Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy.

OBJECTIVES: To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features. MATERIALS AND METHODS: One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were used to evaluate the predictive performance of the scoring system. RESULTS: Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001). CONCLUSION: ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach. CRITICAL RELEVANCE STATEMENT: The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis. KEY POINTS: Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.

Sarcopenia and Its Influencing Factors in Patients With Colorectal Cancer.

Sarcopenia can lead to more postoperative complications and poorer prognosis in patients with colorectal cancer; however there is limited research exploring the incidence and influencing factors of different stages of sarcopenia in patients with colorectal cancer. We investigated 312 patients with colorectal cancer. Sarcopenia was determined by measuring muscle mass, grip strength, and gait speed. According to the European Working Group on Sarcopenia in Older People (EWGSOP) 2010, patients were classified into four groups: nonsarcopenia, presarcopenia, sarcopenia, and severe sarcopenia. The incidence of sarcopenia and severe sarcopenia was 10.3% and 8.7%, respectively. Multivariate logistic regression analysis showed that age, TNM stage smoking history, total protein, upper arm circumference, waist circumference, and Patient-Generated Subjective Global Assessment (PG-SGA) score were independently associated with sarcopenia at different stages. This study suggests that routine data should be used to provide targeted care during hospitalization for patients with colorectal cancer in order to reduce the incidence of sarcopenia and improve prognosis.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

The Value of LI-RADS and Radiomic Features From MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma within 5 cm.

RATIONALE AND OBJECTIVES: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm. METHODS: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively. RESULTS: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort. CONCLUSION: Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC＜5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy.

Gastric tubular adenocarcinoma with diffuse neutrophils infiltrating: characteristics and probable treatment strategy.

BACKGROUND: Gastric adenocarcinoma is a highly heterogeneous malignancy with varying prognoses. In clinicopathological practice, we noticed a special tubular adenocarcinoma with diffuse neutrophils infiltrating (TADNI). However, the proportion and characteristics of TADNI remain unclear. This study aimed to evaluate the features of TADNI and explore probable treatments. METHODS: We divided 289 tubular adenocarcinoma cases into the TADNI and non-TADNI (nTADNI) groups by histological neutrophil quantity and performed immunohistochemistry of treatment-associated markers (CXCR1, CXCR2, PD-L1, CD8, HER2 and VEGFR2). Then we evaluated the clinical and morphological features in these cases. We also compared the value of histological features and peripheral blood neutrophil test. In addition, multiomics bioinformatic analyses were performed using the public datasets. RESULTS: In our cohort, TADNI accounted for 10.4% of all tubular adenocarcinoma cases. These cases had worse prognoses (especially the neutrophils mainly outside the tubes) than nTADNI cases. The histological identification of TADNI had more prognostic value than peripheral blood neutrophils. CXCR1/CXCR2 expression was significantly high in TADNI group which indicated that CXCR1/CXCR2 inhibitors might be beneficial for TADNI patients. There were no significant differences in the expression of PD-L1, CD8, HER2 and VEGFR2. The analyses of TCGA data confirmed that TADNI cases had poorer prognoses and higher CXCR1/CXCR2 expression. Bioinformatic results also revealed molecular features (more hsa-mir-223 expression, fewer CD8-positive T cells and regulatory T cells, tighter communication between tumor cells' CXCR1/CXCR2 and neutrophils' CXCL5/CXCL8) of this type. CONCLUSIONS: TADNI is a special morphological subtype with poorer prognoses and unique molecular characteristics, which might benefit from CXCR1/CXCR2 inhibitors.

Predicted factors of surgical site infection in glioblastoma patients: A meta-analysis.

Surgical site infection (SSI) is one of the common postoperative complications after craniotomy for glioblastoma patients. Previous studies have investigated the risk factors for SSI in patients with glioblastoma. Whereas big differences in research results exist, and the correlation coefficients of different research results are quite different. A meta-analysis was conducted to examine the risk factors related to surgical site infection in patients with glioblastoma. We searched English databases to collect case-control studies or cohort studies published before 15 October 2023 including PubMed, Web of Science, Embase. The risk of bias of the included studies was assessed via Newcastle-Ottawa Scale. The analysis was performed using RevMan 5.4.1 tool. A total of 4 articles (n = 2222) were selected in this meta-analysis. The following risk factors were presented to be correlated with SSI in glioblastoma: irradiation (OR = 1.88, 95% CI [0.46, 7.60]), more than 3 surgeries (OR = 2.99, 95% CI [1.47, 6.08]). Occurrence of SSI is influenced by a variety of factors. Thus, we should pay close attention to high-risk subjects and take crucial targeted interventions to lower the SSI risk following craniotomy. Owing to the limited quality and quantity of the included studies, more rigorous studies with adequate sample sizes are needed to verify the conclusion.

