Endosome-microautophagy targeting chimera (eMIATAC) for targeted proteins degradation and enhance CAR-T cell anti-tumor therapy.

Rationale: Since oncogene expression products often exhibit upregulation or abnormally activated activity, developing a technique to regulate abnormal protein levels represent a viable approach for treating tumors and protein abnormality-related diseases. Methods: We first screened out eMIATAC components with high targeted degradation efficiency and explored the mechanism by which eMIATAC induced target protein degradation, and verified the degradation efficiency of the target protein by protein imprinting and flow cytometry. Next, we recombined eMIATAC with some controllable elements to verify the regulatable degradation performance of the target protein. Subsequently, we constructed eMIATAC that can express targeted degradation of AKT1 and verified its effect on GBM cell development in vitro and in vivo. Finally, we concatenated eMIATAC with CAR sequences to construct CAR-T cells with low BATF protein levels and verified the changes in their anti-tumor efficacy. Results: we developed a system based on the endosome-microautophagy-lysosome pathway for degrading endogenous proteins: endosome-MicroAutophagy TArgeting Chimera (eMIATAC), dependent on Vps4A instead of lysosomal-associated membrane protein 2A (LAMP2A) to bind to the chaperone Hsc70 and the protein of interest (POI). The complex was then transported to the lysosome by late endosomes, where degradation occurred similarly to microautophagy. The eMIATACs demonstrated accuracy, efficiency, reversibility, and controllability in degrading the target protein EGFP. Moreover, eMIATAC exhibited excellent performance in knocking down POI when targeting endogenous proteins in vivo and in vitro. Conclusions: The eMIATACs could not only directly knock down abnormal proteins for glioma treatment but also enhance the therapeutic effect of CAR-T cell therapy for tumors by knocking down T cell exhaustion-related proteins. The newly developed eMIATAC system holds promise as a novel tool for protein knockdown strategies. By enabling direct control over endogenous protein levels, eMIATAC has the potential to revolutionize treatment for cancer and genetic diseases.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

ZNF384 transcriptionally activated MGST1 to confer TMZ resistance of glioma cells by negatively regulating ferroptosis.

BACKGROUND: Drug resistance is one of the major reasons of the poor prognosis and recurs frequently in glioma. Ferroptosis is considered to be a new therapeutic strategy for glioma. METHODS: Microsomal glutathione S-transferase 1 (MGST1) expression in glioma samples was ensured through GAPIA database, qRT-PCR, western blotting assay and immunohistochemistry. The interaction between zinc finger protein 384 (ZNF384) and MGST1 promoter was analyzed through UCSC and JASPAR databases and further verified by ChIP and luciferase reporter assay. Cell viability and IC50 value of temozolomide (TMZ) was measured by CCK-8 assay. The production of MDA, GSH and ROS and the level of Fe2+ were determined using the corresponding kit. RESULTS: MGST1 expression was increased in clinical glioma tissues and glioma cells. MGST1 expression was increased but ferroptosis was suppressed in TMZ-resistant cells when contrasted to parent cells. MGST1 silencing downregulated IC50 value of TMZ and cell viability but facilitated ferroptosis in TMZ-resistant cells and parent glioma cells. Moreover, our data indicated that ZNF384 interacted with MGST1 promoter and facilitated MGST1 expression. ZNF384 was also increased expression in TMZ-resistant cells, and showed a positive correlation with MGST1 expression in clinical level. ZNF384 decreasing enhanced the sensitivity of resistant cells to TMZ, while the effect of ZNF384 could be reversed by overexpression of MGST1. CONCLUSION: MGST1 transcription is regulated by transcription factor ZNF384 in TMZ-resistant cells. ZNF384 confers the resistance of glioma cells to TMZ through inhibition of ferroptosis by positively regulating MGST1 expression. The current study may provide some new understand to the mechanism of TMZ resistance in glioma.

Exosomal HMGB3 released by glioma cells confers the activation of NLRP3 inflammasome and pyroptosis in tumor-associated macrophages.

