A Comprehensive Interaction Network Constructed Using miRNAs and mRNAs Provides New Insights into Potato Tuberization under High Temperatures.

High temperatures delay tuberization and decrease potato (Solanum tuberosum L.) yields. However, the molecular mechanisms and regulatory networks underlying tuberization under high temperatures remain largely unknown. Here, we performed the mRNA and miRNA sequencing of leaves and stems to identify genes and regulatory networks involved in tuberization under high temperatures. A total of 2804 and 5001 differentially expressed genes (DEGs) under high-temperature stress were identified in leaves and stems, respectively. These genes were significantly enriched in gene ontology terms regarding meristem development, the sucrose biosynthetic process, and response to heat. Meanwhile, 101 and 75 differentially expressed miRNAs (DEmiRNAs) were identified in leaves and stems, respectively. We constructed an interaction network between DEmiRNAs and DEGs, identifying 118 and 150 DEmiRNA-DEG pairs in leaves and stems, respectively. We found three miRNA-mRNA candidate modules involved in tuberization under high temperatures, including stu-miR8030-5p/StCPY714, stu-miR7981f-p5/StAGL8a, and stu-miR10532A/StAGL8b. Our study constructed an interaction network between miRNAs and target genes and proposes candidate miRNA-gene modules that regulate tuber formation under high temperatures. Our study provides new insights for revealing the regulatory mechanism of the high-temperature inhibition of tuberization and also provides gene resources for improving the heat tolerance in potatoes.

Best Overall Response-associated Signature to Doxorubicin in Soft Tissue Sarcomas: A Transcriptomic Analysis from ANNOUNCE.

PURPOSE: This exploratory analysis evaluated the tumor samples of the patients treated with doxorubicin (with or without olaratumab) in a negative phase-3 ANNOUNCE trial to better understand the complexity of advanced soft tissue sarcomas (STS) and to potentially identify its predictive markers. EXPERIMENTAL DESIGN: RNA sequencing was performed on pretreatment tumor samples (n=273) from the ANNOUNCE trial to evaluate response patterns and identify potential predictive treatment markers for doxorubicin. A BOR-associated signature to doxorubicin (REDSARC) was created by evaluating tumors with radiographic response versus progression. An external cohort of doxorubicin-treated patients from the Spanish Group for Research on Sarcomas (GEIS) was utilized for refinement and validation. RESULTS: A total of 259 samples from the trial were considered for analysis. Comparative analyses by the treatment arm did not explain the negative trial. However, there was an association between the BOR signature and histologic subtype (χ2 P=2.0e-7) and grade (P=0.002). There were no associations between the BOR signature and gender, age, ethnicity or stage. Applied to survival outcomes, REDSARC was also predictive for progression-free (PFS) and overall survival (OS). Using the GEIS cohort, a refined 25-gene signature was identified and applied to the ANNOUNCE cohort, where it was predictive of PFS and OS in leiomyosarcoma, liposarcoma, and other sarcoma subtypes, but not in undifferentiated pleomorphic sarcoma. CONCLUSIONS: The refined REDSARC signature provides a potential tool to direct the application of doxorubicin in sarcomas and other malignancies. Validation and further refinement of the signature in other potentially subtype specific prospective cohorts is recommended.

Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma.

PURPOSE: To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL). METHODS: A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves. RESULTS: In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05). CONCLUSIONS: Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.

Solution Sequential Deposition Pseudo-Planar Heterojunction: An Efficient Strategy for State-of-Art Organic Solar Cells.

Organic solar cells (OSCs) are considered as a promising new generation of clean energy. Bulk heterojunction (BHJ) structure has been widely employed in the active layer of efficient OSCs. However, precise regulation of morphology in BHJ is still challenging due to the competitive coupling between crystallization and phase separation. Recently, a novel pseudo-planar heterojunction (PPHJ) structure, prepared through solution sequential deposition, has attracted much attention. It is an easy-to-prepare structure in which the phase separation structures, interfaces, and molecular packing can be separately controlled. Employing PPHJ structure, the properties of OSCs, such as power conversion efficiency, stability, transparency, flexibility, and so on, are usually better than its BHJ counterpart. Hence, a comprehensive understanding of the film-forming process, morphology control, and device performance of PPHJ structure should be considered. In terms of the representative works about PPHJ, this review first introduces the fabrication process of active layers based on PPHJ structure. Second, the widely applied morphology control methods in PPHJ structure are summarized. Then, the influences of PPHJ structure on device performance and other property are reviewed, which largely expand its application. Finally, a brief prospect and development tendency of PPHJ devices are discussed with the consideration of their challenges.