Progress in cancer neuroscience.

Cancer of the central nervous system (CNS) can crosstalk systemically and locally in the tumor microenvironment and has become a topic of attention for tumor initiation and advancement. Recently studied neuronal and cancer interaction fundamentally altered the knowledge about glioma and metastases, indicating how cancers invade complex neuronal networks. This review systematically discussed the interactions between neurons and cancers and elucidates new therapeutic avenues. We have overviewed the current understanding of direct or indirect communications of neuronal cells with cancer and the mechanisms associated with cancer invasion. Besides, tumor-associated neuronal dysfunction and the influence of cancer therapies on the CNS are highlighted. Furthermore, interactions between peripheral nervous system and various cancers have also been discussed separately. Intriguingly and importantly, it cannot be ignored that exosomes could mediate the "wireless communications" between nervous system and cancer. Finally, promising future strategies targeting neuronal-brain tumor interactions were reviewed. A great deal of work remains to be done to elucidate the neuroscience of cancer, and future more research should be directed toward clarifying the precise mechanisms of cancer neuroscience, which hold enormous promise to improve outcomes for a wide range of malignancies.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer.

OBJECTIVE: To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis. METHODS: A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model. RESULTS: In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010). CONCLUSION: The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery. CRITICAL RELEVANCE STATEMENT: MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC. KEY POINTS: • Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.

Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer.

Objectives: Sarcopenia is associated with a poor prognosis in patients with colorectal cancer. However, the clinical factors that lead to colorectal cancer patients with sarcopenia are still unclear. The objective of this study is to develop and validate a nomogram for predicting the occurrence of sarcopenia and to provide healthcare professionals with a reliable tool for early identification of high-risk patients with colorectal cancer associated sarcopenia. Methods: A total of 359 patients diagnosed with colorectal cancer from July 2021 to May 2022 were included. All patients were randomly divided into a training (n = 287) cohort and a validation cohort (n = 72) at the ratio of 80/20. Univariate and multivariate logistic analysis were performed to evaluate the factors associated with sarcopenia. The diagnostic nomogram of sarcopenia in patients with colorectal cancer was constructed in the training cohort and validated in the validation cohort. Various evaluation metrics were employed to assess the performance of the developed nomogram, including the ROC curve, calibration curve, and Hosmer-Lemeshow test. Results: Smoking history, drinking history, diabetes, TNM stage, nutritional status, and physical activity were included in the nomogram for the prediction of sarcopenia. The diagnostic nomograms demonstrated excellent discrimination, with AUC values of 0.971 and 0.922 in the training and validation cohorts, respectively. Moreover, the calibration performance of the nomogram is also excellent, as evidenced by the Hosmer-Lemeshow test result of 0.886. Conclusions: The nomogram consisting of preoperative factors was able to successfully predict the occurrence of sarcopenia in colorectal cancer patients, aiding in the early identification of high-risk patients and facilitating timely implementation of appropriate intervention measures.

Combined CT and serum CA19-9 for stratifying risk for progression in patients with locally advanced pancreatic cancer receiving intraoperative radiotherapy.

Background and purpose: The aim of this study was to evaluate the significance of baseline computed tomography (CT) imaging features and carbohydrate antigen 19-9 (CA19-9) in predicting prognosis of locally advanced pancreatic cancer (LAPC) receiving intraoperative radiotherapy (IORT) and to establish a progression risk nomogram that helps to identify the potential beneficiary of IORT. Methods: A total of 88 LAPC patients with IORT as their initial treatment were enrolled retrospectively. Clinical data and CT imaging features were analyzed. Cox regression analyses were performed to identify the independent risk factors for progression-free survival (PFS) and to establish a nomogram. A risk-score was calculated by the coefficients of the regression model to stratify the risk of progression. Results: Multivariate analyses revealed that relative enhanced value in portal-venous phase (REV-PVP), peripancreatic fat infiltration, necrosis, and CA19-9 were significantly associated with PFS (all p < 0.05). The nomogram was constructed according to the above variables and showed a good performance in predicting the risk of progression with a concordance index (C-index) of 0.779. Our nomogram stratified patients with LAPC into low- and high-risk groups with distinct differences in progression after IORT (p < 0.001). Conclusion: The integrated nomogram would help clinicians to identify appropriate patients who might benefit from IORT before treatment and to adapt an individualized treatment strategy.