BACKGROUND: Previous evidences has highlighted the pivotal role of NOD-like receptor family pyrin domain-containing 3 (NLRP3)-mediated inflammasomes and pyroptosis activation in driving tumor malignancy and shaping the tumor microenvironment. Herein, we aimed to elucidate the impact of high-mobility group box 3 (HMGB3) released in glioma-derived exosomes on macrophage infiltration in gliomas, NLRP3 inflammasome activation and polarization. METHODS: Transcripts and protein levels of HMGB3, and cytokines associated with macrophage phenotypes and pyroptosis were assessed in glioma tissues and cell lines (U251, LN229, T98G, A172) using qRT-PCR and/or Western blot analysis. Exosomes secreted from LN229 and NHA cells were isolated via differential ultracentrifugation and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and analysis of exosome-related markers. PKH67 staining was employed to examine exosomes uptake by THP-1 differentiated macrophages. Flow cytometry was utilized to assess macrophage pyroptotic rates and polarization-related markers. RESULTS: HMGB3 expression was elevated in glioma tissues, serum samples and tumor cell lines. Kaplan-Meier curves revealed a positive correlation between higher HMGB3 expression and poor overall survival and recurrence-free survival. Moreover, glioma tissues with increased HMGB3 expression exhibited significant upregulation of M2 macrophages markers (CD68, CD206, Arg1) and NLRP3 inflammasome components (NLRP3, IL-1ß, ASC), suggesting that HMGB3 was closely associated with macrophage infiltration and NLRP3 inflammasome activation. Notably, HMGB3 was found to be enriched in glioma cell- secreted exosomes and could be internalized by macrophages. Knockdown of HMGB3 in glioma cell exosomes could restrain M2 macrophage polarization, NLRP3 inflammasome activation and pyroptosis. CONCLUSION: These findings suggested that glioma cells secreted exosomal HMGB3 could facilitate macrophage M2 polarization, pyroptosis and inflammatory infiltration, indicating HMGB3 might be a poor prognosis factor for glioma.

Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) remain uncertain. Here, the authors apply their recently developed automated deep learning technique to quantify total bleeding volume (TBV) in patients with aSAH and investigate associations between quantitative TBV and secondary complications, adverse long-term functional outcomes, and death. METHODS: Electronic health record data were extracted for adult patients admitted to a single institution within 72 hours of aSAH onset between 2018 and 2021. An automatic deep learning model was used to fully segment and quantify TBV on admission noncontrast head CT images. Patients were subgrouped by TBV quartile, and multivariable logistic regression, restricted cubic splines, and subgroup analysis were used to explore the relationships between TBV and each clinical outcome. RESULTS: A total of 819 patients were included in the study. Sixty-six (8.1%) patients developed hydrocephalus, while 43 (5.3%) experienced rebleeding, 141 (17.2%) had delayed cerebral ischemia, 88 (10.7%) died in the 12 months after discharge, and 208 (25.7%) had a modified Rankin Scale score ≥ 3 12 months after discharge. On multivariable analysis, patients in the highest TBV quartile (> 37.94 ml) had an increased risk of hydrocephalus (adjusted OR [aOR] 4.38, 95% CI 1.61-11.87; p = 0.004), rebleeding (aOR 3.26, 95% CI 1.03-10.33; p = 0.045), death (aOR 6.92, 95% CI 1.89-25.37; p = 0.004), and 12-month disability (aOR 3.30, 95% CI 1.62-6.72; p = 0.001) compared with the lowest TBV quantile (< 8.34 ml). The risks of hydrocephalus (nonlinear, p = 0.025), rebleeding, death, and disability (linear, p > 0.05) were positively associated with TBV by restricted cubic splines. In subgroup analysis, TBV had a stronger effect on 12-month outcome in female than male patients (p for interaction = 0.0499) and on rebleeding prevalence in patients with endovascular coiling than those with surgical clipping (p for interaction = 0.008). CONCLUSIONS: Elevated TBV is associated with a greater risk of hydrocephalus, rebleeding, death, and poor prognosis.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). METHODS: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. RESULTS: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. CONCLUSIONS: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001). CONCLUSION: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG).

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.

Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non-Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial.

