Characterization of Tfgal-9: A galectin in innate immune system of Trachidermus fasciatus - Insights into its sequence analysis, expression patterns, and in vitro bioactivities.

An in-depth understanding of the immune system of endangered species is crucial for successful conservation efforts. Galectins, as members of the lectin family, play a crucial role in the fish innate immune system. Galectin-9 (Tfgal-9) was cloned from endangered species Trachidermus fasciatus, revealing a cDNA sequence of 1453 bp with an open reading frame of 900 bp encoding a protein of 299 amino acids. Tfgal-9 protein features two repeated carbohydrate-binding domains, each characterized by two conserved galactose-binding sites (H-NPR and WG-EER), and it possesses neither a signal peptide nor a transmembrane domain. The qRT-PCR analysis revealed that Tfgal-9 was widely expressed across all examined tissues, with the highest expression in the intestine, followed by the blood, heart and brain. Expression was notably up-regulated in the blood, skin, liver, stomach, and heart when challenged with LPS. Following induction by the heavy metal solution containing Cu, Pb, Cd, and Hg, the expression Tfgal-9 was dramatically induced to 32 times higher than that of the control group in the brain. The recombinant Tfgal-9 protein exhibits calcium-independent binding and agglutination of selected bacteria and yeast. Antimicrobial activity of recombinant Tfgal-9 protein against Gram positive bacteria Staphylococcus aureus was confirmed using the cylinder-plate method. In vitro antioxidant experiments showed that radical scavenging activity of DPPH was 50.38 % when Tfgal-9 concentration reached 200 µg/mL. These results indicate that Tfgal-9 may play important roles in the immune response against microbial infections and the maintaining of redox homeostasis.

TNC upregulation promotes glioma tumourigenesis through TDG-mediated active DNA demethylation.

Gliomas represent the most predominant primary malignant tumor in central nervous system. Thymine DNA glycosylase (TDG) is a central component in active DNA demethylation. However, the specific mechanisms of TDG-mediated active DNA demethylation in gliomas remain unclear. This research indicates TDG expression is overexpressed in gliomas and correlated with poor prognosis. TDG knockdown suppressed the malignant phenotype of gliomas both in vitro and vivo. Notably, RNA-seq analysis revealed a strong association between TDG and tenascin-C (TNC). ChIP-qPCR and MeDIP-qPCR assays were undertaken to confirm that TDG participates in TNC active DNA demethylation process, revealing decreased DNA methylation levels and elevated TNC expression as a result. Silencing TNC expression also suppressed the tumor malignant phenotype in both in vitro and in vivo experiments. Additionally, simultaneous silencing of TNC reduced or even reversed the glioma promotion caused by TDG overexpression. Based on our findings, we conclude that TDG exerts an indispensable role in TNC active DNA demethylation in gliomas. The DNA demethylation process leads to alternations in TNC methylation levels and promotes its expression, thereby contributing to the development of gliomas. These results suggest a novel epigenetic therapeutic strategy targeting active DNA demethylation in gliomas.

How and when does proactive personality predict career adaptability? A study of the moderated mediation model.

In the present study, we explored the relationship between proactive personality and career adaptability to construct a cross-level moderated mediation model based on the conservation of resources (COR) theory. By conducting a time-lagged study involving three data collection points from 587 employees across 104 teams in China, we examined how and when proactive personality predicts employees' career adaptability using strengths use as a mediator and managerial coaching as boundary conditions. The results revealed that proactive personality predicted strengths use, which, in turn, influenced career adaptability, with managerial coaching moderating the indirect relationship between proactive personality and career adaptability. Consequently, our findings suggest that, in contexts where managerial coaching lacks guidance, facilitation, and inspiration, a proactive personality encourages employees to leverage their strengths, subsequently enhancing their career adaptability. Finally, we discuss the theoretical and practical implications of our findings, address limitations, and propose directions for future research.

An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images.

Computer-aided diagnosis (CAD) for thyroid nodules has been studied for years, yet there are still reliability and interpretability challenges due to the lack of clinically-relevant evidence. To address this issue, inspired by Thyroid Imaging Reporting and Data System (TI-RADS), we propose a novel interpretable two-branch bi-coordinate network based on multi-grained domain knowledge. First, we transform the two types of domain knowledge provided by TI-RADS, namely region-based and boundary-based knowledge, into labels at multi-grained levels: coarse-grained classification labels, and fine-grained region segmentation masks and boundary localization vectors. We combine these two labels to form the Multi-grained Domain Knowledge Representation (MG-DKR) of TI-RADS. Then we design a Two-branch Bi-coordinate network (TB2C-net) which utilizes two branches to predict MG-DKR from both Cartesian and polar images, and uses an attention-based integration module to integrate the features of the two branches for benign-malignant classification. We validated our method on a large cohort containing 3245 patients (with 3558 nodules and 6466 ultrasound images). Results show that our method achieves competitive performance with AUC of 0.93 and ACC of 0.87 compared with other state-of-the-art methods. Ablation experiment results demonstrate the effectiveness of the TB2C-net and MG-DKR, and the knowledge attention map from the integration module provides the interpretability for benign-malignant classification.

Spermacoce alata Aubl. Essential Oil: Chemical Composition, In Vitro Antioxidant Activity, and Inhibitory Effects of Acetylcholinesterase, α-Glucosidase and ß-Lactamase.