Prediction of Microvascular Invasion in Solitary AFP-Negative Hepatocellular Carcinoma ≤ 5 cm Using a Combination of Imaging Features and Quantitative Dual-Layer Spectral-Detector CT Parameters.

RATIONALE AND OBJECTIVES: AFP-negative hepatocellular carcinoma (AFPN-HCC) within 5 cm is a special subgroup of HCC. This study aimed to investigate the value of dual-layer spectral-detector CT (DLCT) and construct a scoring model based on imaging features as well as DLCT for predicting microvascular invasion (MVI) in AFPN-HCC within 5 cm. METHODS: This retrospective study enrolled 104 HCC patients who underwent multiphase contrast-enhanced DLCT studies preoperatively. Combined radiological features (CR) and combined DLCT quantitative parameter (CDLCT) were constructed to predict MVI. Multivariable logistic regression was applied to identify potential predictors of MVI. Based on the coefficient of the regression model, a scoring model was developed. The predictive efficacy was assessed through ROC analysis. RESULTS: Microvascular invasion (MVI) was found in 28 (26.9%) AFPN-HCC patients. Among single parameters, the effective atomic number in arterial phase demonstrated the best predictive efficiency for MVI with an area under the curve (AUC) of 0.792. CR and CDLCT showed predictive performance with AUCs of 0.848 and 0.849, respectively. A risk score (RS) was calculated using the independent predictors of MVI as follows: RS = 2 × (mosaic architecture) + 2 × (corona enhancement) + 2 × (incomplete tumor capsule) + 2 × (2-trait predictor of venous invasion [TTPVI]) + 3 × (CDLCT > -1.229). Delong's test demonstrated this scoring system could significantly improve the AUC to 0.929 compared with CR (p = 0.016) and CDLCT (p = 0.034). CONCLUSION: The scoring model combining radiological features with DLCT provides a promising tool for predicting MVI in solitary AFPN-HCC within 5 cm preoperatively.

Percutaneous Full Endoscopic Management of Spinal Foraminal Schwannomas: Case Series.

BACKGROUND: Schwannoma, a benign peripheral nerve sheath tumor, is perhaps only secondary to degenerative pathology as the most common lesion at neural foramen. The surgical dilemma here is either risking nerve injury because of inadequate exposure or the need for internal fixation because of facet joint sacrifice. OBJECTIVE: To evaluate the feasibility and safety of management of foraminal schwannomas by percutaneous full-endoscopic technique. METHODS: A single-center retrospective review was conducted on patients who underwent full-endoscopic resection of neural foraminal schwannomas. Tumors were grouped into either medial type or lateral type based on relevant location to the neural foramen, and respective surgical approaches were adopted. Data including preoperative neurological status, tumor size, surgery time, the extension of resection, and clinical outcomes were collected. The learning curve was plotted as surgical time/tumor size against case number. RESULTS: A total of 25 patients were treated between May 2015 and March 2022. Gross total resection was achieved in 24 patients, and near-total resection in 1 case, with 1 patient experienced transient voiding difficulty. No tumor recurrence or spinal instability was detected in the short-term follow-up (median follow-up 22 months, range 3 months-6 years). Surgical efficiency improved with the number of cases operated on and remained stable after the initial 10 cases. CONCLUSION: Percutaneous full-endoscopic technique is a safe and minimally invasive technique for the resection of foraminal schwannomas.

The safety of an MRI simulation-guided boost after short-course preoperative radiotherapy for unresectable rectal cancer (SUNRISE): interim analysis of a randomized phase II trial.