Importance: Adjuvant and neoadjuvant immunotherapy have improved clinical outcomes for patients with early-stage non-small cell lung cancer (NSCLC). However, the optimal combination of checkpoint inhibition with chemotherapy remains unknown. Objective: To determine whether toripalimab in combination with platinum-based chemotherapy will improve event-free survival and major pathological response in patients with stage II or III resectable NSCLC compared with chemotherapy alone. Design, Setting, and Participants: This randomized clinical trial enrolled patients with stage II or III resectable NSCLC (without EGFR or ALK alterations for nonsquamous NSCLC) from March 12, 2020, to June 19, 2023, at 50 participating hospitals in China. The data cutoff date for this interim analysis was November 30, 2022. Interventions: Patients were randomized in a 1:1 ratio to receive 240 mg of toripalimab or placebo once every 3 weeks combined with platinum-based chemotherapy for 3 cycles before surgery and 1 cycle after surgery, followed by toripalimab only (240 mg) or placebo once every 3 weeks for up to 13 cycles. Main Outcomes and Measures: The primary outcomes were event-free survival (assessed by the investigators) and the major pathological response rate (assessed by blinded, independent pathological review). The secondary outcomes included the pathological complete response rate (assessed by blinded, independent pathological review) and adverse events. Results: Of the 501 patients randomized, 404 had stage III NSCLC (202 in the toripalimab + chemotherapy group and 202 in the placebo + chemotherapy group) and 97 had stage II NSCLC and were excluded from this interim analysis. The median age was 62 years (IQR, 56-65 years), 92% of patients were male, and the median follow-up was 18.3 months (IQR, 12.7-22.5 months). For the primary outcome of event-free survival, the median length was not estimable (95% CI, 24.4 months-not estimable) in the toripalimab group compared with 15.1 months (95% CI, 10.6-21.9 months) in the placebo group (hazard ratio, 0.40 [95% CI, 0.28-0.57], P < .001). The major pathological response rate (another primary outcome) was 48.5% (95% CI, 41.4%-55.6%) in the toripalimab group compared with 8.4% (95% CI, 5.0%-13.1%) in the placebo group (between-group difference, 40.2% [95% CI, 32.2%-48.1%], P < .001). The pathological complete response rate (secondary outcome) was 24.8% (95% CI, 19.0%-31.3%) in the toripalimab group compared with 1.0% (95% CI, 0.1%-3.5%) in the placebo group (between-group difference, 23.7% [95% CI, 17.6%-29.8%]). The incidence of immune-related adverse events occurred more frequently in the toripalimab group. No unexpected treatment-related toxic effects were identified. The incidence of grade 3 or higher adverse events, fatal adverse events, and adverse events leading to discontinuation of treatment were comparable between the groups. Conclusions and Relevance: The addition of toripalimab to perioperative chemotherapy led to a significant improvement in event-free survival for patients with resectable stage III NSCLC and this treatment strategy had a manageable safety profile. Trial Registration: ClinicalTrials.gov Identifier: NCT04158440.

EGFR co-mutation is associated with the risk of recurrence in invasive lung adenocarcinoma with the micropapillary component.

BACKGROUND: Invasive lung adenocarcinoma (LUAD) patients with the micropapillary (MPP) component tend to have extremely poor prognosis. To optimize clinical outcomes, a better understanding of specific concurrent gene alterations and their impact on the prognosis of patients with the MPP component is necessary. METHOD: A total of 621 Chinese patients with surgically resected invasive LUAD who underwent genetic testing for lung cancer were enrolled in this retrospective study. The genomic profiling of major lung cancer-related genes based on next-generation sequencing (NGS) was carried out on formalin-fixed paraffin-embedded tumor samples. RESULT: Among 621 patients with invasive LUAD, 154 (24.8%, 154/621) had the MPP component. We found that PIK3CA (4.5% vs 1.3%), KRAS (9.1% vs 4.7%), and ROS1 (2.6% vs 0.4%) were more frequent in patients with the MPP component than those without the MPP component (P < 0.05). The co-mutation occurred in 66 patients (10.6%, 66/621), of which 19 patients with the MPP component. Most of them were EGFR co-mutations (89.5%, 17/19), including EGFR and PIK3CA, EGFR and ERBB2, and other types. Patients with the MPP component who harbored EGFR co-mutations showed significantly worse recurrence-free survival (RFS) than single EGFR mutation (median RFS 20.1 vs 30.5 months; hazard ratio [HR]: 8.008; 95% confidence interval [CI]: 1.322-48.508). CONCLUSIONS: Patients with the MPP component harbored the co-mutation of driver genes had a higher risk of recurrence after surgery, especially in patients with EGFR co-mutation. EGFR co-mutation was a significant prognostic factor for RFS in patients with the MPP component.