Multiparametric MRI-Based Machine Learning Models for the Characterization of Cystic Renal Masses Compared to the Bosniak Classification, Version 2019: A Multicenter Study.

RATIONALE AND OBJECTIVE: Accurate differentiation between benign and malignant cystic renal masses (CRMs) is challenging in clinical practice. This study aimed to develop MRI-based machine learning models for differentiating between benign and malignant CRMs and compare the best-performing model with the Bosniak classification, version 2019 (BC, version 2019). METHODS: Between 2009 and 2021, consecutive surgery-proven CRM patients with renal MRI were enrolled in this multicenter study. Models were constructed to differentiate between benign and malignant CRMs using logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms, respectively. Meanwhile, two radiologists classified CRMs into I-IV categories according to the BC, version 2019 in consensus in the test set. A subgroup analysis was conducted to investigate the performance of the best-performing model in complicated CRMs (II-IV lesions in the test set). The performances of models and BC, version 2019 were evaluated using the area under the receiver operating characteristic curve (AUC). Performance was statistically compared between the best-performing model and the BC, version 2019. RESULTS: 278 and 48 patients were assigned to the training and test sets, respectively. In the test set, the AUC and accuracy of the LR model, the RF model, the SVM model, and the BC, version 2019 were 0.884 and 75.0%, 0.907 and 83.3%, 0.814 and 72.9%, and 0.893 and 81.2%, respectively. Neither the AUC nor the accuracy of the RF model that performed best were significantly different from the BC, version 2019 (P = 0.780, P = 0.065). The RF model achieved an AUC and accuracy of 0.880 and 81.0% in complicated CRMs. CONCLUSIONS: The MRI-based RF model can accurately differentiate between benign and malignant CRMs with comparable performance to the BC, version 2019, and has good performance in complicated CRMs, which may facilitate treatment decision-making and is less affected by interobserver disagreements.

Dual Function of the Third Component in Ternary Organic Solar Cells: Broaden the Spectrum and Optimize the Morphology.

Ternary organic solar cells (T-OSCs) have attracted significant attention as high-performance devices. In recent years, T-OSCs have achieved remarkable progress with power conversion efficiency (PCE) exceeding 19%. However, the introduction of the third component complicates the intermolecular interaction compared to the binary blend, resulting in poor controllability of active layer and limiting performance improvement. To address these issues, dual-functional third components have been developed that not only broaden the spectral range but also optimize morphology. In this review, the effect of the third component on expanding the absorption range of T-OSCs is first discussed. Second, the extra functions of the third component are introduced, including adjusting the crystallinity and molecular stack in active layer, regulating phase separation and purity, altering molecular orientation of the donor or acceptor. Finally, a summary of the current research progress is provided, followed by a discussion of future research directions.

Tethered Small-Molecule Acceptor Refines Hierarchical Morphology in Ternary Polymer Solar Cells: Enhanced Stability and 19% Efficiency.

Polymer solar cells (PSCs) are promising for efficient solar energy conversion, but achieving high efficiency and device longevity within a bulk-heterojunction (BHJ) structure remains a challenge. Traditional small-molecule acceptors (SMAs) in the BHJ blend show thermodynamic instability affecting the morphology. In contrast, tethered SMAs exhibit higher glass transition temperatures, mitigating these concerns. Yet, they might not integrate well with polymer donors, causing pronounced phase separation and overpurification of mixed domains. Herein, a novel ternary device is introduced that uses DY-P2EH, a tethered dimeric SMA with conjugated side-chains as host acceptor, and BTP-ec9, a monomeric SMA as secondary acceptor, which respectively possess hypomiscibility and hypermiscibility with the polymer donor PM6. This unique combination affords a parallel-connected ternary BHJ blend, leading to a hierarchical and stable morphology. The ternary device achieves a remarkable fill factor of 80.61% and an impressive power conversion efficiency of 19.09%. Furthermore, the ternary device exhibits exceptional stability, retaining over 85% of its initial efficiency even after enduring 1100 h of thermal stress at 85 °C. These findings highlight the potential advantage of tethered SMAs in the design of ternary devices with a refined hierarchical structure for more efficient and durable solar energy conversion technologies.