PURPOSE: The safety of an MRI simulation-guided boost after short-course preoperative radiotherapy (SCPRT) for unresectable rectal cancer is assessed with a planned interim analysis. METHODS AND MATERIALS: Patients diagnosed with clinical stage T3-4 or regional lymph node-positive disease with positive mesorectal fascia or T4b disease evaluated by pelvic MRI were randomly assigned to the SCPRT-boost group (25 Gy in 5 fractions plus 4 Gy delivered to the gross tumor volume, followed by four cycles of chemotherapy) or preoperative chemoradiotherapy group (50 Gy in 25 fractions with concurrent chemotherapy). Then, patients received total mesorectal excision surgery after preoperative treatment. The primary endpoint was the R0 resection rate. The interim analysis was performed when 42 patients completed their assigned treatments. RESULTS: From October 2018 to November 2019, a total of 43 patients were enrolled, and 42 patients were included in the interim analysis. During preoperative therapy, grade 3 or above toxicities were observed in 10/21 (47.6%) patients in the experimental group, and 4/21 (19.0%) patients in the control group. A total of 17 (81.0%) and 13 (61.9%) patients in the experimental group and control group underwent surgery, respectively. Overall, 65.1% of the patients achieved R0 resection in the intention-to-treat analysis. Surgery-related adverse complications were observed in 2 patients (11.8%) in the experimental group and 1 patient (7.7%) in the control group. CONCLUSION: Our results show that the toxicity of an MRI simulation-guided boost after SCPRT for unresectable rectal cancer is acceptable. Thus, this clinical trial will be continued as planned.

Immunological classification of glioblastoma and its prognostic implications.

OBJECTIVES: The progress of immunotherapy for glioblastoma (GBM) is currently slow. To improve immunotherapy, we need a deeper understanding of the immune microenvironment of GBM. Here, we aimed to establish a classification system based on immune expression profile in GBM. METHODS: Immune gene expression profiles of 152 patients with GBM from The Cancer Genome Atlas (TCGA) were used to identify subtypes by consensus clustering, and the classification system was reproduced in the two validation datasets (CGGA and GSE16011). Clinical information, molecular characteristics, immune infiltration, and genomic variation were integrated to characterize the subtypes. RESULTS: Two distinct immune subtypes in GBM were successfully identified and validated. The Im2 subtype was closely related to IDH-wildtype and combined +7/-10, while the Im1 subtype was associated with IDH mutation. Survival curve analysis showed that the Im2 subtype was associated with significantly shorter survival than the Im1 subtype. Im2 showed a high immune score and stromal score, low tumor purity, enrichment of macrophages, and high immune checkpoint and HLA gene expression. Im1 was characterized by low immune score and stromal score, high tumor purity, enrichment of lymphocytes, and low immune checkpoint and HLA gene expression. Finally, we developed an immune-related signature in GBM with better prognosis prediction. CONCLUSIONS: Our study confirmed the immune heterogeneity of GBM and might provide valuable classification for immunotherapy.

Identification of a 6-RBP gene signature for a comprehensive analysis of glioma and ischemic stroke: Cognitive impairment and aging-related hypoxic stress.

There is mounting evidence that ischemic cerebral infarction contributes to vascular cognitive impairment and dementia in elderly. Ischemic stroke and glioma are two majorly fatal diseases worldwide, which promote each other's development based on some common underlying mechanisms. As a post-transcriptional regulatory protein, RNA-binding protein is important in the development of a tumor and ischemic stroke (IS). The purpose of this study was to search for a group of RNA-binding protein (RBP) gene markers related to the prognosis of glioma and the occurrence of IS, and elucidate their underlying mechanisms in glioma and IS. First, a 6-RBP (POLR2F, DYNC1H1, SMAD9, TRIM21, BRCA1, and ERI1) gene signature (RBPS) showing an independent overall survival prognostic prediction was identified using the transcriptome data from TCGA-glioma cohort (n = 677); following which, it was independently verified in the CGGA-glioma cohort (n = 970). A nomogram, including RBPS, 1p19q codeletion, radiotherapy, chemotherapy, grade, and age, was established to predict the overall survival of patients with glioma, convenient for further clinical transformation. In addition, an automatic machine learning classification model based on radiomics features from MRI was developed to stratify according to the RBPS risk. The RBPS was associated with immunosuppression, energy metabolism, and tumor growth of gliomas. Subsequently, the six RBP genes from blood samples showed good classification performance for IS diagnosis (AUC = 0.95, 95% CI: 0.902-0.997). The RBPS was associated with hypoxic responses, angiogenesis, and increased coagulation in IS. Upregulation of SMAD9 was associated with dementia, while downregulation of POLR2F was associated with aging-related hypoxic stress. Irf5/Trim21 in microglia and Taf7/Trim21 in pericytes from the mouse cerebral cortex were identified as RBPS-related molecules in each cell type under hypoxic conditions. The RBPS is expected to serve as a novel biomarker for studying the common mechanisms underlying glioma and IS.

Computational Design of Extraordinarily Stable Peptide Structures through Side-Chain-Locked Knots.