PFKP confers chemoresistance by upregulating ABCC2 transporter in non-small cell lung cancer.

Background: Chemoresistance is a significant factor contributing to tumor recurrence and treatment failure in non-small cell lung cancer (NSCLC). The phosphofructokinase, platelet (PFKP) is highly expressed in NSCLC and is associated with a poor prognosis. Exploring the molecular mechanism and identifying effective strategies to overcome chemoresistance will have important clinical significance in improving the diagnosis and treatment of NSCLC. Methods: The correlation between PFKP and cisplatin resistance in NSCLC patients was assessed by organoids and immunohistochemistry. The impact of PFKP on the prognosis of NSCLC patients was analyzed using The Cancer Genome Atlas (TCGA) database. In NSCLC cell lines, the expression of PFKP was modulated using lentivirus, and cisplatin sensitivity was assessed by flow cytometry. Subsequently, the therapeutic effect of cisplatin was tested in BALB/c nude mice implanted subcutaneously with tumor cells. We performed luciferase assay and immunohistochemistry (IHC) to investigate the correlation between PFKP and ABCC2 (ATP-binding cassette sub-family C member 2). Results: Overexpression of PFKP was correlated with poorer survival rates in NSCLC patients who received platinum-based chemotherapy. Using NSCLC organoid, we found that the expression of PFKP was elevated in cisplatin (CDDP)-resistant patients with NSCLC. Overexpression of PFKP decreased the sensitivity of NSCLC cells to CDDP, while genetic inhibition of PFKP enhanced CDDP sensitivity both in vitro and in vivo. Furthermore, we found that PFKP upregulated ABCC2 by increasing the levels of phosphorylation of IκBα and nuclear p65 NF-κB subunit protein. Conclusions: PFKP can regulate the expression of ABCC2 through the activation of NF-κB, which in turn promotes chemoresistance in NSCLC. PFKP has the potential to be a personalized therapeutic target for NSCLC patients with chemoresistance.

Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach.

The escalating global greenhouse gas emission crisis necessitates a robust scientific carbon accounting framework and innovative development approaches. Achieving emission peaks remains the primary goal for emission reduction. Guangdong Province, a pivotal region in China, faces pressure to reduce carbon emissions. In this study, data was leveraged from the China Carbon Accounting Database (CEADS) and panel data from the "Guangdong Statistical Yearbook" spanning 1997 to 2022. Factors impacting carbon emissions were selected based on Guangdong Province's carbon reduction goals, macroeconomic development strategies, and economic-population dynamics. To address multicollinearity, lasso regression identified key factors, including population size, economic development level, energy intensity, and technology factors. A novel STIRPAT extended model, combined with the BP neural network optimized using the TPE algorithm, enhanced carbon emission predictions for Guangdong Province. Employing scenario analysis, five scenarios were generated in alignment with the planning policies of Guangdong Province, to forecast carbon emissions from 2020 to 2050. The results suggest that to achieve a win-win situation for both economic development and environmental protection, Guangdong Province should prioritize the energy-saving scenario (S2), which aligns with the "13th Five-Year Plan's" ecological and green development directives, to reach a projected carbon peak of 637.05Mt by 2030. In conclusion, recommendations for carbon reduction are proposed in the areas of low-carbon transformation for the population, sustainable economic development, and the development of low-carbon technologies.

The novel EGFR mutations (p.E746_S752delinsI, p.T751_I759delinsG, p.L747_S752delinsAA) in patients with non-small cell lung cancer and the clinical treatment strategy: three case reports.

Epidermal growth factor receptor (EGFR) is an established driver gene in non-small cell lung cancer (NSCLC) and the common Exon 19 del mutation (p.E746_A750 del) has exhibited remarkable responses for EGFR tyrosine kinase inhibitors (TKIs). However, there is even less comprehension of the treatment strategy in NSCLC patients harboring uncommon Exon 19 delins mutation. Here, we identified three novel EGFR Exon 19 mutations (p.E746_S752delinsI, p.T751_I759delinsG, p.L747_S752delinsAA), and described the clinical treatment process. To our knowledge, the EGFR p.E746_S752delinsI mutation of the patient with advanced NSCLC could benefit from the treatment with Icotinib. Otherwise, for the NSCLC patients with early-stage, one harboring p.T751_I759delinsG mutation had an excellent recovery and the other harboring p.L747_S752delinsAA experienced a relapse after receiving horacoscopic radical resection, which means the patients with different Exon 19 delins mutation might have different prognosis. Our study also demonstrated that next-generation sequencing (NGS) is a crucial tool in guiding clinical treatment decisions in NSCLC. Furthermore, the real incidence of these mutation is not known, the routinely use of NGS surely will increase the detection of EGFR del-ins respect to the old tools used to screen for EGFR mutations.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS: A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION: The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

