18-Octadec-9-enolide (6.04%), and 3-Hexen-1-ol (4.55%). The antioxidant activity of the essential oils was determined by using ABTS assay, DPPH assay, and FRAP assay, with IC50 values of 2.12 ± 0.05 mg/mL, 4.73 ± 0.91 mg/mL against the ABTS, DPPH, and FRAP value 117.42 ± 8.10 mM/g. The result showed that it had moderate antioxidant activities in the experiment, which why it is likely that it will be used as an antioxidant. At the same time, the EOs also showed moderate anti-acetylcholinesterase activity. This study expands the chemical and biological knowledge of the EOs of T. triquetrum.

A 30% Efficient High-Output Voltage Fully Integrated Self-Biased Gate RF Rectifier Topology for Neural Implants.

This paper presents a fully integrated RF energy harvester (EH) with 30% end-to-end power harvesting efficiency (PHE) and supports high output voltage operation, up to 9.3V, with a 1.07 GHz input and under the electrode model for neural applications. The EH is composed of a novel 10-stage self-biased gate (SBG) rectifier with an on-chip matching network. The SBG topology elevates the gate-bias of transistors in a non-linear manner to enable higher conductivity. The design also achieves >20% PHE range of 12-dB. The design was fabricated in 65 nm CMOS technology and occupies an area of 0.0732-mm2 with on-chip matching network. In addition to standalone EH characterization measurement results, animal tissue stimulation test was performed to evaluate its performance in a realistic neural implant application.

Chemical Composition and In Vitro Antioxidant Activity of Sida rhombifolia L. Volatile Organic Compounds.

In the current study, the phytochemical constituents of volatile organic compounds (VOCs) obtained from Sida rhombifolia L. were identified by GC-FID and GC-MS analysis. A total of 73 volatile organic compounds were identified. The major components of S. rhombifolia VOCs were identified as palmitic acid (21.56%), phytol (7.02%), 6,10,14-trimethyl-2-pentadecanone (6.30%), oleic acid (5.48%), 2-pentyl-furan (5.23%), and linoleic acid (3.21%). The VOCs are rich in fatty acids (32.50%), olefine aldehyde (9.59%), ketone (9.41%), enol (9.02%), aldehyde (8.63%), and ketene (6.41%). The antioxidant capacity of S. rhombifolia VOCs was determined by 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azinobis-(3-ethylbenzothiazolin-6-sulfonic acid) diammonium salt (ABTS), and ferric reducing/antioxidant power (FRAP) methods with butylated hydroxytoluene (BHT) and Trolox as standard. The VOCs showed dose-dependent antioxidant activity with IC50 (50% inhibitory concentration) values of 5.48 ± 0.024 and 1.47 ± 0.012 mg/mL for DPPH and ABTS assays, respectively. FRAP antioxidant capacity was 83.10 ± 1.66 mM/g. The results show that the VOCs distilled from S. rhombifolia have a moderate antioxidant property that can be utilized as a natural botanical supplement or an antioxidant.

Research on Straightness Perception Compensation Model of FBG Scraper Conveyor Based on Rotation Error Angle.

The accurate perception of straightness of a scraper conveyor is important for the construction of intelligent working faces in coal mines. In this paper, we propose a precision compensation model based on rotation error angle to improve the accuracy of the fiber Bragg grating (FBG) curvature sensor of a scraper conveyor. The correctness of the model is verified by theoretical analysis, numerical simulation, and experiments. Finally, the feasibility of the model is analyzed and discussed for field application in a coal mine. When the rotation error angle is within the range of 0~90°, according to the strain of FBG obtained by numerical simulation, the radius of the curvature is inversely calculated by the compensation model. The relative error of each discrete point is within ±0.9%, and the relative error after fitting is less than 0.2%. The experiment shows that the relative error of the curvature radius after fitting according to the theoretical formula is less than ±3%, and the relative error of the curvature radius value obtained by the inverse deduction of each discrete point is less than ±6%, which verifies the correctness and applicability of the compensation model. In addition, the compensation model with the FBG curvature sensor has broad application prospects in coal mine underground conveyors, submarine pipelines and ground pipelines.