Extraordinarily stable protein and peptide structures are critically demanded in many applications. Typical approaches to enhance protein and peptide stability are strengthening certain interactions. Here, we develop a very different approach: stabilizing peptide structures through side-chain-locked knots. More specifically, a peptide core consists of a knot, which is prevented from unknotting and unfolding by large side chains of amino acids at knot boundaries. These side chains impose free energy barriers for unknotting. The free energy barriers are quantified using all-atom and coarse-grained simulations. The barriers become infinitely high for large side chains and tight knot cores, resulting in stable peptide structures, which never unfold unless one chemical bond is broken. The extraordinary stability is essentially kinetic stability. Our new approach lifts the thermodynamic restriction in designing peptide structures, provides extra freedom in selecting sequence and structural motifs that are thermodynamically unstable, and should expand the functionality of peptides. This work also provides a bottom-up understanding of how knotting enhances protein stability.

Prognostic Factors and Treatments Efficacy in Spontaneous Spinal Epidural Hematoma: A Multicenter Retrospective Study.

BACKGROUND AND OBJECTIVES: Spontaneous spinal epidural hematoma (SSEH) is an uncommon but serious condition with a high morbidity rate. Although SSEH is related to numerous risk factors, its etiology remains unclear. There is a paucity of data on its prognostic factors. We aim to evaluate prognostic factors for SSEH in this study. METHOD: A retrospective study was performed on patients who were admitted for SSEH in 3 academic neurosurgical centers from January 2010 to June 2021. Clinical parameters, including clinical condition on admission, anticoagulants use, imaging modality, the timing and type of surgery performed, and outcomes, were collected. Prognostic factors were analyzed. The Frankel scale was used to assess the clinical condition. RESULTS: A total of 105 patients with SSEH were retrieved from medical records, with a mean age of 51.3 years. Eighty-three patients (79%) complained of acute onset of severe neck or back pain. Eighty-two patients (78%) suffered from moderate to severe neurologic deficits (Frankel scale A-C). Anticoagulation usage was found in 20% of cases. Lower thoracic spine (p = 0.046), use of anticoagulants (p = 0.019), sphincter function disfunction (p = 0.008), severe neurologic deficits at admission (p < 0.001), and rapid deterioration (<1 hour, p = 0.004) were found to be associated with poor outcomes. Surgical decompression was performed in 74 (70%) cases. The univariate and multivariate analysis revealed that preoperative severe neurologic deficits (p = 0.005) and extended paraplegia time (>12 hours, p = 0.004) were independent adverse prognostic factors. The univariate analysis revealed that lower thoracic spine location (p = 0.08) and rapid progression (<6 hours, p = 0.005) were correlated with poor prognosis, but the multivariate analysis failed to identify them as independent prognostic factors. DISCUSSION: Adverse prognostic factors for SSEH might include thoracic segment location, use of anticoagulation, severe neurologic deficits on admission, sphincter dysfunction, and rapid progression. Preoperative neurologic deficit and extended paraplegia time were strongly correlated with the prognosis in the subset of patients who underwent surgical decompression. Timely surgical decompression is recommended for patients with moderate/severe neurologic deficits or progressive neurologic deterioration.

Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer.

Background: This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients' prognosis, so as to provide a basis for clinical individualized precision treatment. Methods: One hundred twenty-nine newly diagnosed LAGC patients who underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this study. Pathological tumor regression grade (TRG) served as the reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological responders (pR) and pathological non-responders (pNR) groups were analyzed. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS was compared using the Kaplan-Meier method and the log-rank test. Results: Sixty-nine patients were classified as pR and 60 were pNR. Ktrans, kep, and ve values in the pR group were significantly higher, while ADCstandard and D values were significantly lower than those in the pNR group. Multivariate logistic regression analysis demonstrated that Ktrans, kep, ve, and D values were independent predictors for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an area under the curve (AUC) of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by the IVIM-DWI model were independent predictors of poor RFS. The IVIM-DWI model could significantly stratify median RFS (52 vs. 15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC. Conclusion: Pretreatment DCE-MRI quantitative parameters Ktrans, kep, ve, and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI, and their combination could help RFS stratification of LAGC patients.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.

Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance.

BACKGROUND: Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas. METHODS: A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan-Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics. RESULTS: In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group. CONCLUSIONS: We developed a TNF family-based signature to predict the prognosis of patients with glioma. Video abstract.