Effect of Surgical Clipping versus Endovascular Coiling on the Incidence of Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage: A Multicenter Observational Cohort Study with Propensity Score Matching.

OBJECTIVE: The effect of surgical clipping (SC) and endovascular coiling (EC) on the incidence of delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH) has always been a controversial topic. Hence, it is necessary to reanalyze the effects of the 2 surgical methods on DCI, which determines the choice of the most favorable method for patients who are suitable for both surgical modalities. METHODS: A multicenter retrospective observational cohort study was performed to evaluate all consecutive patients with aSAH admitted to 5 medical centers in China between April 2019 and June 2021. Univariable and multivariable analyses were used to confirm risk factors of DCI after aSAH. A 1:1 propensity score matching model was generated in the EC and SC groups to reduce the influence of all confounding factors on DCI. RESULTS: A total of 412 patients were included, and 115 patients (27.9%) developed DCI. After propensity score matching for controlling demographic information, past medical history, admission clinical status, aneurysm characteristics, and inflammatory factors associated with DCI, 133 patients with SC and 133 patients with EC treatment were matched. The results of the matched cohorts indicate a significantly lower incidence of DCI when patients received EC than SC (31.9% vs. 20%; adjusted odds ratio, 1.87; 95% confidence interval, 1.08-3.29; P = 0.027). CONCLUSIONS: The study found that the patients who received SC treatment had a higher incidence of DCI than did those who received EC and suggested that ruptured intracerebral aneurysm is preferentially coiled rather than clipped if the aneurysm is suitable for both surgical modalities.

DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma.

Growing evidences indicate DNA methylation plays a crucial regulatory role in inflammation, innate immunity, and immunotherapy. However, the overall landscape of various DNA methylation regulatory genes and their relationship with the infiltration of immune cells into the tumor microenvironment (TME) as well as the response to immunotherapy in gliomas is still not clear. Therefore, we comprehensively analyzed the correlation between DNA methylation regulator patterns, infiltration of immune cell-types, and tumor immune response status in gather glioma cohorts. Furthermore, we calculated the DNA methylation score (DMS) for individual glioma samples, then evaluated the relationship between DMS, clinicopathological characteristics, and overall survival (OS) in patients with gliomas. Our results showed three distinct DNA methylation regulator patterns among the glioma patients which correlated with three distinct tumor immune response phenotypes, namely, immune-inflamed, immune-excluded, and immune desert. We then calculated DMS for individual glioma samples based on the expression of DNA methylation-related gene clusters. Furthermore, DMS, tumor mutation burden (TMB), programmed death 1 (PD-1) expression, immune cell infiltration status in the TME, and Tumor Immune Dysfunction and Exclusion (TIDE) scores were associated with survival outcomes and clinical responses to immune checkpoint blockade therapy. We also validated the predictive value of DMS in two independent immunotherapy cohorts. In conclusion, our results demonstrated that three DNA methylation regulator patterns that correlated with three tumor immune response phenotypes. Moreover, we demonstrated that DMS was an independent predictive biomarker that correlated with survival outcomes of glioma patients and their responses to immunotherapy therapeutic regimens.

Comprehensive analysis of the prognostic implications and functional exploration of PAK gene family in human cancer.

