Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.

ß-Cyclodextrin based Pickering emulsions for α-tocopherol delivery: Antioxidation stability and bioaccessibility.

ß-Cyclodextrin (ß-CD) Pickering emulsion and cinnamaldehyde/ß-cyclodextrin (CIN/ß-CD) Pickering emulsion were prepared and the influences of oxidation and digestion were investigated. CIN/ß-CD composite was better dispersed at the oil-water interface than ß-CD. Hydrophobic group of CIN anchored in the oil phase and Hydrophilic hydroxyl group of ß-CD extended into the aqueous phase, which allowed CIN/ß-CD composite to be oriented at the oil-water interface and formed a more stable oil-water interface layer. ß-CD Pickering emulsion was more susceptible to oxidative deterioration than CIN/ß-CD Pickering emulsion, its malondialdehyde (MDA) value was as high as 509.41 ± 9.37 nmol/L. Digestion experiment indicated that CIN/ß-CD Pickering emulsion was released inner oil phase in the small intestine and free fatty acid (FFA) release rate was 44.32 ± 1.08%. Pharmacokinetic parameters manifested that α-tocopherol peak concentration (Cmax) was 64.32 ± 6.45 mg/L and the peak time (Tmax) appeared at 5 h after administration of CIN/ß-CD Pickering emulsion.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Inhibition of autophagy can promote the apoptosis of bladder cancer cells induced by SC66 through the endoplasmic reticulum stress pathway.

Bladder cancer is among the ten most prevalent cancer types worldwide, and its prognosis has not improved significantly in the past three decades because of cognitive limitations in the molecular mechanisms that drive the malignant progression of bladder cancer. Therefore, there is an urgent need to identify new therapeutic drugs or molecular targets to improve the prognosis of patients with bladder cancer. SC66, a novel allosteric inhibitor of AKT, has recently been reported to exert potent anticancer effects on various cancer cells. However, the mechanisms underlying its anticancer effects in bladder cancer remain largely unknown. Consequently, this study aimed to conduct a series of molecular and cellular biology experiments to verify the anticancer effect and potential mechanism of action of SC66 in bladder cancer in vitro. A xenograft tumor model was established to confirm its anticancer role in vivo. Our results showed that SC66 inhibited cell proliferation, triggered mitochondria-mediated apoptosis, and initiated autophagy in bladder cancer cells dose-dependently. In addition, our results suggested that SC66-caused apoptosis and autophagy were endoplasmic reticulum stress-dependent. Interestingly, the activation of autophagy can partially protect bladder cancer cells from apoptosis under endoplasmic reticulum stress induced by SC66 treatment. This study shows that SC66 exerts its anticancer impact on bladder cancer by inhibiting cell proliferation and inducing apoptosis. It also reveals that inhibiting autophagy can increase the cytotoxic effects of SC66 in bladder cancer. Overall, this is the first study on the anticancer effect of SC66 mediated by the endoplasmic reticulum stress pathway and the first report on the AKT-independent anticancer mechanism of SC66 in bladder cancer. Conclusively, exploring the relationship between apoptosis, autophagy, and endoplasmic reticulum stress induced by SC66 indicates that SC66 is a promising novel agent for patients with bladder cancer.

Switchable enzyme mimics based on self-assembled peptides for polyethylene terephthalate degradation.

Polyethylene terephthalate (PET), the most abundant polyester plastic, has become a global concern due to its refractoriness and accumulation in the environment. In this study, inspired by the structure and catalytic mechanism of the native enzyme, peptides, based on supramolecular self-assembly, were developed to construct enzyme mimics for PET degradation, which were achieved by combining the enzymatic active sites of serine, histidine and aspartate with the self-assembling polypeptide MAX. The two designed peptides with differences in hydrophobic residues at two positions exhibited a conformational transition from random coil to ß-sheet by changing the pH and temperature, and the catalytic activity followed the self-assembly "switch" with the fibrils formed ß-sheet, which could catalyze PET efficiently. Although the two peptides possessed same catalytic site, they showed different catalytic activities. Analysis of the structure - activity relationship of the enzyme mimics suggested that the high catalytic activity of the enzyme mimics for PET could be attributed to the formation of stable fibers of peptides and ordered arrangement of molecular conformation; in addition, hydrogen bonding and hydrophobic interactions, as the major forces, promoted effects of enzyme mimics on PET degradation. Enzyme mimics with PET-hydrolytic activity are a promising material for degrading PET and reducing environmental pollution.