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

OBJECTIVE: The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy. MATERIALS AND METHODS: We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set. RESULTS: Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000). CONCLUSION: Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

Overall Survival and Exploratory Biomarker Analyses of Abemaciclib plus Trastuzumab with or without Fulvestrant versus Trastuzumab plus Chemotherapy in HR+, HER2+ Metastatic Breast Cancer Patients.

PURPOSE: The monarcHER trial has shown that abemaciclib, a cyclin-dependent kinase 4 and 6 inhibitor, combined with fulvestrant and trastuzumab, improves progression-free survival (PFS) in hormone receptor-positive (HR+), HER2-positive (HER2+) advanced breast cancer (ABC) compared with standard-of-care (SOC) chemotherapy combined with trastuzumab. We report the final overall survival (OS) analysis, updated safety and efficacy data, and exploratory biomarker results from monarcHER. PATIENTS AND METHODS: monarcHER (NCT02675231), a randomized, multicenter, open-label, phase II trial, enrolled 237 patients across Arm A (abemaciclib, trastuzumab, fulvestrant), Arm B (abemaciclib, trastuzumab), and Arm C (SOC chemotherapy, trastuzumab). Following the statistical plan, OS and PFS were estimated in all arms. RNA sequencing (RNA-seq) was performed on archival tissue. RESULTS: Median OS was 31.1 months in Arm A, 29.2 months in Arm B, and 20.7 months in Arm C [A vs. C: HR, 0.71; 95% confidence interval (CI), 0.48-1.05; nominal two-sided P value 0.086; B vs. C: HR 0.83 (95% CI, 0.57-1.23); nominal two-sided P value 0.365]. Updated PFS and safety findings were consistent with previous results. The most frequently reported treatment-emergent adverse events included diarrhea, fatigue, nausea, neutrophil count decrease, and anemia. In exploratory RNA-seq analyses, Luminal subtypes were associated with longer PFS [8.6 vs. 5.4 months (HR, 0.54; 95% CI, 0.38-0.79)] and OS [31.7 vs. 19.7 months (HR, 0.68; 95% CI, 0.46-1.00)] compared with non-Luminal. CONCLUSIONS: In this phase II trial, abemaciclib + trastuzumab ± fulvestrant numerically improved median OS in women with HR+, HER2+ ABC compared with SOC chemotherapy + trastuzumab.

The protective effects of sevoflurane on subarachnoid hemorrhage.

Sevoflurane has become an important volatile anesthetic in clinic and has been widely studied in recent years. Numerous studies have demonstrated the efficacy of sevoflurane in safeguarding against brain damage across various domains. For example, it has played a neuroprotective role in subarachnoid hemorrhage (SAH), traumatic brain injury, and ischemia/reperfusion injury. The ensuing critique will focus on the significance of sevoflurane in experimental SAH and shed light on the underlying mechanisms. The findings of the current investigation demonstrate that sevoflurane possesses neuroprotective capabilities and clarify that it effectively attenuates secondary damage resulting from SAH through anti-inflammatory and anti-apoptotic pathways. More specifically, sevoflurane is observed to mitigate arterial vasospasm, diminish microvascular thrombosis, and alleviate cerebral edema. In light of these discoveries, we maintain that sevoflurane exhibits significant promise in the management of SAH, and it merits additional investigation to facilitate its prompt clinical implementation. Therefore, a thorough understanding of the neuroprotective properties of sevoflurane is beneficial to exploring novel therapeutic solutions for SAH and providing clinicians with alternative treatment modalities.