BACKGROUND: The p21-activated kinase (PAK) family (PAKs) plays a key role in the formation and development of human tumors. However, a systematic analysis of PAKs in human cancers is lacking and the potential role of PAKs in cancer immunity has not been explored. METHODS: We used datasets from in The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression database (GTEx). RESULTS: Based on TCGA datasets most PAKs show noteworthy differences in expression between tumors and corresponding normal tissues or across different tumor tissues. Patients with high expression of PAKs often show a worse prognosis. However, copy number variation, mutation, and DNA methylation of PAKs have limited impact on tumor development. Further analysis showed that the impact of PAKs on immunity varies with the type of tumor and the respective tumor microenvironment. PAK1 and PAK4 may be stronger predictors of immune characteristics, and are more suitable as drugs and molecular therapeutic targets. Furthermore, Cox regression analysis revealed that a PAK gene signature could be used as an independent prognostic factor for lower grade glioma (LGG) and glioblastoma (GBM). Gene set enrichment analysis (GSEA) analysis indicated that PAK genes may affect the occurrence and development of GBM through the PI3K signaling pathway. Further experiments verified that PAK1 and AKT1 have a significant interaction in GBM cells, and inhibiting the overactivation of PAK1 can significantly inhibit the proliferation of GBM cells. CONCLUSIONS: Our study provides a rationale for further research on the prognostic and therapeutic potential of PAKs in human tumors.

Immune Characteristics and Prognosis Analysis of the Proteasome 20S Subunit Beta 9 in Lower-Grade Gliomas.

Glioma is a common intracranial malignancy in adults and has a high mortality due to its poor prognosis and high recurrence rate. Dysregulation of protein degradation is one of the main promoting factors in glioma development. As an indispensable unit of the proteasome, Proteasome 20S Subunit Beta 9 (PSMB9) is one of the major enzymes in ubiquitin-dependent protein degradation in cells. In addition, proteasomes also participate in a series of cellular processing, like immune regulation, nerve signal transduction, material transport through channels, cell adhesion, and various signaling pathways. However, the relationship between the PSMB9 expression and the occurrence of lower-grade glioma (LGG) is still unknown. First, we collected the RNA-seq and clinical information about LGG clinical samples from The Cancer Genome Atlas (TCGA) cohort, Chinese Glioma Genome Atlas (CGGA; including CGGAseq1 and CGGAseq2) cohort, and Gene Expression Omnibus (GEO; GSE16011, GSE61374, and Rembrandt) cohort. Then, these data were used for differential analysis, survival analysis, enrichment analysis, clinical model construction, etc. In addition, we combine immune-related data for immune-related analysis, including immune infiltration and immunotherapy. Through the above research, we have provided a new biomarker for LGG prognosis prediction and more comprehensively explained the role of PSMB9 in the development of LGG. This study determined that PSMB9 can be used as an immunotherapy target through the analysis of immune data, providing new ideas for the clinical treatment of LGG.

Prognosis of patients with esophageal squamous cell carcinoma undergoing surgery versus no surgery after neoadjuvant chemoradiotherapy: a retrospective cohort study.

Background: Esophageal surgery is an invasive surgical method with high surgical risk, and seriously affects postoperative quality of life. This study compared the prognosis of patients with locally advanced esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (Neo-CRT) plus surgery and Neo-CRT alone, in order to explore the necessity of continuing operation after Neo-CRT. Methods: We retrospectively analyzed 223 patients who received Neo-CRT in Taizhou Hospital Affiliated to Wenzhou Medical University from June 2007 to December 2014. According to the treatment, the patients were divided into Neo-CRT plus surgery group (operation group, n=185) and single Neo-CRT group (non-operation group, n=38). Patients in both groups were followed up for a long time until death or deadline. The overall survival (OS), adverse reactions, recurrence and death results of the two groups were evaluated. The risk factors of poor prognosis were analyzed. Results: The two groups were comparable. The median follow-up time was 23.5 months in non-operation group and 112.9 months in operation group. The 1-year survival rate, 2-year survival rate and 5-year survival rate in non-operation group were 69.9%, 47.7% and 31.8%, respectively. The rates in operation group were 94.0%, 79.3% and 65.0%, respectively. The incidence of low hemoglobin was 73.7% (non-operation group) and 53.0% (operation group). The infection rates were 15.8% and 2.7%, respectively. There was no significant difference in the incidence of leukopenia, neutropenia and thrombocytopenia between the two groups. Multivariate analysis showed that recurrence and treatment were independent risk factors affecting the prognosis of patients. Conclusions: To sum up, no matter in terms of recurrence rate or OS rate, the prognosis of patients in the operation group was better than that in the non-operation group. Therefore, Neo-CRT combined with esophagectomy is recommended for locally advanced ESCC with acceptable surgical risk.