Potential mechanisms of osthole against bladder cancer cells based on network pharmacology, molecular docking, and experimental validation.

BACKGROUND: Osthole was traditionally used in treatment for various diseases. However, few studies had demonstrated that osthole could suppress bladder cancer cells and its mechanism was unclear. Therefore, we performed a research to explore the potential mechanism for osthole against bladder cancer. METHODS: Internet web servers SwissTargetPrediction, PharmMapper, SuperPRED, and TargetNet were used to predict the Osthole targets. GeneCards and the OMIM database were used to indicate bladder cancer targets. The intersection of two target gene fragments was used to obtain the key target genes. Protein-protein interaction (PPI) analysis was performed using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Furthermore, we used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular function of target genes. AutoDock software was then used to perform molecular docking of target genes,osthole and co-crystal ligand. Finally, an in vitro experiment was conducted to validate bladder cancer inhibition by osthole. RESULTS: Our analysis identified 369 intersection genes for osthole, the top ten target genes included MAPK1, AKT1, SRC, HRAS, HASP90AA1, PIK3R1, PTPN11, MAPK14, CREBBP, and RXRA. The GO and KEGG pathway enrichment results revealed that the PI3K-AKT pathway was closely correlated with osthole against bladder cancer. The osthole had cytotoxic effect on bladder cancer cells according to the cytotoxic assay. Additionally, osthole blocked the bladder cancer epithelial-mesenchymal transition and promoted bladder cancer cell apoptosis by inhibiting the PI3K-AKT and Janus kinase/signal transducer and activator of transcription (JAK/STAT3) pathways. CONCLUSIONS: We found that osthole had cytotoxic effect on bladder cancer cells and inhibited invasion, migration, and epithelial-mesenchymal transition by inhibiting PI3K-AKT and JAK/STAT3 pathways in in vitro experiment. Above all, osthole might have potential significance in treatment of bladder cancer. SUBJECTS: Bioinformatics, Computational Biology, Molecular Biology.

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.

BACKGROUND AND OBJECTIVES: Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). METHODS: A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. RESULTS: The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. CONCLUSIONS: The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.

[Corrigendum] Thymoquinone inhibits epithelial­mesenchymal transition in prostate cancer cells by negatively regulating the TGF­ß/Smad2/3 signaling pathway.

After the publication of the article, an interested reader drew to the authors' attention that the Du145 'Control' migration panel in Fig. 2C appeared to overlap with the Du145 'Control' invasion panel in Fig. 5A; furthermore, two of the Du145 panels in Fig. 5A also appeared to overlap. The authors have consulted their original data, and realize that these figures were inadvertently assembled incorrectly. The corrected versions of Figs. 2 and 5, incorporating the correct data for the Du145 'Control' panel in Fig. 2C, and the TQ­/TGF­ß OE­ invasion and migration panels, and the TQ+/TGF­ß OE+ migration panel, in Fig. 5A, are shown on the next page. These further corrections do not grossly affect the results or the conclusions reported in this work. The authors all agree to this Corrigendum, and are grateful to the Editor of Oncology Reports for granting them the opportunity to correct the errors that were made during the assembly of these figures. Lastly, the authors apologize to the readership for any inconvenience these errors may have caused. [Oncology Reports 38: 3592­3598, 2017; DOI: 10.3892/or.2017.6012].

The effective constituent puerarin, from Pueraria lobata, inhibits the proliferation and inflammation of vascular smooth muscle in atherosclerosis through the miR-29b-3p/IGF1 pathway.