The relationship between atrial fibrillation and NLRP3 inflammasome: a gut microbiota perspective.

Atrial fibrillation (AF) is a common clinical arrhythmia whose pathogenesis has not been fully elucidated, and the inflammatory response plays an important role in the development of AF. The inflammasome is an important component of innate immunity and is involved in a variety of pathophysiologic processes. The NLRP3 inflammasome is by far the best studied and validated inflammasome that recognizes multiple pathogens through pattern recognition receptors of innate immunity and mediates inflammatory responses through activation of Caspase-1. Several studies have shown that NLRP3 inflammasome activation contributes to the onset and development of AF. Ecological dysregulation of the gut microbiota has been associated with the development of AF, and some evidence suggests that gut microbiota components, functional byproducts, or metabolites may induce or exacerbate the development of AF by directly or indirectly modulating the NLRP3 inflammasome. In this review, we report on the interconnection of NLRP3 inflammasomes and gut microbiota and whether this association is related to the onset and persistence of AF. We discuss the potential value of pharmacological and dietary induction in the management of AF in the context of the association between the NLRP3 inflammasome and gut microbiota. It is hoped that this review will lead to new therapeutic targets for the future management of AF.

The efficacy and safety of general anesthesia vs. conscious sedation for endovascular treatment in patients with acute ischemic stroke: a systematic review and meta-analysis.

Background: Endovascular thrombectomy (EVT) is an important treatment for patients with acute ischemic stroke (AIS). A number of studies have suggested that anesthesia type (conscious sedation vs. general anesthesia) during intra-arterial treatment for acute ischemic stroke has implications for patient outcomes. Methods: PubMed, EMBASE, Cochrane Library and clinicaltrials.gov were searched for randomized controlled trials (RCTs) that were performed to evaluate general anesthesia (GA) and conscious sedation (CS) up to May 30, 2023. Review Manager 5.3 software was used to assess the data. The risk ratio (RR) and mean difference (MD) were analyzed and calculated with a fixed effect model. Results: We pooled 930 patients from seven RCTs. We conducted a meta-analysis comparing the outcomes of GA and CS in the included trials. The rate of functional independence in the GA group was higher than that in the CS group (RR: 1.17, 95% CI: 1.00-1.35; P = 0.04; I2 = 16%). The GA group had a higher successful recanalization rate than the CS group (RR: 1.15, 95% CI: 1.08-1.22; P < 0.0001; I2 = 26%). The GA group had a higher pneumonia rate than the CS group (RR: 1.69, 95% CI: 1.22-2.34; P = 0.002; I2 = 26%). In addition, there was no significant difference between GA and CS with respect to the National Institutes of Health Stroke Scale (NIHSS) score at 24 h (P = 0.62), Modified Rankin Scale (mRS) score at 90 days (P = 0.25), intracerebral hemorrhage (P = 0.54), and mortality at 3 months (P = 0.61). Conclusion: GA demonstrated superiority over CS in achieving successful recanalization and functional independence at 3 months when performing EVT in AIS patients. However, it was also associated with a higher risk of pneumonia. Further studies, particularly those with long-term follow-ups, are necessary to identify precise strategies for selecting the appropriate anesthetic modality in EVT patients. Systematic review registration: INPLASY202370116.

Endothelial cell-derived RSPO3 activates Gαi1/3-Erk signaling and protects neurons from ischemia/reperfusion injury.