Spermacoce alata Aubl. is widely available in the market as traditional Chinese medicine and animal feed, due to its properties of clearing heat and treating malaria and its high-protein and crude fiber content. In this study, the essential oil of S. alata was obtained through hydrodistillation. GC-MS and GC-FID methods were used to identify the chemical components and their relative abundance. Furthermore, the antioxidant capacity was measured using DPPH, ABTS, and FRAP assays, and the inhibitory effects of acetylcholinesterase, α-glucosidase, and ß-lactamase were also evaluated. A total of 67 compounds were identified, with the major constituents being palmitic acid (30.74%), linoleic acid (16.13%), and phenylheptatriyne (8.07%). The essential oil exhibited moderate antioxidant activity against DPPH (IC50 > 10 mg/mL), while the IC50 value for the ABTS assay was 3.84 ± 2.12 mg/mL and the FRAP assay value was 87.22 ± 12.22 µM/g. Additionally, the essential oil showed moderate anti-acetylcholinesterase activity (IC50 = 286.0 ± 79.04 µg/mL), significant anti-α-glucosidase activity (IC50 = 174.7 ± 13.12 µg/mL), and potent anti-ß-lactamase activity (IC50 = 37.56 ± 3.48 µg/mL). The results suggest that S. alata has the potential for application in pharmacology, warranting further exploration and investigation.

RAD51 Expression as a Biomarker to Predict Efficacy of Platinum-Based Chemotherapy and PD-L1 Blockade for Muscle-Invasive Bladder Cancer.

RAD51, a key recombinase that catalyzes homologous recombination (HR), is commonly overexpressed in multiple cancers. It is curial for DNA damage repair (DDR) to maintain genomic integrity which could further determine the therapeutic response. Herein, we attempt to explore the clinical value of RAD51 in therapeutic guidance in muscle-invasive bladder cancer (MIBC). In this retrospective study, a total of 823 patients with MIBC were included. Zhongshan hospital (ZSHS) cohort (n=134) and The Cancer Genome Atlas-Bladder Cancer (TCGA-BLCA) cohort (n=391) were included for the investigation of chemotherapeutic response. The IMvigor210 cohort (n=298) was utilized to interrogate the predictive efficacy of RAD51 status to programmed cell death ligand-1 (PD-L1) blockade. In addition, the association of RAD51 with genomic instability and tumor immune contexture was investigated. Patients with RAD51 overexpression were more likely to benefit from both platinum-based chemotherapy and immunotherapy rather than RAD51-low patients. The TMB high PD-L1 high RAD51 high subgroup possessed the best clinical benefits from PD-L1 blockade. RAD51-high tumors featured by genomic instability were correlated to highly inflamed and immunogenic contexture with activated immunotherapeutic pathway in MIBC. RAD51 could serve as a prognosticator for treatment response to platinum-based chemotherapy and PD-L1 inhibitor in MIBC patients. Besides, it could also improve the predictive efficacy of TMB and PD-L1.

Overall survival benefits of cancer drugs initially approved by the US Food and Drug Administration on the basis of immature survival data: a retrospective analysis.

BACKGROUND: New cancer drugs can be approved by the US Food and Drug Administration (FDA) on the basis of surrogate endpoints while data on overall survival are still incomplete or immature, with too few deaths for meaningful analysis. We aimed to evaluate whether clinical trials with immature survival data generated evidence of overall survival benefit during the period after marketing authorisation, and where that evidence was reported. METHODS: In this retrospective analysis, we searched Drugs@FDA to identify cancer drug indications approved between Jan 1, 2001, and Dec 31, 2018, on the basis of immature survival data. We systematically collected publicly available data on postapproval overall survival results in labelling (Drugs@FDA), journal publications (MEDLINE via PubMed), and clinical trial registries (ClinicalTrials.gov). The primary outcome was availability of statistically significant overall survival benefits during the period after marketing authorisation (until March 31, 2023). Additionally, we evaluated the availability and timing of overall survival findings in labelling, journal publications, and ClinicalTrials.gov records. FINDINGS: During the study period, the FDA granted marketing authorisation to 223 cancer drug indications, 95 of which had overall survival as an endpoint. 39 (41%) of these 95 indications had immature survival data. After a minimum of 4·3 years of follow-up during the period after marketing authorisation (and median 8·2 years [IQR 5·3-12·0] since FDA approval), additional survival data from the pivotal trials became available in either revised labelling or publications, or both, for 38 (97%) of 39 indications. Additional data on overall survival showed a statistically significant benefit in 12 (32%) of 38 indications, whereas mature data yielded statistically non-significant overall survival findings for 24 (63%) indications. Statistically significant evidence of overall survival benefit was reported in either labelling or publications a median of 1·5 years (IQR 0·8-2·3) after initial approval. The median time to availability of statistically non-significant overall survival results was 3·3 years (2·2-4·5). The availability of overall survival results on ClinicalTrials.gov varied considerably. INTERPRETATION: Fewer than a third of indications approved with immature survival data showed a statistically significant overall survival benefit after approval. Notable inconsistencies in timing and availability of information after approval across different sources emphasise the need for better reporting standards. FUNDING: None.

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.

An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting.

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.

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.

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.

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.

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.

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.

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.

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.