CONTEXT: Atherosclerosis (AS) is the main cause of cardiovascular and cerebrovascular diseases. Pueraria lobata (Willd.) Ohwi (Fabaceae) has a positive effect on improving these diseases. OBJECTIVE: The P. lobata effect on the proliferation and inflammation of vascular smooth muscle in AS and the potential mechanism were investigated. MATERIALS AND METHODS: By feeding a high-fat diet to 8-week-old apolipoprotein E knockout mice, an atherosclerosis model was created. H&E and IHC staining were used to analyse the histopathology of mice. CCK-8, TUNEL, and scratch tests were used to detect cell proliferation, apoptosis, and migration after 24 h treatment, respectively. ELISA was performed to evaluate the level of IL-6 and IL-8. The target miRNA and its downstream target gene were screened by the bioinformatics method; RT-qPCR has conducted to analyse the expression of these genes. RESULTS: In the aortic tissue and serum of AS mice, puerarin can lower the expression of α-SMA and the inflammatory proteins IL-6 and IL-8. Puerarin (200 M) decreased hVSMC proliferation, migration, and IL-6 and IL-8 secretion by more than half. The inhibitory impact of puerarin on hVSMC was decreased by overexpression of miR-29b-3p. IGF1 was miR-29b-3p's downstream target gene. IGF1 expression increased almost 3-fold in AS mice and hVSMC, but miR-29b-3p mimic inhibited it. The effect of miR-29b-3p on hVSMC was reversed when IGF1 was overexpressed. DISCUSSION AND CONCLUSIONS: Puerarin inhibits the proliferation and inflammation of vascular smooth muscle in AS through the miR-29b-3p/IGF1 pathway. Puerarin may have a beneficial effect in the treatment of atherosclerosis and offer a novel therapy option.

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC). METHODS: A total of 215 tumours (129 for training and 31 for internal validation, centre 1; 55 for external validation, centre 2) were included. MIBC was confirmed by pathological examination. VI-RADS scores were provided by two groups of radiologists (readers 1 and readers 2) independently. A deep convolutional neural network was constructed in the training set, and validation was conducted on the internal and external validation sets. ROC analysis was performed to evaluate the performance for MIBC diagnosis. RESULTS: The AUCs of the DL model, readers 1, and readers 2 were as follows: in the internal validation set, 0.963, 0.843, and 0.852, respectively; in the external validation set, 0.861, 0.808, and 0.876, respectively. The accuracy of the DL model in the tumours scored VI-RADS 2 or 3 was higher than that of radiologists in the external validation set: for readers 1, 0.886 vs. 0.600, p = 0.006; for readers 2, 0.879 vs. 0.636, p = 0.021. The average processing time (38 s and 43 s in two validation sets) of the DL method was much shorter than the readers, with a reduction of over 100 s in both validation sets. CONCLUSIONS: Compared to radiologists using VI-RADS, the DL method had a better diagnostic performance, shorter processing time, and robust generalisability, indicating good potential for diagnosing MIBC. KEY POINTS: • The DL model shows robust performance for MIBC diagnosis in both internal and external validation. • The diagnostic performance of the DL model in the tumours scored VI-RADS 2 or 3 is better than that obtained by radiologists using VI-RADS. • The DL method shows potential in the preoperative assessment of MIBC.

False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study.

BACKGROUND: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS: Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.

[Mechanism of Cyathulae Radix in treatment of knee osteoarthritis based on metabolomics].

Metabolomics based on ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS) was employed to analyze the effect of Cyathulae Radix on serum and urine in rats with knee osteoarthritis, and to decipher the mechanism of Cyathulae Radix in the treatment of knee osteoarthritis. SD rats were randomized into a normal group, a model group, a positive drug group, and a Cyathulae Radix group. The knee osteoarthritis model was established by injecting 2% pa-pain and 0.03 mol·L~(-1) cysteine, and the serum levels of tumor necrosis factor-α(TNF-α) and matrix metalloproteinase-3(MMP-3) in the model group was measured to preliminarily evaluate the therapeutic effect of Cyathulae Radix on knee osteoarthritis. UHPLC-Q-TOF-MS was employed to establish the metabolic profile of endogenous small molecule metabolites in the four groups. Potential biomarkers were screened out by multivariate analysis methods such as partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis(OPLS-DA) in combination with t-test, variable importance for projection(VIP), and fold-change. The related metabolic pathways were enriched with the help of MetaboAnalyst 5.0. The results showed that Cyathulae Radix alleviated the general signs of rats with knee osteoarthritis, and reduced the levels of TNF-α and MMP-3 in the rat serum. Twenty-eight differential metabolites that might be associated with the therapeutic effect of Cyathulae Radix were screened out from serum and urine. They were mainly involved in arginine biosynthesis, nicotinate and nicotinamide metabolism, tricarboxylic acid cycle, alanine, aspartate and glutamate metabolism, riboflavin metabolism, glyoxylate and dicarboxylate metabolism, and pyrimidine metabolism. Through metabonomics analysis, this study predicted the possible mechanism of Cyathulae Radix in the treatment of knee osteoarthritis, which laid a foundation for further research.