The current study explores the potential function and the underlying mechanisms of endothelial cell-derived R-spondin 3 (RSPO3) neuroprotection against ischemia/reperfusion-induced neuronal cell injury. In both neuronal cells (Neuro-2a) and primary murine cortical neurons, pretreatment with RSPO3 ameliorated oxygen and glucose deprivation (OGD)/re-oxygenation (OGD/R)-induced neuronal cell death and oxidative injury. In neurons RSPO3 activated the Akt, Erk and ß-Catenin signaling cascade, but only Erk inhibitors reversed RSPO3-induced neuroprotection against OGD/R. In mouse embryonic fibroblasts (MEFs) and neuronal cells, RSPO3-induced LGR4-Gab1-Gαi1/3 association was required for Erk activation, and either silencing or knockout of Gαi1 and Gαi3 abolished RSPO3-induced neuroprotection. In mice, middle cerebral artery occlusion (MCAO) increased RSPO3 expression and Erk activation in ischemic penumbra brain tissues. Endothelial knockdown or knockout of RSPO3 inhibited Erk activation in the ischemic penumbra brain tissues and increased MCAO-induced cerebral ischemic injury in mice. Conversely, endothelial overexpression of RSPO3 ameliorated MCAO-induced cerebral ischemic injury. We conclude that RSPO3 activates Gαi1/3-Erk signaling to protect neuronal cells from ischemia/reperfusion injury.

Methodological evolution of potato yield prediction: a comprehensive review.

Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

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Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18.

This study aims to discriminate between leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis and gamma-aminobutyric acid B (GABAB) receptor antibody encephalitis using a convolutional neural network (CNN) model. A total of 81 patients were recruited for this study. ResNet18, VGG16, and ResNet50 were trained and tested separately using 3828 positron emission tomography image slices that contained the medial temporal lobe (MTL) or basal ganglia (BG). Leave-one-out cross-validation at the patient level was used to evaluate the CNN models. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were generated to evaluate the CNN models. Based on the prediction results at slice level, a decision strategy was employed to evaluate the CNN models' performance at patient level. The ResNet18 model achieved the best performance at the slice (AUC = 0.86, accuracy = 80.28%) and patient levels (AUC = 0.98, accuracy = 96.30%). Specifically, at the slice level, 73.28% (1445/1972) of image slices with GABAB receptor antibody encephalitis and 87.72% (1628/1856) of image slices with LGI1 antibody encephalitis were accurately detected. At the patient level, 94.12% (16/17) of patients with GABAB receptor antibody encephalitis and 96.88% (62/64) of patients with LGI1 antibody encephalitis were accurately detected. Heatmaps of the image slices extracted using gradient-weighted class activation mapping indicated that the model focused on the MTL and BG for classification. In general, the ResNet18 model is a potential approach for discriminating between LGI1 and GABAB receptor antibody encephalitis. Metabolism in the MTL and BG is important for discriminating between these two encephalitis subtypes.

Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma.

PURPOSE: Plexiform neurofibromas (PNF) are peripheral nerve sheath tumors that cause significant morbidity in persons with neurofibromatosis type 1 (NF1), yet treatment options remain limited. To identify novel therapeutic targets for PNF, we applied an integrated multi-omic approach to quantitatively profile kinome enrichment in a mouse model that has predicted therapeutic responses in clinical trials for NF1-associated PNF with high fidelity. EXPERIMENTAL DESIGN: Utilizing RNA sequencing combined with chemical proteomic profiling of the functionally enriched kinome using multiplexed inhibitor beads coupled with mass spectrometry, we identified molecular signatures predictive of response to CDK4/6 and RAS/MAPK pathway inhibition in PNF. Informed by these results, we evaluated the efficacy of the CDK4/6 inhibitor, abemaciclib, and the ERK1/2 inhibitor, LY3214996, alone and in combination in reducing PNF tumor burden in Nf1flox/flox;PostnCre mice. RESULTS: Converging signatures of CDK4/6 and RAS/MAPK pathway activation were identified within the transcriptome and kinome that were conserved in both murine and human PNF. We observed robust additivity of the CDK4/6 inhibitor, abemaciclib, in combination with the ERK1/2 inhibitor, LY3214996, in murine and human NF1(Nf1) mutant Schwann cells. Consistent with these findings, the combination of abemaciclib (CDK4/6i) and LY3214996 (ERK1/2i) synergized to suppress molecular signatures of MAPK activation and exhibited enhanced antitumor activity in Nf1flox/flox;PostnCre mice in vivo. CONCLUSIONS: These findings provide rationale for the clinical translation of CDK4/6 inhibitors alone and in combination with therapies targeting the RAS/MAPK pathway for the treatment of PNF and other peripheral nerve sheath tumors in persons with NF1.