A novel signature based on pyroptosis-related genes for predicting prognosis and treatment response in prostate cancer patients.

Background: Pyroptosis is a form of programmed cell death accompanied by specific inflammatory and immune responses, and it is closely related to the occurrence and progression of various cancers. However, the roles of pyroptosis-related genes (PRGs) in the prognosis, treatment response, and tumor microenvironment (TME) of prostate cancer (PCa) remain to be investigated. Methods: The mRNA expression data and clinical information of PCa patients were obtained from the Cancer Genome Atlas database (TCGA) and the cBioPortal for Cancer Genomics website, and the 52 PRGs were obtained from the published papers. The univariate, multivariate, and LASSO Cox regression algorithms were used to obtain prognostic hub PRGs. Meanwhile, qRT-PCR was used to validate the expression of hub genes between PCa lines and normal prostate epithelial cell lines. We then constructed and validated a risk model associated with the patient's disease-free survival (DFS). Finally, the relationships between risk score and clinicopathological characteristics, tumor immune microenvironment, and drug treatment response of PCa were systematically analyzed. Results: A prognostic risk model was constructed with 6 hub PRGs (CHMP4C, GSDMB, NOD2, PLCG1, CYCS, GPX4), and patients were divided into high and low-risk groups by median risk score. The risk score was confirmed to be an independent prognostic factor for PCa in both the training and external validation sets. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they had more increased somatic mutations, higher immune cell infiltration and higher expression of immune checkpoint-related genes. Moreover, they were more sensitive to cell cycle-related chemotherapeutic drugs and might be more responsive to immunotherapy. Conclusion: In our study, pyroptosis played a significant role in the management of the prognosis and tumor microenvironment of PCa. Meanwhile, the established model might help to develop more effective individual treatment strategies.

Kinematic alignment versus mechanical alignment in total knee arthroplasty: An up-to-date meta-analysis.

PURPOSE: The purpose of this meta-analysis was to compare the efficacy and imaging parameters of kinematic alignment (KA) and mechanical alignment (MA) in total knee arthroplasty (TKA) and to evaluate whether patients undergoing KA-TKA benefited more than those undergoing MA-TKA. METHODS: Studies comparing the efficacy of KA-TKA and MA-TKA were included after searching and screening in the database, including PubMed, Embase, Web of Science and Cochrane Database Library. A total of 1420 patients were enrolled in the study, with 736 MA-TKA and 738 KA-TKA. The primary outcomes were postoperative knee function scores including KSS series, WOMAC, KOOS and OKS. Secondary outcomes included the operative time, the length of hospital stay, knee extension/flexion angle, and some imaging parameters. The risk of bias for included studies was assessed using the Cochrane Collaborative risk-of-bias assessment tool or the Newcastle-Ottawa Scale(NOS). RESULTS: Sixteen studies were included in this meta-analysis (11 randomized controlled studies and 5 cohort studies). Primary outcomes: Knee Society score (KSS, MD = 8.36, 95% Cl: 0.83-15.90) and combined KSS (MD = 15.24, 95% CI: 5.41-25.07) were higher in KA-TKA than in MA-TKA, and other functional scores were not statistically significant in KA-TKA and MA-TKA, including knee injury and osteoarthritis outcome score (KOOS), Oxford knee score (OKS), Knee Function score (KFS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Secondary outcomes: KA-TKA resulted in smaller medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) compared to MA-TKA. For other outcome measures, KA-TKA showed similar results compared to MA-TKA, including hip-knee-ankle (HKA) angle, extension/flexion angle, tibial component slope angle, joint line orientation angle (JLOA), the operation time, the length of hospital stay and ligament release rate. CONCLUSIONS: In our analysis results, patients undergoing KA-TKA benefit as much as patients undergoing MA-TKA. KA may be a viable reference in total knee replacement.

Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study.

In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals.

A Qualitative Signature to Identify TERT Promoter Mutant High-Risk Tumors in Low-Grade Gliomas.

Background: Telomerase reverse transcriptase promoter (TERT-p) mutation has been frequently found, but associated with contrary prognosis, in both low-grade gliomas and glioblastomas. For the low-grade gliomas (Grades II-III), TERT-p mutant patients have a better prognosis than the wildtype patients, whereas for the GBMs (Grade IV), TERT-p mutation is related to a poor prognosis. We hypothesize that there exist high-risk patients in LGGs who share GBM-like molecular features, including TERT-p mutation, and need more intensive treatment than other LGGs. A molecular signature is needed to identify these high-risk patients for an accurate and timely treatment. Methods: Using the within-sample relative expression orderings of gene pairs, we identified the gene pairs with significantly stable REOs, respectively, in both the TERT-p mutant LGGs and GBMs but with opposite directions in the two groups. These reversely stable gene pairs were used as the molecular signature to stratify the LGGs into high-risk and low-risk groups. Results: A signature consisting of 21 gene pairs was developed, which can classify LGGs into two groups with significantly different overall survival. The high-risk group has a similar genetic mutation profile and a similar survival profile as GBMs, and these high-risk tumors may progress to a more malignant state. Conclusion: The 21 gene-pair signature based on REOs is capable of identifying high-risk patients in LGGs and guiding the clinical choice for appropriate and timely intervention.

Homologous Recombination Related Signatures Predict Prognosis and Immunotherapy Response in Metastatic Urothelial Carcinoma.

Objective: This study used homologous recombination (HR) related signatures to develop a clinical prediction model for screening immune checkpoint inhibitors (ICIs) advantaged populations and identify hub genes in advanced metastatic urothelial carcinoma. Methods: The single-sample gene enrichment analysis and weighted gene co-expression network analysis were applied to identify modules associated with immune response and HR in IMvigor210 cohort samples. The principal component analysis was utilized to determine the differences in HR-related module gene signature scores across different tissue subtypes and clinical variables. Risk prediction models and nomograms were developed using differential gene expression analysis associated with HR scores, least absolute shrinkage and selection operator, and multivariate proportional hazards model regression. Additionally, hub genes were identified by analyzing the contribution of HR-related genes to principal components and overall survival analysis. Finally, clinical features from GSE133624, GSE13507, the TCGA, and other data sets were analyzed to validate the relationship between hub genes and tumor growth and mutation. Results: The HR score was significantly higher in the complete/partial response group than in the stable/progressive disease group. The majority of genes associated with HR were discovered to be involved in the cell cycle and others. Genomically unstable, high tumor level, and high immune level samples all exhibited significantly higher HR score than other sample categories, and higher HR scores were related to improved survival following ICIs treatment. The risk scores for AUNIP, SEPT, FAM72D, CAMKV, CXCL9, and FOXN4 were identified, and the training and verification groups had markedly different survival times. The risk score, tumor neoantigen burden, mismatch repair, and cell cycle regulation were discovered to be independent predictors of survival time following immunotherapy. Patients with a high level of expression of hub genes such as EME1, RAD51AP1, and RAD54L had a greater chance of surviving following immunotherapy. These genes are expressed at significantly higher levels in tumors, high-grade cancer, and invasive cancer than other categories, and are associated with TP53 and RB1 mutations. Conclusion: HR-related genes are upregulated in genomically unstable samples, the survival time of mUC patients after treatment with ICIs can be predicted using a normogram model based on HR signature.

Surface Modulation of 3D Porous CoNiP Nanoarrays In Situ Grown on Nickel Foams for Robust Overall Water Splitting.

The careful design of nanostructures and multi-compositions of non-noble metal-based electrocatalysts for highly efficient electrocatalytic hydrogen and oxygen evolution reaction (HER and OER) is of great significance to realize sustainable hydrogen release. Herein, bifunctional electrocatalysts of the three-dimensional (3D) cobalt-nickel phosphide nanoarray in situ grown on nickel foams (CoNiP NA/NF) were synthesized through a facile hydrothermal method followed by phosphorization. Due to the unique self-template nanoarray structure and tunable multicomponent system, the CoNiP NA/NF samples present exceptional activity and durability for HER and OER. The optimized sample of CoNiP NA/NF-2 afforded a current density of 10 mA cm-2 at a low overpotential of 162 mV for HER and 499 mV for OER, corresponding with low Tafel slopes of 114.3 and 79.5 mV dec-1, respectively. Density functional theory (DFT) calculations demonstrate that modulation active sites with appropriate electronic properties facilitate the interaction between the catalyst surface and intermediates, especially for the adsorption of absorbed H* and *OOH intermediates, resulting in an optimized energy barrier for HER and OER. The 3D nanoarray structure, with a large specific surface area and abundant ion channels, can enrich the electroactive sites and enhance mass transmission. This work provides novel strategies and insights for the design of robust non-precious metal catalysts.